diff --git a/.github/workflows/unit_test.yml b/.github/workflows/unit_test.yml index 7625a08cfe515..b805eb928b94c 100644 --- a/.github/workflows/unit_test.yml +++ b/.github/workflows/unit_test.yml @@ -38,8 +38,8 @@ jobs: with: # v0 makes it easy to bust the cache if needed # just increase the integer to start with a fresh cache - gha-cache-key: v0-py${{ matrix.python_version }} - named-caches-hash: ${{ hashFiles('**/pyproject.toml') }} + gha-cache-key: v1-py${{ matrix.python_version }} + named-caches-hash: v1-py${{ matrix.python_version }} pants-python-version: ${{ matrix.python-version }} pants-ci-config: pants.toml - name: Check BUILD files diff --git a/.gitignore b/.gitignore index 31e6bc9056658..c40424b7fa0a6 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,10 @@ dist/ migration_scripts/ venv/ +.venv/ .ipynb_checkpoints .__pycache__ +__pycache__ dev_notebooks/ llamaindex_registry.txt +packages_to_bump_deduped.txt diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 6e13d2aedbdb2..4bab7acfdcb9a 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -288,7 +288,7 @@ cd ./llama-index-packs llamaindex-cli new-package --kind "packs" --name "my new pack" cd ./llama-index-integrations/readers -llamaindex-cli new-pacakge --kind "readers" --name "new reader" +llamaindex-cli new-package --kind "readers" --name "new reader" ``` Executing the first set of shell commands will create a new folder called `llama-index-packs-my-new-pack` diff --git a/Makefile b/Makefile index ef71f85190e7e..a350215cd0313 100644 --- a/Makefile +++ b/Makefile @@ -11,7 +11,7 @@ lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files test: ## Run tests via pants - pants --no-local-cache --changed-since=origin/main test + pants --no-local-cache --changed-since=origin/main --changed-dependents=transitive test test-core: ## Run tests via pants pants --no-local-cache test llama-index-core/:: diff --git a/docs/community/integrations/uptrain.md b/docs/community/integrations/uptrain.md index ee522f99e5da6..d07e25fcbf1b5 100644 --- a/docs/community/integrations/uptrain.md +++ b/docs/community/integrations/uptrain.md @@ -1,4 +1,4 @@ -# How to use UpTrain with LlamaIndex +# Perform Evaluations on LlamaIndex with UpTrain **Overview**: In this example, we will see how to use UpTrain with LlamaIndex. @@ -12,6 +12,365 @@ 1. LlamaIndex solves the first problem by allowing you to perform Retrieval Augmented Generation (RAG) with a retriever that is fine-tuned on your own data. This allows you to use your own data to fine-tune a retriever, and then use that retriever to perform RAG. 2. UpTrain solves the second problem by allowing you to perform evaluations on the generated responses. This helps you to ensure that the responses are relevant to the prompt, align with the desired tone or the context, and are not offensive etc. +# How to go about it? + +There two ways you can use UpTrain with LlamaIndex: + +1. **Using the UpTrain Callback Handler**: This method allows you to seamlessly integrate UpTrain with LlamaIndex. You can simply add UpTrainCallbackHandler to your existing LlamaIndex pipeline and it will take care of sending the generated responses to the UpTrain Managed Service for evaluations. This is the recommended method as it is the easiest to use and provides you with dashboards and insights with minimal effort. + +2. **Using UpTrain's EvalLlamaIndex**: This method allows you to use UpTrain to perform evaluations on the generated responses. You can use the EvalLlamaIndex object to generate responses for the queries and then perform evaluations on the responses. You can find a detailed tutorial on how to do this below. This method offers more flexibility and control over the evaluations, but requires more effort to set up and use. + +# 1. Using the UpTrain Callback Handler Open In Colab + +Three additional evaluations for Llamaindex have been introduced, complementing existing ones. These evaluations run automatically, with results displayed in the output. More details on UpTrain's evaluations can be found [here](https://github.com/uptrain-ai/uptrain?tab=readme-ov-file#pre-built-evaluations-we-offer-). + +Selected operators from the LlamaIndex pipeline are highlighted for demonstration: + +## 1. **RAG Query Engine Evaluations**: + +The RAG query engine plays a crucial role in retrieving context and generating responses. To ensure its performance and response quality, we conduct the following evaluations: + +- **Context Relevance**: Determines if the context extracted from the query is relevant to the response. +- **Factual Accuracy**: Assesses if the LLM is hallcuinating or providing incorrect information. +- **Response Completeness**: Checks if the response contains all the information requested by the query. + +## 2. **Sub-Question Query Generation Evaluation**: + +The SubQuestionQueryGeneration operator decomposes a question into sub-questions, generating responses for each using a RAG query engine. Given the complexity, we include the previous evaluations and add: + +- **Sub Query Completeness**: Assures that the sub-questions accurately and comprehensively cover the original query. + +## 3. **Re-Ranking Evaluations**: + +Re-ranking involves reordering nodes based on relevance to the query and choosing top n nodes. Different evaluations are performed based on the number of nodes returned after re-ranking. + +a. Same Number of Nodes + +- **Context Reranking**: Checks if the order of re-ranked nodes is more relevant to the query than the original order. + +b. Different Number of Nodes: + +- **Context Conciseness**: Examines whether the reduced number of nodes still provides all the required information. + +These evaluations collectively ensure the robustness and effectiveness of the RAG query engine, SubQuestionQueryGeneration operator, and the re-ranking process in the LlamaIndex pipeline. + +#### **Note:** + +- We have performed evaluations using basic RAG query engine, the same evaluations can be performed using the advanced RAG query engine as well. +- Same is true for Re-Ranking evaluations, we have performed evaluations using CohereRerank, the same evaluations can be performed using other re-rankers as well. + +## Install Dependencies and Import Libraries + +Install notebook dependencies. + +```bash +pip install -q html2text llama-index pandas tqdm uptrain cohere +``` + +Import libraries. + +```python +from llama_index import ( + ServiceContext, + VectorStoreIndex, +) +from llama_index.node_parser import SentenceSplitter +from llama_index.readers import SimpleWebPageReader +from llama_index.callbacks import CallbackManager, UpTrainCallbackHandler +from llama_index.postprocessor.cohere_rerank import CohereRerank +from llama_index.service_context import set_global_service_context +from llama_index.query_engine.sub_question_query_engine import ( + SubQuestionQueryEngine, +) +from llama_index.tools.query_engine import QueryEngineTool +from llama_index.tools.types import ToolMetadata +``` + +## Setup + +You can choose between the following options for evaluating using UpTrain: + +### 1. **UpTrain's Open-Source Software (OSS)**: + +You can use the open-source evaluation service to evaluate your model. +In this case, you will need to provide an OpenAI API key. You can get yours [here](https://platform.openai.com/account/api-keys). + +Parameters: + +- key_type="openai" +- api_key="OPENAI_API_KEY" +- project_name_prefix="PROJECT_NAME_PREFIX" + +### 2. **UpTrain Managed Service and Dashboards**: + +You can create a free UpTrain account [here](https://uptrain.ai/) and get free trial credits. If you want more trial credits, [book a call with the maintainers of UpTrain here](https://calendly.com/uptrain-sourabh/30min). + +UpTrain Managed service provides: + +1. Dashboards with advanced drill-down and filtering options +1. Insights and common topics among failing cases +1. Observability and real-time monitoring of production data +1. Regression testing via seamless integration with your CI/CD pipelines + +The notebook contains some screenshots of the dashboards and the insights that you can get from the UpTrain managed service. + +Parameters: + +- key_type="uptrain" +- api_key="UPTRAIN_API_KEY" +- project_name_prefix="PROJECT_NAME_PREFIX" + +**Note:** The `project_name_prefix` will be used as prefix for the project names in the UpTrain dashboard. These will be different for different types of evals. For example, if you set project_name_prefix="llama" and perform the sub_question evaluation, the project name will be "llama_sub_question_answering". + +```python +callback_handler = UpTrainCallbackHandler( + key_type="openai", + api_key="sk-******************************", + project_name_prefix="llama", +) +callback_manager = CallbackManager([callback_handler]) +service_context = ServiceContext.from_defaults( + callback_manager=callback_manager +) +set_global_service_context(service_context) +``` + +## Load and Parse Documents + +Load documents from Paul Graham's essay "What I Worked On". + +```python +documents = SimpleWebPageReader().load_data( + [ + "https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt" + ] +) +``` + +Parse the document into nodes. + +```python +parser = SentenceSplitter() +nodes = parser.get_nodes_from_documents(documents) +``` + +# 1. RAG Query Engine Evaluation + +UpTrain callback handler will automatically capture the query, context and response once generated and will run the following three evaluations _(Graded from 0 to 1)_ on the response: + +- **Context Relevance**: Check if the context extractedfrom the query is relevant to the response. +- **Factual Accuracy**: Check how factually accurate the response is. +- **Response Completeness**: Check if the response contains all the information that the query is asking for. + +```python +index = VectorStoreIndex.from_documents( + documents, service_context=service_context +) +query_engine = index.as_query_engine() + +max_characters_per_line = 80 +queries = [ + "What did Paul Graham do growing up?", + "When and how did Paul Graham's mother die?", + "What, in Paul Graham's opinion, is the most distinctive thing about YC?", + "When and how did Paul Graham meet Jessica Livingston?", + "What is Bel, and when and where was it written?", +] +for query in queries: + response = query_engine.query(query) +``` + + Question: What did Paul Graham do growing up? + Context Relevance Score: 0.0 + Factual Accuracy Score: 1.0 + Response Completeness Score: 0.0 + + + Question: When and how did Paul Graham's mother die? + Context Relevance Score: 0.0 + Factual Accuracy Score: 1.0 + Response Completeness Score: 0.0 + + + Question: What, in Paul Graham's opinion, is the most distinctive thing about YC? + Context Relevance Score: 1.0 + Factual Accuracy Score: 1.0 + Response Completeness Score: 1.0 + + + Question: When and how did Paul Graham meet Jessica Livingston? + Context Relevance Score: 1.0 + Factual Accuracy Score: 1.0 + Response Completeness Score: 0.5 + + + Question: What is Bel, and when and where was it written? + Context Relevance Score: 1.0 + Factual Accuracy Score: 1.0 + Response Completeness Score: 0.0 + +Here's an example of the dashboard showing how you can filter and drill down to the failing cases and get insights on the failing cases: +![image-2.png](https://uptrain-assets.s3.ap-south-1.amazonaws.com/images/llamaindex/image-2.png) + +# 2. Sub-Question Query Engine Evaluation + +The **sub question query engine** is used to tackle the problem of answering a complex query using multiple data sources. It first breaks down the complex query into sub questions for each relevant data source, then gather all the intermediate responses and synthesizes a final response. + +UpTrain callback handler will automatically capture the sub-question and the responses for each of them once generated and will run the following three evaluations _(Graded from 0 to 1)_ on the response: + +- **Context Relevance**: Check if the context extractedfrom the query is relevant to the response. +- **Factual Accuracy**: Check how factually accurate the response is. +- **Response Completeness**: Check if the response contains all the information that the query is asking for. + +In addition to the above evaluations, the callback handler will also run the following evaluation: + +- **Sub Query Completeness**: Checks if the sub-questions accurately and completely cover the original query. + +```python +# build index and query engine +vector_query_engine = VectorStoreIndex.from_documents( + documents=documents, use_async=True, service_context=service_context +).as_query_engine() + +query_engine_tools = [ + QueryEngineTool( + query_engine=vector_query_engine, + metadata=ToolMetadata( + name="documents", + description="Paul Graham essay on What I Worked On", + ), + ), +] + +query_engine = SubQuestionQueryEngine.from_defaults( + query_engine_tools=query_engine_tools, + service_context=service_context, + use_async=True, +) + +response = query_engine.query( + "How was Paul Grahams life different before, during, and after YC?" +) +``` + + Question: What did Paul Graham work on during YC? + Context Relevance Score: 0.5 + Factual Accuracy Score: 1.0 + Response Completeness Score: 0.5 + + + Question: What did Paul Graham work on after YC? + Context Relevance Score: 0.5 + Factual Accuracy Score: 1.0 + Response Completeness Score: 0.5 + + + Question: What did Paul Graham work on before YC? + Context Relevance Score: 1.0 + Factual Accuracy Score: 1.0 + Response Completeness Score: 0.0 + + + Question: How was Paul Grahams life different before, during, and after YC? + Sub Query Completeness Score: 1.0 + +Here's an example of the dashboard visualizing the scores of the sub-questions in the form of a bar chart: + +![image.png](https://uptrain-assets.s3.ap-south-1.amazonaws.com/images/llamaindex/image.png) + +# 3. Re-ranking + +Re-ranking is the process of reordering the nodes based on their relevance to the query. There are multiple classes of re-ranking algorithms offered by Llamaindex. We have used CohereRerank for this example. + +The re-ranker allows you to enter the number of top n nodes that will be returned after re-ranking. If this value remains the same as the original number of nodes, the re-ranker will only re-rank the nodes and not change the number of nodes. Otherwise, it will re-rank the nodes and return the top n nodes. + +We will perform different evaluations based on the number of nodes returned after re-ranking. + +## 3a. Re-ranking (With same number of nodes) + +If the number of nodes returned after re-ranking is the same as the original number of nodes, the following evaluation will be performed: + +- **Context Reranking**: Check if the order of the re-ranked nodes is more relevant to the query than the original order. + +```python +api_key = "**********************************" # Insert cohere API key here +cohere_rerank = CohereRerank( + api_key=api_key, top_n=5 +) # In this example, the number of nodes before re-ranking is 5 and after re-ranking is also 5. + +index = VectorStoreIndex.from_documents( + documents=documents, service_context=service_context +) + +query_engine = index.as_query_engine( + similarity_top_k=10, + node_postprocessors=[cohere_rerank], + service_context=service_context, +) + +response = query_engine.query( + "What did Sam Altman do in this essay?", +) +``` + + Question: What did Sam Altman do in this essay? + Context Reranking Score: 0.0 + +# 3b. Re-ranking (With different number of nodes) + +If the number of nodes returned after re-ranking is the lesser as the original number of nodes, the following evaluation will be performed: + +- **Context Conciseness**: If the re-ranked nodes are able to provide all the information required by the query. + +```python +api_key = "**********************************" # insert cohere API key here +cohere_rerank = CohereRerank( + api_key=api_key, top_n=2 +) # In this example, the number of nodes before re-ranking is 5 and after re-ranking is 2. + +index = VectorStoreIndex.from_documents( + documents=documents, service_context=service_context +) +query_engine = index.as_query_engine( + similarity_top_k=10, + node_postprocessors=[cohere_rerank], + service_context=service_context, +) + +# Use your advanced RAG +response = query_engine.query( + "What did Sam Altman do in this essay?", +) +``` + + Question: What did Sam Altman do in this essay? + Context Conciseness Score: 1.0 + +# UpTrain's Managed Service Dashboard and Insights + +The UpTrain Managed Service offers the following features: + +1. Advanced dashboards with drill-down and filtering options. +1. Identification of insights and common themes among unsuccessful cases. +1. Real-time observability and monitoring of production data. +1. Integration with CI/CD pipelines for seamless regression testing. + +To define the UpTrain callback handler, the only change required is to set the `key_type` and `api_key` parameters. The rest of the code remains the same. + +```python +callback_handler = UpTrainCallbackHandler( + key_type="uptrain", + api_key="up-******************************", + project_name_prefix="llama", +) +``` + +Here's a short GIF showcasing the dashboard and the insights that you can get from the UpTrain managed service: + +![output.gif](https://uptrain-assets.s3.ap-south-1.amazonaws.com/images/llamaindex/output.gif) + +# 2. Using EvalLlamaIndex Open In Colab + ## Install UpTrain and LlamaIndex ```bash @@ -182,16 +541,17 @@ pd.DataFrame(results) Histogram of score vs number of cases with that score -![image.png](UpTrain_files/image.png) +![dashboard.png](https://uptrain-assets.s3.ap-south-1.amazonaws.com/images/llamaindex/nyc_wiki_results.png) ### Insights: You can filter failure cases and generate common topics among them. This can help identify the core issue and help fix it -![LlamaIndex_Integration.gif](UpTrain_files/LlamaIndex_Integration.gif) +![LlamaIndex_Integration.gif](https://uptrain-assets.s3.ap-south-1.amazonaws.com/images/llamaindex/LlamaIndex_Integration.gif) ## Learn More -1. [Colab Notebook on UpTrain Integration with LlamaIndex](https://colab.research.google.com/github/uptrain-ai/llama_index/blob/uptrain_integration/docs/examples/evaluation/UpTrain.ipynb) +1. [Colab Notebook on UpTrainCallbackHandler](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/callbacks/UpTrainCallback.ipynb) +1. [Colab Notebook on UpTrain Integration with LlamaIndex](https://colab.research.google.com/github/run-llama/llama_index/blob/main/docs/examples/evaluation/UpTrain.ipynb) 1. [UpTrain Github Repository](https://github.com/uptrain-ai/uptrain) 1. [UpTrain Documentation](https://docs.uptrain.ai/) diff --git a/docs/examples/callbacks/UpTrainCallback.ipynb b/docs/examples/callbacks/UpTrainCallback.ipynb new file mode 100644 index 0000000000000..7ccc72565c127 --- /dev/null +++ b/docs/examples/callbacks/UpTrainCallback.ipynb @@ -0,0 +1,643 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# UpTrain Callback Handler\n", + "\n", + "This notebook showcases the UpTrain callback handler seamlessly integrating into your pipeline, facilitating diverse evaluations. Three additional evaluations for Llamaindex have been introduced, complementing existing ones. These evaluations run automatically, with results displayed in the output. More details on UpTrain's evaluations can be found [here](https://github.com/uptrain-ai/uptrain?tab=readme-ov-file#pre-built-evaluations-we-offer-). \n", + "\n", + "Selected operators from the LlamaIndex pipeline are highlighted for demonstration:\n", + "\n", + "## 1. **RAG Query Engine Evaluations**:\n", + "The RAG query engine plays a crucial role in retrieving context and generating responses. To ensure its performance and response quality, we conduct the following evaluations:\n", + "\n", + "- **Context Relevance**: Determines if the context extracted from the query is relevant to the response.\n", + "- **Factual Accuracy**: Assesses if the LLM is hallcuinating or providing incorrect information.\n", + "- **Response Completeness**: Checks if the response contains all the information requested by the query.\n", + "\n", + "## 2. **Sub-Question Query Generation Evaluation**:\n", + "The SubQuestionQueryGeneration operator decomposes a question into sub-questions, generating responses for each using a RAG query engine. Given the complexity, we include the previous evaluations and add:\n", + "\n", + "- **Sub Query Completeness**: Assures that the sub-questions accurately and comprehensively cover the original query.\n", + "\n", + "## 3. **Re-Ranking Evaluations**:\n", + "Re-ranking involves reordering nodes based on relevance to the query and chosing top n nodes. Different evaluations are performed based on the number of nodes returned after re-ranking.\n", + "\n", + "a. Same Number of Nodes\n", + "- **Context Reranking**: Checks if the order of re-ranked nodes is more relevant to the query than the original order.\n", + "\n", + "b. Different Number of Nodes:\n", + "- **Context Conciseness**: Examines whether the reduced number of nodes still provides all the required information.\n", + "\n", + "These evaluations collectively ensure the robustness and effectiveness of the RAG query engine, SubQuestionQueryGeneration operator, and the re-ranking process in the LlamaIndex pipeline." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### **Note:** \n", + "- We have performed evaluations using basic RAG query engine, the same evaluations can be performed using the advanced RAG query engine as well.\n", + "- Same is true for Re-Ranking evaluations, we have performed evaluations using CohereRerank, the same evaluations can be performed using other re-rankers as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Install Dependencies and Import Libraries\n", + "\n", + "Install notebook dependencies." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-postprocessor-cohere-rerank\n", + "%pip install llama-index-readers-web\n", + "%pip install llama-index-callback-uptrain\n", + "%pip install -q html2text llama-index pandas tqdm uptrain cohere" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import libraries.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.settings import Settings\n", + "from llama_index.core import VectorStoreIndex\n", + "from llama_index.core.node_parser import SentenceSplitter\n", + "from llama_index.readers.web import SimpleWebPageReader\n", + "from llama_index.core.callbacks import CallbackManager\n", + "from llama_index.callbacks.uptrain.base import UpTrainCallbackHandler\n", + "from llama_index.core.query_engine import SubQuestionQueryEngine\n", + "from llama_index.core.tools import QueryEngineTool, ToolMetadata\n", + "from llama_index.core.postprocessor.llm_rerank import LLMRerank\n", + "from llama_index.llms.openai import OpenAI\n", + "\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup\n", + "\n", + "You can choose between the following options for evaluating using UpTrain:\n", + "### 1. **UpTrain's Open-Source Software (OSS)**: \n", + "You can use the open-source evaluation service to evaluate your model.\n", + "In this case, you will need to provie an OpenAI API key. You can get yours [here](https://platform.openai.com/account/api-keys).\n", + "\n", + "Parameters:\n", + "- key_type=\"openai\"\n", + "- api_key=\"OPENAI_API_KEY\"\n", + "- project_name_prefix=\"PROJECT_NAME_PREFIX\"\n", + "\n", + "\n", + "### 2. **UpTrain Managed Service and Dashboards**: \n", + "You can create a free UpTrain account [here](https://uptrain.ai/) and get free trial credits. If you want more trial credits, [book a call with the maintainers of UpTrain here](https://calendly.com/uptrain-sourabh/30min).\n", + "\n", + "UpTrain Managed service provides:\n", + "1. Dashboards with advanced drill-down and filtering options\n", + "1. Insights and common topics among failing cases\n", + "1. Observability and real-time monitoring of production data\n", + "1. Regression testing via seamless integration with your CI/CD pipelines\n", + "\n", + "The notebook contains some screenshots of the dashboards and the insights that you can get from the UpTrain managed service.\n", + "\n", + "Parameters:\n", + "- key_type=\"uptrain\"\n", + "- api_key=\"UPTRAIN_API_KEY\"\n", + "- project_name_prefix=\"PROJECT_NAME_PREFIX\"\n", + "\n", + "\n", + "**Note:** The `project_name_prefix` will be used as prefix for the project names in the UpTrain dashboard. These will be different for different types of evals. For example, if you set project_name_prefix=\"llama\" and perform the sub_question evaluation, the project name will be \"llama_sub_question_answering\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "callback_handler = UpTrainCallbackHandler(\n", + " key_type=\"openai\",\n", + " api_key=\"sk-...\", # replace with your OpenAI API key\n", + " project_name_prefix=\"llama\",\n", + ")\n", + "Settings.callback_manager = CallbackManager([callback_handler])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Load and Parse Documents\n", + "\n", + "Load documents from Paul Graham's essay \"What I Worked On\"." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "documents = SimpleWebPageReader().load_data(\n", + " [\n", + " \"https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt\"\n", + " ]\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Parse the document into nodes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parser = SentenceSplitter()\n", + "nodes = parser.get_nodes_from_documents(documents)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1. RAG Query Engine Evaluation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "UpTrain callback handler will automatically capture the query, context and response once generated and will run the following three evaluations *(Graded from 0 to 1)* on the response:\n", + "- **Context Relevance**: Check if the context extractedfrom the query is relevant to the response.\n", + "- **Factual Accuracy**: Check how factually accurate the response is.\n", + "- **Response Completeness**: Check if the response contains all the information that the query is asking for." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-14 16:04:09.869\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: What did Paul Graham do growing up?\n", + "Response: Growing up, Paul Graham worked on writing and programming. He wrote short stories and also tried his hand at programming on the IBM 1401 computer that his school district had. He later got a microcomputer, a TRS-80, and started programming more extensively, creating simple games and even a word processor.\n", + "Context Relevance Score: 0.5\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 1.0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-14 16:04:36.895\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: When and how did Paul Graham's mother die?\n", + "Response: The context information does not provide any information about Paul Graham's mother or her death.\n", + "Context Relevance Score: 0.0\n", + "Factual Accuracy Score: 0.0\n", + "Response Completeness Score: 0.0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-14 16:04:55.245\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: What, in Paul Graham's opinion, is the most distinctive thing about YC?\n", + "Response: The most distinctive thing about YC, according to Paul Graham's opinion, is that it provides a sense of community and support for startup founders. It solves the problem of isolation that founders often face by connecting them with colleagues who understand the challenges they are going through and can offer guidance and support. Additionally, YC fosters a tight-knit alumni community where startups can help each other and even become each other's customers.\n", + "Context Relevance Score: 0.0\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 0.5\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-14 16:05:24.705\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: When and how did Paul Graham meet Jessica Livingston?\n", + "Response: Paul Graham met Jessica Livingston at a party at his house in October 2003. They were introduced to each other by a mutual friend named Maria Daniels. A couple of days later, Paul asked Jessica out and they started dating.\n", + "Context Relevance Score: 0.5\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 1.0\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-14 16:05:52.062\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: What is Bel, and when and where was it written?\n", + "Response: Bel is a new Lisp that was written in Arc. It was written over a period of 4 years, from March 26, 2015, to October 12, 2019. The majority of Bel was written in England, as the author moved there in the summer of 2016.\n", + "Context Relevance Score: 1.0\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 0.5\n", + "\n" + ] + } + ], + "source": [ + "index = VectorStoreIndex.from_documents(\n", + " documents,\n", + ")\n", + "query_engine = index.as_query_engine()\n", + "\n", + "max_characters_per_line = 80\n", + "queries = [\n", + " \"What did Paul Graham do growing up?\",\n", + " \"When and how did Paul Graham's mother die?\",\n", + " \"What, in Paul Graham's opinion, is the most distinctive thing about YC?\",\n", + " \"When and how did Paul Graham meet Jessica Livingston?\",\n", + " \"What is Bel, and when and where was it written?\",\n", + "]\n", + "for query in queries:\n", + " response = query_engine.query(query)" + ] + }, + { + "attachments": { + "image-2.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's an example of the dashboard showing how you can filter and drill down to the failing cases and get insights on the failing cases:\n", + "![image-2.png](attachment:image-2.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 2. Sub-Question Query Engine Evaluation\n", + "\n", + "The **sub question query engine** is used to tackle the problem of answering a complex query using multiple data sources. It first breaks down the complex query into sub questions for each relevant data source, then gather all the intermediate reponses and synthesizes a final response.\n", + "\n", + "UpTrain callback handler will automatically capture the sub-question and the responses for each of them once generated and will run the following three evaluations *(Graded from 0 to 1)* on the response:\n", + "- **Context Relevance**: Check if the context extractedfrom the query is relevant to the response.\n", + "- **Factual Accuracy**: Check how factually accurate the response is.\n", + "- **Response Completeness**: Check if the response contains all the information that the query is asking for.\n", + "\n", + "In addition to the above evaluations, the callback handler will also run the following evaluation:\n", + "- **Sub Query Completeness**: Checks if the sub-questions accurately and completely cover the original query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Generated 3 sub questions.\n", + "\u001b[1;3;38;2;237;90;200m[documents] Q: What did Paul Graham work on before YC?\n", + "\u001b[0m\u001b[1;3;38;2;90;149;237m[documents] Q: What did Paul Graham work on during YC?\n", + "\u001b[0m\u001b[1;3;38;2;11;159;203m[documents] Q: What did Paul Graham work on after YC?\n", + "\u001b[0m\u001b[1;3;38;2;237;90;200m[documents] A: Before Y Combinator (YC), Paul Graham worked on a startup called Viaweb.\n", + "\u001b[0m\u001b[1;3;38;2;11;159;203m[documents] A: After leaving Y Combinator, Paul Graham focused on painting. He wanted to see how good he could get at painting if he dedicated his time and effort to it. He spent most of 2014 working on his painting skills, but eventually ran out of steam in November.\n", + "\u001b[0m\u001b[1;3;38;2;90;149;237m[documents] A: During his time at Y Combinator (YC), Paul Graham worked on various projects. He initially intended to work on three things: hacking, writing essays, and working on YC. However, as YC grew and he became more excited about it, it started to take up a lot more of his attention. He also worked on writing essays and was responsible for writing all of YC's internal software in Arc.\n", + "\u001b[0m" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-14 08:24:08.958\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n", + "\u001b[32m2024-02-14 08:24:34.450\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: What did Paul Graham work on before YC?\n", + "Response: Before Y Combinator (YC), Paul Graham worked on a startup called Viaweb.\n", + "Context Relevance Score: 0.0\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 0.5\n", + "\n", + "\n", + "Question: What did Paul Graham work on after YC?\n", + "Response: After leaving Y Combinator, Paul Graham focused on painting. He wanted to see how good he could get at painting if he dedicated his time and effort to it. He spent most of 2014 working on his painting skills, but eventually ran out of steam in November.\n", + "Context Relevance Score: 1.0\n", + "Factual Accuracy Score: 0.0\n", + "Response Completeness Score: 0.0\n", + "\n", + "\n", + "Question: What did Paul Graham work on during YC?\n", + "Response: During his time at Y Combinator (YC), Paul Graham worked on various projects. He initially intended to work on three things: hacking, writing essays, and working on YC. However, as YC grew and he became more excited about it, it started to take up a lot more of his attention. He also worked on writing essays and was responsible for writing all of YC's internal software in Arc.\n", + "Context Relevance Score: 0.5\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 0.5\n", + "\n", + "\n", + "Question: How was Paul Grahams life different before, during, and after YC?\n", + "Sub Query Completeness Score: 1.0\n", + "\n" + ] + } + ], + "source": [ + "# build index and query engine\n", + "vector_query_engine = VectorStoreIndex.from_documents(\n", + " documents=documents,\n", + " use_async=True,\n", + ").as_query_engine()\n", + "\n", + "query_engine_tools = [\n", + " QueryEngineTool(\n", + " query_engine=vector_query_engine,\n", + " metadata=ToolMetadata(\n", + " name=\"documents\",\n", + " description=\"Paul Graham essay on What I Worked On\",\n", + " ),\n", + " ),\n", + "]\n", + "\n", + "query_engine = SubQuestionQueryEngine.from_defaults(\n", + " query_engine_tools=query_engine_tools,\n", + " use_async=True,\n", + ")\n", + "\n", + "response = query_engine.query(\n", + " \"How was Paul Grahams life different before, during, and after YC?\"\n", + ")" + ] + }, + { + "attachments": { + "image.png": { + "image/png": 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" + } + }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's an example of the dashboard visualizing the scores of the sub-questions in the form of a bar chart:\n", + "\n", + "![image.png](attachment:image.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3. Re-ranking \n", + "\n", + "Re-ranking is the process of reordering the nodes based on their relevance to the query. There are multiple classes of re-ranking algorithms offered by Llamaindex. We have used CohereRerank for this example.\n", + "\n", + "The re-ranker allows you to enter the number of top n nodes that will be returned after re-ranking. If this value remains the same as the original number of nodes, the re-ranker will only re-rank the nodes and not change the number of nodes. Otherwise, it will re-rank the nodes and return the top n nodes.\n", + "\n", + "We will perform different evaluations based on the number of nodes returned after re-ranking." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3a. Re-ranking (With same number of nodes)\n", + "\n", + "If the number of nodes returned after re-ranking is the same as the original number of nodes, the following evaluation will be performed:\n", + "- **Context Reranking**: Check if the order of the re-ranked nodes is more relevant to the query than the original order." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-13 20:00:17.459\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: What did Sam Altman do in this essay?\n", + "Context Relevance Score: 1.0\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 1.0\n", + "\n" + ] + } + ], + "source": [ + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\" # Replace with your OpenAI API key\n", + "llm = OpenAI(model=\"gpt-4-turbo-preview\")\n", + "\n", + "cohere_rerank = LLMRerank(\n", + " llm=llm, top_n=5\n", + ") # In this example, the number of nodes before re-ranking is 5 and after re-ranking is also 5.\n", + "\n", + "index = VectorStoreIndex.from_documents(\n", + " documents=documents,\n", + ")\n", + "\n", + "query_engine = index.as_query_engine(\n", + " similarity_top_k=10,\n", + " node_postprocessors=[cohere_rerank],\n", + ")\n", + "\n", + "response = query_engine.query(\n", + " \"What did Sam Altman do in this essay?\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 3b. Re-ranking (With different number of nodes)\n", + "\n", + "If the number of nodes returned after re-ranking is the lesser as the original number of nodes, the following evaluation will be performed:\n", + "- **Context Conciseness**: If the re-ranked nodes are able to provide all the information required by the query." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\u001b[32m2024-02-13 20:01:39.343\u001b[0m | \u001b[1mINFO \u001b[0m | \u001b[36muptrain.framework.evalllm\u001b[0m:\u001b[36mevaluate\u001b[0m:\u001b[36m110\u001b[0m - \u001b[1mSending evaluation request for rows 0 to <50 to the Uptrain\u001b[0m\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Question: What did Sam Altman do in this essay?\n", + "Context Relevance Score: 0.5\n", + "Factual Accuracy Score: 1.0\n", + "Response Completeness Score: 1.0\n", + "\n" + ] + } + ], + "source": [ + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\" # Replace with your OpenAI API key\n", + "llm = OpenAI(model=\"gpt-4-turbo-preview\")\n", + "\n", + "cohere_rerank = LLMRerank(\n", + " llm=llm, top_n=2\n", + ") # In this example, the number of nodes before re-ranking is 5 and after re-ranking is 2.\n", + "\n", + "index = VectorStoreIndex.from_documents(\n", + " documents=documents,\n", + ")\n", + "query_engine = index.as_query_engine(\n", + " similarity_top_k=10,\n", + " node_postprocessors=[cohere_rerank],\n", + ")\n", + "\n", + "# Use your advanced RAG\n", + "response = query_engine.query(\n", + " \"What did Sam Altman do in this essay?\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# UpTrain's Managed Service Dashboard and Insights\n", + "\n", + "The UpTrain Managed Service offers the following features:\n", + "\n", + "1. Advanced dashboards with drill-down and filtering options.\n", + "1. Identification of insights and common themes among unsuccessful cases.\n", + "1. Real-time observability and monitoring of production data.\n", + "1. Integration with CI/CD pipelines for seamless regression testing.\n", + "\n", + "To define the UpTrain callback handler, the only change required is to set the `key_type` and `api_key` parameters. The rest of the code remains the same.\n", + "\n", + "```python\n", + "callback_handler = UpTrainCallbackHandler(\n", + " key_type=\"uptrain\",\n", + " api_key=\"up-******************************\",\n", + " project_name_prefix=\"llama\",\n", + ")\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here's a short GIF showcasing the dashboard and the insights that you can get from the UpTrain managed service:\n", + "\n", + "![output.gif](https://uptrain-assets.s3.ap-south-1.amazonaws.com/images/llamaindex/output.gif)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "phoenixdev", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/docs/examples/embeddings/optimum_intel.ipynb b/docs/examples/embeddings/optimum_intel.ipynb new file mode 100644 index 0000000000000..3a3c78cb0d9f3 --- /dev/null +++ b/docs/examples/embeddings/optimum_intel.ipynb @@ -0,0 +1,87 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\"Open" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Optimized Embedding Model using Optimum-Intel\n", + "\n", + "LlamaIndex has support for loading quantized embedding models for Intel, using the [Optimum-Intel library](https://huggingface.co/docs/optimum/main/en/intel/index). \n", + "\n", + "Optimized models are smaller and faster, with minimal accuracy loss, see the [documentation](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc) and an [optimization guide](https://huggingface.co/docs/optimum/main/en/intel/optimization_inc) using the IntelLabs/fastRAG library. \n", + "\n", + "In order to be able to load and use the quantized models, install the required dependency `pip install optimum[exporters] optimum-intel neural-compressor`. \n", + "\n", + "Loading is done using the class `IntelEmbedding`; usage is similar to any HuggingFace local embedding model. See example." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-embeddings-huggingface-optimum-intel" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.embeddings.huggingface_optimum_intel import IntelEmbedding\n", + "\n", + "embed_model = IntelEmbedding(\"Intel/bge-small-en-v1.5-rag-int8-static\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "384\n", + "[-0.0032782123889774084, -0.013396517373621464, 0.037944991141557693, -0.04642259329557419, 0.027709005400538445]\n" + ] + } + ], + "source": [ + "embeddings = embed_model.get_text_embedding(\"Hello World!\")\n", + "print(len(embeddings))\n", + "print(embeddings[:5])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/docs/examples/evaluation/UpTrain.ipynb b/docs/examples/evaluation/UpTrain.ipynb index 285bc8a5cbb74..6cb85c2340c9e 100644 --- a/docs/examples/evaluation/UpTrain.ipynb +++ b/docs/examples/evaluation/UpTrain.ipynb @@ -5,7 +5,7 @@ "id": "ec212316", "metadata": {}, "source": [ - "\"Open" + "\"Open" ] }, { diff --git a/docs/examples/evaluation/faithfulness_eval.ipynb b/docs/examples/evaluation/faithfulness_eval.ipynb index fbf01350a9448..30410a705a5de 100644 --- a/docs/examples/evaluation/faithfulness_eval.ipynb +++ b/docs/examples/evaluation/faithfulness_eval.ipynb @@ -19,7 +19,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install llama-index-llms-openai" + "%pip install llama-index-llms-openai pandas[jinja2] spacy" ] }, { @@ -38,16 +38,13 @@ { "cell_type": "code", "execution_count": null, - "id": "9080b39e", + "id": "190a6684", "metadata": {}, "outputs": [], "source": [ - "# configuring logger to INFO level\n", - "import logging\n", - "import sys\n", + "import os\n", "\n", - "logging.basicConfig(stream=sys.stdout, level=logging.INFO)\n", - "logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))" + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" ] }, { @@ -58,7 +55,6 @@ "outputs": [], "source": [ "from llama_index.core import (\n", - " TreeIndex,\n", " VectorStoreIndex,\n", " SimpleDirectoryReader,\n", " Response,\n", @@ -123,8 +119,11 @@ "metadata": {}, "outputs": [], "source": [ + "from llama_index.core.evaluation import EvaluationResult\n", + "\n", + "\n", "# define jupyter display function\n", - "def display_eval_df(response: Response, eval_result: str) -> None:\n", + "def display_eval_df(response: Response, eval_result: EvaluationResult) -> None:\n", " if response.source_nodes == []:\n", " print(\"no response!\")\n", " return\n", @@ -133,6 +132,7 @@ " \"Response\": str(response),\n", " \"Source\": response.source_nodes[0].node.text[:1000] + \"...\",\n", " \"Evaluation Result\": \"Pass\" if eval_result.passing else \"Fail\",\n", + " \"Reasoning\": eval_result.feedback,\n", " },\n", " index=[0],\n", " )\n", @@ -176,32 +176,34 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 ResponseSourceEvaluation ResultResponseSourceEvaluation ResultReasoning
0New York City got its name from the English explorer Henry Hudson, who rediscovered New York Harbor in 1609 while searching for the Northwest Passage. He named the area New York after the Duke of York, who later became King James II of England.He claimed the area for France and named it Nouvelle Angoulême (New Angoulême).A Spanish expedition, led by the Portuguese captain Estêvão Gomes sailing for Emperor Charles V, arrived in New York Harbor in January 1525 and charted the mouth of the Hudson River, which he named Río de San Antonio ('Saint Anthony's River').The Padrón Real of 1527, the first scientific map to show the East Coast of North America continuously, was informed by Gomes' expedition and labeled the northeastern United States as Tierra de Esteban Gómez in his honor.In 1609, the English explorer Henry Hudson rediscovered New York Harbor while searching for the Northwest Passage to the Orient for the Dutch East India Company.He proceeded to sail up what the Dutch would name the North River (now the Hudson River), named first by Hudson as the Mauritius after Maurice, Prince of Orange.Hudson's first mate described the harbor as \"a very good Harbour for all windes\" and the river as \"a mile broad\" and \"full of fish\".Hud...Fail0New York City got its name when it came under British control in 1664. It was renamed New York after King Charles II of England granted the lands to his brother, the Duke of York.The city came under British control in 1664 and was renamed New York after King Charles II of England granted the lands to his brother, the Duke of York. The city was regained by the Dutch in July 1673 and was renamed New Orange for one year and three months; the city has been continuously named New York since November 1674. New York City was the capital of the United States from 1785 until 1790, and has been the largest U.S. city since 1790. The Statue of Liberty greeted millions of immigrants as they came to the U.S. by ship in the late 19th and early 20th centuries, and is a symbol of the U.S. and its ideals of liberty and peace. In the 21st century, New York City has emerged as a global node of creativity, entrepreneurship, and as a symbol of freedom and cultural diversity. The New York Times has won the most Pulitzer Prizes for journalism and remains the U.S. media's \"newspaper of record\". In 2019, New York City was voted the greatest city in the world in a survey of over 30,000 p...PassYES
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -229,22 +231,23 @@ "metadata": {}, "outputs": [ { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "WARNING:llama_index.indices.service_context:chunk_size_limit is deprecated, please specify chunk_size instead\n", - "chunk_size_limit is deprecated, please specify chunk_size instead\n", - "chunk_size_limit is deprecated, please specify chunk_size instead\n" + "/Users/loganmarkewich/giant_change/llama_index/llama-index-core/llama_index/core/evaluation/dataset_generation.py:212: DeprecationWarning: Call to deprecated class DatasetGenerator. (Deprecated in favor of `RagDatasetGenerator` which should be used instead.)\n", + " return cls(\n", + "/Users/loganmarkewich/giant_change/llama_index/llama-index-core/llama_index/core/evaluation/dataset_generation.py:309: DeprecationWarning: Call to deprecated class QueryResponseDataset. (Deprecated in favor of `LabelledRagDataset` which should be used instead.)\n", + " return QueryResponseDataset(queries=queries, responses=responses_dict)\n" ] }, { "data": { "text/plain": [ "['What is the population of New York City as of 2020?',\n", - " 'Which borough of New York City is home to the headquarters of the United Nations?',\n", - " 'How many languages are spoken in New York City, making it the most linguistically diverse city in the world?',\n", - " 'Who founded the trading post on Manhattan Island that would later become New York City?',\n", - " 'What was New York City named after in 1664?']" + " 'Which city is the second-largest in the United States?',\n", + " 'How many people live within 250 miles of New York City?',\n", + " 'What are the five boroughs of New York City?',\n", + " 'What is the gross metropolitan product of the New York metropolitan area?']" ] }, "execution_count": null, @@ -297,36 +300,6 @@ "name": "stdout", "output_type": "stream", "text": [ - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=35 request_id=b36e17a843c31e827f0b7034e603cf28 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=35 request_id=b36e17a843c31e827f0b7034e603cf28 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=35 request_id=b36e17a843c31e827f0b7034e603cf28 response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=35 request_id=5acb726518065db9312da9f23beef411 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=35 request_id=5acb726518065db9312da9f23beef411 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=35 request_id=5acb726518065db9312da9f23beef411 response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=46 request_id=4af43bfbe4e24fdae0ec33312ee7491e response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=46 request_id=4af43bfbe4e24fdae0ec33312ee7491e response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=46 request_id=4af43bfbe4e24fdae0ec33312ee7491e response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=37 request_id=e30413546fe5f96d3890606767f2ec53 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=37 request_id=e30413546fe5f96d3890606767f2ec53 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=37 request_id=e30413546fe5f96d3890606767f2ec53 response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=33 request_id=01f0a8dada4dae80c97a9a412f03b84f response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=33 request_id=01f0a8dada4dae80c97a9a412f03b84f response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/embeddings processing_ms=33 request_id=01f0a8dada4dae80c97a9a412f03b84f response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=282 request_id=ed7b1f8ba68ae32b1d8e24e0d0764e86 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=282 request_id=ed7b1f8ba68ae32b1d8e24e0d0764e86 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=282 request_id=ed7b1f8ba68ae32b1d8e24e0d0764e86 response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=820 request_id=b4532c6d665b6cfd644861ed69819cb9 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=820 request_id=b4532c6d665b6cfd644861ed69819cb9 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=820 request_id=b4532c6d665b6cfd644861ed69819cb9 response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=847 request_id=4d9bbc71a95b7e0bb69a048e251772c8 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=847 request_id=4d9bbc71a95b7e0bb69a048e251772c8 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=847 request_id=4d9bbc71a95b7e0bb69a048e251772c8 response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=952 request_id=d1657940d881929d500b1fddc46b5866 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=952 request_id=d1657940d881929d500b1fddc46b5866 response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=952 request_id=d1657940d881929d500b1fddc46b5866 response_code=200\n", - "INFO:openai:message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=1482 request_id=c4456f75580d227f846d3a044e5eef1b response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=1482 request_id=c4456f75580d227f846d3a044e5eef1b response_code=200\n", - "message='OpenAI API response' path=https://api.openai.com/v1/chat/completions processing_ms=1482 request_id=c4456f75580d227f846d3a044e5eef1b response_code=200\n", "finished query\n", "score: 5/5\n" ] diff --git a/docs/examples/evaluation/relevancy_eval.ipynb b/docs/examples/evaluation/relevancy_eval.ipynb index e5a2cab5610c2..970651a1ec875 100644 --- a/docs/examples/evaluation/relevancy_eval.ipynb +++ b/docs/examples/evaluation/relevancy_eval.ipynb @@ -18,7 +18,7 @@ "metadata": {}, "outputs": [], "source": [ - "%pip install llama-index-llms-openai" + "%pip install llama-index-llms-openai pandas[jinja2] spacy" ] }, { @@ -96,7 +96,32 @@ "execution_count": null, "id": "41f0e53f-77a6-40d5-94ae-3f81b01af75c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n" + ] + } + ], "source": [ "# create vector index\n", "splitter = SentenceSplitter(chunk_size=512)\n", @@ -112,14 +137,20 @@ "metadata": {}, "outputs": [], "source": [ + "from llama_index.core.evaluation import EvaluationResult\n", + "\n", + "\n", "# define jupyter display function\n", - "def display_eval_df(query: str, response: Response, eval_result: str) -> None:\n", + "def display_eval_df(\n", + " query: str, response: Response, eval_result: EvaluationResult\n", + ") -> None:\n", " eval_df = pd.DataFrame(\n", " {\n", " \"Query\": query,\n", " \"Response\": str(response),\n", " \"Source\": response.source_nodes[0].node.text[:1000] + \"...\",\n", " \"Evaluation Result\": \"Pass\" if eval_result.passing else \"Fail\",\n", + " \"Reasoning\": eval_result.feedback,\n", " },\n", " index=[0],\n", " )\n", @@ -148,7 +179,20 @@ "execution_count": null, "id": "180a5d2e-9286-477b-9cd0-a5976d18d845", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], "source": [ "query_str = (\n", " \"What battles took place in New York City in the American Revolution?\"\n", @@ -170,37 +214,39 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 QueryResponseSourceEvaluation ResultQueryResponseSourceEvaluation ResultReasoning
0What battles took place in New York City in the American Revolution?The Battle of Long Island was the largest battle of the American Revolutionary War that took place in New York City.=== American Revolution ===\n", + " 0What battles took place in New York City in the American Revolution?The Battle of Long Island was the largest battle of the American Revolutionary War that took place in New York City.=== American Revolution ===\n", "\n", "The Stamp Act Congress met in New York in October 1765, as the Sons of Liberty organization emerged in the city and skirmished over the next ten years with British troops stationed there. The Battle of Long Island, the largest battle of the American Revolutionary War, was fought in August 1776 within the modern-day borough of Brooklyn. After the battle, in which the Americans were defeated, the British made the city their military and political base of operations in North America. The city was a haven for Loyalist refugees and escaped slaves who joined the British lines for freedom newly promised by the Crown for all fighters. As many as 10,000 escaped slaves crowded into the city during the British occupation. When the British forces evacuated at the close of the war in 1783, they transported 3,000 freedmen for resettlement in Nova Scotia. They resettled other freedmen in England and the Caribbean.\n", "The only attempt at a peaceful solution to the war took pl...PassPassThe context confirms that the Battle of Long Island, which was the largest battle of the American Revolutionary War, took place in New York City.
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -216,7 +262,20 @@ "execution_count": null, "id": "97f3ddf1-8dc2-4fb8-831f-2c06649e0955", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], "source": [ "query_str = \"What are the airports in New York City?\"\n", "query_engine = vector_index.as_query_engine()\n", @@ -236,44 +295,42 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", - " \n", + "New York City's public bus fleet runs 24/7 and is the largest in North America. The Port Authority Bus Terminal, the main intercity bus terminal of the city, serves 7,000 buse...\n", + " \n", + " \n", " \n", " \n", "
 QueryResponseSourceEvaluation ResultQueryResponseSourceEvaluation ResultReasoning
0What are the airports in New York City?The airports in New York City include John F. Kennedy International Airport, Newark Liberty International Airport, LaGuardia Airport, Stewart International Airport, Long Island MacArthur Airport, Trenton-Mercer Airport, and Westchester County Airport.Like the New York City Subway, the PATH operates 24 hours a day; meaning three of the six rapid transit systems in the world which operate on 24-hour schedules are wholly or partly in New York (the others are a portion of the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n", + " 0What are the airports in New York City?The airports in New York City include John F. Kennedy International Airport, Newark Liberty International Airport, LaGuardia Airport, Stewart International Airport, Long Island MacArthur Airport, Trenton-Mercer Airport, and Westchester County Airport.along the Northeast Corridor, and long-distance train service to other North American cities.The Staten Island Railway rapid transit system solely serves Staten Island, operating 24 hours a day. The Port Authority Trans-Hudson (PATH train) links Midtown and Lower Manhattan to northeastern New Jersey, primarily Hoboken, Jersey City, and Newark. Like the New York City Subway, the PATH operates 24 hours a day; meaning three of the six rapid transit systems in the world which operate on 24-hour schedules are wholly or partly in New York (the others are a portion of the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).\n", + "Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n", "\n", "\n", "==== Buses ====\n", "\n", - "New York City's public bus fleet runs 24/7 and is the largest in North America. The Port Authority Bus Terminal, the main intercity bus terminal of the city, serves 7,000 buses and 200,000 commuters daily, making it the busiest bus station in the world.\n", - "\n", - "\n", - "=== Air ===\n", - "\n", - "New York's airspace is the busiest in the United States and one of the world's busiest air transportation corridors. The three busiest airports in the New York metropolitan area include John F. Kennedy International Airport, Newark Liberty International...PassPassThe context provides information about the airports in New York City, which includes John F. Kennedy International Airport, Newark Liberty International Airport, LaGuardia Airport, Stewart International Airport, Long Island MacArthur Airport, Trenton-Mercer Airport, and Westchester County Airport. This matches the response to the query.
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -289,7 +346,20 @@ "execution_count": null, "id": "8b860691-9f6a-4b2f-bfef-5a5a56693a18", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], "source": [ "query_str = \"Who is the mayor of New York City?\"\n", "query_engine = vector_index.as_query_engine()\n", @@ -309,36 +379,38 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", - " \n", + " \n", + " \n", " \n", " \n", "
 QueryResponseSourceEvaluation ResultQueryResponseSourceEvaluation ResultReasoning
0Who is the mayor of New York City?The mayor of New York City is Eric Adams.=== Politics ===\n", + " 0Who is the mayor of New York City?The mayor of New York City is Eric Adams.=== Politics ===\n", "The present mayor is Eric Adams. He was elected in 2021 with 67% of the vote, and assumed office on January 1, 2022.\n", "The Democratic Party holds the majority of public offices. As of April 2016, 69% of registered voters in the city are Democrats and 10% are Republicans. New York City has not been carried by a Republican presidential election since President Calvin Coolidge won the five boroughs in 1924. A Republican candidate for statewide office has not won all five boroughs of the city since it was incorporated in 1898. In 2012, Democrat Barack Obama became the first presidential candidate of any party to receive more than 80% of the overall vote in New York City, sweeping all five boroughs. Party platforms center on affordable housing, education, and economic development, and labor politics are of importance in the city. Thirteen out of 27 U.S. congressional districts in the state of New York include portions of New York City.New York is one of the most important so...PassPassThe context confirms that Eric Adams is the current mayor of New York City, as stated in the response.
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -397,7 +469,22 @@ "execution_count": null, "id": "0e3674ed-2632-4f48-9102-895fea94e340", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/embeddings \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], "source": [ "# NOTE: you can set response_mode=\"no_text\" to get just the sources\n", "query_str = \"What are the airports in New York City?\"\n", @@ -428,23 +515,24 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", - " \n", + " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 SourceEval ResultSourceEval Result
0Like the New York City Subway, the PATH operates 24 hours a day; meaning three of the six rapid transit systems in the world which operate on 24-hour schedules are wholly or partly in New York (the others are a portion of the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n", + " 0along the Northeast Corridor, and long-distance train service to other North American cities.The Staten Island Railway rapid transit system solely serves Staten Island, operating 24 hours a day. The Port Authority Trans-Hudson (PATH train) links Midtown and Lower Manhattan to northeastern New Jersey, primarily Hoboken, Jersey City, and Newark. Like the New York City Subway, the PATH operates 24 hours a day; meaning three of the six rapid transit systems in the world which operate on 24-hour schedules are wholly or partly in New York (the others are a portion of the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).\n", + "Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n", "\n", "\n", "==== Buses ====\n", @@ -455,11 +543,14 @@ "=== Air ===\n", "\n", "New York's airspace is the busiest in the United States and one of the world's busiest air transportation corridors. The three busiest airports in the New York metropolitan area include John F. Kennedy International Airport, Newark Liberty International Airport, and LaGuardia Airport; 130.5 million travelers used these three airports in 2016. JFK and Newark Liberty were the busiest and fourth busiest U.S. gateways for international air passengers, respectively, in 2012; as of 2011, JFK was the busiest airport for international passengers in North America.Plans have advanced to expand passenger volume at a fourth airport, Stewart International Airport near Newburgh, New York, by the Port Authority of New York and New Jersey. Plans were announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.PassPass
1=== Parks ===\n", + " 1See or edit raw graph data.\n", + "\n", + "\n", + "=== Parks ===\n", "\n", "The city of New York has a complex park system, with various lands operated by the National Park Service, the New York State Office of Parks, Recreation and Historic Preservation, and the New York City Department of Parks and Recreation. In its 2018 ParkScore ranking, the Trust for Public Land reported that the park system in New York City was the ninth-best park system among the fifty most populous U.S. cities. ParkScore ranks urban park systems by a formula that analyzes median park size, park acres as percent of city area, the percent of city residents within a half-mile of a park, spending of park services per resident, and the number of playgrounds per 10,000 residents. In 2021, the New York City Council banned the use of synthetic pesticides by city agencies and instead required organic lawn management. The effort was started by teacher Paula Rogovin's kindergarten class at P.S. 290.\n", "\n", @@ -468,30 +559,19 @@ "\n", "Gateway National Recreation Area contains over 26,000 acres (110 km2), most of it in New York City. In Brooklyn and Queens, the park contains over 9,000 acres (36 km2) of salt marsh, wetlands, islands, and water, including most of Jamaica Bay and the Jamaica Bay Wildlife Refuge. Also in Queens, the park includes a significant portion of the western Rockaway Peninsula, most notably Jacob Riis Park and Fort Tilden. In Staten Island, it includes Fort Wadsworth, with historic pre-Civil War era Battery Weed and Fort Tompkins, and Great Kills Park, with beaches, trails, and a marina.\n", "The Statue of Liberty National Monument and Ellis Island Immigration Museum are managed by the National Park Service and are in both New York and New Jersey. They are joined in the harbor by Governors Island National Monument. Historic sites under federal management on Manhattan Island include Stonewall National Monument; Castle Clinton National Monument; Federal Hall National Memorial; Theodore Roosevelt Birthplace National Historic Site; General Grant National Memorial (Grant's Tomb); African Burial Ground National Monument; and Hamilton Grange National Memorial. Hundreds of properties are listed on the National Register of Historic Places or as a National Historic Landmark.FailFail
2Providing continuous 24/7 service and contributing to the nickname The City That Never Sleeps, the New York City Subway is the largest single-operator rapid transit system in the world with 472 passenger rail stations, and Penn Station in Midtown Manhattan is the busiest transportation hub in the Western Hemisphere.The city has over 120 colleges and universities, including Columbia University, an Ivy League university routinely ranked among the world's top universities, New York University, and the City University of New York system, the largest urban public university system in the nation.Anchored by Wall Street in the Financial District of Lower Manhattan, New York City has been called both the world's leading financial and fintech center and the most economically powerful city in the world, and is home to the world's two largest stock exchanges by total market capitalization, the New York Stock Exchange and Nasdaq.The Stonewall Inn in Greenwich Village, part of the Stonewall National Monument, is considered the historic epicenter of LGBTQ+ culture and the birthplace of the modern gay rights movement.New York City is the headquarters of the global art market, with numerous art galleries and auction houses collectively hosting half of the world’s art auctions, and the Metropolitan Museum of Art is both the largest art museum and the most visited museum in the United States.Governors Island in New York Harbor is planned to host a US$1 billion research and education center as a leader the climate crisis.\n", - "\n", - "\n", - "== Etymology ==\n", - "\n", - "In 1664, New York was named in honor of the Duke of York, who would become King James II of England. James's elder brother, King Charles II, appointed the Duke as proprietor of the former territory of New Netherland, including the city of New Amsterdam, when England seized it from Dutch control.\n", - "\n", - "\n", - "== History ==\n", - "\n", - "\n", - "=== Early history ===\n", - "In the pre-Columbian era, the area of present-day New York City was inhabited by Algonquian Native Americans, including the Lenape.Their homeland, known as Lenapehoking, included the present-day areas of Staten Island, Manhattan, the Bronx, the western portion of Long Island (including the areas that would later become the boroughs of Brooklyn and Queens), and the Lower Hudson Valley.The first documented visit into New York Harbor by a European was in 1524 by Italian Giovanni da Verrazzano, an explorer from Florence in the service of the French crown.Fail2New York has witnessed a growing combined volume of international and domestic tourists, reflecting over 60 million visitors to the city per year, the world's busiest tourist destination. Approximately 12 million visitors to New York City have been from outside the United States, with the highest numbers from the United Kingdom, Canada, Brazil, and China. Multiple sources have called New York the most photographed city in the world.I Love New York (stylized I ❤ NY) is both a logo and a song that are the basis of an advertising campaign and have been used since 1977 to promote tourism in New York City, and later to promote New York State as well. The trademarked logo, owned by New York State Empire State Development, appears in souvenir shops and brochures throughout the city and state, some licensed, many not. The song is the state song of New York.\n", + "The majority of the most high-profile tourist destinations to the city are situated in Manhattan. These include Times Square; Broadway theater productions; the Empire State Building; the Statue of Liberty; Ellis Island; the United Nations headquarters; the World Trade Center (including the National September 11 Memorial & Museum and One World Trade Center); the art museums along Museum Mile; green spaces such as Central Park, Washington Square Park, the High Line, and the medieval gardens of The Cloisters; the Stonewall Inn; Rockefeller Center; ethnic enclaves including the Manhattan Chinatown, Koreatown, Curry Hill, Harlem, Spanish Harlem, Little Italy, and Little Australia; luxury shopping along Fifth and Madison Avenues; and events such as the Halloween Parade in Greenwich Village; the Brooklyn Bridge (shared with Brooklyn); the Macy's Thanksgiving Day Parade; the lighting of the Rockefeller Center Christmas Tree; the St. Patrick's Day Parade; seasonal activities such as ice skating in Central Park in the wintertime; the Tribeca Film Festival; and free performances in Central Park at SummerStage.Points of interest have also developed in the city outside Manhattan and have made the outer boroughs tourist destinations in their own right.Fail
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -507,7 +587,20 @@ "execution_count": null, "id": "cdc340a9-1853-414e-9e87-820aa5d82db7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "INFO:httpx:HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n", + "HTTP Request: POST https://api.openai.com/v1/chat/completions \"HTTP/1.1 200 OK\"\n" + ] + } + ], "source": [ "# NOTE: you can set response_mode=\"no_text\" to get just the sources\n", "query_str = \"Who is the mayor of New York City?\"\n", @@ -537,76 +630,63 @@ "data": { "text/html": [ "\n", - "\n", + "
\n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + "Gateway National Recreation Area contains over 26,000 acres (110 km2), most of it in New York City. In Brooklyn and Queens, the park contains over 9,000 acres (36 km2) of salt marsh, wetlands, islands, and water, including most of Jamaica Bay and the Jamaica Bay Wildlife Refuge. Also in Queens, the park includes a significant portion of the western Rockaway Peninsula, most notably Jacob Riis Park and Fort Tilden. In Staten Island, it includes Fort Wadsworth, with historic pre-Civil War era Battery Weed and Fort Tompkins, and Great Kills Park, with beaches, trails, and a marina.\n", + "The Statue of Liberty National Monument and Ellis Island Immigration Museum are managed by the National Park Service and are in both New York and New Jersey. They are joined in the harbor by Governors Island National Monument. Historic sites under federal management on Manhattan Island include Stonewall National Monument; Castle Clinton National Monument; Federal Hall National Memorial; Theodore Roosevelt Birthplace National Historic Site; General Grant National Memorial (Grant's Tomb); African Burial Ground National Monument; and Hamilton Grange National Memorial. Hundreds of properties are listed on the National Register of Historic Places or as a National Historic Landmark.\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", "
 SourceEval ResultSourceEval Result
0by geographic population boundaries. Each term for the mayor and council members lasts four years and has a two consecutive-term limit, which is reset after a four-year break. The New York City Administrative Code, the New York City Rules, and the City Record are the code of local laws, compilation of regulations, and official journal, respectively.Each borough is coextensive with a judicial district of the state Unified Court System, of which the Criminal Court and the Civil Court are the local courts, while the New York Supreme Court conducts major trials and appeals. Manhattan hosts the First Department of the Supreme Court, Appellate Division while Brooklyn hosts the Second Department. There are also several extrajudicial administrative courts, which are executive agencies and not part of the state Unified Court System.\n", - "Uniquely among major American cities, New York is divided between, and is host to the main branches of, two different U.S. district courts: the District Court for the Southern District of New York, whose main courthouse is on Foley Square near City Hall in Manhattan and whose jurisdiction includes Manhattan and the Bronx; and the District Court for the Eastern District of New York, whose main courthouse is in Brooklyn and whose jurisdiction includes Brooklyn, Queens, and Staten Island. The U.S. Court of Appeals for the Second Circuit and U.S. Court of International Trade are also based in New York, also on Foley Square in Manhattan.\n", + " 0along the Northeast Corridor, and long-distance train service to other North American cities.The Staten Island Railway rapid transit system solely serves Staten Island, operating 24 hours a day. The Port Authority Trans-Hudson (PATH train) links Midtown and Lower Manhattan to northeastern New Jersey, primarily Hoboken, Jersey City, and Newark. Like the New York City Subway, the PATH operates 24 hours a day; meaning three of the six rapid transit systems in the world which operate on 24-hour schedules are wholly or partly in New York (the others are a portion of the Chicago \"L\", the PATCO Speedline serving Philadelphia, and the Copenhagen Metro).\n", + "Multibillion-dollar heavy rail transit projects under construction in New York City include the Second Avenue Subway, and the East Side Access project.\n", "\n", "\n", - "=== Politics ===\n", - "The present mayor is Eric Adams. He was elected in 2021 with 67% of the vote, and assumed office on January 1, 2022.\n", - "The Democratic Party holds the majority of public offices. As of April 2016, 69% of registered voters in the city are Democrats and 10% are Republicans. New York City has not been carried by a Republican presidential election since President Calvin Coolidge won the five boroughs in 1924. A Republican candidate for statewide office has not won all five boroughs of the city since it was incorporated in 1898. In 2012, Democrat Barack Obama became the first presidential candidate of any party to receive more than 80% of the overall vote in New York City, sweeping allNO
1for the Second Circuit and U.S. Court of International Trade are also based in New York, also on Foley Square in Manhattan.\n", + "==== Buses ====\n", "\n", + "New York City's public bus fleet runs 24/7 and is the largest in North America. The Port Authority Bus Terminal, the main intercity bus terminal of the city, serves 7,000 buses and 200,000 commuters daily, making it the busiest bus station in the world.\n", "\n", - "=== Politics ===\n", - "The present mayor is Eric Adams. He was elected in 2021 with 67% of the vote, and assumed office on January 1, 2022.\n", - "The Democratic Party holds the majority of public offices. As of April 2016, 69% of registered voters in the city are Democrats and 10% are Republicans. New York City has not been carried by a Republican presidential election since President Calvin Coolidge won the five boroughs in 1924. A Republican candidate for statewide office has not won all five boroughs of the city since it was incorporated in 1898. In 2012, Democrat Barack Obama became the first presidential candidate of any party to receive more than 80% of the overall vote in New York City, sweeping all five boroughs. Party platforms center on affordable housing, education, and economic development, and labor politics are of importance in the city. Thirteen out of 27 U.S. congressional districts in the state of New York include portions of New York City.New York is one of the most important sources of political fundraising in the United States. At least four of the top five ZIP Codes in the nation for political contributions were in Manhattan for the 2004, 2006, and 2008 elections. The top ZIP Code, 10021 on the Upper East Side, generated the most money for the 2004 presidential campaigns of George W. Bush and John Kerry. The city has a strong imbalance of payments with the national and state governments. It receives 83 cents in services for every $1 it sends to the federal government in taxes (or annually sends $11.4 billion more than it receives back). City residents and businesses also sent an additional $4.1 billion in the 2009–2010 fiscal year to the state of New York than the city received in return.\n", "\n", + "=== Air ===\n", "\n", - "== Transportation ==\n", + "New York's airspace is the busiest in the United States and one of the world's busiest air transportation corridors. The three busiest airports in the New York metropolitan area include John F. Kennedy International Airport, Newark Liberty International Airport, and LaGuardia Airport; 130.5 million travelers used these three airports in 2016. JFK and Newark Liberty were the busiest and fourth busiest U.S. gateways for international air passengers, respectively, in 2012; as of 2011, JFK was the busiest airport for international passengers in North America.Plans have advanced to expand passenger volume at a fourth airport, Stewart International Airport near Newburgh, New York, by the Port Authority of New York and New Jersey. Plans were announced in July 2015 to entirely rebuild LaGuardia Airport in a multibillion-dollar project to replace its aging facilities. Other commercial airports in or serving the New York metropolitan area include Long Island MacArthur Airport, Trenton–Mercer Airport and Westchester County Airport. The primary general aviation airport serving the area is Teterboro Airport.Fail
1See or edit raw graph data.\n", "\n", - "New York City's comprehensive transportation system is both complex and extensive.\n", "\n", + "=== Parks ===\n", "\n", - "=== Rapid transit ===\n", - "Mass transit in New York City, most of which runs 24 hours a day, accounts for one in every three users of mass transit inYES
2ed.). New York: The New Press. ISBN 978-1-56584-321-9.\n", - "Holli, Melvin G., and Jones, Peter d'A., eds. Biographical Dictionary of American Mayors, 1820-1980 (Greenwood Press, 1981) short scholarly biographies each of the city's mayors 1820 to 1980. online; see index at p. 410 for list.Jackson, Kenneth T., ed. (1995). The Encyclopedia of New York City. New Haven: Yale University Press. ISBN 0300055366.\n", - "Jackson, Kenneth T.; Dunbar, David S., eds. (2005). Empire City: New York Through the Centuries. Columbia University Press. ISBN 978-0-231-10909-3.\n", - "Lankevich, George L. (1998). American Metropolis: A History of New York City. NYU Press. ISBN 978-0-8147-5186-2.\n", - "White, E.B. (1949). Here is New York (2000 reissue ed.). Little Bookroom.\n", - "White, Norval & Willensky, Elliot (2000). AIA Guide to New York City (4th ed.). New York: Three Rivers Press. ISBN 978-0-8129-3107-5.\n", - "Whitehead, Colson (2003). The Colossus of New York: A City in 13 Parts. New York: Doubleday. ISBN 978-0-385-50794-3.\n", + "The city of New York has a complex park system, with various lands operated by the National Park Service, the New York State Office of Parks, Recreation and Historic Preservation, and the New York City Department of Parks and Recreation. In its 2018 ParkScore ranking, the Trust for Public Land reported that the park system in New York City was the ninth-best park system among the fifty most populous U.S. cities. ParkScore ranks urban park systems by a formula that analyzes median park size, park acres as percent of city area, the percent of city residents within a half-mile of a park, spending of park services per resident, and the number of playgrounds per 10,000 residents. In 2021, the New York City Council banned the use of synthetic pesticides by city agencies and instead required organic lawn management. The effort was started by teacher Paula Rogovin's kindergarten class at P.S. 290.\n", "\n", "\n", - "== External links ==\n", + "==== National parks ====\n", "\n", - "Official website \n", - "NYC Go, official tourism website\n", - "New York City at Curlie\n", - " Geographic data related to New York City at OpenStreetMap\n", - "Collections, 145,000 NYC photographs at the Museum of the City of New York\n", - "\"The New New York Skyline (interactive)\". National Geographic. November 2015.NOFail
2New York has witnessed a growing combined volume of international and domestic tourists, reflecting over 60 million visitors to the city per year, the world's busiest tourist destination. Approximately 12 million visitors to New York City have been from outside the United States, with the highest numbers from the United Kingdom, Canada, Brazil, and China. Multiple sources have called New York the most photographed city in the world.I Love New York (stylized I ❤ NY) is both a logo and a song that are the basis of an advertising campaign and have been used since 1977 to promote tourism in New York City, and later to promote New York State as well. The trademarked logo, owned by New York State Empire State Development, appears in souvenir shops and brochures throughout the city and state, some licensed, many not. The song is the state song of New York.\n", + "The majority of the most high-profile tourist destinations to the city are situated in Manhattan. These include Times Square; Broadway theater productions; the Empire State Building; the Statue of Liberty; Ellis Island; the United Nations headquarters; the World Trade Center (including the National September 11 Memorial & Museum and One World Trade Center); the art museums along Museum Mile; green spaces such as Central Park, Washington Square Park, the High Line, and the medieval gardens of The Cloisters; the Stonewall Inn; Rockefeller Center; ethnic enclaves including the Manhattan Chinatown, Koreatown, Curry Hill, Harlem, Spanish Harlem, Little Italy, and Little Australia; luxury shopping along Fifth and Madison Avenues; and events such as the Halloween Parade in Greenwich Village; the Brooklyn Bridge (shared with Brooklyn); the Macy's Thanksgiving Day Parade; the lighting of the Rockefeller Center Christmas Tree; the St. Patrick's Day Parade; seasonal activities such as ice skating in Central Park in the wintertime; the Tribeca Film Festival; and free performances in Central Park at SummerStage.Points of interest have also developed in the city outside Manhattan and have made the outer boroughs tourist destinations in their own right.Fail
\n" ], "text/plain": [ - "" + "" ] }, "metadata": {}, diff --git a/docs/examples/llm/llama_2_llama_cpp.ipynb b/docs/examples/llm/llama_2_llama_cpp.ipynb index c57992d27ccb4..81c7dacde38ff 100644 --- a/docs/examples/llm/llama_2_llama_cpp.ipynb +++ b/docs/examples/llm/llama_2_llama_cpp.ipynb @@ -55,7 +55,7 @@ "source": [ "from llama_index.core import SimpleDirectoryReader, VectorStoreIndex\n", "from llama_index.llms.llama_cpp import LlamaCPP\n", - "from llama_index.core.llms.llama_utils import (\n", + "from llama_index.llms.llama_cpp.llama_utils import (\n", " messages_to_prompt,\n", " completion_to_prompt,\n", ")" diff --git a/docs/examples/llm/nvidia_tensorrt.ipynb b/docs/examples/llm/nvidia_tensorrt.ipynb index b08d424a67981..64853f12f9a65 100644 --- a/docs/examples/llm/nvidia_tensorrt.ipynb +++ b/docs/examples/llm/nvidia_tensorrt.ipynb @@ -57,7 +57,7 @@ "metadata": {}, "outputs": [], "source": [ - "!pip3 install tensorrt_llm -U --extra-index-url https://pypi.nvidia.com" + "!pip install tensorrt_llm==0.7.0 --extra-index-url https://pypi.nvidia.com --extra-index-url https://download.pytorch.org/whl/cu121" ] }, { diff --git a/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb b/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb index dc91554f5c686..f5fc5df33f12c 100644 --- a/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb +++ b/docs/examples/metadata_extraction/MetadataExtraction_LLMSurvey.ipynb @@ -626,7 +626,82 @@ "execution_count": null, "id": "5672f974-1a4d-435f-904a-77222eaaed16", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**`Final Response:`** Metrics for evaluating text generation quality can vary depending on the task. However, some commonly used metrics include BLEU, ROUGE, and exact match metrics. \n", + "\n", + "BLEU and ROUGE are often used for evaluating machine translation and summarization tasks. They measure the n-gram overlap between the generated text and a reference text. However, these metrics have limitations. For example, they may not be suitable for tasks like abstractive summarization or dialogue, where a wide variety of responses are possible. This is because they rely on exact match and n-gram overlap, which may not capture the semantic quality or creativity of the generated text.\n", + "\n", + "Exact match metrics, such as BLEU and ROUGE, have the downside of not considering the possibility of a good response with zero n-gram overlap with the reference. This means that even if the generated text is a good response, it may receive a low score if it does not have any n-gram overlap with the reference.\n", + "\n", + "In addition to their limitations in capturing text quality, these metrics also have poor adaptability to a wider variety of tasks. Using a metric proposed for one task to evaluate another task may not be appropriate. \n", + "\n", + "Furthermore, these metrics have poor reproducibility. There can be high variance in scores across different studies, possibly due to variations in human judgment collection or metric parameter settings. This lack of consistency makes it challenging to compare results across different studies.\n", + "\n", + "Overall, while metrics like BLEU and ROUGE provide some quantitative measure of text generation quality, they have downsides in terms of adaptability, reproducibility, and their ability to capture the creativity and diversity of generated text." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "---" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**`Source Node 1/1`**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Node ID:** 256dfc5a-cd91-4ff1-9f28-15693775e354
**Similarity:** 0.8402930255994321
**Text:** require creativity and\n", + "diversity](https://arxiv.org/abs/2303.16634).\n", + "\n", + "Second, these metrics often have **poor adaptability to a wider variety of\n", + "tasks**. Adopting a metric proposed for one task to another is not always\n", + "prudent. For example, exact match metrics such as BLEU and ROUGE are a poor\n", + "fit for tasks like abstractive summarization or dialogue. Since they’re based\n", + "on n-gram overlap between output and reference, they don’t make sense for a\n", + "dialogue task where a wide variety of responses are possible. An output can\n", + "have zero n-gram overlap with the reference but yet be a good response.\n", + "\n", + "Third, these metrics have **poor reproducibility**. Even for the same metric,\n", + "[high variance is reported across different\n", + "studies](https://arxiv.org/abs/2008.12009), possibly due to variations in\n", + "human judgment collection or metric parameter settings. Another study of\n", + "[ROUGE scores](https://aclanthology.org/2023.acl-long.107/) across 2,000\n", + "studies found that scores were hard
**Metadata:** {}
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_response(\n", " response0, source_length=1000, show_source=True, show_source_metadata=True\n", @@ -672,7 +747,82 @@ "execution_count": null, "id": "7093adf9-5fdd-47e3-9114-fc2d264df81c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**`Final Response:`** Metrics for evaluating text generation quality can vary depending on the task at hand. However, some commonly used metrics include BLEU (Bilingual Evaluation Understudy), ROUGE (Recall-Oriented Understudy for Gisting Evaluation), and exact match metrics.\n", + "\n", + "BLEU and ROUGE are often used for evaluating machine translation and summarization tasks. They measure the overlap of n-grams (sequences of words) between the generated text and a reference text. Higher scores indicate better quality, as it suggests that the generated text is similar to the reference.\n", + "\n", + "Exact match metrics, on the other hand, focus on the exact match between the generated text and the reference. These metrics are commonly used for tasks like question answering or dialogue systems. They determine if the generated text matches the reference exactly, without considering partial matches or variations in wording.\n", + "\n", + "While these metrics can provide some insights into text generation quality, they have their downsides. First, they may lack adaptability to different tasks. For example, exact match metrics like BLEU and ROUGE may not be suitable for tasks like abstractive summarization or dialogue, where a wide variety of responses are possible.\n", + "\n", + "Second, these metrics may have poor reproducibility. Different studies have reported high variance in scores, possibly due to variations in human judgment collection or metric parameter settings. This can make it challenging to compare results across different studies or reproduce the same scores.\n", + "\n", + "In summary, metrics for evaluating text generation quality can be useful but should be chosen carefully based on the task. It is important to consider their adaptability, reproducibility, and limitations when interpreting the results." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "---" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**`Source Node 1/1`**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Node ID:** 5161474e-bb2b-4642-9f07-692aa7db4375
**Similarity:** 0.8352202179927705
**Text:** require creativity and\n", + "diversity](https://arxiv.org/abs/2303.16634).\n", + "\n", + "Second, these metrics often have **poor adaptability to a wider variety of\n", + "tasks**. Adopting a metric proposed for one task to another is not always\n", + "prudent. For example, exact match metrics such as BLEU and ROUGE are a poor\n", + "fit for tasks like abstractive summarization or dialogue. Since they’re based\n", + "on n-gram overlap between output and reference, they don’t make sense for a\n", + "dialogue task where a wide variety of responses are possible. An output can\n", + "have zero n-gram overlap with the reference but yet be a good response.\n", + "\n", + "Third, these metrics have **poor reproducibility**. Even for the same metric,\n", + "[high variance is reported across different\n", + "studies](https://arxiv.org/abs/2008.12009), possibly due to variations in\n", + "human judgment collection or metric parameter settings. Another study of\n", + "[ROUGE scores](https://aclanthology.org/2023.acl-long.107/) across 2,000\n", + "studies found that scores were hard
**Metadata:** {'questions_this_excerpt_can_answer': '1. What are some limitations of using exact match metrics like BLEU and ROUGE for tasks such as abstractive summarization or dialogue?\\n2. Why is it not always prudent to adopt a metric proposed for one task to another?\\n3. What are some challenges in reproducing and comparing metric scores across different studies?'}
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_response(\n", " response1, source_length=1000, show_source=True, show_source_metadata=True\n", @@ -684,7 +834,78 @@ "execution_count": null, "id": "b188a0dc-cb46-4370-ad28-ce3ca9d9fa4f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**`Final Response:`** Metrics for evaluating text generation quality include BERTScore and MoverScore. BERTScore measures the similarity between generated text and reference text by considering contextual embeddings. On the other hand, MoverScore measures the effort required to transform one text sequence into another by mapping semantically related words. \n", + "\n", + "However, there are downsides to using conventional benchmarks and metrics for text generation evaluation. Firstly, these metrics have been found to have poor correlation with human judgments. For example, metrics like BLEU and ROUGE have shown negative correlation with human evaluations of fluency and moderate to less correlation with human adequacy scores. Additionally, they have low correlation with tasks that require creativity and diversity.\n", + "\n", + "Secondly, these metrics often have poor adaptability to different tasks. Metrics like BLEU and ROUGE, which are based on n-gram overlap between output and reference, are not suitable for tasks like abstractive summarization or dialogue. Therefore, adopting a metric proposed for one task to another may not be appropriate." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "---" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**`Source Node 1/1`**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Node ID:** 59031c3f-c150-4d84-82f6-a32bb9920a0a
**Similarity:** 0.8382547930335613
**Text:** is to measure the distance that words would\n", + "have to move to convert one sequence to another.\n", + "\n", + "However, there are several pitfalls to using these conventional benchmarks and\n", + "metrics.\n", + "\n", + "First, there’s **poor correlation between these metrics and human judgments.**\n", + "BLEU, ROUGE, and others have had [negative correlation with how humans\n", + "evaluate fluency](https://arxiv.org/abs/2008.12009). They also showed moderate\n", + "to less correlation with human adequacy scores. In particular, BLEU and ROUGE\n", + "have [low correlation with tasks that require creativity and\n", + "diversity](https://arxiv.org/abs/2303.16634).\n", + "\n", + "Second, these metrics often have **poor adaptability to a wider variety of\n", + "tasks**. Adopting a metric proposed for one task to another is not always\n", + "prudent. For example, exact match metrics such as BLEU and ROUGE are a poor\n", + "fit for tasks like abstractive summarization or dialogue. Since they’re based\n", + "on n-gram overlap between output and reference, they don’t make sense for a\n", + "dialogue task where ...
**Metadata:** {'prev_section_summary': 'The section discusses the comparison between BERTScore and MoverScore, two metrics used to evaluate the quality of text generation models. MoverScore is described as a metric that measures the effort required to transform one text sequence into another by mapping semantically related words. The section also highlights the limitations of conventional benchmarks and metrics, such as poor correlation with human judgments and low correlation with tasks requiring creativity.', 'next_section_summary': 'The section discusses the limitations of current evaluation metrics in natural language processing tasks. It highlights three main issues: lack of creativity and diversity in metrics, poor adaptability to different tasks, and poor reproducibility. The section mentions specific metrics like BLEU and ROUGE, and also references studies that have reported high variance in metric scores.', 'section_summary': 'The section discusses the limitations of conventional benchmarks and metrics used to measure the distance between word sequences. It highlights two main issues: the poor correlation between these metrics and human judgments, and their limited adaptability to different tasks. The section mentions specific metrics like BLEU and ROUGE, which have been found to have low correlation with human evaluations of fluency, adequacy, creativity, and diversity. It also points out that metrics based on n-gram overlap, such as BLEU and ROUGE, are not suitable for tasks like abstractive summarization or dialogue.', 'questions_this_excerpt_can_answer': '1. What are the limitations of conventional benchmarks and metrics in measuring the distance between word sequences?\\n2. How do metrics like BLEU and ROUGE correlate with human judgments in terms of fluency, adequacy, creativity, and diversity?\\n3. Why are metrics based on n-gram overlap, such as BLEU and ROUGE, not suitable for tasks like abstractive summarization or dialogue?'}
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_response(\n", " response2, source_length=1000, show_source=True, show_source_metadata=True\n", @@ -724,7 +945,75 @@ "execution_count": null, "id": "4db36ec6-b64b-45da-aff1-ed51b79c571f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**`Final Response:`** BERTScore is a metric that is useful because it can account for synonyms and paraphrasing, unlike simpler metrics like BLEU and ROUGE. It uses contextualized embeddings to compute the distance between tokens in the generated output and reference. The formula for BERTScore is:\n", + "\n", + "\\[ F_{\\text{BERT}} = \\frac{2 \\cdot P_{\\text{BERT}} \\cdot R_{\\text{BERT}}}{P_{\\text{BERT}} + R_{\\text{BERT}}} \\]\n", + "\n", + "MoverScore, on the other hand, also uses contextualized embeddings to compute the distance between tokens in the generated output and reference. However, unlike BERTScore, which is based on one-to-one matching of tokens, MoverScore allows for many-to-one matching. This is also known as \"soft alignment.\" Unfortunately, the formula for MoverScore is not provided in the given context." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "---" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**`Source Node 1/1`**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Node ID:** b15a1c51-3c52-42c2-aba0-1f7aa805e6ce
**Similarity:** 0.8393536980456234
**Text:** = F_{\\text{BERT}} =\n", + "\\frac{2 \\cdot P_{\\text{BERT}} \\cdot R_{\\text{BERT}}}{P_{\\text{BERT}} +\n", + "R_{\\text{BERT}}}\\\\]\n", + "\n", + "BERTScore is useful because it can account for synonyms and paraphrasing.\n", + "Simpler metrics like BLEU and ROUGE can’t do this due to their reliance on\n", + "exact matches. BERTScore has been shown to have better correlation for tasks\n", + "such as image captioning and machine translation.\n", + "\n", + "**[MoverScore](https://arxiv.org/abs/1909.02622)** also uses contextualized\n", + "embeddings to compute the distance between tokens in the generated output and\n", + "reference. But unlike BERTScore, which is based on one-to-one matching (or\n", + "“hard alignment”) of tokens, MoverScore allows for many-to-one matching (or\n", + "“soft alignment”).\n", + "\n", + "![BERTScore \\(left\\) vs. MoverScore
**Metadata:** {}
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_response(\n", " response0, source_length=1000, show_source=True, show_source_metadata=True\n", @@ -736,7 +1025,73 @@ "execution_count": null, "id": "ddef5ae7-1030-4257-ba16-8340e46bd548", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**`Final Response:`** BERTScore is a metric that is useful because it can account for synonyms and paraphrasing, unlike simpler metrics like BLEU and ROUGE. It uses contextualized embeddings to compute the distance between tokens in the generated output and reference. The formula for BERTScore is F_{BERT} = \\frac{2 \\cdot P_{BERT} \\cdot R_{BERT}}{P_{BERT} + R_{BERT}}.\n", + "\n", + "MoverScore, on the other hand, also uses contextualized embeddings to compute the distance between tokens in the generated output and reference. However, unlike BERTScore, which is based on one-to-one matching of tokens, MoverScore allows for many-to-one matching. Unfortunately, the formula for MoverScore is not provided in the given context." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "---" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**`Source Node 1/1`**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Node ID:** c7d7dcb1-48a8-4433-9a58-a0a16ce9b14e
**Similarity:** 0.8485439966069306
**Text:** = F_{\\text{BERT}} =\n", + "\\frac{2 \\cdot P_{\\text{BERT}} \\cdot R_{\\text{BERT}}}{P_{\\text{BERT}} +\n", + "R_{\\text{BERT}}}\\\\]\n", + "\n", + "BERTScore is useful because it can account for synonyms and paraphrasing.\n", + "Simpler metrics like BLEU and ROUGE can’t do this due to their reliance on\n", + "exact matches. BERTScore has been shown to have better correlation for tasks\n", + "such as image captioning and machine translation.\n", + "\n", + "**[MoverScore](https://arxiv.org/abs/1909.02622)** also uses contextualized\n", + "embeddings to compute the distance between tokens in the generated output and\n", + "reference. But unlike BERTScore, which is based on one-to-one matching (or\n", + "“hard alignment”) of tokens, MoverScore allows for many-to-one matching (or\n", + "“soft alignment”).\n", + "\n", + "![BERTScore \\(left\\) vs. MoverScore
**Metadata:** {'questions_this_excerpt_can_answer': '1. What is the advantage of using BERTScore over simpler metrics like BLEU and ROUGE?\\n2. How does MoverScore differ from BERTScore in terms of token matching?\\n3. What tasks have shown better correlation with BERTScore, such as image captioning and machine translation?'}
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_response(\n", " response1, source_length=1000, show_source=True, show_source_metadata=True\n", @@ -748,7 +1103,75 @@ "execution_count": null, "id": "43c607eb-6133-43f9-83c7-f6e4e03d78df", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/markdown": [ + "**`Final Response:`** BERTScore is a method used to compute the similarity between generated output and reference in tasks like image captioning and machine translation. It considers synonyms and paraphrasing, unlike simpler metrics such as BLEU and ROUGE. BERTScore uses one-to-one matching of tokens to calculate its score.\n", + "\n", + "MoverScore, on the other hand, also utilizes contextualized embeddings to compute the distance between tokens in the generated output and reference. Unlike BERTScore, which uses one-to-one matching, MoverScore allows for many-to-one matching. It solves an optimization problem to find the minimum effort required to transform one text into another by measuring the distance words would have to move.\n", + "\n", + "Unfortunately, the formulas for BERTScore and MoverScore are not provided in the given context." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "---" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**`Source Node 1/1`**" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/markdown": [ + "**Node ID:** 21664308-0009-435f-93fc-1ff7de4a9091
**Similarity:** 0.8474860535677818
**Text:** = F_{\\text{BERT}} =\n", + "\\frac{2 \\cdot P_{\\text{BERT}} \\cdot R_{\\text{BERT}}}{P_{\\text{BERT}} +\n", + "R_{\\text{BERT}}}\\\\]\n", + "\n", + "BERTScore is useful because it can account for synonyms and paraphrasing.\n", + "Simpler metrics like BLEU and ROUGE can’t do this due to their reliance on\n", + "exact matches. BERTScore has been shown to have better correlation for tasks\n", + "such as image captioning and machine translation.\n", + "\n", + "**[MoverScore](https://arxiv.org/abs/1909.02622)** also uses contextualized\n", + "embeddings to compute the distance between tokens in the generated output and\n", + "reference. But unlike BERTScore, which is based on one-to-one matching (or\n", + "“hard alignment”) of tokens, MoverScore allows for many-to-one matching (or\n", + "“soft alignment”).\n", + "\n", + "![BERTScore \\(left\\) vs. MoverScore
**Metadata:** {'next_section_summary': 'The key topics of this section are BERTScore and MoverScore, which are methods used to compute the similarity between generated output and reference in tasks like image captioning and machine translation. BERTScore uses one-to-one matching of tokens, while MoverScore allows for many-to-one matching. MoverScore solves an optimization problem to find the minimum effort required to transform one text into another by measuring the distance words would have to move.', 'section_summary': \"The section discusses the importance of BERTScore and MoverScore in evaluating the quality of generated text. BERTScore is advantageous as it considers synonyms and paraphrasing, unlike simpler metrics such as BLEU and ROUGE. It has shown better correlation in tasks like image captioning and machine translation. On the other hand, MoverScore also utilizes contextualized embeddings but allows for many-to-one matching, unlike BERTScore's one-to-one matching.\", 'questions_this_excerpt_can_answer': \"1. What are the key differences between BERTScore and MoverScore in terms of their matching approaches?\\n2. How does BERTScore account for synonyms and paraphrasing, and why is this advantageous compared to metrics like BLEU and ROUGE?\\n3. What is the advantage of MoverScore allowing for many-to-one matching compared to BERTScore's one-to-one matching?\"}
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "display_response(\n", " response2, source_length=1000, show_source=True, show_source_metadata=True\n", diff --git a/docs/examples/multi_modal/multi_modal_video_RAG.ipynb b/docs/examples/multi_modal/multi_modal_video_RAG.ipynb index 3530070970ed0..726c3abdb5031 100644 --- a/docs/examples/multi_modal/multi_modal_video_RAG.ipynb +++ b/docs/examples/multi_modal/multi_modal_video_RAG.ipynb @@ -39,7 +39,8 @@ "outputs": [], "source": [ "%pip install llama-index-multi-modal-llms-openai\n", - "%pip install llama-index-vector-stores-lancedb" + "%pip install llama-index-vector-stores-lancedb\n", + "%pip install llama-index-embeddings-clip" ] }, { @@ -49,6 +50,7 @@ "outputs": [], "source": [ "%pip install llama_index ftfy regex tqdm\n", + "%pip install -U openai-whisper\n", "%pip install git+https://github.com/openai/CLIP.git\n", "%pip install torch torchvision\n", "%pip install matplotlib scikit-image\n", @@ -57,7 +59,8 @@ "%pip install pytube\n", "%pip install pydub\n", "%pip install SpeechRecognition\n", - "%pip install ffmpeg-python" + "%pip install ffmpeg-python\n", + "%pip install soundfile" ] }, { @@ -67,7 +70,6 @@ "outputs": [], "source": [ "from moviepy.editor import VideoFileClip\n", - "from pydub import AudioSegment\n", "from pathlib import Path\n", "import speech_recognition as sr\n", "from pytube import YouTube\n", @@ -230,46 +232,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Moviepy - Writing frames ./mixed_data/frame%04d.png.\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Moviepy - Done writing frames ./mixed_data/frame%04d.png.\n", - "MoviePy - Writing audio in ./mixed_data/output_audio.wav\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - " \r" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MoviePy - Done.\n", - "Text data saved to file\n", - "Audio file removed\n" - ] - } - ], + "outputs": [], "source": [ "try:\n", " metadata_vid = download_video(video_url, output_video_path)\n", @@ -420,7 +383,7 @@ { "data": { "text/markdown": [ - "**Node ID:** 2b0dab05-1469-48a2-9f36-2103458ba252
**Similarity:** 0.742850661277771
**Text:** The basic function underlying a normal distribution, aka a Gaussian, is e to the negative x squared. But you might wonder why this function? Of all the expressions we could dream up that give you s...
" + "**Node ID:** bda08ef1-137c-4d69-9bcc-b7005a41a13c
**Similarity:** 0.7431071996688843
**Text:** The basic function underlying a normal distribution, aka a Gaussian, is e to the negative x squared. But you might wonder why this function? Of all the expressions we could dream up that give you s...
" ], "text/plain": [ "" @@ -432,7 +395,7 @@ { "data": { "text/markdown": [ - "**Node ID:** 6856d43b-9978-4882-a1ff-09d11913157b
**Similarity:** 0.7337126731872559
**Text:** This step is actually pretty technical, it goes a little beyond what I want to talk about here. Often use these objects called moment generating functions, that gives you a very abstract argument t...
" + "**Node ID:** 7d6d0f32-ce16-461b-be54-883241252e50
**Similarity:** 0.7335695028305054
**Text:** This step is actually pretty technical, it goes a little beyond what I want to talk about here. Often use these objects called moment generating functions, that gives you a very abstract argument t...
" ], "text/plain": [ "" @@ -444,7 +407,7 @@ { "data": { "text/markdown": [ - "**Node ID:** 27e3d1f6-4b30-4087-a93d-5484edc814d3
**Similarity:** 0.7068501114845276
**Text:** This is the important point. All of the stuff that's involving s is now entirely separate from the integrated variable. This remaining integral is a little bit tricky. I did a whole video on it. It...
" + "**Node ID:** 519fb788-3927-4842-ad5c-88be61deaf65
**Similarity:** 0.7069740295410156
**Text:** The essence of what we want to compute is what the convolution between two copies of this function looks like. If you remember, in the last video, we had two different ways to visualize convolution...
" ], "text/plain": [ "" @@ -456,7 +419,7 @@ { "data": { "text/markdown": [ - "**Node ID:** 320ed2fc-eeaa-48a3-a216-b90e0316b1a8
**Similarity:** 0.7065496444702148
**Text:** The essence of what we want to compute is what the convolution between two copies of this function looks like. If you remember, in the last video, we had two different ways to visualize convolution...
" + "**Node ID:** f265c3fb-3c9f-4f36-aa2a-fb15efff9783
**Similarity:** 0.706935465335846
**Text:** This is the important point. All of the stuff that's involving s is now entirely separate from the integrated variable. This remaining integral is a little bit tricky. I did a whole video on it. It...
" ], "text/plain": [ "" @@ -503,63 +466,63 @@ "name": "stdout", "output_type": "stream", "text": [ - "('The video titled \"A pretty reason why Gaussian + Gaussian = Gaussian\" by '\n", - " '3Blue1Brown covers several aspects of the Gaussian function, also known as '\n", - " 'the normal distribution. Here are the key points discussed in the video:\\n'\n", + "('The video by 3Blue1Brown, titled \"A pretty reason why Gaussian + Gaussian = '\n", + " 'Gaussian,\" covers several aspects of the Gaussian function, also known as '\n", + " \"the normal distribution. Here's a summary of the key points discussed in the \"\n", + " 'video:\\n'\n", " '\\n'\n", " '1. **Central Limit Theorem**: The video begins by discussing the central '\n", " 'limit theorem, which states that the sum of multiple copies of a random '\n", - " 'variable tends to look like a normal distribution. As the number of summed '\n", + " 'variable tends to look like a normal distribution. As the number of '\n", " 'variables increases, the approximation to a normal distribution becomes '\n", " 'better.\\n'\n", " '\\n'\n", " '2. **Convolution of Random Variables**: The process of adding two random '\n", " 'variables is mathematically represented by a convolution of their respective '\n", - " 'distributions. The video explains how to visualize the convolution operation '\n", - " 'using two methods, with a focus on the second method involving diagonal '\n", - " 'slices.\\n'\n", + " 'distributions. The video explains the concept of convolution and how it is '\n", + " 'used to find the distribution of the sum of two random variables.\\n'\n", " '\\n'\n", " '3. **Gaussian Function**: The Gaussian function is more complex than just '\n", - " '\\\\(e^{-x^2}\\\\). The full formula includes a normalization factor to ensure '\n", - " 'the area under the curve is one, making it a valid probability distribution. '\n", - " 'The standard deviation (\\\\(\\\\sigma\\\\)) is used to describe the spread of the '\n", - " 'distribution, and the mean (\\\\(\\\\mu\\\\)) can be included to shift the center '\n", - " 'of the distribution.\\n'\n", + " '\\\\( e^{-x^2} \\\\). The full formula includes a scaling factor to ensure the '\n", + " 'area under the curve is 1 (making it a valid probability distribution), a '\n", + " 'standard deviation parameter \\\\( \\\\sigma \\\\) to describe the spread, and a '\n", + " 'mean parameter \\\\( \\\\mu \\\\) to shift the center. However, the video focuses '\n", + " 'on centered distributions with \\\\( \\\\mu = 0 \\\\).\\n'\n", " '\\n'\n", - " '4. **Convolution of Two Gaussians**: The video explores what happens when '\n", - " 'you add two normally distributed random variables, which is equivalent to '\n", - " 'computing the convolution of two Gaussian functions. The author presents a '\n", - " 'visual approach to understand this calculation by exploiting the rotational '\n", - " 'symmetry of the graph of \\\\(e^{-x^2}\\\\).\\n'\n", + " '4. **Visualizing Convolution**: The video presents a visual method to '\n", + " 'understand the convolution of two Gaussian functions using diagonal slices '\n", + " 'on the xy-plane. This method involves looking at the probability density of '\n", + " 'landing on a point (x, y) as \\\\( f(x) \\\\times g(y) \\\\), where f and g are '\n", + " 'the two distributions being convolved.\\n'\n", " '\\n'\n", - " '5. **Rotational Symmetry and Slices**: The video demonstrates that the graph '\n", - " 'of \\\\(e^{-x^2} \\\\cdot e^{-y^2}\\\\) is rotationally symmetric around the '\n", - " 'origin, which is a unique property of Gaussian functions. By examining '\n", - " 'diagonal slices of this graph, the author shows how to compute the area '\n", - " 'under these slices, which corresponds to the convolution of the two '\n", - " 'functions.\\n'\n", + " '5. **Rotational Symmetry**: A key property of the Gaussian function is its '\n", + " 'rotational symmetry, which is unique to bell curves. This symmetry is '\n", + " 'exploited in the video to simplify the calculation of the convolution. By '\n", + " 'rotating the graph 45 degrees, the computation becomes easier because the '\n", + " 'integral only involves one variable.\\n'\n", " '\\n'\n", - " '6. **Resulting Distribution**: The convolution of two Gaussian functions is '\n", - " 'shown to be another Gaussian function. This is a special property because '\n", - " 'convolutions typically result in a different kind of function. The video '\n", - " 'explains that this property is key to understanding why the Gaussian '\n", - " 'function is the universal shape approached by the central limit theorem.\\n'\n", + " '6. **Result of Convolution**: The video demonstrates that the convolution of '\n", + " 'two Gaussian functions is another Gaussian function. This is a special '\n", + " 'property because convolutions typically result in a different kind of '\n", + " 'function. The standard deviation of the resulting Gaussian is \\\\( \\\\sqrt{2} '\n", + " '\\\\times \\\\sigma \\\\) if the original Gaussians had the same standard '\n", + " 'deviation.\\n'\n", " '\\n'\n", - " '7. **Standard Deviation of the Result**: When reintroducing the constants '\n", - " 'for a normal distribution, the video concludes that the convolution of two '\n", - " 'normal distributions with mean 0 and standard deviation \\\\(\\\\sigma\\\\) '\n", - " 'results in another normal distribution with a standard deviation of '\n", - " '\\\\(\\\\sqrt{2} \\\\cdot \\\\sigma\\\\).\\n'\n", + " '7. **Proof of Central Limit Theorem**: The video explains that the '\n", + " 'convolution of two Gaussians being another Gaussian is a crucial step in '\n", + " 'proving the central limit theorem. It shows that the Gaussian function is a '\n", + " 'fixed point in the space of distributions, and since all distributions with '\n", + " 'finite variance tend towards a single universal shape, that shape must be '\n", + " 'the Gaussian.\\n'\n", " '\\n'\n", - " '8. **Implications for the Central Limit Theorem**: The video emphasizes that '\n", - " 'the computation of the convolution of two Gaussians is fundamental to the '\n", - " 'central limit theorem. It shows that the Gaussian distribution is a fixed '\n", - " 'point in the space of distributions, which is why it is the shape that '\n", - " 'emerges from the central limit theorem.\\n'\n", + " '8. **Connection to Pi**: The video also touches on the connection between '\n", + " 'the Gaussian function and the number Pi, which appears in the formula for '\n", + " 'the normal distribution.\\n'\n", " '\\n'\n", - " 'Throughout the video, the author provides visual examples and explanations '\n", - " 'to help viewers understand the mathematical concepts involved in the '\n", - " 'Gaussian function and its properties related to probability and statistics.')\n" + " 'The video aims to provide an intuitive geometric argument for why the sum of '\n", + " 'two normally distributed random variables is also normally distributed, and '\n", + " 'how this relates to the central limit theorem and the special properties of '\n", + " 'the Gaussian function.')\n" ] } ], diff --git a/docs/examples/node_postprocessor/PII.ipynb b/docs/examples/node_postprocessor/PII.ipynb index 71a45c8be01cc..01acaae96495d 100644 --- a/docs/examples/node_postprocessor/PII.ipynb +++ b/docs/examples/node_postprocessor/PII.ipynb @@ -279,6 +279,110 @@ "new_nodes[0].node.metadata[\"__pii_node_info__\"]" ] }, + { + "cell_type": "markdown", + "id": "6cc87ed4", + "metadata": {}, + "source": [ + "### Option 3: Use Presidio for PII Masking\n", + "\n", + "Use presidio to identify and anonymize PII" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ac215117", + "metadata": {}, + "outputs": [], + "source": [ + "# load documents\n", + "text = \"\"\"\n", + "Hello Paulo Santos. The latest statement for your credit card account \\\n", + "4095-2609-9393-4932 was mailed to Seattle, WA 98109. \\\n", + "IBAN GB90YNTU67299444055881 and social security number is 474-49-7577 were verified on the system. \\\n", + "Further communications will be sent to paulo@presidio.site \n", + "\"\"\"\n", + "presidio_node = TextNode(text=text)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a745520", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.postprocessor.presidio import PresidioPIINodePostprocessor\n", + "\n", + "processor = PresidioPIINodePostprocessor()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "89cb17ed", + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.schema import NodeWithScore\n", + "\n", + "presidio_new_nodes = processor.postprocess_nodes(\n", + " [NodeWithScore(node=presidio_node)]\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8fe9cef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'\\nHello . The latest statement for your credit card account was mailed to , . IBAN and social security number is were verified on the system. Further communications will be sent to \\n'" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# view redacted text\n", + "presidio_new_nodes[0].node.get_text()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80203af0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'': 'paulo@presidio.site',\n", + " '': '474-49-7577',\n", + " '': 'GB90YNTU67299444055881',\n", + " '': 'WA 98109',\n", + " '': 'Seattle',\n", + " '': '4095-2609-9393-4932',\n", + " '': 'Paulo Santos'}" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# get mapping in metadata\n", + "# NOTE: this is not sent to the LLM!\n", + "presidio_new_nodes[0].node.metadata[\"__pii_node_info__\"]" + ] + }, { "attachments": {}, "cell_type": "markdown", diff --git a/docs/examples/output_parsing/__pycache__/directory.cpython-311.pyc b/docs/examples/output_parsing/__pycache__/directory.cpython-311.pyc deleted file mode 100644 index 0352eb02f06a0..0000000000000 Binary files a/docs/examples/output_parsing/__pycache__/directory.cpython-311.pyc and /dev/null differ diff --git a/docs/examples/vector_stores/MongoDBAtlasVectorSearchRAGOpenAI.ipynb b/docs/examples/vector_stores/MongoDBAtlasVectorSearchRAGOpenAI.ipynb new file mode 100644 index 0000000000000..393a811c91db9 --- /dev/null +++ b/docs/examples/vector_stores/MongoDBAtlasVectorSearchRAGOpenAI.ipynb @@ -0,0 +1,1242 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index\n", + "!pip install llama-index-vector-stores-mongodb\n", + "!pip install llama-index-embeddings-openai\n", + "!pip install pymongo\n", + "!pip install datasets\n", + "!pip install pandas" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%env OPENAI_API_KEY=OPENAI_API_KEY" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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awardsmetacriticratedfullplottitlewriterslanguagesplotplot_embeddingruntimecountriesgenresdirectorscasttypeimdbposternum_mflix_comments
0{'nominations': 0, 'text': '1 win.', 'wins': 1}NaNNoneYoung Pauline is left a lot of money when her ...The Perils of Pauline[Charles W. Goddard (screenplay), Basil Dickey...[English]Young Pauline is left a lot of money when her ...[0.00072939653, -0.026834568, 0.013515796, -0....199.0[USA][Action][Louis J. Gasnier, Donald MacKenzie][Pearl White, Crane Wilbur, Paul Panzer, Edwar...movie{'id': 4465, 'rating': 7.6, 'votes': 744}https://m.media-amazon.com/images/M/MV5BMzgxOD...0
1{'nominations': 1, 'text': '1 nomination.', 'w...NaNTV-GAs a penniless man worries about how he will m...From Hand to Mouth[H.M. Walker (titles)][English]A penniless young man tries to save an heiress...[-0.022837115, -0.022941574, 0.014937485, -0.0...22.0[USA][Comedy, Short, Action][Alfred J. Goulding, Hal Roach][Harold Lloyd, Mildred Davis, 'Snub' Pollard, ...movie{'id': 10146, 'rating': 7.0, 'votes': 639}https://m.media-amazon.com/images/M/MV5BNzE1OW...0
2{'nominations': 0, 'text': '1 win.', 'wins': 1}NaNNoneMichael \"Beau\" Geste leaves England in disgrac...Beau Geste[Herbert Brenon (adaptation), John Russell (ad...[English]Michael \"Beau\" Geste leaves England in disgrac...[0.00023330493, -0.028511643, 0.014653289, -0....101.0[USA][Action, Adventure, Drama][Herbert Brenon][Ronald Colman, Neil Hamilton, Ralph Forbes, A...movie{'id': 16634, 'rating': 6.9, 'votes': 222}None0
3{'nominations': 0, 'text': '1 win.', 'wins': 1}NaNNoneA nobleman vows to avenge the death of his fat...The Black Pirate[Douglas Fairbanks (story), Jack Cunningham (a...NoneSeeking revenge, an athletic young man joins t...[-0.005927917, -0.033394486, 0.0015323418, -0....88.0[USA][Adventure, Action][Albert Parker][Billie Dove, Tempe Pigott, Donald Crisp, Sam ...movie{'id': 16654, 'rating': 7.2, 'votes': 1146}https://m.media-amazon.com/images/M/MV5BMzU0ND...1
4{'nominations': 1, 'text': '1 nomination.', 'w...NaNPASSEDThe Uptown Boy, J. Harold Manners (Lloyd) is a...For Heaven's Sake[Ted Wilde (story), John Grey (story), Clyde B...[English]An irresponsible young millionaire changes his...[-0.0059373598, -0.026604708, -0.0070914757, -...58.0[USA][Action, Comedy, Romance][Sam Taylor][Harold Lloyd, Jobyna Ralston, Noah Young, Jim...movie{'id': 16895, 'rating': 7.6, 'votes': 918}https://m.media-amazon.com/images/M/MV5BMTcxMT...0
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awardsmetacriticratedfullplottitlewriterslanguagesplotruntimecountriesgenresdirectorscasttypeimdbposternum_mflix_comments
0{'nominations': 0, 'text': '1 win.', 'wins': 1}NaNNoneYoung Pauline is left a lot of money when her ...The Perils of Pauline[Charles W. Goddard (screenplay), Basil Dickey...[English]Young Pauline is left a lot of money when her ...199.0[USA][Action][Louis J. Gasnier, Donald MacKenzie][Pearl White, Crane Wilbur, Paul Panzer, Edwar...movie{'id': 4465, 'rating': 7.6, 'votes': 744}https://m.media-amazon.com/images/M/MV5BMzgxOD...0
1{'nominations': 1, 'text': '1 nomination.', 'w...NaNTV-GAs a penniless man worries about how he will m...From Hand to Mouth[H.M. Walker (titles)][English]A penniless young man tries to save an heiress...22.0[USA][Comedy, Short, Action][Alfred J. Goulding, Hal Roach][Harold Lloyd, Mildred Davis, 'Snub' Pollard, ...movie{'id': 10146, 'rating': 7.0, 'votes': 639}https://m.media-amazon.com/images/M/MV5BNzE1OW...0
2{'nominations': 0, 'text': '1 win.', 'wins': 1}NaNNoneMichael \"Beau\" Geste leaves England in disgrac...Beau Geste[Herbert Brenon (adaptation), John Russell (ad...[English]Michael \"Beau\" Geste leaves England in disgrac...101.0[USA][Action, Adventure, Drama][Herbert Brenon][Ronald Colman, Neil Hamilton, Ralph Forbes, A...movie{'id': 16634, 'rating': 6.9, 'votes': 222}None0
3{'nominations': 0, 'text': '1 win.', 'wins': 1}NaNNoneA nobleman vows to avenge the death of his fat...The Black Pirate[Douglas Fairbanks (story), Jack Cunningham (a...NoneSeeking revenge, an athletic young man joins t...88.0[USA][Adventure, Action][Albert Parker][Billie Dove, Tempe Pigott, Donald Crisp, Sam ...movie{'id': 16654, 'rating': 7.2, 'votes': 1146}https://m.media-amazon.com/images/M/MV5BMzU0ND...1
4{'nominations': 1, 'text': '1 nomination.', 'w...NaNPASSEDThe Uptown Boy, J. Harold Manners (Lloyd) is a...For Heaven's Sake[Ted Wilde (story), John Grey (story), Clyde B...[English]An irresponsible young millionaire changes his...58.0[USA][Action, Comedy, Romance][Sam Taylor][Harold Lloyd, Jobyna Ralston, Noah Young, Jim...movie{'id': 16895, 'rating': 7.6, 'votes': 918}https://m.media-amazon.com/images/M/MV5BMTcxMT...0
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Walker (titles)] [English] \n", + "2 [Herbert Brenon (adaptation), John Russell (ad... [English] \n", + "3 [Douglas Fairbanks (story), Jack Cunningham (a... None \n", + "4 [Ted Wilde (story), John Grey (story), Clyde B... [English] \n", + "\n", + " plot runtime countries \\\n", + "0 Young Pauline is left a lot of money when her ... 199.0 [USA] \n", + "1 A penniless young man tries to save an heiress... 22.0 [USA] \n", + "2 Michael \"Beau\" Geste leaves England in disgrac... 101.0 [USA] \n", + "3 Seeking revenge, an athletic young man joins t... 88.0 [USA] \n", + "4 An irresponsible young millionaire changes his... 58.0 [USA] \n", + "\n", + " genres directors \\\n", + "0 [Action] [Louis J. Gasnier, Donald MacKenzie] \n", + "1 [Comedy, Short, Action] [Alfred J. Goulding, Hal Roach] \n", + "2 [Action, Adventure, Drama] [Herbert Brenon] \n", + "3 [Adventure, Action] [Albert Parker] \n", + "4 [Action, Comedy, Romance] [Sam Taylor] \n", + "\n", + " cast type \\\n", + "0 [Pearl White, Crane Wilbur, Paul Panzer, Edwar... movie \n", + "1 [Harold Lloyd, Mildred Davis, 'Snub' Pollard, ... movie \n", + "2 [Ronald Colman, Neil Hamilton, Ralph Forbes, A... movie \n", + "3 [Billie Dove, Tempe Pigott, Donald Crisp, Sam ... movie \n", + "4 [Harold Lloyd, Jobyna Ralston, Noah Young, Jim... movie \n", + "\n", + " imdb \\\n", + "0 {'id': 4465, 'rating': 7.6, 'votes': 744} \n", + "1 {'id': 10146, 'rating': 7.0, 'votes': 639} \n", + "2 {'id': 16634, 'rating': 6.9, 'votes': 222} \n", + "3 {'id': 16654, 'rating': 7.2, 'votes': 1146} \n", + "4 {'id': 16895, 'rating': 7.6, 'votes': 918} \n", + "\n", + " poster num_mflix_comments \n", + "0 https://m.media-amazon.com/images/M/MV5BMzgxOD... 0 \n", + "1 https://m.media-amazon.com/images/M/MV5BNzE1OW... 0 \n", + "2 None 0 \n", + "3 https://m.media-amazon.com/images/M/MV5BMzU0ND... 1 \n", + "4 https://m.media-amazon.com/images/M/MV5BMTcxMT... 0 " + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Remove data point where fullplot coloumn is missing\n", + "dataset_df = dataset_df.dropna(subset=[\"fullplot\"])\n", + "print(\"\\nNumber of missing values in each column after removal:\")\n", + "print(dataset_df.isnull().sum())\n", + "\n", + "# Remove the plot_embedding from each data point in the dataset as we are going to create new embeddings with the new OpenAI emebedding Model \"text-embedding-3-small\"\n", + "dataset_df = dataset_df.drop(columns=[\"plot_embedding\"])\n", + "\n", + "dataset_df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.settings import Settings\n", + "from llama_index.llms.openai import OpenAI\n", + "from llama_index.embeddings.openai import OpenAIEmbedding\n", + "\n", + "embed_model = OpenAIEmbedding(model=\"text-embedding-3-small\", dimensions=256)\n", + "llm = OpenAI()\n", + "\n", + "Settings.llm = llm\n", + "Settings.embed_model = embed_model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "The LLM sees this: \n", + " Metadata: awards=>{\"nominations\": 0, \"text\": \"1 win.\", \"wins\": 1}\n", + "rated=>None\n", + "title=>The Perils of Pauline\n", + "writers=>[\"Charles W. Goddard (screenplay)\", \"Basil Dickey (screenplay)\", \"Charles W. Goddard (novel)\", \"George B. Seitz\", \"Bertram Millhauser\"]\n", + "languages=>[\"English\"]\n", + "plot=>Young Pauline is left a lot of money when her wealthy uncle dies. However, her uncle's secretary has been named as her guardian until she marries, at which time she will officially take ...\n", + "runtime=>199.0\n", + "countries=>[\"USA\"]\n", + "genres=>[\"Action\"]\n", + "directors=>[\"Louis J. Gasnier\", \"Donald MacKenzie\"]\n", + "cast=>[\"Pearl White\", \"Crane Wilbur\", \"Paul Panzer\", \"Edward Jos\\u00e8\"]\n", + "type=>movie\n", + "imdb=>{\"id\": 4465, \"rating\": 7.6, \"votes\": 744}\n", + "poster=>https://m.media-amazon.com/images/M/MV5BMzgxODk1Mzk2Ml5BMl5BanBnXkFtZTgwMDg0NzkwMjE@._V1_SY1000_SX677_AL_.jpg\n", + "num_mflix_comments=>0\n", + "-----\n", + "Content: Young Pauline is left a lot of money when her wealthy uncle dies. However, her uncle's secretary has been named as her guardian until she marries, at which time she will officially take possession of her inheritance. Meanwhile, her \"guardian\" and his confederates constantly come up with schemes to get rid of Pauline so that he can get his hands on the money himself.\n", + "\n", + "The Embedding model sees this: \n", + " Metadata: awards=>{\"nominations\": 0, \"text\": \"1 win.\", \"wins\": 1}\n", + "title=>The Perils of Pauline\n", + "writers=>[\"Charles W. Goddard (screenplay)\", \"Basil Dickey (screenplay)\", \"Charles W. Goddard (novel)\", \"George B. Seitz\", \"Bertram Millhauser\"]\n", + "languages=>[\"English\"]\n", + "plot=>Young Pauline is left a lot of money when her wealthy uncle dies. However, her uncle's secretary has been named as her guardian until she marries, at which time she will officially take ...\n", + "countries=>[\"USA\"]\n", + "genres=>[\"Action\"]\n", + "directors=>[\"Louis J. Gasnier\", \"Donald MacKenzie\"]\n", + "cast=>[\"Pearl White\", \"Crane Wilbur\", \"Paul Panzer\", \"Edward Jos\\u00e8\"]\n", + "type=>movie\n", + "imdb=>{\"id\": 4465, \"rating\": 7.6, \"votes\": 744}\n", + "-----\n", + "Content: Young Pauline is left a lot of money when her wealthy uncle dies. However, her uncle's secretary has been named as her guardian until she marries, at which time she will officially take possession of her inheritance. Meanwhile, her \"guardian\" and his confederates constantly come up with schemes to get rid of Pauline so that he can get his hands on the money himself.\n" + ] + } + ], + "source": [ + "import json\n", + "from llama_index.core import Document\n", + "from llama_index.core.schema import MetadataMode\n", + "\n", + "# Convert the DataFrame to a JSON string representation\n", + "documents_json = dataset_df.to_json(orient=\"records\")\n", + "# Load the JSON string into a Python list of dictionaries\n", + "documents_list = json.loads(documents_json)\n", + "\n", + "llama_documents = []\n", + "\n", + "for document in documents_list:\n", + " # Value for metadata must be one of (str, int, float, None)\n", + " document[\"writers\"] = json.dumps(document[\"writers\"])\n", + " document[\"languages\"] = json.dumps(document[\"languages\"])\n", + " document[\"genres\"] = json.dumps(document[\"genres\"])\n", + " document[\"cast\"] = json.dumps(document[\"cast\"])\n", + " document[\"directors\"] = json.dumps(document[\"directors\"])\n", + " document[\"countries\"] = json.dumps(document[\"countries\"])\n", + " document[\"imdb\"] = json.dumps(document[\"imdb\"])\n", + " document[\"awards\"] = json.dumps(document[\"awards\"])\n", + "\n", + " # Create a Document object with the text and excluded metadata for llm and embedding models\n", + " llama_document = Document(\n", + " text=document[\"fullplot\"],\n", + " metadata=document,\n", + " excluded_llm_metadata_keys=[\"fullplot\", \"metacritic\"],\n", + " excluded_embed_metadata_keys=[\n", + " \"fullplot\",\n", + " \"metacritic\",\n", + " \"poster\",\n", + " \"num_mflix_comments\",\n", + " \"runtime\",\n", + " \"rated\",\n", + " ],\n", + " metadata_template=\"{key}=>{value}\",\n", + " text_template=\"Metadata: {metadata_str}\\n-----\\nContent: {content}\",\n", + " )\n", + "\n", + " llama_documents.append(llama_document)\n", + "\n", + "# Observing an example of what the LLM and Embedding model receive as input\n", + "print(\n", + " \"\\nThe LLM sees this: \\n\",\n", + " llama_documents[0].get_content(metadata_mode=MetadataMode.LLM),\n", + ")\n", + "print(\n", + " \"\\nThe Embedding model sees this: \\n\",\n", + " llama_documents[0].get_content(metadata_mode=MetadataMode.EMBED),\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "llama_documents[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.node_parser import SentenceSplitter\n", + "\n", + "parser = SentenceSplitter()\n", + "nodes = parser.get_nodes_from_documents(llama_documents)\n", + "\n", + "for node in nodes:\n", + " node_embedding = embed_model.get_text_embedding(\n", + " node.get_content(metadata_mode=\"all\")\n", + " )\n", + " node.embedding = node_embedding" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Ensure your databse, collection and vector store index is setup on MongoDB Atlas for the collection or the following step won't work appropriately on MongoDB.\n", + "\n", + "\n", + " - For assistance with database cluster setup and obtaining the URI, refer to this [guide](https://www.mongodb.com/docs/guides/atlas/cluster/) for setting up a MongoDB cluster, and this [guide](https://www.mongodb.com/docs/guides/atlas/connection-string/) to get your connection string. \n", + "\n", + " - Once you have successfully created a cluster, create the database and collection within the MongoDB Atlas cluster by clicking “+ Create Database”. The database will be named movies, and the collection will be named movies_records.\n", + "\n", + " - Creating a vector search index within the movies_records collection is essential for efficient document retrieval from MongoDB into our development environment. To achieve this, refer to the official [guide](https://www.mongodb.com/docs/atlas/atlas-vector-search/create-index/) on vector search index creation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Connection to MongoDB successful\n" + ] + } + ], + "source": [ + "import pymongo\n", + "from google.colab import userdata\n", + "\n", + "\n", + "def get_mongo_client(mongo_uri):\n", + " \"\"\"Establish connection to the MongoDB.\"\"\"\n", + " try:\n", + " client = pymongo.MongoClient(mongo_uri)\n", + " print(\"Connection to MongoDB successful\")\n", + " return client\n", + " except pymongo.errors.ConnectionFailure as e:\n", + " print(f\"Connection failed: {e}\")\n", + " return None\n", + "\n", + "\n", + "mongo_uri = userdata.get(\"MONGO_URI\")\n", + "if not mongo_uri:\n", + " print(\"MONGO_URI not set in environment variables\")\n", + "\n", + "mongo_client = get_mongo_client(mongo_uri)\n", + "\n", + "DB_NAME = \"movies\"\n", + "COLLECTION_NAME = \"movies_records\"\n", + "\n", + "db = mongo_client[DB_NAME]\n", + "collection = db[COLLECTION_NAME]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "DeleteResult({'n': 0, 'electionId': ObjectId('7fffffff000000000000000a'), 'opTime': {'ts': Timestamp(1708000722, 1), 't': 10}, 'ok': 1.0, '$clusterTime': {'clusterTime': Timestamp(1708000722, 1), 'signature': {'hash': b'\\xd8\\x1a\\xaci\\xf5EN+\\xe2\\xd1\\xb3y8.${u5P\\xf3', 'keyId': 7320226449804230661}}, 'operationTime': Timestamp(1708000722, 1)}, acknowledged=True)" + ] + }, + "execution_count": null, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# To ensure we are working with a fresh collection\n", + "# delete any existing records in the collection\n", + "collection.delete_many({})" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch\n", + "\n", + "vector_store = MongoDBAtlasVectorSearch(\n", + " mongo_client,\n", + " db_name=DB_NAME,\n", + " collection_name=COLLECTION_NAME,\n", + " index_name=\"vector_index\",\n", + ")\n", + "vector_store.add(nodes)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import VectorStoreIndex, StorageContext\n", + "\n", + "index = VectorStoreIndex.from_vector_store(vector_store)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "**`Final Response:`** The movie \"Romancing the Stone\" would be a suitable romantic movie for the Christmas season. It is a romantic adventure film that follows a romance writer who sets off on a dangerous adventure to rescue her kidnapped sister. The movie has elements of romance, adventure, and comedy, making it an entertaining choice for the holiday season. Additionally, the movie has received positive reviews and has been nominated for awards, indicating its quality." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[NodeWithScore(node=TextNode(id_='c6bbc236-e21d-49ab-b43d-db920b4946e6', embedding=None, metadata={'awards': '{\"nominations\": 2, \"text\": \"Nominated for 1 Oscar. Another 6 wins & 2 nominations.\", \"wins\": 7}', 'metacritic': None, 'rated': 'PG', 'fullplot': \"Joan Wilder, a mousy romance novelist, receives a treasure map in the mail from her recently murdered brother-in-law. Meanwhile, her sister Elaine is kidnapped in Colombia and the two criminals responsible demand that she travel to Colombia to exchange the map for her sister. Joan does, and quickly becomes lost in the jungle after being waylayed by Zolo, a vicious and corrupt Colombian cop who will stop at nothing to obtain the map. There, she meets an irreverent soldier-of-fortune named Jack Colton who agrees to bring her back to civilization. Together, they embark upon an adventure that could be straight out of Joan's novels.\", 'title': 'Romancing the Stone', 'writers': '[\"Diane Thomas\"]', 'languages': '[\"English\", \"Spanish\", \"French\"]', 'plot': 'A romance writer sets off to Colombia to ransom her kidnapped sister, and soon finds herself in the middle of a dangerous adventure.', 'runtime': 106.0, 'countries': '[\"USA\", \"Mexico\"]', 'genres': '[\"Action\", \"Adventure\", \"Comedy\"]', 'directors': '[\"Robert Zemeckis\"]', 'cast': '[\"Michael Douglas\", \"Kathleen Turner\", \"Danny DeVito\", \"Zack Norman\"]', 'type': 'movie', 'imdb': '{\"id\": 88011, \"rating\": 6.9, \"votes\": 59403}', 'poster': 'https://m.media-amazon.com/images/M/MV5BMDAwNjljMzEtMTc3Yy00NDg2LThjNDAtNjc0NGYyYjM2M2I1XkEyXkFqcGdeQXVyNDE5MTU2MDE@._V1_SY1000_SX677_AL_.jpg', 'num_mflix_comments': 0}, excluded_embed_metadata_keys=['fullplot', 'metacritic', 'poster', 'num_mflix_comments', 'runtime', 'rated'], excluded_llm_metadata_keys=['fullplot', 'metacritic'], relationships={: RelatedNodeInfo(node_id='e50144b0-96ba-4a5a-b90a-3a2419f5b380', node_type=, metadata={'awards': '{\"nominations\": 2, \"text\": \"Nominated for 1 Oscar. Another 6 wins & 2 nominations.\", \"wins\": 7}', 'metacritic': None, 'rated': 'PG', 'fullplot': \"Joan Wilder, a mousy romance novelist, receives a treasure map in the mail from her recently murdered brother-in-law. Meanwhile, her sister Elaine is kidnapped in Colombia and the two criminals responsible demand that she travel to Colombia to exchange the map for her sister. Joan does, and quickly becomes lost in the jungle after being waylayed by Zolo, a vicious and corrupt Colombian cop who will stop at nothing to obtain the map. There, she meets an irreverent soldier-of-fortune named Jack Colton who agrees to bring her back to civilization. Together, they embark upon an adventure that could be straight out of Joan's novels.\", 'title': 'Romancing the Stone', 'writers': '[\"Diane Thomas\"]', 'languages': '[\"English\", \"Spanish\", \"French\"]', 'plot': 'A romance writer sets off to Colombia to ransom her kidnapped sister, and soon finds herself in the middle of a dangerous adventure.', 'runtime': 106.0, 'countries': '[\"USA\", \"Mexico\"]', 'genres': '[\"Action\", \"Adventure\", \"Comedy\"]', 'directors': '[\"Robert Zemeckis\"]', 'cast': '[\"Michael Douglas\", \"Kathleen Turner\", \"Danny DeVito\", \"Zack Norman\"]', 'type': 'movie', 'imdb': '{\"id\": 88011, \"rating\": 6.9, \"votes\": 59403}', 'poster': 'https://m.media-amazon.com/images/M/MV5BMDAwNjljMzEtMTc3Yy00NDg2LThjNDAtNjc0NGYyYjM2M2I1XkEyXkFqcGdeQXVyNDE5MTU2MDE@._V1_SY1000_SX677_AL_.jpg', 'num_mflix_comments': 0}, hash='b984e4f203b7b67eae14afa890718adb800a5816661ac2edf412aa96fd7dc10b'), : RelatedNodeInfo(node_id='f895e43a-038a-4a1c-8a82-0e22868e35d7', node_type=, metadata={'awards': '{\"nominations\": 1, \"text\": \"1 nomination.\", \"wins\": 0}', 'metacritic': None, 'rated': 'R', 'fullplot': \"Chicago psychiatrist Judd Stevens (Roger Moore) is suspected of murdering one of his patients when the man turns up stabbed to death in the middle of the city. After repeated attempts to convince cops Rod Steiger and Elliott Gould of his innocence, Dr.Stevens is forced to go after the real villains himself, and he finds himself up against one of the city's most notorious Mafia kingpins.\", 'title': 'The Naked Face', 'writers': '[\"Bryan Forbes\", \"Sidney Sheldon (novel)\"]', 'languages': '[\"English\"]', 'plot': 'Chicago psychiatrist Judd Stevens (Roger Moore) is suspected of murdering one of his patients when the man turns up stabbed to death in the middle of the city. After repeated attempts to ...', 'runtime': 103.0, 'countries': '[\"USA\"]', 'genres': '[\"Action\", \"Mystery\", \"Thriller\"]', 'directors': '[\"Bryan Forbes\"]', 'cast': '[\"Roger Moore\", \"Rod Steiger\", \"Elliott Gould\", \"Art Carney\"]', 'type': 'movie', 'imdb': '{\"id\": 87777, \"rating\": 5.3, \"votes\": 654}', 'poster': 'https://m.media-amazon.com/images/M/MV5BMTg0NDM4MTY0NV5BMl5BanBnXkFtZTcwNTcwOTc2NA@@._V1_SY1000_SX677_AL_.jpg', 'num_mflix_comments': 1}, hash='066e2b3d12c5fab61175f52dd625ec41fb1fce1fe6fe4c892774227c576fdbbd'), : RelatedNodeInfo(node_id='e31f1142-c6b6-4183-b14b-1634166b9d1f', node_type=, metadata={}, hash='9b9127e21d18792749a7a35321e04d29b8d77f7b454b0133205f9de1090038b4')}, text=\"Joan Wilder, a mousy romance novelist, receives a treasure map in the mail from her recently murdered brother-in-law. Meanwhile, her sister Elaine is kidnapped in Colombia and the two criminals responsible demand that she travel to Colombia to exchange the map for her sister. Joan does, and quickly becomes lost in the jungle after being waylayed by Zolo, a vicious and corrupt Colombian cop who will stop at nothing to obtain the map. There, she meets an irreverent soldier-of-fortune named Jack Colton who agrees to bring her back to civilization. Together, they embark upon an adventure that could be straight out of Joan's novels.\", start_char_idx=0, end_char_idx=635, text_template='Metadata: {metadata_str}\\n-----\\nContent: {content}', metadata_template='{key}=>{value}', metadata_seperator='\\n'), score=0.7502920627593994),\n", + " NodeWithScore(node=TextNode(id_='5c7cef95-79e3-4c96-a009-4154ea125240', embedding=None, metadata={'awards': '{\"nominations\": 2, \"text\": \"Nominated for 2 Oscars. Another 1 win & 2 nominations.\", \"wins\": 3}', 'metacritic': 64.0, 'rated': 'PG-13', 'fullplot': 'In 1880, four men travel together to the city of Silverado. They come across with many dangers before they finally engage the \"bad guys\" and bring peace and equality back to the city.', 'title': 'Silverado', 'writers': '[\"Lawrence Kasdan\", \"Mark Kasdan\"]', 'languages': '[\"English\"]', 'plot': 'A misfit bunch of friends come together to right the injustices which exist in a small town.', 'runtime': 133.0, 'countries': '[\"USA\"]', 'genres': '[\"Action\", \"Crime\", \"Drama\"]', 'directors': '[\"Lawrence Kasdan\"]', 'cast': '[\"Kevin Kline\", \"Scott Glenn\", \"Kevin Costner\", \"Danny Glover\"]', 'type': 'movie', 'imdb': '{\"id\": 90022, \"rating\": 7.2, \"votes\": 26415}', 'poster': 'https://m.media-amazon.com/images/M/MV5BYTljNTE5YmUtMGEyZi00ZjI4LWEzYjUtZDY2YWEwNzVmZjRkXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_SY1000_SX677_AL_.jpg', 'num_mflix_comments': 1}, excluded_embed_metadata_keys=['fullplot', 'metacritic', 'poster', 'num_mflix_comments', 'runtime', 'rated'], excluded_llm_metadata_keys=['fullplot', 'metacritic'], relationships={: RelatedNodeInfo(node_id='decbc30c-c17e-4ba4-bd1e-72dce4ce383a', node_type=, metadata={'awards': '{\"nominations\": 2, \"text\": \"Nominated for 2 Oscars. Another 1 win & 2 nominations.\", \"wins\": 3}', 'metacritic': 64.0, 'rated': 'PG-13', 'fullplot': 'In 1880, four men travel together to the city of Silverado. They come across with many dangers before they finally engage the \"bad guys\" and bring peace and equality back to the city.', 'title': 'Silverado', 'writers': '[\"Lawrence Kasdan\", \"Mark Kasdan\"]', 'languages': '[\"English\"]', 'plot': 'A misfit bunch of friends come together to right the injustices which exist in a small town.', 'runtime': 133.0, 'countries': '[\"USA\"]', 'genres': '[\"Action\", \"Crime\", \"Drama\"]', 'directors': '[\"Lawrence Kasdan\"]', 'cast': '[\"Kevin Kline\", \"Scott Glenn\", \"Kevin Costner\", \"Danny Glover\"]', 'type': 'movie', 'imdb': '{\"id\": 90022, \"rating\": 7.2, \"votes\": 26415}', 'poster': 'https://m.media-amazon.com/images/M/MV5BYTljNTE5YmUtMGEyZi00ZjI4LWEzYjUtZDY2YWEwNzVmZjRkXkEyXkFqcGdeQXVyNTI4MjkwNjA@._V1_SY1000_SX677_AL_.jpg', 'num_mflix_comments': 1}, hash='80b77d835c7dfad9d57d300cf69ba388704e6f282f49dc23106489db03b8b441'), : RelatedNodeInfo(node_id='1c04fb7f-ff8f-4e8c-84f6-74c57251446a', node_type=, metadata={'awards': '{\"nominations\": 5, \"text\": \"Nominated for 3 Oscars. 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When Pete finally falls for her and she turns down Jefty\\'s marriage proposal, they must face Jefty\\'s murderous jealousy and his twisted plots to \"punish\" the two.', 'title': 'Road House', 'writers': '[\"Edward Chodorov (screen play)\", \"Margaret Gruen (story)\", \"Oscar Saul (story)\"]', 'languages': '[\"English\"]', 'plot': 'A night club owner becomes infatuated with a torch singer and frames his best friend/manager for embezzlement when the chanteuse falls in love with him.', 'runtime': 95.0, 'countries': '[\"USA\"]', 'genres': '[\"Action\", \"Drama\", \"Film-Noir\"]', 'directors': '[\"Jean Negulesco\"]', 'cast': '[\"Ida Lupino\", \"Cornel Wilde\", \"Celeste Holm\", \"Richard Widmark\"]', 'type': 'movie', 'imdb': '{\"id\": 40740, \"rating\": 7.3, \"votes\": 1353}', 'poster': 'https://m.media-amazon.com/images/M/MV5BMjc1ZTNkM2UtYzY3Yi00ZWZmLTljYmEtNjYxZDNmYzk2ZjkzXkEyXkFqcGdeQXVyMjUxODE0MDY@._V1_SY1000_SX677_AL_.jpg', 'num_mflix_comments': 2}, hash='040b4a77fcc8fbb5347620e99a217d67b85dcdbd370d91bd23877722a499079f'), : RelatedNodeInfo(node_id='75f37fbc-d75e-4a76-b86f-f15d9260afd1', node_type=, metadata={}, hash='9941706d03783561f3fc3200c26527493a62307f8532dcda60b20948c886b330')}, text=\"Dick Powell stars as Haven, a government private investigator assigned to investigate the murders of two cavalrymen. 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"display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/llama-index-cli/.gitignore b/llama-index-cli/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-cli/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-cli/BUILD b/llama-index-cli/BUILD new file mode 100644 index 0000000000000..0b67818b466ca --- /dev/null +++ b/llama-index-cli/BUILD @@ -0,0 +1,5 @@ +python_sources() + +poetry_requirements( + name="poetry", +) diff --git a/llama-index-cli/CHANGELOG.MD b/llama-index-cli/CHANGELOG.MD new file mode 100644 index 0000000000000..109adf94b711b --- /dev/null +++ b/llama-index-cli/CHANGELOG.MD @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.0] - 2024-02-15 + +- Initial release of cli (ripped out from core) diff --git a/llama-index-cli/Makefile b/llama-index-cli/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-cli/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-cli/README.md b/llama-index-cli/README.md new file mode 100644 index 0000000000000..3232c32f3fcd9 --- /dev/null +++ b/llama-index-cli/README.md @@ -0,0 +1,30 @@ +# LlamaIndex CLI + +## Installation + +```sh +pip install llama-index-cli +``` + +## Usage + +```sh +llamaindex -h + +usage: llamaindex [-h] {rag,download-llamapack,download-llamadataset,upgrade,upgrade-file,new-package} ... + +LlamaIndex CLI tool. + +options: + -h, --help show this help message and exit + +commands: + {rag,download-llamapack,download-llamadataset,upgrade,upgrade-file,new-package} + rag Ask a question to a document / a directory of documents. + download-llamapack Download a llama-pack + download-llamadataset + Download a llama-dataset + upgrade Upgrade a directory containing notebooks or python files. + upgrade-file Upgrade a single notebook or python file. + new-package Initialize a new llama-index package +``` diff --git a/llama-index-cli/llama_index/cli/BUILD b/llama-index-cli/llama_index/cli/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-cli/llama_index/cli/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-cli/llama_index/cli/__init__.py b/llama-index-cli/llama_index/cli/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-cli/llama_index/cli/command_line.py b/llama-index-cli/llama_index/cli/command_line.py new file mode 100644 index 0000000000000..34af87a864c57 --- /dev/null +++ b/llama-index-cli/llama_index/cli/command_line.py @@ -0,0 +1,272 @@ +import argparse +from typing import Any, Optional + +from llama_index.cli.rag.base import RagCLI, default_ragcli_persist_dir +from llama_index.cli.upgrade.base import upgrade_dir, upgrade_file +from llama_index.cli.new_package.base import init_new_package +from llama_index.core.ingestion import IngestionCache, IngestionPipeline +from llama_index.core.llama_dataset.download import ( + LLAMA_DATASETS_LFS_URL, + LLAMA_DATASETS_SOURCE_FILES_GITHUB_TREE_URL, + LLAMA_HUB_URL, + download_llama_dataset, +) +from llama_index.core.llama_pack.download import ( + LLAMA_PACKS_CONTENTS_URL, + download_llama_pack, +) +from llama_index.core.storage.docstore import SimpleDocumentStore +from llama_index.core.text_splitter import SentenceSplitter + + +def handle_init_package( + name: str, kind: str, prefix: Optional[str] = None, **kwargs: Any +): + init_new_package(integration_name=name, integration_type=kind, prefix=prefix) + print(f"Successfully initialized package") + + +def handle_download_llama_pack( + llama_pack_class: Optional[str] = None, + download_dir: Optional[str] = None, + llama_pack_url: str = LLAMA_PACKS_CONTENTS_URL, + **kwargs: Any, +) -> None: + assert llama_pack_class is not None + assert download_dir is not None + + download_llama_pack( + llama_pack_class=llama_pack_class, + download_dir=download_dir or "./custom_llama_pack", + llama_pack_url=llama_pack_url, + ) + print(f"Successfully downloaded {llama_pack_class} to {download_dir}") + + +def handle_download_llama_dataset( + llama_dataset_class: Optional[str] = None, + download_dir: Optional[str] = None, + llama_hub_url: str = LLAMA_HUB_URL, + llama_datasets_lfs_url: str = LLAMA_DATASETS_LFS_URL, + llama_datasets_source_files_tree_url: str = LLAMA_DATASETS_SOURCE_FILES_GITHUB_TREE_URL, + **kwargs: Any, +) -> None: + assert llama_dataset_class is not None + assert download_dir is not None + + download_llama_dataset( + llama_dataset_class=llama_dataset_class, + download_dir=download_dir, + llama_hub_url=llama_hub_url, + llama_datasets_lfs_url=llama_datasets_lfs_url, + llama_datasets_source_files_tree_url=llama_datasets_source_files_tree_url, + show_progress=True, + load_documents=False, + ) + + print(f"Successfully downloaded {llama_dataset_class} to {download_dir}") + + +def default_rag_cli() -> RagCLI: + import chromadb # pants: no-infer-dep + from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep + from llama_index.vector_stores.chroma import ( + ChromaVectorStore, + ) # pants: no-infer-dep + + persist_dir = default_ragcli_persist_dir() + chroma_client = chromadb.PersistentClient(path=persist_dir) + chroma_collection = chroma_client.create_collection("default", get_or_create=True) + vector_store = ChromaVectorStore( + chroma_collection=chroma_collection, persist_dir=persist_dir + ) + docstore = SimpleDocumentStore() + + ingestion_pipeline = IngestionPipeline( + transformations=[SentenceSplitter(), OpenAIEmbedding()], + vector_store=vector_store, + docstore=docstore, + cache=IngestionCache(), + ) + try: + from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep + except ImportError: + OpenAIEmbedding = None + + try: + import chromadb + + from llama_index.vector_stores.chroma import ( + ChromaVectorStore, + ) + except ImportError: + ChromaVectorStore = None + + if OpenAIEmbedding and ChromaVectorStore: + persist_dir = default_ragcli_persist_dir() + chroma_client = chromadb.PersistentClient(path=persist_dir) + chroma_collection = chroma_client.create_collection( + "default", get_or_create=True + ) + vector_store = ChromaVectorStore( + chroma_collection=chroma_collection, persist_dir=persist_dir + ) + docstore = SimpleDocumentStore() + + ingestion_pipeline = IngestionPipeline( + transformations=[SentenceSplitter(), OpenAIEmbedding()], + vector_store=vector_store, + docstore=docstore, + cache=IngestionCache(), + ) + try: + ingestion_pipeline.load(persist_dir=persist_dir) + except FileNotFoundError: + pass + + return RagCLI( + ingestion_pipeline=ingestion_pipeline, + verbose=False, + persist_dir=persist_dir, + ) + else: + print( + "Default RagCLI was not built. There are packages missing. Please" + " install required dependencies by running " + "`pip install llama-index-embeddings-openai llama-index-llms-openai chromadb llama-index-vector-stores-chroma`" + ) + return None + + +def main() -> None: + parser = argparse.ArgumentParser(description="LlamaIndex CLI tool.") + + # Subparsers for the main commands + subparsers = parser.add_subparsers(title="commands", dest="command", required=True) + + # llama rag command + llamarag_parser = subparsers.add_parser( + "rag", help="Ask a question to a document or a directory of documents." + ) + RagCLI.add_parser_args(llamarag_parser, default_rag_cli) + + # download llamapacks command + llamapack_parser = subparsers.add_parser( + "download-llamapack", help="Download a llama-pack" + ) + llamapack_parser.add_argument( + "llama_pack_class", + type=str, + help=( + "The name of the llama-pack class you want to download, " + "such as `GmailOpenAIAgentPack`." + ), + ) + llamapack_parser.add_argument( + "-d", + "--download-dir", + type=str, + default="./llama_packs", + help="Custom dirpath to download the pack into.", + ) + llamapack_parser.add_argument( + "--llama-hub-url", + type=str, + default=LLAMA_HUB_URL, + help="URL to llama hub.", + ) + llamapack_parser.set_defaults( + func=lambda args: handle_download_llama_pack(**vars(args)) + ) + + # download llamadatasets command + llamadataset_parser = subparsers.add_parser( + "download-llamadataset", help="Download a llama-dataset" + ) + llamadataset_parser.add_argument( + "llama_dataset_class", + type=str, + help=( + "The name of the llama-dataset class you want to download, " + "such as `PaulGrahamEssayDataset`." + ), + ) + llamadataset_parser.add_argument( + "-d", + "--download-dir", + type=str, + default="./llama_datasets", + help="Custom dirpath to download the pack into.", + ) + llamadataset_parser.add_argument( + "--llama-hub-url", + type=str, + default=LLAMA_HUB_URL, + help="URL to llama hub.", + ) + llamadataset_parser.add_argument( + "--llama-datasets-lfs-url", + type=str, + default=LLAMA_DATASETS_LFS_URL, + help="URL to llama datasets.", + ) + llamadataset_parser.set_defaults( + func=lambda args: handle_download_llama_dataset(**vars(args)) + ) + + # Upgrade command + upgrade_parser = subparsers.add_parser( + "upgrade", help="Upgrade a directory containing notebooks or python files." + ) + upgrade_parser.add_argument( + "directory", + type=str, + help="The directory to upgrade. Will run on only .ipynb or .py files.", + ) + upgrade_parser.set_defaults(func=lambda args: upgrade_dir(args.directory)) + + # Upgrade command + upgrade_file_parser = subparsers.add_parser( + "upgrade-file", help="Upgrade a single notebook or python file." + ) + upgrade_file_parser.add_argument( + "path", + type=str, + help="The directory to upgrade. Will run on only .ipynb or .py files.", + ) + upgrade_file_parser.set_defaults(func=lambda args: upgrade_file(args.path)) + + # init package command + new_package_parser = subparsers.add_parser( + "new-package", help="Initialize a new llama-index package" + ) + new_package_parser.add_argument( + "-k", + "--kind", + type=str, + help="Kind of package, e.g., llm, embedding, pack, etc.", + ) + new_package_parser.add_argument( + "-n", + "--name", + type=str, + help="Name of python package", + ) + new_package_parser.add_argument( + "-p", + "--prefix", + type=str, + required=False, + help="Name of prefix package", + ) + new_package_parser.set_defaults(func=lambda args: handle_init_package(**vars(args))) + + # Parse the command-line arguments + args = parser.parse_args() + + # Call the appropriate function based on the command + args.func(args) + + +if __name__ == "__main__": + main() diff --git a/llama-index-cli/llama_index/cli/new_package/BUILD b/llama-index-cli/llama_index/cli/new_package/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-cli/llama_index/cli/new_package/__init__.py b/llama-index-cli/llama_index/cli/new_package/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-cli/llama_index/cli/new_package/base.py b/llama-index-cli/llama_index/cli/new_package/base.py new file mode 100644 index 0000000000000..68e573babfb96 --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/base.py @@ -0,0 +1,120 @@ +import os +import shutil +from pathlib import Path +from llama_index.cli.new_package.templates import ( + pyproject_str, + readme_str, + init_str, + init_with_prefix_str, +) +from typing import Optional + + +def _create_init_file(dir: str): + # create __init__.py + Path(dir + "/__init__.py").touch() + + +def _create_test_file(filename: str): + Path(filename).touch() + + +def _makedirs(dir: str): + try: + os.makedirs(dir) + except FileExistsError as e: + pass + + +def init_new_package( + integration_type: str, + integration_name: str, + prefix: Optional[str] = None, +): + # create new directory, works in current directory + pkg_name = ( + f"llama-index-{integration_type}-{integration_name}".replace(" ", "-") + .replace("_", "-") + .lower() + if prefix is None + else f"llama-index-{prefix}-{integration_type}-{integration_name}".replace( + " ", "-" + ) + .replace("_", "-") + .lower() + ) + pkg_path = os.path.join(os.getcwd(), pkg_name) + tests_path = os.path.join(pkg_path, "tests") + examples_path = os.path.join(pkg_path, "examples") + pkg_src_dir = os.path.join( + pkg_path, + ( + f"llama_index/{integration_type}/{integration_name}".replace( + " ", "_" + ).lower() + if prefix is None + else f"llama_index/{prefix}/{integration_type}/{integration_name}".replace( + " ", "_" + ).lower() + ), + ) + + # make dirs + _makedirs(pkg_path) + _makedirs(tests_path) + _makedirs(examples_path) + _makedirs(pkg_src_dir) + + # create init files + _create_init_file(tests_path) + with open(pkg_src_dir + "/__init__.py", "w") as f: + init_string = ( + init_str.format( + TYPE=integration_type.replace(" ", "_").lower(), + NAME=integration_name.replace(" ", "_").lower(), + ) + if prefix is None + else init_with_prefix_str.format( + TYPE=integration_type.replace(" ", "-").lower(), + NAME=integration_name.replace(" ", "-").lower(), + PREFIX=prefix.replace(" ", "_").lower(), + ) + ) + f.write(init_string) + + # create pyproject.toml + with open(pkg_path + "/pyproject.toml", "w") as f: + f.write( + pyproject_str.format( + PACKAGE_NAME=pkg_name, + TYPE=integration_type.lower(), + NAME=integration_name.lower(), + ) + ) + + # create readme + with open(pkg_path + "/README.md", "w") as f: + f.write( + readme_str.format( + PACKAGE_NAME=pkg_name, + TYPE=integration_type.lower().title(), + NAME=integration_name.lower().title(), + ) + ) + + # create an empty test file + test_file_name = tests_path + ( + f"/test_{integration_type}_{integration_name}.py".replace(" ", "_").lower() + if prefix is None + else f"/test_{prefix}_{integration_type}_{integration_name}.py".replace( + " ", "_" + ).lower() + ) + _create_test_file(test_file_name) + + # copy common files to folders + script_path = Path(__file__).parent.resolve() + common_path = os.path.join(script_path, "common") + shutil.copyfile(common_path + "/.gitignore", pkg_path + "/.gitignore") + shutil.copyfile(common_path + "/Makefile", pkg_path + "/Makefile") + shutil.copyfile(common_path + "/BUILD", pkg_path + "/BUILD") diff --git a/llama-index-cli/llama_index/cli/new_package/common/.gitignore b/llama-index-cli/llama_index/cli/new_package/common/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/common/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-cli/llama_index/cli/new_package/common/BUILD b/llama-index-cli/llama_index/cli/new_package/common/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/common/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-cli/llama_index/cli/new_package/common/Makefile b/llama-index-cli/llama_index/cli/new_package/common/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/common/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-cli/llama_index/cli/new_package/templates/BUILD b/llama-index-cli/llama_index/cli/new_package/templates/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/templates/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-cli/llama_index/cli/new_package/templates/__init__.py b/llama-index-cli/llama_index/cli/new_package/templates/__init__.py new file mode 100644 index 0000000000000..1e8fff1879b1b --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/templates/__init__.py @@ -0,0 +1,8 @@ +from llama_index.cli.new_package.templates.pyproject import pyproject_str +from llama_index.cli.new_package.templates.readme import readme_str +from llama_index.cli.new_package.templates.init import ( + init_str, + init_with_prefix_str, +) + +__all__ = ["pyproject_str", "readme_str", "init_str", "init_with_prefix_str"] diff --git a/llama-index-cli/llama_index/cli/new_package/templates/init.py b/llama-index-cli/llama_index/cli/new_package/templates/init.py new file mode 100644 index 0000000000000..c91d15eb5c007 --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/templates/init.py @@ -0,0 +1,11 @@ +init_str = """from llama_index.{TYPE}.{NAME}.base import + + +__all__ = [""] +""" + +init_with_prefix_str = """from llama_index.{PREFIX}.{TYPE}.{NAME}.base import + + +__all__ = [""] +""" diff --git a/llama-index-cli/llama_index/cli/new_package/templates/pyproject.py b/llama-index-cli/llama_index/cli/new_package/templates/pyproject.py new file mode 100644 index 0000000000000..7efdadb178a28 --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/templates/pyproject.py @@ -0,0 +1,50 @@ +pyproject_str = """[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +name = "{PACKAGE_NAME}" +version = "0.1.0" +description = "llama-index {TYPE} {NAME} integration" +authors = ["Your Name "] +license = "MIT" +readme = "README.md" +packages = [{{include = "llama_index/"}}] + +[tool.poetry.dependencies] +python = ">=3.8.1,<4.0" +llama-index-core = "^0.10.0" + +[tool.poetry.group.dev.dependencies] +black = {{extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"}} +codespell = {{extras = ["toml"], version = ">=v2.2.6"}} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" +""" diff --git a/llama-index-cli/llama_index/cli/new_package/templates/readme.py b/llama-index-cli/llama_index/cli/new_package/templates/readme.py new file mode 100644 index 0000000000000..01b7924c68a62 --- /dev/null +++ b/llama-index-cli/llama_index/cli/new_package/templates/readme.py @@ -0,0 +1,2 @@ +readme_str = """# LlamaIndex {TYPE} Integration: {NAME} +""" diff --git a/llama-index-cli/llama_index/cli/rag/BUILD b/llama-index-cli/llama_index/cli/rag/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-cli/llama_index/cli/rag/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-cli/llama_index/cli/rag/__init__.py b/llama-index-cli/llama_index/cli/rag/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-cli/llama_index/cli/rag/base.py b/llama-index-cli/llama_index/cli/rag/base.py new file mode 100644 index 0000000000000..041ab25ddf669 --- /dev/null +++ b/llama-index-cli/llama_index/cli/rag/base.py @@ -0,0 +1,389 @@ +import asyncio +import os +import shutil +from argparse import ArgumentParser +from glob import iglob +from pathlib import Path +from typing import Any, Callable, Dict, Optional, Union, cast + +from llama_index.core import ( + Response, + ServiceContext, + SimpleDirectoryReader, + VectorStoreIndex, +) +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.core.base.response.schema import RESPONSE_TYPE, StreamingResponse +from llama_index.core.bridge.pydantic import BaseModel, Field, validator +from llama_index.core.chat_engine import CondenseQuestionChatEngine +from llama_index.core.ingestion import IngestionPipeline +from llama_index.core.llms import LLM +from llama_index.core.query_engine import CustomQueryEngine +from llama_index.core.query_pipeline.components.function import FnComponent +from llama_index.core.query_pipeline.query import QueryPipeline +from llama_index.core.readers.base import BaseReader +from llama_index.core.response_synthesizers import CompactAndRefine +from llama_index.core.utils import get_cache_dir + + +def _try_load_openai_llm(): + try: + from llama_index.llms.openai import OpenAI # pants: no-infer-dep + + return OpenAI(model="gpt-3.5-turbo", streaming=True) + except ImportError: + raise ImportError( + "`llama-index-llms-openai` package not found, " + "please run `pip install llama-index-llms-openai`" + ) + + +RAG_HISTORY_FILE_NAME = "files_history.txt" + + +def default_ragcli_persist_dir() -> str: + return str(Path(get_cache_dir()) / "rag_cli") + + +def query_input(query_str: Optional[str] = None) -> str: + return query_str or "" + + +class QueryPipelineQueryEngine(CustomQueryEngine): + query_pipeline: QueryPipeline = Field( + description="Query Pipeline to use for Q&A.", + ) + + def custom_query(self, query_str: str) -> RESPONSE_TYPE: + return self.query_pipeline.run(query_str=query_str) + + async def acustom_query(self, query_str: str) -> RESPONSE_TYPE: + return await self.query_pipeline.arun(query_str=query_str) + + +class RagCLI(BaseModel): + """ + CLI tool for chatting with output of a IngestionPipeline via a QueryPipeline. + """ + + ingestion_pipeline: IngestionPipeline = Field( + description="Ingestion pipeline to run for RAG ingestion." + ) + verbose: bool = Field( + description="Whether to print out verbose information during execution.", + default=False, + ) + persist_dir: str = Field( + description="Directory to persist ingestion pipeline.", + default_factory=default_ragcli_persist_dir, + ) + llm: LLM = Field( + description="Language model to use for response generation.", + default_factory=lambda: _try_load_openai_llm(), + ) + query_pipeline: Optional[QueryPipeline] = Field( + description="Query Pipeline to use for Q&A.", + default=None, + ) + chat_engine: Optional[CondenseQuestionChatEngine] = Field( + description="Chat engine to use for chatting.", + default_factory=None, + ) + file_extractor: Optional[Dict[str, BaseReader]] = Field( + description="File extractor to use for extracting text from files.", + default=None, + ) + + class Config: + arbitrary_types_allowed = True + + @validator("query_pipeline", always=True) + def query_pipeline_from_ingestion_pipeline( + cls, query_pipeline: Any, values: Dict[str, Any] + ) -> Optional[QueryPipeline]: + """ + If query_pipeline is not provided, create one from ingestion_pipeline. + """ + if query_pipeline is not None: + return query_pipeline + + ingestion_pipeline = cast(IngestionPipeline, values["ingestion_pipeline"]) + if ingestion_pipeline.vector_store is None: + return None + verbose = cast(bool, values["verbose"]) + query_component = FnComponent( + fn=query_input, output_key="output", req_params={"query_str"} + ) + llm = cast(LLM, values["llm"]) + + # get embed_model from transformations if possible + embed_model = None + if ingestion_pipeline.transformations is not None: + for transformation in ingestion_pipeline.transformations: + if isinstance(transformation, BaseEmbedding): + embed_model = transformation + break + + service_context = ServiceContext.from_defaults( + llm=llm, embed_model=embed_model or "default" + ) + retriever = VectorStoreIndex.from_vector_store( + ingestion_pipeline.vector_store, service_context=service_context + ).as_retriever(similarity_top_k=8) + response_synthesizer = CompactAndRefine( + service_context=service_context, streaming=True, verbose=verbose + ) + + # define query pipeline + query_pipeline = QueryPipeline(verbose=verbose) + query_pipeline.add_modules( + { + "query": query_component, + "retriever": retriever, + "summarizer": response_synthesizer, + } + ) + query_pipeline.add_link("query", "retriever") + query_pipeline.add_link("retriever", "summarizer", dest_key="nodes") + query_pipeline.add_link("query", "summarizer", dest_key="query_str") + return query_pipeline + + @validator("chat_engine", always=True) + def chat_engine_from_query_pipeline( + cls, chat_engine: Any, values: Dict[str, Any] + ) -> Optional[CondenseQuestionChatEngine]: + """ + If chat_engine is not provided, create one from query_pipeline. + """ + if chat_engine is not None: + return chat_engine + + if values.get("query_pipeline", None) is None: + values["query_pipeline"] = cls.query_pipeline_from_ingestion_pipeline( + query_pipeline=None, values=values + ) + + query_pipeline = cast(QueryPipeline, values["query_pipeline"]) + if query_pipeline is None: + return None + query_engine = QueryPipelineQueryEngine(query_pipeline=query_pipeline) # type: ignore + verbose = cast(bool, values["verbose"]) + llm = cast(LLM, values["llm"]) + return CondenseQuestionChatEngine.from_defaults( + query_engine=query_engine, llm=llm, verbose=verbose + ) + + async def handle_cli( + self, + files: Optional[str] = None, + question: Optional[str] = None, + chat: bool = False, + verbose: bool = False, + clear: bool = False, + create_llama: bool = False, + **kwargs: Dict[str, Any], + ) -> None: + """ + Entrypoint for local document RAG CLI tool. + """ + if clear: + # delete self.persist_dir directory including all subdirectories and files + if os.path.exists(self.persist_dir): + # Ask for confirmation + response = input( + f"Are you sure you want to delete data within {self.persist_dir}? [y/N] " + ) + if response.strip().lower() != "y": + print("Aborted.") + return + os.system(f"rm -rf {self.persist_dir}") + print(f"Successfully cleared {self.persist_dir}") + + self.verbose = verbose + ingestion_pipeline = cast(IngestionPipeline, self.ingestion_pipeline) + if self.verbose: + print("Saving/Loading from persist_dir: ", self.persist_dir) + if files is not None: + documents = [] + for _file in iglob(files, recursive=True): + _file = os.path.abspath(_file) + if os.path.isdir(_file): + reader = SimpleDirectoryReader( + input_dir=_file, + filename_as_id=True, + file_extractor=self.file_extractor, + ) + else: + reader = SimpleDirectoryReader( + input_files=[_file], + filename_as_id=True, + file_extractor=self.file_extractor, + ) + + documents.extend(reader.load_data(show_progress=verbose)) + + await ingestion_pipeline.arun(show_progress=verbose, documents=documents) + ingestion_pipeline.persist(persist_dir=self.persist_dir) + + # Append the `--files` argument to the history file + with open(f"{self.persist_dir}/{RAG_HISTORY_FILE_NAME}", "a") as f: + f.write(files + "\n") + + if create_llama: + if shutil.which("npx") is None: + print( + "`npx` is not installed. Please install it by calling `npm install -g npx`" + ) + else: + history_file_path = Path(f"{self.persist_dir}/{RAG_HISTORY_FILE_NAME}") + if not history_file_path.exists(): + print( + "No data has been ingested, " + "please specify `--files` to create llama dataset." + ) + else: + with open(history_file_path) as f: + stored_paths = {line.strip() for line in f if line.strip()} + if len(stored_paths) == 0: + print( + "No data has been ingested, " + "please specify `--files` to create llama dataset." + ) + elif len(stored_paths) > 1: + print( + "Multiple files or folders were ingested, which is not supported by create-llama. " + "Please call `llamaindex-cli rag --clear` to clear the cache first, " + "then call `llamaindex-cli rag --files` again with a single folder or file" + ) + else: + path = stored_paths.pop() + if "*" in path: + print( + "Glob pattern is not supported by create-llama. " + "Please call `llamaindex-cli rag --clear` to clear the cache first, " + "then call `llamaindex-cli rag --files` again with a single folder or file." + ) + elif not os.path.exists(path): + print( + f"The path {path} does not exist. " + "Please call `llamaindex-cli rag --clear` to clear the cache first, " + "then call `llamaindex-cli rag --files` again with a single folder or file." + ) + else: + print(f"Calling create-llama using data from {path} ...") + command_args = [ + "npx", + "create-llama@latest", + "--frontend", + "--template", + "streaming", + "--framework", + "fastapi", + "--ui", + "shadcn", + "--vector-db", + "none", + "--engine", + "context", + f"--files {path}", + ] + os.system(" ".join(command_args)) + + if question is not None: + await self.handle_question(question) + if chat: + await self.start_chat_repl() + + async def handle_question(self, question: str) -> None: + if self.query_pipeline is None: + raise ValueError("query_pipeline is not defined.") + query_pipeline = cast(QueryPipeline, self.query_pipeline) + query_pipeline.verbose = self.verbose + chat_engine = cast(CondenseQuestionChatEngine, self.chat_engine) + response = chat_engine.chat(question) + + if isinstance(response, StreamingResponse): + response.print_response_stream() + else: + response = cast(Response, response) + print(response) + + async def start_chat_repl(self) -> None: + """ + Start a REPL for chatting with the agent. + """ + if self.query_pipeline is None: + raise ValueError("query_pipeline is not defined.") + chat_engine = cast(CondenseQuestionChatEngine, self.chat_engine) + chat_engine.streaming_chat_repl() + + @classmethod + def add_parser_args( + cls, + parser: Union[ArgumentParser, Any], + instance_generator: Optional[Callable[[], "RagCLI"]], + ) -> None: + if instance_generator: + parser.add_argument( + "-q", + "--question", + type=str, + help="The question you want to ask.", + required=False, + ) + + parser.add_argument( + "-f", + "--files", + type=str, + help=( + "The name of the file or directory you want to ask a question about," + 'such as "file.pdf".' + ), + ) + parser.add_argument( + "-c", + "--chat", + help="If flag is present, opens a chat REPL.", + action="store_true", + ) + parser.add_argument( + "-v", + "--verbose", + help="Whether to print out verbose information during execution.", + action="store_true", + ) + parser.add_argument( + "--clear", + help="Clears out all currently embedded data.", + action="store_true", + ) + parser.add_argument( + "--create-llama", + help="Create a LlamaIndex application with your embedded data.", + required=False, + action="store_true", + ) + parser.set_defaults( + func=lambda args: asyncio.run( + instance_generator().handle_cli(**vars(args)) + ) + ) + + def cli(self) -> None: + """ + Entrypoint for CLI tool. + """ + parser = ArgumentParser(description="LlamaIndex RAG Q&A tool.") + subparsers = parser.add_subparsers( + title="commands", dest="command", required=True + ) + llamarag_parser = subparsers.add_parser( + "rag", help="Ask a question to a document / a directory of documents." + ) + self.add_parser_args(llamarag_parser, lambda: self) + # Parse the command-line arguments + args = parser.parse_args() + + # Call the appropriate function based on the command + args.func(args) diff --git a/llama-index-cli/llama_index/cli/upgrade/BUILD b/llama-index-cli/llama_index/cli/upgrade/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-cli/llama_index/cli/upgrade/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-cli/llama_index/cli/upgrade/__init__.py b/llama-index-cli/llama_index/cli/upgrade/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-cli/llama_index/cli/upgrade/base.py b/llama-index-cli/llama_index/cli/upgrade/base.py new file mode 100644 index 0000000000000..20db64956f3aa --- /dev/null +++ b/llama-index-cli/llama_index/cli/upgrade/base.py @@ -0,0 +1,283 @@ +import json +import os +import re +from pathlib import Path +from typing import Dict, List, Tuple + +mappings_path = os.path.join(os.path.dirname(__file__), "mappings.json") + + +def _parse_from_imports( + mappings: Dict[str, str], + installed_modules: List[str], + line_idx: int, + lines: List[str], + verbose: bool = False, +): + new_lines = [] + new_installs = [] + imported_modules = [] + parsing_modules = False + skipped_lines = 0 + + for line in lines[line_idx:]: + skipped_lines += 1 + if "from " in line: + imported_modules = [line, line.strip().split(" import ")[-1].strip()] + if imported_modules[-1].startswith("("): + imported_modules[-1] = [] + parsing_modules = True + else: + imported_modules = [line, imported_modules[-1].split(", ")] + + if parsing_modules: + if ")" in line: + parsing_modules = False + elif "(" not in line: + imported_modules[-1].append(line.strip().replace(",", "")) + + if not parsing_modules and len(imported_modules) > 0: + imported_module_names = [x.strip() for x in imported_modules[-1]] + new_imports = {} + for module in imported_module_names: + if module in mappings: + new_import_parent = mappings[module] + if new_import_parent not in new_imports: + new_imports[new_import_parent] = [module] + else: + new_imports[new_import_parent].append(module) + else: + print(f"Module not found: {module}\nSwitching to core") + new_import_parent = ( + imported_modules[0] + .split(" import ")[0] + .split("from ")[-1] + .replace("llama_index", "llama_index.core") + ) + if new_import_parent not in new_imports: + new_imports[new_import_parent] = [module] + else: + new_imports[new_import_parent].append(module) + + for new_import_parent, new_imports in new_imports.items(): + new_install_parent = new_import_parent.replace(".", "-").replace( + "_", "-" + ) + if new_install_parent not in installed_modules: + overlap = [x for x in installed_modules if x in new_install_parent] + if len(overlap) == 0: + installed_modules.append(new_install_parent) + new_installs.append(f"%pip install {new_install_parent}\n") + new_imports = ", ".join(new_imports) + new_lines.append(f"from {new_import_parent} import {new_imports}\n") + + parsing_modules = False + new_imports = {} + imported_modules = [] + + return new_lines, new_installs, installed_modules, skipped_lines + + elif not parsing_modules: + new_lines.append(line) + + return new_lines, new_installs, installed_modules, skipped_lines + + +def _parse_hub_downloads( + mappings: Dict[str, str], + installed_modules: List[str], + line: str, +): + regex = r"download_loader\([\"']([A-Z,a-z]+)[\"'][\s,a-z,A-Z,_=]*\)|download_tool\([\"']([a-z,A-Z]+)[\"'][A-Z,a-z,\s,_=]*\)" + result = re.search(regex, line) + new_lines = [] + new_installs = [] + if result: + tool, reader = result.groups() + module = tool if tool else reader + if module in mappings: + new_import_parent = mappings[module] + new_lines.append(f"from {new_import_parent} import {module}\n") + new_install_parent = new_import_parent.replace(".", "-").replace("_", "-") + if new_install_parent not in installed_modules: + new_installs.append(f"%pip install {new_install_parent}\n") + installed_modules.append(new_install_parent) + else: + print(f"Reader/Tool not found: {module}\nKeeping line as is.") + new_lines.append(line) + + return new_lines, new_installs, installed_modules + + +def parse_lines( + lines: List[str], installed_modules: List[str], verbose: bool = False +) -> Tuple[List[str], List[str]]: + with open(mappings_path) as f: + mappings = json.load(f) + + new_installs = [] + new_lines = [] + just_found_imports = False + skipped_lines = 0 + + for idx, line in enumerate(lines): + this_new_lines = [] + this_new_installs = [] + this_installed_modules = [] + + if skipped_lines != 0: + skipped_lines -= 1 + + if just_found_imports and skipped_lines > 0: + continue + else: + just_found_imports = False + + if ( + "from llama_index." in line + or "from llama_index import" in line + or "from llama_hub." in line + ): + ( + this_new_lines, + this_new_installs, + this_installed_modules, + skipped_lines, + ) = _parse_from_imports( + mappings=mappings, + installed_modules=installed_modules, + line_idx=idx, + lines=lines, + verbose=verbose, + ) + just_found_imports = True + + elif "download_loader(" in line or "download_tool(" in line: + ( + this_new_lines, + this_new_installs, + this_installed_modules, + ) = _parse_hub_downloads( + mappings=mappings, + installed_modules=installed_modules, + line=line, + ) + + elif not just_found_imports: + this_new_lines = [line] + + new_lines += this_new_lines + new_installs += this_new_installs + installed_modules += this_installed_modules + installed_modules = list(set(installed_modules)) + + return new_lines, list(set(new_installs)) + + +def _cell_installs_llama_hub(cell) -> bool: + lines = cell["source"] + llama_hub_partial_statements = [ + "pip install llama-hub", + "import download_loader", + "import download_tool", + ] + + if len(lines) > 1: + return False + if cell["cell_type"] == "code" and any( + el in lines[0] for el in llama_hub_partial_statements + ): + return True + return False + + +def _format_new_installs(new_installs): + if new_installs: + new_installs = list(set(new_installs)) + return new_installs[:-1] + [new_installs[-1].replace("\n", "")] + return new_installs + + +def upgrade_nb_file(file_path): + print(f"\n=====================\n{file_path}\n", flush=True) + with open(file_path) as f: + notebook = json.load(f) + + verbose = False + if file_path == "../docs/examples/managed/manage_retrieval_benchmark.ipynb": + verbose = True + + installed_modules = ["llama-index-core"] # default installs + cur_cells = [] + new_installs = [] + first_code_idx = -1 + for idx, cell in enumerate(notebook["cells"]): + if cell["cell_type"] == "code": + if verbose: + print(f"cell: {cell}", flush=True) + if first_code_idx == -1: + first_code_idx = idx + + code = cell["source"] + + new_lines, cur_new_installs = parse_lines(code, installed_modules, verbose) + new_installs += cur_new_installs + + cell["source"] = new_lines + + cur_cells.append(cell) + + if len(new_installs) > 0: + notebook["cells"] = cur_cells + new_cell = { + "cell_type": "code", + "metadata": {}, + "execution_count": None, + "outputs": [], + "source": _format_new_installs(new_installs), + } + cur_cells.insert(first_code_idx, new_cell) + + cur_cells = [cell for cell in cur_cells if not _cell_installs_llama_hub(cell)] + notebook["cells"] = cur_cells + with open(file_path, "w") as f: + json.dump(notebook, f, indent=1, ensure_ascii=False) + + +def upgrade_py_md_file(file_path: str) -> None: + with open(file_path) as f: + lines = f.readlines() + + installed_modules = ["llama-index-core"] # default installs + new_lines, new_installs = parse_lines(lines, installed_modules) + + with open(file_path, "w") as f: + f.write("".join(new_lines)) + + if len(new_installs) > 0: + print("New installs:") + for install in new_installs: + print(install.strip().replace("%", "")) + + +def upgrade_file(file_path: str) -> None: + if file_path.endswith(".ipynb"): + upgrade_nb_file(file_path) + elif file_path.endswith((".py", ".md")): + upgrade_py_md_file(file_path) + else: + raise Exception(f"File type not supported: {file_path}") + + +def _is_hidden(path: Path) -> bool: + return any(part.startswith(".") and part not in [".", ".."] for part in path.parts) + + +def upgrade_dir(input_dir: str) -> None: + file_refs = list(Path(input_dir).rglob("*.py")) + file_refs += list(Path(input_dir).rglob("*.ipynb")) + file_refs += list(Path(input_dir).rglob("*.md")) + file_refs = [x for x in file_refs if not _is_hidden(x)] + for file_ref in file_refs: + if file_ref.is_file(): + upgrade_file(str(file_ref)) diff --git a/llama-index-cli/llama_index/cli/upgrade/mappings.json b/llama-index-cli/llama_index/cli/upgrade/mappings.json new file mode 100644 index 0000000000000..59855d154b8d3 --- /dev/null +++ b/llama-index-cli/llama_index/cli/upgrade/mappings.json @@ -0,0 +1,832 @@ +{ + "StorageContext": "llama_index.core", + "ServiceContext": "llama_index.core", + "ComposableGraph": "llama_index.core", + "# indicesSummaryIndex": "llama_index.core", + "VectorStoreIndex": "llama_index.core", + "SimpleKeywordTableIndex": "llama_index.core", + "KeywordTableIndex": "llama_index.core", + "RAKEKeywordTableIndex": "llama_index.core", + "TreeIndex": "llama_index.core", + "DocumentSummaryIndex": "llama_index.core", + "KnowledgeGraphIndex": "llama_index.core", + "# indices - legacy namesGPTKeywordTableIndex": "llama_index.core", + "GPTKnowledgeGraphIndex": "llama_index.core", + "GPTSimpleKeywordTableIndex": "llama_index.core", + "GPTRAKEKeywordTableIndex": "llama_index.core", + "GPTListIndex": "llama_index.core", + "ListIndex": "llama_index.core", + "GPTTreeIndex": "llama_index.core", + "GPTVectorStoreIndex": "llama_index.core", + "GPTDocumentSummaryIndex": "llama_index.core", + "Prompt": "llama_index.core", + "PromptTemplate": "llama_index.core", + "BasePromptTemplate": "llama_index.core", + "ChatPromptTemplate": "llama_index.core", + "SelectorPromptTemplate": "llama_index.core", + "SummaryPrompt": "llama_index.core", + "TreeInsertPrompt": "llama_index.core", + "TreeSelectPrompt": "llama_index.core", + "TreeSelectMultiplePrompt": "llama_index.core", + "RefinePrompt": "llama_index.core", + "QuestionAnswerPrompt": "llama_index.core", + "KeywordExtractPrompt": "llama_index.core", + "QueryKeywordExtractPrompt": "llama_index.core", + "Response": "llama_index.core", + "Document": "llama_index.core", + "SimpleDirectoryReader": "llama_index.core", + "VellumPredictor": "llama_index.core", + "VellumPromptRegistry": "llama_index.core", + "MockEmbedding": "llama_index.core", + "SQLDatabase": "llama_index.core", + "SQLDocumentContextBuilder": "llama_index.core", + "SQLContextBuilder": "llama_index.core", + "PromptHelper": "llama_index.core", + "IndexStructType": "llama_index.core", + "download_loader": "llama_index.core", + "load_graph_from_storage": "llama_index.core", + "load_index_from_storage": "llama_index.core", + "load_indices_from_storage": "llama_index.core", + "QueryBundle": "llama_index.core", + "get_response_synthesizer": "llama_index.core", + "set_global_service_context": "llama_index.core", + "set_global_handler": "llama_index.core", + "set_global_tokenizer": "llama_index.core", + "get_tokenizer": "llama_index.core", + "AgentRunner": "llama_index.core.agent", + "ParallelAgentRunner": "llama_index.core.agent", + "ReActAgentWorker": "llama_index.core.agent", + "ReActAgent": "llama_index.core.agent", + "CustomSimpleAgentWorker": "llama_index.core.agent", + "QueryPipelineAgentWorker": "llama_index.core.agent", + "ReActChatFormatter": "llama_index.core.agent", + "# betaMultimodalReActAgentWorker": "llama_index.core.agent", + "# schema-relatedAgentChatResponse": "llama_index.core.agent", + "Task": "llama_index.core.agent", + "CallbackManager": "llama_index.core.callbacks", + "CBEvent": "llama_index.core.callbacks", + "CBEventType": "llama_index.core.callbacks", + "EventPayload": "llama_index.core.callbacks", + "LlamaDebugHandler": "llama_index.core.callbacks", + "TokenCountingHandler": "llama_index.core.callbacks", + "trace_method": "llama_index.core.callbacks", + "deepeval_callback_handler": "llama_index.callbacks.deepeval", + "OpenInferenceCallbackHandler": "llama_index.callbacks.openinference", + "WandbCallbackHandler": "llama_index.callbacks.wandb", + "argilla_callback_handler": "llama_index.callbacks.argilla", + "honeyhive_callback_handler": "llama_index.callbacks.honeyhive", + "arize_phoenix_callback_handler": "llama_index.callbacks.arize_phoenix", + "AimCallback": "llama_index.callbacks.aim", + "PromptLayerHandler": "llama_index.callbacks.promptlayer", + "SimpleChatEngine": "llama_index.core.chat_engine", + "CondenseQuestionChatEngine": "llama_index.core.chat_engine", + "ContextChatEngine": "llama_index.core.chat_engine", + "CondensePlusContextChatEngine": "llama_index.core.chat_engine", + "QASummaryQueryEngineBuilder": "llama_index.core.composability", + "IndexGraph": "llama_index.core.data_structs", + "KeywordTable": "llama_index.core.data_structs", + "IndexList": "llama_index.core.data_structs", + "IndexDict": "llama_index.core.data_structs", + "StructDatapoint": "llama_index.core.data_structs", + "Node": "llama_index.core.data_structs", + "BaseEmbedding": "llama_index.core.embeddings", + "MultiModalEmbedding": "llama_index.core.embeddings", + "Pooling": "llama_index.core.embeddings", + "BaseEvaluator": "llama_index.core.evaluation", + "AnswerRelevancyEvaluator": "llama_index.core.evaluation", + "ContextRelevancyEvaluator": "llama_index.core.evaluation", + "EvaluationResult": "llama_index.core.evaluation", + "FaithfulnessEvaluator": "llama_index.core.evaluation", + "RelevancyEvaluator": "llama_index.core.evaluation", + "RelevanceEvaluator": "llama_index.core.evaluation", + "DatasetGenerator": "llama_index.core.evaluation", + "QueryResponseDataset": "llama_index.core.evaluation", + "GuidelineEvaluator": "llama_index.core.evaluation", + "CorrectnessEvaluator": "llama_index.core.evaluation", + "SemanticSimilarityEvaluator": "llama_index.core.evaluation", + "PairwiseComparisonEvaluator": "llama_index.core.evaluation", + "BatchEvalRunner": "llama_index.core.evaluation", + "# legacy: kept for backward compatibilityQueryResponseEvaluator": "llama_index.core.evaluation", + "ResponseEvaluator": "llama_index.core.evaluation", + "# retrievalgenerate_qa_embedding_pairs": "llama_index.core.evaluation", + "generate_question_context_pairs": "llama_index.core.evaluation", + "EmbeddingQAFinetuneDataset": "llama_index.core.evaluation", + "BaseRetrievalEvaluator": "llama_index.core.evaluation", + "RetrievalEvalResult": "llama_index.core.evaluation", + "RetrieverEvaluator": "llama_index.core.evaluation", + "MultiModalRetrieverEvaluator": "llama_index.core.evaluation", + "RetrievalMetricResult": "llama_index.core.evaluation", + "resolve_metrics": "llama_index.core.evaluation", + "HitRate": "llama_index.core.evaluation", + "MRR": "llama_index.core.evaluation", + "get_retrieval_results_df": "llama_index.core.evaluation", + "LabelledQADataset": "llama_index.core.evaluation", + "SummaryExtractor": "llama_index.core.extractors", + "QuestionsAnsweredExtractor": "llama_index.core.extractors", + "TitleExtractor": "llama_index.core.extractors", + "KeywordExtractor": "llama_index.core.extractors", + "BaseExtractor": "llama_index.core.extractors", + "PydanticProgramExtractor": "llama_index.core.extractors", + "SimpleGraphStore": "llama_index.core.graph_stores", + "SummaryIndex": "llama_index.core", + "PandasIndex": "llama_index.core.indices", + "SQLStructStoreIndex": "llama_index.core.indices", + "MultiModalVectorStoreIndex": "llama_index.core.indices", + "EmptyIndex": "llama_index.core.indices", + "# legacyGPTKnowledgeGraphIndex": "llama_index.core.indices", + "GPTKeywordTableIndex": "llama_index.core.indices", + "GPTPandasIndex": "llama_index.core.indices", + "GPTSQLStructStoreIndex": "llama_index.core.indices", + "GPTEmptyIndex": "llama_index.core.indices", + "DocstoreStrategy": "llama_index.core.ingestion", + "IngestionCache": "llama_index.core.ingestion", + "IngestionPipeline": "llama_index.core.ingestion", + "run_transformations": "llama_index.core.ingestion", + "arun_transformations": "llama_index.core.ingestion", + "BaseLlamaDataset": "llama_index.core.llama_dataset", + "BaseLlamaDataExample": "llama_index.core.llama_dataset", + "BaseLlamaExamplePrediction": "llama_index.core.llama_dataset", + "BaseLlamaPredictionDataset": "llama_index.core.llama_dataset", + "LabelledRagDataExample": "llama_index.core.llama_dataset", + "LabelledRagDataset": "llama_index.core.llama_dataset", + "LabeledRagDataExample": "llama_index.core.llama_dataset", + "LabeledRagDataset": "llama_index.core.llama_dataset", + "RagExamplePrediction": "llama_index.core.llama_dataset", + "RagPredictionDataset": "llama_index.core.llama_dataset", + "CreatedByType": "llama_index.core.llama_dataset", + "CreatedBy": "llama_index.core.llama_dataset", + "download_llama_dataset": "llama_index.core.llama_dataset", + "EvaluatorExamplePrediction": "llama_index.core.llama_dataset", + "EvaluatorPredictionDataset": "llama_index.core.llama_dataset", + "LabeledEvaluatorDataset": "llama_index.core.llama_dataset", + "LabelledEvaluatorDataset": "llama_index.core.llama_dataset", + "LabelledEvaluatorDataExample": "llama_index.core.llama_dataset", + "LabeledEvaluatorDataExample": "llama_index.core.llama_dataset", + "LabelledPairwiseEvaluatorDataExample": "llama_index.core.llama_dataset", + "LabelledPairwiseEvaluatorDataset": "llama_index.core.llama_dataset", + "LabeledPairwiseEvaluatorDataExample": "llama_index.core.llama_dataset", + "LabeledPairwiseEvaluatorDataset": "llama_index.core.llama_dataset", + "PairwiseEvaluatorExamplePrediction": "llama_index.core.llama_dataset", + "PairwiseEvaluatorPredictionDataset": "llama_index.core.llama_dataset", + "BaseLlamaPack": "llama_index.core.llama_pack", + "download_llama_pack": "llama_index.core.llama_pack", + "CustomLLM": "llama_index.core.llms", + "LLM": "llama_index.core.llms", + "ChatMessage": "llama_index.core.llms", + "ChatResponse": "llama_index.core.llms", + "ChatResponseAsyncGen": "llama_index.core.llms", + "ChatResponseGen": "llama_index.core.llms", + "CompletionResponse": "llama_index.core.llms", + "CompletionResponseAsyncGen": "llama_index.core.llms", + "CompletionResponseGen": "llama_index.core.llms", + "LLMMetadata": "llama_index.core.llms", + "MessageRole": "llama_index.core.llms", + "BaseMemory": "llama_index.core.memory", + "ChatMemoryBuffer": "llama_index.core.memory", + "MultiModalLLMMetadata": "llama_index.core.multi_modal_llms", + "MultiModalLLM": "llama_index.core.multi_modal_llms", + "TokenTextSplitter": "llama_index.core.node_parser", + "SentenceSplitter": "llama_index.core.node_parser", + "CodeSplitter": "llama_index.core.node_parser", + "SimpleFileNodeParser": "llama_index.core.node_parser", + "HTMLNodeParser": "llama_index.core.node_parser", + "MarkdownNodeParser": "llama_index.core.node_parser", + "JSONNodeParser": "llama_index.core.node_parser", + "SentenceWindowNodeParser": "llama_index.core.node_parser", + "SemanticSplitterNodeParser": "llama_index.core.node_parser", + "NodeParser": "llama_index.core.node_parser", + "HierarchicalNodeParser": "llama_index.core.node_parser", + "TextSplitter": "llama_index.core.node_parser", + "MarkdownElementNodeParser": "llama_index.core.node_parser", + "MetadataAwareTextSplitter": "llama_index.core.node_parser", + "LangchainNodeParser": "llama_index.core.node_parser", + "UnstructuredElementNodeParser": "llama_index.core.node_parser", + "get_leaf_nodes": "llama_index.core.node_parser", + "get_root_nodes": "llama_index.core.node_parser", + "# deprecated": "llama_index.core.node_parser", + "for backwards compatibilitySimpleNodeParser": "llama_index.core.node_parser", + "ObjectRetriever": "llama_index.core.objects", + "ObjectIndex": "llama_index.core.objects", + "SimpleObjectNodeMapping": "llama_index.core.objects", + "SimpleToolNodeMapping": "llama_index.core.objects", + "SimpleQueryToolNodeMapping": "llama_index.core.objects", + "SQLTableNodeMapping": "llama_index.core.objects", + "SQLTableSchema": "llama_index.core.objects", + "ChainableOutputParser": "llama_index.core.output_parsers", + "LangchainOutputParser": "llama_index.core.output_parsers", + "PydanticOutputParser": "llama_index.core.output_parsers", + "SelectionOutputParser": "llama_index.core.output_parsers", + "Playground": "llama_index.core.playground", + "DEFAULT_INDEX_CLASSES": "llama_index.core.playground", + "DEFAULT_MODES": "llama_index.core.playground", + "SimilarityPostprocessor": "llama_index.core.postprocessor", + "KeywordNodePostprocessor": "llama_index.core.postprocessor", + "PrevNextNodePostprocessor": "llama_index.core.postprocessor", + "AutoPrevNextNodePostprocessor": "llama_index.core.postprocessor", + "FixedRecencyPostprocessor": "llama_index.core.postprocessor", + "EmbeddingRecencyPostprocessor": "llama_index.core.postprocessor", + "TimeWeightedPostprocessor": "llama_index.core.postprocessor", + "PIINodePostprocessor": "llama_index.core.postprocessor", + "NERPIINodePostprocessor": "llama_index.core.postprocessor", + "LLMRerank": "llama_index.core.postprocessor", + "SentenceEmbeddingOptimizer": "llama_index.core.postprocessor", + "SentenceTransformerRerank": "llama_index.core.postprocessor", + "MetadataReplacementPostProcessor": "llama_index.core.postprocessor", + "LongContextReorder": "llama_index.core.postprocessor", + "BasePydanticProgram": "llama_index.core.program", + "LLMTextCompletionProgram": "llama_index.core.program", + "MultiModalLLMCompletionProgram": "llama_index.core.program", + "LangchainPromptTemplate": "llama_index.core.prompts", + "PromptType": "llama_index.core.prompts", + "display_prompt_dict": "llama_index.core.prompts", + "CitationQueryEngine": "llama_index.core.query_engine", + "CogniswitchQueryEngine": "llama_index.core.query_engine", + "ComposableGraphQueryEngine": "llama_index.core.query_engine", + "RetrieverQueryEngine": "llama_index.core.query_engine", + "TransformQueryEngine": "llama_index.core.query_engine", + "MultiStepQueryEngine": "llama_index.core.query_engine", + "RouterQueryEngine": "llama_index.core.query_engine", + "RetrieverRouterQueryEngine": "llama_index.core.query_engine", + "ToolRetrieverRouterQueryEngine": "llama_index.core.query_engine", + "SubQuestionQueryEngine": "llama_index.core.query_engine", + "SubQuestionAnswerPair": "llama_index.core.query_engine", + "SQLJoinQueryEngine": "llama_index.core.query_engine", + "SQLAutoVectorQueryEngine": "llama_index.core.query_engine", + "RetryQueryEngine": "llama_index.core.query_engine", + "RetrySourceQueryEngine": "llama_index.core.query_engine", + "RetryGuidelineQueryEngine": "llama_index.core.query_engine", + "FLAREInstructQueryEngine": "llama_index.core.query_engine", + "PandasQueryEngine": "llama_index.core.query_engine", + "JSONalyzeQueryEngine": "llama_index.core.query_engine", + "KnowledgeGraphQueryEngine": "llama_index.core.query_engine", + "BaseQueryEngine": "llama_index.core.query_engine", + "CustomQueryEngine": "llama_index.core.query_engine", + "# multimodalSimpleMultiModalQueryEngine": "llama_index.core.query_engine", + "# SQLSQLTableRetrieverQueryEngine": "llama_index.core.query_engine", + "NLSQLTableQueryEngine": "llama_index.core.query_engine", + "PGVectorSQLQueryEngine": "llama_index.core.query_engine", + "AgentFnComponent": "llama_index.core.query_pipeline", + "AgentInputComponent": "llama_index.core.query_pipeline", + "ArgPackComponent": "llama_index.core.query_pipeline", + "FnComponent": "llama_index.core.query_pipeline", + "InputComponent": "llama_index.core.query_pipeline", + "RouterComponent": "llama_index.core.query_pipeline", + "ToolRunnerComponent": "llama_index.core.query_pipeline", + "QueryPipeline": "llama_index.core.query_pipeline", + "LLMQuestionGenerator": "llama_index.core.question_gen", + "SubQuestionOutputParser": "llama_index.core.question_gen", + "ReaderConfig": "llama_index.core.readers", + "PDFReader": "llama_index.readers.file", + "HTMLTagReader": "llama_index.readers.file", + "StringIterableReader": "llama_index.core.readers", + "ResponseMode": "llama_index.core.response_synthesizers", + "BaseSynthesizer": "llama_index.core.response_synthesizers", + "Refine": "llama_index.core.response_synthesizers", + "SimpleSummarize": "llama_index.core.response_synthesizers", + "TreeSummarize": "llama_index.core.response_synthesizers", + "Generation": "llama_index.core.response_synthesizers", + "CompactAndRefine": "llama_index.core.response_synthesizers", + "Accumulate": "llama_index.core.response_synthesizers", + "VectorIndexRetriever": "llama_index.core.retrievers", + "VectorIndexAutoRetriever": "llama_index.core.retrievers", + "SummaryIndexRetriever": "llama_index.core.retrievers", + "SummaryIndexEmbeddingRetriever": "llama_index.core.retrievers", + "SummaryIndexLLMRetriever": "llama_index.core.retrievers", + "KGTableRetriever": "llama_index.core.retrievers", + "KnowledgeGraphRAGRetriever": "llama_index.core.retrievers", + "EmptyIndexRetriever": "llama_index.core.retrievers", + "TreeAllLeafRetriever": "llama_index.core.retrievers", + "TreeSelectLeafEmbeddingRetriever": "llama_index.core.retrievers", + "TreeSelectLeafRetriever": "llama_index.core.retrievers", + "TreeRootRetriever": "llama_index.core.retrievers", + "TransformRetriever": "llama_index.core.retrievers", + "KeywordTableSimpleRetriever": "llama_index.core.retrievers", + "BaseRetriever": "llama_index.core.retrievers", + "RecursiveRetriever": "llama_index.core.retrievers", + "AutoMergingRetriever": "llama_index.core.retrievers", + "RouterRetriever": "llama_index.core.retrievers", + "BM25Retriever": "llama_index.core.retrievers", + "QueryFusionRetriever": "llama_index.core.retrievers", + "# SQLSQLRetriever": "llama_index.core.retrievers", + "NLSQLRetriever": "llama_index.core.retrievers", + "SQLParserMode": "llama_index.core.retrievers", + "# legacyListIndexEmbeddingRetriever": "llama_index.core.retrievers", + "ListIndexRetriever": "llama_index.core.retrievers", + "# imageBaseImageRetriever": "llama_index.core.retrievers", + "LLMSingleSelector": "llama_index.core.selectors", + "LLMMultiSelector": "llama_index.core.selectors", + "EmbeddingSingleSelector": "llama_index.core.selectors", + "PydanticSingleSelector": "llama_index.core.selectors", + "PydanticMultiSelector": "llama_index.core.selectors", + "BaseTool": "llama_index.core.tools", + "adapt_to_async_tool": "llama_index.core.tools", + "AsyncBaseTool": "llama_index.core.tools", + "QueryEngineTool": "llama_index.core.tools", + "RetrieverTool": "llama_index.core.tools", + "ToolMetadata": "llama_index.core.tools", + "ToolOutput": "llama_index.core.tools", + "FunctionTool": "llama_index.core.tools", + "QueryPlanTool": "llama_index.core.tools", + "download_tool": "llama_index.core.tools", + "VectorStoreQuery": "llama_index.core.vector_stores", + "VectorStoreQueryResult": "llama_index.core.vector_stores", + "MetadataFilters": "llama_index.core.vector_stores", + "MetadataFilter": "llama_index.core.vector_stores", + "ExactMatchFilter": "llama_index.core.vector_stores", + "FilterCondition": "llama_index.core.vector_stores", + "FilterOperator": "llama_index.core.vector_stores", + "SimpleVectorStore": "llama_index.core.vector_stores", + "BaseParamTuner": "llama_index.experimental.param_tuner", + "ParamTuner": "llama_index.experimental.param_tuner", + "AsyncParamTuner": "llama_index.experimental.param_tuner", + "RayTuneParamTuner": "llama_index.experimental.param_tuner", + "OpenAIFinetuneEngine": "llama_index.finetuning", + "generate_qa_embedding_pairs": "llama_index.finetuning", + "SentenceTransformersFinetuneEngine": "llama_index.finetuning", + "EmbeddingAdapterFinetuneEngine": "llama_index.finetuning", + "GradientFinetuneEngine": "llama_index.finetuning", + "generate_cohere_reranker_finetuning_dataset": "llama_index.finetuning", + "CohereRerankerFinetuneEngine": "llama_index.finetuning", + "BaseFinetuningHandler": "llama_index.finetuning.callbacks", + "OpenAIFineTuningHandler": "llama_index.finetuning.callbacks", + "CrossEncoderFinetuneEngine": "llama_index.finetuning.cross_encoders", + "CohereRerankerFinetuneDataset": "llama_index.finetuning.rerankers", + "SingleStoreVectorStore": "llama_index.vector_stores.singlestoredb", + "QdrantVectorStore": "llama_index.vector_stores.qdrant", + "PineconeVectorStore": "llama_index.vector_stores.pinecone", + "SupabaseVectorStore": "llama_index.vector_stores.supabase", + "AstraDBVectorStore": "llama_index.vector_stores.astra", + "LanceDBVectorStore": "llama_index.vector_stores.lancedb", + "ElasticsearchStore": "llama_index.vector_stores.elasticsearch", + "PGVectorStore": "llama_index.vector_stores.postgres", + "CassandraVectorStore": "llama_index.vector_stores.cassandra", + "ZepVectorStore": "llama_index.vector_stores.zep", + "RocksetVectorStore": "llama_index.vector_stores.rocksetdb", + "MyScaleVectorStore": "llama_index.vector_stores.myscale", + "TencentVectorDB": "llama_index.vector_stores.tencentvectordb", + "MilvusVectorStore": "llama_index.vector_stores.milvus", + "Neo4jVectorStore": "llama_index.vector_stores.neo4jvector", + "DeepLakeVectorStore": "llama_index.vector_stores.deeplake", + "WeaviateVectorStore": "llama_index.vector_stores.weaviate", + "TimescaleVectorStore": "llama_index.vector_stores.timescalevector", + "DashVectorStore": "llama_index.vector_stores.dashvector", + "JaguarVectorStore": "llama_index.vector_stores.jaguar", + "FaissVectorStore": "llama_index.vector_stores.faiss", + "MongoDBAtlasVectorSearch": "llama_index.vector_stores.mongodb", + "ChromaVectorStore": "llama_index.vector_stores.chroma", + "BagelVectorStore": "llama_index.vector_stores.bagel", + "TxtaiVectorStore": "llama_index.vector_stores.txtai", + "EpsillaVectorStore": "llama_index.vector_stores.epsilla", + "LanternVectorStore": "llama_index.vector_stores.lantern", + "AwaDBVectorStore": "llama_index.vector_stores.awadb", + "AzureCosmosDBMongoDBVectorSearch": "llama_index.vector_stores.azurecosmosmongo", + "TypesenseVectorStore": "llama_index.vector_stores.typesense", + "PGVectoRsStore": "llama_index.vector_stores.pgvecto_rs", + "OpensearchVectorStore": "llama_index.vector_stores.opensearch", + "DocArrayInMemoryVectorStore": "llama_index.vector_stores.docarray", + "DocArrayHnswVectorStore": "llama_index.vector_stores.docarray", + "DynamoDBVectorStore": "llama_index.vector_stores.dynamodb", + "ChatGPTRetrievalPluginClient": "llama_index.vector_stores.chatgpt_plugin", + "TairVectorStore": "llama_index.vector_stores.tair", + "CognitiveSearchVectorStore": "llama_index.vector_stores.cogsearch", + "RedisVectorStore": "llama_index.vector_stores.redis", + "set_google_config": "llama_index.vector_stores.google", + "GoogleVectorStore": "llama_index.vector_stores.google", + "MetalVectorStore": "llama_index.vector_stores.metal", + "PathwayRetriever": "llama_index.retrievers.pathway", + "YouRetriever": "llama_index.retrievers.you", + "ZillizCloudPipelineIndex": "llama_index.indices.managed.zilliz", + "ZillizCloudPipelineRetriever": "llama_index.indices.managed.zilliz", + "ColbertIndex": "llama_index.indices.managed.colbert", + "VectaraIndex": "llama_index.indices.managed.vectara", + "VectaraRetriever": "llama_index.indices.managed.vectara", + "VectaraAutoRetriever": "llama_index.indices.managed.vectara", + "GoogleIndex": "llama_index.indices.managed.google", + "SalesforceToolSpec": "llama_index.tools.salesforce", + "PythonFileToolSpec": "llama_index.tools.python_file", + "TextToImageToolSpec": "llama_index.tools.text_to_image", + "OpenAPIToolSpec": "llama_index.tools.openapi", + "IonicShoppingToolSpec": "llama_index.tools.ionic_shopping", + "ShopifyToolSpec": "llama_index.tools.shopify", + "MetaphorToolSpec": "llama_index.tools.metaphor", + "ACTION_URL_TMPL": "llama_index.tools.zapier", + "ExaToolSpec": "llama_index.tools.exa", + "ZapierToolSpec": "llama_index.tools.zapier", + "WikipediaToolSpec": "llama_index.tools.wikipedia", + "GoogleCalendarToolSpec": "llama_index.tools.google", + "GmailToolSpec": "llama_index.tools.google", + "GoogleSearchToolSpec": "llama_index.tools.google", + "QUERY_URL_TMPL": "llama_index.tools.google", + "AzureTranslateToolSpec": "llama_index.tools.azure_translate", + "ENDPOINT_BASE_URL": "llama_index.tools.azure_translate", + "MultionToolSpec": "llama_index.tools.multion", + "DatabaseToolSpec": "llama_index.tools.database", + "ChatGPTPluginToolSpec": "llama_index.tools.chatgpt_plugin", + "Neo4jQueryToolSpec": "llama_index.tools.neo4j", + "BingSearchToolSpec": "llama_index.tools.bing_search", + "CogniswitchToolSpec": "llama_index.tools.cogniswitch", + "AzureCVToolSpec": "llama_index.tools.azure_cv", + "CV_URL_TMPL": "llama_index.tools.azure_cv", + "NotionToolSpec": "llama_index.tools.notion", + "YelpToolSpec": "llama_index.tools.yelp", + "OpenWeatherMapToolSpec": "llama_index.tools.weather", + "AzureSpeechToolSpec": "llama_index.tools.azure_speech", + "WolframAlphaToolSpec": "llama_index.tools.wolfram_alpha", + "SlackToolSpec": "llama_index.tools.slack", + "TavilyToolSpec": "llama_index.tools.tavily_research", + "ArxivToolSpec": "llama_index.tools.arxiv", + "VectorDB": "llama_index.tools.vector_db", + "VectorDBToolSpec": "llama_index.tools.vector_db", + "CodeInterpreterToolSpec": "llama_index.tools.code_interpreter", + "WaiiToolSpec": "llama_index.tools.waii", + "INVALID_URL_PROMPT": "llama_index.tools.requests", + "RequestsToolSpec": "llama_index.tools.requests", + "OpenAIImageGenerationToolSpec": "llama_index.tools.openai", + "GraphQLToolSpec": "llama_index.tools.graphql", + "PlaygroundsSubgraphConnectorToolSpec": "llama_index.tools.playgrounds", + "PlaygroundsSubgraphInspectorToolSpec": "llama_index.tools.playgrounds", + "ClipEmbedding": "llama_index.embeddings.clip", + "AnyscaleEmbedding": "llama_index.embeddings.anyscale", + "LLMRailsEmbedding": "llama_index.embeddings.llm_rails", + "LLMRailsEmbeddings": "llama_index.embeddings.llm_rails", + "AdapterEmbeddingModel": "llama_index.embeddings.adapter", + "TogetherEmbedding": "llama_index.embeddings.together", + "VoyageEmbedding": "llama_index.embeddings.voyageai", + "GradientEmbedding": "llama_index.embeddings.gradient", + "HuggingFaceEmbedding": "llama_index.embeddings.huggingface", + "HuggingFaceInferenceAPIEmbedding": "llama_index.embeddings.huggingface", + "HuggingFaceInferenceAPIEmbeddings": "llama_index.embeddings.huggingface", + "OllamaEmbedding": "llama_index.embeddings.ollama", + "SageMakerEmbedding": "llama_index.embeddings.sagemaker_endpoint", + "OpenAIEmbedding": "llama_index.embeddings.openai", + "OpenAIEmbeddingMode": "llama_index.embeddings.openai", + "OpenAIEmbeddingModelType": "llama_index.embeddings.openai", + "OpenAIEmbeddingModeModel": "llama_index.embeddings.openai", + "CohereEmbedding": "llama_index.embeddings.cohere", + "JinaEmbedding": "llama_index.embeddings.jinaai", + "NomicEmbedding": "llama_index.embeddings.nomic", + "GeminiEmbedding": "llama_index.embeddings.gemini", + "FastEmbedEmbedding": "llama_index.embeddings.fastembed", + "InstructorEmbedding": "llama_index.embeddings.instructor", + "OptimumEmbedding": "llama_index.embeddings.huggingface_optimum", + "LangchainEmbedding": "llama_index.embeddings.langchain", + "GooglePaLMEmbedding": "llama_index.embeddings.google", + "GoogleUnivSentEncoderEmbedding": "llama_index.embeddings.google", + "MistralAIEmbedding": "llama_index.embeddings.mistralai", + "BedrockEmbedding": "llama_index.embeddings.bedrock", + "ClarifaiEmbedding": "llama_index.embeddings.clarifai", + "ElasticsearchEmbedding": "llama_index.embeddings.elasticsearch", + "AzureOpenAIEmbedding": "llama_index.embeddings.azure_openai", + "TextEmbeddingsInference": "llama_index.embeddings.text_embeddings_inference", + "LongLLMLinguaPostprocessor": "llama_index.postprocessor.longllmlingua", + "RankGPTRerank": "llama_index.postprocessor.rankgpt_rerank", + "CohereRerank": "llama_index.postprocessor.cohere_rerank", + "FlagEmbeddingReranker": "llama_index.postprocessor.flag_embedding_reranker", + "Neo4jGraphStore": "llama_index.graph_stores.neo4j", + "FalkorDBGraphStore": "llama_index.graph_stores.falkordb", + "KuzuGraphStore": "llama_index.graph_stores.kuzu", + "NebulaGraphStore": "llama_index.graph_stores.nebula", + "OpenAIMultiModal": "llama_index.multi_modal_llms.openai", + "AzureOpenAIMultiModal": "llama_index.multi_modal_llms.azure_openai", + "ReplicateMultiModal": "llama_index.multi_modal_llms.replicate", + "GeminiMultiModal": "llama_index.multi_modal_llms.gemini", + "OllamaMultiModal": "llama_index.multi_modal_llms.ollama", + "DashScopeMultiModal": "llama_index.multi_modal_llms.dashscope", + "DashScopeMultiModalModels": "llama_index.multi_modal_llms.dashscope", + "OpenAIAgent": "llama_index.agent.openai", + "OpenAIAgentWorker": "llama_index.agent.openai", + "OpenAIAssistantAgent": "llama_index.agent.openai", + "BaseOpenAIAgent": "llama_index.agent.openai_legacy", + "FnRetrieverOpenAIAgent": "llama_index.agent.openai_legacy", + "ContextRetrieverOpenAIAgent": "llama_index.agent.openai_legacy", + "PostgresKVStore": "llama_index.storage.kvstore.postgres", + "RedisKVStore": "llama_index.storage.kvstore.redis", + "FirestoreKVStore": "llama_index.storage.kvstore.firestore", + "MongoDBKVStore": "llama_index.storage.kvstore.mongodb", + "S3DBKVStore": "llama_index.storage.kvstore.s3", + "DynamoDBKVStore": "llama_index.storage.kvstore.dynamodb", + "DynamoDBDocumentStore": "llama_index.storage.docstore.dynamodb", + "MongoDocumentStore": "llama_index.storage.docstore.mongodb", + "FirestoreDocumentStore": "llama_index.storage.docstore.firestore", + "RedisDocumentStore": "llama_index.storage.docstore.redis", + "PostgresDocumentStore": "llama_index.storage.docstore.postgres", + "PostgresIndexStore": "llama_index.storage.index_store.postgres", + "DynamoDBIndexStore": "llama_index.storage.index_store.dynamodb", + "FirestoreIndexStore": "llama_index.storage.index_store.firestore", + "MongoIndexStore": "llama_index.storage.index_store.mongodb", + "RedisIndexStore": "llama_index.storage.index_store.redis", + "RedisChatStore": "llama_index.storage.chat_store.redis", + "GuardrailsOutputParser": "llama_index.output_parsers.guardrails", + "OpenAIPydanticProgram": "llama_index.program.openai", + "LMFormatEnforcerPydanticProgram": "llama_index.program.lmformatenforcer", + "GuidancePydanticProgram": "llama_index.program.guidance", + "BaseEvaporateProgram": "llama_index.program.evaporate", + "DFEvaporateProgram": "llama_index.program.evaporate", + "GoogleTextSynthesizer": "llama_index.response_synthesizers.google", + "SynthesizedResponse": "llama_index.response_synthesizers.google", + "AnswerConsistencyEvaluator": "llama_index.evaluation.tonic_validate", + "AnswerConsistencyBinaryEvaluator": "llama_index.evaluation.tonic_validate", + "AnswerSimilarityEvaluator": "llama_index.evaluation.tonic_validate", + "AugmentationAccuracyEvaluator": "llama_index.evaluation.tonic_validate", + "AugmentationPrecisionEvaluator": "llama_index.evaluation.tonic_validate", + "RetrievalPrecisionEvaluator": "llama_index.evaluation.tonic_validate", + "TonicValidateEvaluator": "llama_index.evaluation.tonic_validate", + "BaseReader": "llama_index.core.readers.base", + "DocugamiReader": "llama_index.readers.docugami", + "SlackReader": "llama_index.readers.slack", + "GeniusReader": "llama_index.readers.genius", + "YoutubeTranscriptReader": "llama_index.readers.youtube_transcript", + "MboxReader": "llama_index.readers.mbox", + "StackoverflowReader": "llama_index.readers.stackoverflow", + "PdbAbstractReader": "llama_index.readers.pdb", + "SnowflakeReader": "llama_index.readers.snowflake", + "DiscordReader": "llama_index.readers.discord", + "EarningsCallTranscript": "llama_index.readers.earnings_call_transcript", + "SimpleCouchDBReader": "llama_index.readers.couchdb", + "CouchbaseReader": "llama_index.readers.couchbase", + "SingleStoreReader": "llama_index.readers.singlestore", + "JSONReader": "llama_index.readers.json", + "TrelloReader": "llama_index.readers.trello", + "JaguarReader": "llama_index.readers.jaguar", + "JiraReader": "llama_index.readers.jira", + "PDFTableReader": "llama_index.readers.pdf_table", + "KibelaReader": "llama_index.readers.kibela", + "DashVectorReader": "llama_index.readers.dashvector", + "BilibiliTranscriptReader": "llama_index.readers.bilibili", + "SmartPDFLoader": "llama_index.readers.smart_pdf_loader", + "AgentSearchReader": "llama_index.readers.agent_search", + "MacrometaGDNReader": "llama_index.readers.macrometa_gdn", + "TwitterTweetReader": "llama_index.readers.twitter", + "AstraDBReader": "llama_index.readers.astra_db", + "OpendalReader": "llama_index.readers.opendal", + "OpendalAzblobReader": "llama_index.readers.opendal", + "OpendalGcsReader": "llama_index.readers.opendal", + "OpendalS3Reader": "llama_index.readers.opendal", + "AzStorageBlobReader": "llama_index.readers.azstorage_blob", + "ReadwiseReader": "llama_index.readers.readwise", + "ApifyActor": "llama_index.readers.apify", + "ApifyDataset": "llama_index.readers.apify", + "SimpleArangoDBReader": "llama_index.readers.arango_db", + "AthenaReader": "llama_index.readers.athena", + "DatabaseReader": "llama_index.readers.database", + "RemoteReader": "llama_index.readers.remote", + "AsanaReader": "llama_index.readers.asana", + "HiveReader": "llama_index.readers.hive", + "DocstringWalker": "llama_index.readers.docstring_walker", + "AirbyteSalesforceReader": "llama_index.readers.airbyte_salesforce", + "StripeDocsReader": "llama_index.readers.stripe_docs", + "MilvusReader": "llama_index.readers.milvus", + "PDFNougatOCR": "llama_index.readers.nougat_ocr", + "AssemblyAIAudioTranscriptReader": "llama_index.readers.assemblyai", + "GraphDBCypherReader": "llama_index.readers.graphdb_cypher", + "ConfluenceReader": "llama_index.readers.confluence", + "LilacReader": "llama_index.readers.lilac", + "GithubClient": "llama_index.readers.github", + "GithubRepositoryReader": "llama_index.readers.github", + "GitHubRepositoryCollaboratorsReader": "llama_index.readers.github", + "GitHubRepositoryIssuesReader": "llama_index.readers.github", + "GitHubIssuesClient": "llama_index.readers.github", + "WikipediaReader": "llama_index.readers.wikipedia", + "SimpleMongoReader": "llama_index.readers.mongodb", + "NotionPageReader": "llama_index.readers.notion", + "WordpressReader": "llama_index.readers.wordpress", + "XMLReader": "llama_index.readers.file", + "PagedCSVReader": "llama_index.readers.file", + "AirbyteTypeformReader": "llama_index.readers.airbyte_typeform", + "AirbyteZendeskSupportReader": "llama_index.readers.airbyte_zendesk_support", + 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file mode 100644 index 0000000000000..ea9fda4493b1e --- /dev/null +++ b/llama-index-cli/pyproject.toml @@ -0,0 +1,63 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["llamaindex"] +description = "llama-index cli" +license = "MIT" +maintainers = [ + "Andrei Fajardo ", + "Haotian Zhang ", + "Jerry Liu ", + "Logan Markewich ", + "Simon Suo ", + "Sourabh Desai ", +] +name = "llama-index-cli" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +llama-index-core = "^0.10.3" +llama-index-vector-stores-chroma = "^0.1.1" +llama-index-embeddings-openai = "^0.1.1" +llama-index-llms-openai = "^0.1.1" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" + +[tool.poetry.scripts] +llamaindex-cli = 'llama_index.cli.command_line:main' diff --git a/llama-index-cli/tests/BUILD b/llama-index-cli/tests/BUILD new file mode 100644 index 0000000000000..41045da0d0fa5 --- /dev/null +++ b/llama-index-cli/tests/BUILD @@ -0,0 +1,3 @@ +python_tests( + skip_tests=True, +) diff --git a/llama-index-cli/tests/__init__.py b/llama-index-cli/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-cli/tests/test_cli.py b/llama-index-cli/tests/test_cli.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-core/BUILD b/llama-index-core/BUILD index 0896ca890d8bf..e229987fbbc30 100644 --- a/llama-index-core/BUILD +++ b/llama-index-core/BUILD @@ -1,3 +1,4 @@ poetry_requirements( name="poetry", + module_mapping={"llamaindex-py-client": ["llama_index_client"]}, ) diff --git a/llama-index-core/llama_index/core/agent/runner/base.py b/llama-index-core/llama_index/core/agent/runner/base.py index 6ad2cb087d02c..b8442a1f159c4 100644 --- a/llama-index-core/llama_index/core/agent/runner/base.py +++ b/llama-index-core/llama_index/core/agent/runner/base.py @@ -253,8 +253,10 @@ def from_llm( ) try: - from llama_index.llms.openai import OpenAI - from llama_index.llms.openai.utils import is_function_calling_model + from llama_index.llms.openai import OpenAI # pants: no-infer-dep + from llama_index.llms.openai.utils import ( + is_function_calling_model, + ) # pants: no-infer-dep except ImportError: raise ImportError( "`llama-index-llms-openai` package not found. Please " @@ -262,7 +264,7 @@ def from_llm( ) if isinstance(llm, OpenAI) and is_function_calling_model(llm.model): - from llama_index.agent.openai import OpenAIAgent + from llama_index.agent.openai import OpenAIAgent # pants: no-infer-dep return OpenAIAgent.from_tools( tools=tools, diff --git a/llama-index-core/llama_index/core/callbacks/base.py b/llama-index-core/llama_index/core/callbacks/base.py index 71722880b6c07..cc6b67119513c 100644 --- a/llama-index-core/llama_index/core/callbacks/base.py +++ b/llama-index-core/llama_index/core/callbacks/base.py @@ -139,6 +139,11 @@ def set_handlers(self, handlers: List[BaseCallbackHandler]) -> None: """Set handlers as the only handlers on the callback manager.""" self.handlers = handlers + @classmethod + def __modify_schema__(cls, schema: Dict[str, Any]) -> None: + """Avoids serialization errors.""" + schema.update(type="object", default={}) + @contextmanager def event( self, diff --git a/llama-index-core/llama_index/core/command_line/command_line.py b/llama-index-core/llama_index/core/command_line/command_line.py index 39539215df583..f96366183305b 100644 --- a/llama-index-core/llama_index/core/command_line/command_line.py +++ b/llama-index-core/llama_index/core/command_line/command_line.py @@ -99,7 +99,7 @@ def default_rag_cli() -> RagCLI: from llama_index.vector_stores.chroma import ( ChromaVectorStore, - ) + ) # pants: no-infer-dep except ImportError: ChromaVectorStore = None diff --git a/llama-index-core/llama_index/core/constants.py b/llama-index-core/llama_index/core/constants.py index 6e6024eb27031..48aac453f8439 100644 --- a/llama-index-core/llama_index/core/constants.py +++ b/llama-index-core/llama_index/core/constants.py @@ -27,3 +27,9 @@ GRAPH_STORE_KEY = "graph_store" INDEX_STORE_KEY = "index_store" DOC_STORE_KEY = "doc_store" + +# llama-cloud constants +DEFAULT_PIPELINE_NAME = "default" +DEFAULT_PROJECT_NAME = "default" +DEFAULT_BASE_URL = "https://api.cloud.llamaindex.ai" +DEFAULT_APP_URL = "https://cloud.llamaindex.ai" diff --git a/llama-index-core/llama_index/core/evaluation/eval_utils.py b/llama-index-core/llama_index/core/evaluation/eval_utils.py index be097c3a7d0b0..ee06573957332 100644 --- a/llama-index-core/llama_index/core/evaluation/eval_utils.py +++ b/llama-index-core/llama_index/core/evaluation/eval_utils.py @@ -5,14 +5,24 @@ """ import asyncio +import os +import subprocess +import tempfile from collections import defaultdict from typing import Any, List, Optional, Tuple import numpy as np import pandas as pd + +from llama_index_client import ProjectCreate +from llama_index_client.client import PlatformApi +from llama_index_client.types.eval_question_create import EvalQuestionCreate + from llama_index.core.async_utils import asyncio_module from llama_index.core.base.base_query_engine import BaseQueryEngine +from llama_index.core.constants import DEFAULT_BASE_URL, DEFAULT_PROJECT_NAME from llama_index.core.evaluation.base import EvaluationResult +from llama_index.core.llama_dataset import LabelledRagDataset async def aget_responses( @@ -61,6 +71,100 @@ def get_results_df( return pd.DataFrame(metric_dict) +def _download_llama_dataset_from_hub(llama_dataset_id: str) -> LabelledRagDataset: + """Uses a subprocess and llamaindex-cli to download a dataset from llama-hub.""" + with tempfile.TemporaryDirectory() as tmp: + try: + subprocess.run( + [ + "llamaindex-cli", + "download-llamadataset", + f"{llama_dataset_id}", + "--download-dir", + f"{tmp}", + ] + ) + return LabelledRagDataset.from_json(f"{tmp}/rag_dataset.json") + except FileNotFoundError as err: + raise ValueError( + "No dataset associated with the supplied `llama_dataset_id`" + ) from err + + +def upload_eval_dataset( + dataset_name: str, + questions: Optional[List[str]] = None, + llama_dataset_id: Optional[str] = None, + project_name: str = DEFAULT_PROJECT_NAME, + base_url: Optional[str] = None, + api_key: Optional[str] = None, + overwrite: bool = False, + append: bool = False, +) -> str: + """Upload questions to platform dataset.""" + if questions is None and llama_dataset_id is None: + raise ValueError( + "Must supply either a list of `questions`, or a `llama_dataset_id` to import from llama-hub." + ) + + base_url = base_url or os.environ.get("LLAMA_CLOUD_BASE_URL", DEFAULT_BASE_URL) + assert base_url is not None + + api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) + + client = PlatformApi(base_url=base_url, token=api_key) + + project = client.project.upsert_project(request=ProjectCreate(name=project_name)) + assert project.id is not None + + existing_datasets = client.project.get_datasets_for_project(project_id=project.id) + + # check if dataset already exists + cur_dataset = None + for dataset in existing_datasets: + if dataset.name == dataset_name: + if overwrite: + assert dataset.id is not None + client.eval.delete_dataset(dataset_id=dataset.id) + break + elif not append: + raise ValueError( + f"Dataset {dataset_name} already exists in project {project_name}." + " Set overwrite=True to overwrite or append=True to append." + ) + else: + cur_dataset = dataset + break + + # either create new dataset or use existing one + if cur_dataset is None: + eval_dataset = client.project.create_eval_dataset_for_project( + project_id=project.id, name=dataset_name + ) + else: + eval_dataset = cur_dataset + + assert eval_dataset.id is not None + + # create questions + if questions: + questions = questions + else: + # download `LabelledRagDataset` from llama-hub + assert llama_dataset_id is not None + rag_dataset = _download_llama_dataset_from_hub(llama_dataset_id) + questions = [example.query for example in rag_dataset[:]] + + eval_questions = client.eval.create_questions( + dataset_id=eval_dataset.id, + request=[EvalQuestionCreate(content=q) for q in questions], + ) + + assert len(eval_questions) == len(questions) + print(f"Uploaded {len(questions)} questions to dataset {dataset_name}") + return eval_dataset.id + + def default_parser(eval_response: str) -> Tuple[Optional[float], Optional[str]]: """ Default parser function for evaluation response. diff --git a/llama-index-core/llama_index/core/indices/knowledge_graph/base.py b/llama-index-core/llama_index/core/indices/knowledge_graph/base.py index 472c8d53da284..86f29a5de8303 100644 --- a/llama-index-core/llama_index/core/indices/knowledge_graph/base.py +++ b/llama-index-core/llama_index/core/indices/knowledge_graph/base.py @@ -138,6 +138,7 @@ def as_retriever( object_map=self._object_map, llm=self._llm, embed_model=embed_model or self._embed_model, + retriever_mode=retriever_mode, **kwargs, ) @@ -240,17 +241,28 @@ def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: rel_embedding = self._embed_model.get_text_embedding(triplet_str) self._index_struct.add_to_embedding_dict(triplet_str, rel_embedding) - def upsert_triplet(self, triplet: Tuple[str, str, str]) -> None: - """Insert triplets. + # Update the storage context's index_store + self._storage_context.index_store.add_index_struct(self._index_struct) + + def upsert_triplet( + self, triplet: Tuple[str, str, str], include_embeddings: bool = False + ) -> None: + """Insert triplets and optionally embeddings. Used for manual insertion of KG triplets (in the form of (subject, relationship, object)). Args: - triplet (str): Knowledge triplet - + triplet (tuple): Knowledge triplet + embedding (Any, optional): Embedding option for the triplet. Defaults to None. """ self._graph_store.upsert_triplet(*triplet) + triplet_str = str(triplet) + if include_embeddings: + set_embedding = self._service_context.embed_model.get_text_embedding( + triplet_str + ) + self._index_struct.add_to_embedding_dict(str(triplet), set_embedding) def add_node(self, keywords: List[str], node: BaseNode) -> None: """Add node. @@ -266,7 +278,10 @@ def add_node(self, keywords: List[str], node: BaseNode) -> None: self._docstore.add_documents([node], allow_update=True) def upsert_triplet_and_node( - self, triplet: Tuple[str, str, str], node: BaseNode + self, + triplet: Tuple[str, str, str], + node: BaseNode, + include_embeddings: bool = False, ) -> None: """Upsert KG triplet and node. @@ -277,11 +292,18 @@ def upsert_triplet_and_node( Args: keywords (List[str]): Keywords to index the node. node (Node): Node to be indexed. + include_embeddings (bool): Option to add embeddings for triplets. Defaults to False """ subj, _, obj = triplet self.upsert_triplet(triplet) self.add_node([subj, obj], node) + triplet_str = str(triplet) + if include_embeddings: + set_embedding = self._service_context.embed_model.get_text_embedding( + triplet_str + ) + self._index_struct.add_to_embedding_dict(str(triplet), set_embedding) def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None: """Delete a node.""" diff --git a/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py b/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py index 71e8197b41482..38cd852fd32a0 100644 --- a/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py +++ b/llama-index-core/llama_index/core/indices/knowledge_graph/retrievers.py @@ -1,4 +1,5 @@ """KG Retrievers.""" + import logging from collections import defaultdict from enum import Enum @@ -120,7 +121,11 @@ def __init__( self.query_keyword_extract_template = query_keyword_extract_template or DQKET self.similarity_top_k = similarity_top_k self._include_text = include_text - self._retriever_mode = KGRetrieverMode(retriever_mode) + self._retriever_mode = ( + KGRetrieverMode(retriever_mode) + if retriever_mode + else KGRetrieverMode.KEYWORD + ) self._llm = llm or llm_from_settings_or_context(Settings, index.service_context) self._embed_model = embed_model or embed_model_from_settings_or_context( diff --git a/llama-index-core/llama_index/core/indices/managed/base.py b/llama-index-core/llama_index/core/indices/managed/base.py index a788af24d02c5..35d375b729545 100644 --- a/llama-index-core/llama_index/core/indices/managed/base.py +++ b/llama-index-core/llama_index/core/indices/managed/base.py @@ -93,4 +93,6 @@ def from_documents( **kwargs: Any, ) -> IndexType: """Build an index from a sequence of documents.""" - raise NotImplementedError("ref_doc_info not implemented for BaseManagedIndex.") + raise NotImplementedError( + "from_documents not implemented for BaseManagedIndex." + ) diff --git a/llama-index-core/llama_index/core/ingestion/api_utils.py b/llama-index-core/llama_index/core/ingestion/api_utils.py new file mode 100644 index 0000000000000..36fdeda5a625c --- /dev/null +++ b/llama-index-core/llama_index/core/ingestion/api_utils.py @@ -0,0 +1,151 @@ +import os +from typing import List, Optional + +from llama_index_client import ( + ConfigurableDataSourceNames, + ConfigurableTransformationNames, + ConfiguredTransformationItem, + DataSourceCreate, + PipelineCreate, + PipelineType, + ProjectCreate, +) +from llama_index_client.client import AsyncPlatformApi, PlatformApi + +from llama_index.core.constants import ( + DEFAULT_APP_URL, + DEFAULT_BASE_URL, + DEFAULT_PROJECT_NAME, +) +from llama_index.core.ingestion.data_sources import ( + ConfiguredDataSource, +) +from llama_index.core.ingestion.transformations import ( + ConfiguredTransformation, +) +from llama_index.core.node_parser import SentenceSplitter +from llama_index.core.readers.base import ReaderConfig +from llama_index.core.schema import BaseNode, TransformComponent + + +def default_transformations() -> List[TransformComponent]: + """Default transformations.""" + from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep + + return [ + SentenceSplitter(), + OpenAIEmbedding(), + ] + + +def get_client( + api_key: Optional[str] = None, + base_url: Optional[str] = None, + app_url: Optional[str] = None, + timeout: int = 60, +) -> PlatformApi: + """Get the sync platform API client.""" + base_url = base_url or os.environ.get("LLAMA_CLOUD_BASE_URL", DEFAULT_BASE_URL) + app_url = app_url or os.environ.get("LLAMA_CLOUD_APP_URL", DEFAULT_APP_URL) + api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) + + return PlatformApi(base_url=base_url, token=api_key, timeout=timeout) + + +def get_aclient( + api_key: Optional[str] = None, + base_url: Optional[str] = None, + app_url: Optional[str] = None, + timeout: int = 60, +) -> AsyncPlatformApi: + """Get the async platform API client.""" + base_url = base_url or os.environ.get("LLAMA_CLOUD_BASE_URL", DEFAULT_BASE_URL) + app_url = app_url or os.environ.get("LLAMA_CLOUD_APP_URL", DEFAULT_APP_URL) + api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) + + return AsyncPlatformApi(base_url=base_url, token=api_key, timeout=timeout) + + +def get_pipeline_create( + pipeline_name: str, + client: PlatformApi, + pipeline_type: PipelineType, + project_name: str = DEFAULT_PROJECT_NAME, + transformations: Optional[List[TransformComponent]] = None, + readers: Optional[List[ReaderConfig]] = None, + input_nodes: Optional[List[BaseNode]] = None, +) -> PipelineCreate: + """Get a pipeline create object.""" + transformations = transformations or [] + readers = readers or [] + input_nodes = input_nodes or [] + + configured_transformations: List[ConfiguredTransformation] = [] + for transformation in transformations: + try: + configured_transformations.append( + ConfiguredTransformation.from_component(transformation) + ) + except ValueError: + raise ValueError(f"Unsupported transformation: {type(transformation)}") + + configured_transformation_items: List[ConfiguredTransformationItem] = [] + for item in configured_transformations: + name = ConfigurableTransformationNames[ + item.configurable_transformation_type.name + ] + configured_transformation_items.append( + ConfiguredTransformationItem( + transformation_name=name, + component=item.component, + configurable_transformation_type=item.configurable_transformation_type.name, + ) + ) + + # remove callback manager + configured_transformation_items[-1].component.pop("callback_manager", None) # type: ignore + + data_sources = [] + for reader in readers: + if reader.reader.is_remote: + configured_data_source = ConfiguredDataSource.from_component( + reader, + ) + source_type = ConfigurableDataSourceNames[ + configured_data_source.configurable_data_source_type.name + ] + data_sources.append( + DataSourceCreate( + name=configured_data_source.name, + source_type=source_type, + component=configured_data_source.component, + ) + ) + else: + documents = reader.read() + input_nodes += documents + + for node in input_nodes: + configured_data_source = ConfiguredDataSource.from_component(node) + source_type = ConfigurableDataSourceNames[ + configured_data_source.configurable_data_source_type.name + ] + data_sources.append( + DataSourceCreate( + name=configured_data_source.name, + source_type=source_type, + component=node, + ) + ) + + project = client.project.upsert_project(request=ProjectCreate(name=project_name)) + assert project.id is not None, "Project ID should not be None" + + # upload + return PipelineCreate( + name=pipeline_name, + configured_transformations=configured_transformation_items, + data_sources=data_sources, + data_sinks=[], + pipeline_type=pipeline_type, + ) diff --git a/llama-index-core/llama_index/core/ingestion/data_sinks.py b/llama-index-core/llama_index/core/ingestion/data_sinks.py new file mode 100644 index 0000000000000..9a1b88b05803e --- /dev/null +++ b/llama-index-core/llama_index/core/ingestion/data_sinks.py @@ -0,0 +1,176 @@ +from enum import Enum +from typing import Generic, Type, TypeVar + +from llama_index.core.bridge.pydantic import BaseModel, Field, GenericModel +from llama_index.core.vector_stores.types import BasePydanticVectorStore + + +class DataSink(BaseModel): + """ + A class containing metadata for a type of data sink. + """ + + name: str = Field( + description="Unique and human-readable name for the type of data sink" + ) + component_type: Type[BasePydanticVectorStore] = Field( + description="Type of component that implements the data sink" + ) + + +def build_conifurable_data_sink_enum(): + """ + Build an enum of configurable data sinks. + But conditional on if the corresponding vector store is available. + """ + + class ConfigurableComponent(Enum): + @classmethod + def from_component( + cls, component: BasePydanticVectorStore + ) -> "ConfigurableDataSinks": + component_class = type(component) + for component_type in cls: + if component_type.value.component_type == component_class: + return component_type + raise ValueError( + f"Component {component} is not a supported data sink component." + ) + + def build_configured_data_sink( + self, component: BasePydanticVectorStore + ) -> "ConfiguredDataSink": + component_type = self.value.component_type + if not isinstance(component, component_type): + raise ValueError( + f"The enum value {self} is not compatible with component of " + f"type {type(component)}" + ) + return ConfiguredDataSink[component_type]( # type: ignore + component=component, name=self.value.name + ) + + enum_members = [] + + try: + from llama_index.vector_stores.chroma import ( + ChromaVectorStore, + ) # pants: no-infer-dep + + enum_members.append( + ( + "CHROMA", + DataSink( + name="Chroma", + component_type=ChromaVectorStore, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.vector_stores.pinecone import ( + PineconeVectorStore, + ) # pants: no-infer-dep + + enum_members.append( + ( + "PINECONE", + DataSink( + name="Pinecone", + component_type=PineconeVectorStore, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.vector_stores.postgres import ( + PGVectorStore, + ) # pants: no-infer-dep + + enum_members.append( + ( + "POSTGRES", + DataSink( + name="PostgreSQL", + component_type=PGVectorStore, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.vector_stores.qdrant import ( + QdrantVectorStore, + ) # pants: no-infer-dep + + enum_members.append( + ( + "QDRANT", + DataSink( + name="Qdrant", + component_type=QdrantVectorStore, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.vector_stores.weaviate import ( + WeaviateVectorStore, + ) # pants: no-infer-dep + + enum_members.append( + ( + "WEAVIATE", + DataSink( + name="Weaviate", + component_type=WeaviateVectorStore, + ), + ) + ) + except ImportError: + pass + + return ConfigurableComponent("ConfigurableDataSinks", enum_members) + + +ConfigurableDataSinks = build_conifurable_data_sink_enum() + + +T = TypeVar("T", bound=BasePydanticVectorStore) + + +class ConfiguredDataSink(GenericModel, Generic[T]): + """ + A class containing metadata & implementation for a data sink in a pipeline. + """ + + name: str + component: T = Field(description="Component that implements the data sink") + + @classmethod + def from_component(cls, component: BasePydanticVectorStore) -> "ConfiguredDataSink": + """ + Build a ConfiguredDataSink from a component. + This should be the preferred way to build a ConfiguredDataSink + as it will ensure that the component is supported as indicated by having a + corresponding enum value in DataSources. + This has the added bonus that you don't need to specify the generic type + like ConfiguredDataSink[Document]. The return value of + this ConfiguredDataSink.from_component(document) will be + ConfiguredDataSink[Document] if document is + a Document object. + """ + return ConfigurableDataSinks.from_component( + component + ).build_configured_data_sink(component) + + @property + def configurable_data_sink_type(self) -> ConfigurableDataSinks: + return ConfigurableDataSinks.from_component(self.component) diff --git a/llama-index-core/llama_index/core/ingestion/data_sources.py b/llama-index-core/llama_index/core/ingestion/data_sources.py new file mode 100644 index 0000000000000..d728a3b29078d --- /dev/null +++ b/llama-index-core/llama_index/core/ingestion/data_sources.py @@ -0,0 +1,376 @@ +import uuid +from enum import Enum +from pathlib import Path +from typing import Any, Generic, Iterable, List, Optional, Type, TypeVar, cast + +from llama_index.core.bridge.pydantic import BaseModel, Field, GenericModel +from llama_index.core.readers.base import BasePydanticReader, ReaderConfig +from llama_index.core.schema import BaseComponent, Document, TextNode + + +class DataSource(BaseModel): + """ + A class containing metadata for a type of data source. + """ + + name: str = Field( + description="Unique and human-readable name for the type of data source" + ) + component_type: Type[BaseComponent] = Field( + description="Type of component that implements the data source" + ) + + +class DocumentGroup(BasePydanticReader): + """ + A group of documents, usually separate pages from a single file. + """ + + file_path: str = Field(description="Path to the file containing the documents") + documents: List[Document] = Field( + description="Sequential group of documents, usually separate pages from a single file." + ) + + @property + def file_name(self) -> str: + return Path(self.file_path).name + + @classmethod + def class_name(cls) -> str: + return "DocumentGroup" + + def lazy_load_data(self, *args: Any, **load_kwargs: Any) -> Iterable[Document]: + """Load data from the input directory lazily.""" + return self.documents + + +def build_conifurable_data_source_enum(): + """ + Build an enum of configurable data sources. + But conditional on if the corresponding reader is available. + """ + + class ConfigurableComponent(Enum): + @classmethod + def from_component(cls, component: BaseComponent) -> "ConfigurableDataSources": + component_class = type(component) + for component_type in cls: + if component_type.value.component_type == component_class: + return component_type + raise ValueError( + f"Component {component} is not a supported data source component." + ) + + def build_configured_data_source( + self, component: BaseComponent, name: Optional[str] = None + ) -> "ConfiguredDataSource": + component_type = self.value.component_type + if not isinstance(component, component_type): + raise ValueError( + f"The enum value {self} is not compatible with component of " + f"type {type(component)}" + ) + elif isinstance(component, BasePydanticReader): + reader_config = ReaderConfig(loader=component) + return ConfiguredDataSource[ReaderConfig]( + component=reader_config + ) # type: ignore + + if isinstance(component, DocumentGroup) and name is None: + # if the component is a DocumentGroup, we want to use the + # full file path as the name of the data source + component = cast(DocumentGroup, component) + name = component.file_path + + if name is None: + suffix = uuid.uuid1() + name = self.value.name + f" [{suffix}]]" + return ConfiguredDataSource[component_type]( # type: ignore + component=component, name=name + ) + + enum_members = [] + + try: + from llama_index.readers.discord import DiscordReader # pants: no-infer-dep + + enum_members.append( + ( + "DISCORD", + DataSource( + name="Discord", + component_type=DiscordReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.elasticsearch import ( + ElasticsearchReader, + ) # pants: no-infer-dep + + enum_members.append( + ( + "ELASTICSEARCH", + DataSource( + name="Elasticsearch", + component_type=ElasticsearchReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.notion import NotionPageReader # pants: no-infer-dep + + enum_members.append( + ( + "NOTION_PAGE", + DataSource( + name="Notion Page", + component_type=NotionPageReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.slack import SlackReader # pants: no-infer-dep + + enum_members.append( + ( + "SLACK", + DataSource( + name="Slack", + component_type=SlackReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.twitter import ( + TwitterTweetReader, + ) # pants: no-infer-dep + + enum_members.append( + ( + "TWITTER", + DataSource( + name="Twitter", + component_type=TwitterTweetReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.web import SimpleWebPageReader # pants: no-infer-dep + + enum_members.append( + ( + "SIMPLE_WEB_PAGE", + DataSource( + name="Simple Web Page", + component_type=SimpleWebPageReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.web import TrafilaturaWebReader # pants: no-infer-dep + + enum_members.append( + ( + "TRAFILATURA_WEB_PAGE", + DataSource( + name="Trafilatura Web Page", + component_type=TrafilaturaWebReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.web import ( + BeautifulSoupWebReader, + ) # pants: no-infer-dep + + enum_members.append( + ( + "BEAUTIFUL_SOUP_WEB_PAGE", + DataSource( + name="Beautiful Soup Web Page", + component_type=BeautifulSoupWebReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.web import RssReader # pants: no-infer-dep + + enum_members.append( + ( + "RSS", + DataSource( + name="RSS", + component_type=RssReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.wikipedia import WikipediaReader # pants: no-infer-dep + + enum_members.append( + ( + "WIKIPEDIA", + DataSource( + name="Wikipedia", + component_type=WikipediaReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.youtube_transcript import ( + YoutubeTranscriptReader, + ) # pants: no-infer-dep + + enum_members.append( + ( + "YOUTUBE_TRANSCRIPT", + DataSource( + name="Youtube Transcript", + component_type=YoutubeTranscriptReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.google import GoogleDocsReader # pants: no-infer-dep + + enum_members.append( + ( + "GOOGLE_DOCS", + DataSource( + name="Google Docs", + component_type=GoogleDocsReader, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.readers.google import GoogleSheetsReader # pants: no-infer-dep + + enum_members.append( + ( + "GOOGLE_SHEETS", + DataSource( + name="Google Sheets", + component_type=GoogleSheetsReader, + ), + ) + ) + except ImportError: + pass + + enum_members.append( + ( + "READER", + DataSource( + name="Reader", + component_type=ReaderConfig, + ), + ) + ) + + enum_members.append( + ( + "DOCUMENT_GROUP", + DataSource( + name="Document Group", + component_type=DocumentGroup, + ), + ) + ) + + enum_members.append( + ( + "TEXT_NODE", + DataSource( + name="Text Node", + component_type=TextNode, + ), + ) + ) + + enum_members.append( + ( + "DOCUMENT", + DataSource( + name="Document", + component_type=Document, + ), + ) + ) + + return ConfigurableComponent("ConfigurableDataSources", enum_members) + + +ConfigurableDataSources = build_conifurable_data_source_enum() + +T = TypeVar("T", bound=BaseComponent) + + +class ConfiguredDataSource(GenericModel, Generic[T]): + """ + A class containing metadata & implementation for a data source in a pipeline. + """ + + name: str + component: T = Field(description="Component that implements the data source") + + @classmethod + def from_component( + cls, component: BaseComponent, name: Optional[str] = None + ) -> "ConfiguredDataSource": + """ + Build a ConfiguredDataSource from a component. + + This should be the preferred way to build a ConfiguredDataSource + as it will ensure that the component is supported as indicated by having a + corresponding enum value in DataSources. + + This has the added bonus that you don't need to specify the generic type + like ConfiguredDataSource[Document]. The return value of + this ConfiguredDataSource.from_component(document) will be + ConfiguredDataSource[Document] if document is + a Document object. + """ + return ConfigurableDataSources.from_component( + component + ).build_configured_data_source(component, name) + + @property + def configurable_data_source_type(self) -> ConfigurableDataSources: + return ConfigurableDataSources.from_component(self.component) diff --git a/llama-index-core/llama_index/core/ingestion/pipeline.py b/llama-index-core/llama_index/core/ingestion/pipeline.py index c8cc3734b28bf..86c841ebc951b 100644 --- a/llama-index-core/llama_index/core/ingestion/pipeline.py +++ b/llama-index-core/llama_index/core/ingestion/pipeline.py @@ -1,5 +1,6 @@ import asyncio import multiprocessing +import os import re import warnings from concurrent.futures import ProcessPoolExecutor @@ -8,14 +9,37 @@ from hashlib import sha256 from itertools import repeat from pathlib import Path -from typing import Any, Generator, List, Optional, Sequence, Union +from typing import Any, Generator, List, Optional, Sequence, Union, cast from fsspec import AbstractFileSystem +from llama_index_client import ( + ConfigurableDataSourceNames, + ConfigurableTransformationNames, + Pipeline, + PipelineType, + Project, + ProjectCreate, +) +from llama_index_client.client import PlatformApi + +from llama_index.core.constants import ( + DEFAULT_APP_URL, + DEFAULT_BASE_URL, + DEFAULT_PIPELINE_NAME, + DEFAULT_PROJECT_NAME, +) from llama_index.core.bridge.pydantic import BaseModel, Field from llama_index.core.ingestion.cache import DEFAULT_CACHE_NAME, IngestionCache +from llama_index.core.ingestion.data_sources import ( + ConfigurableDataSources, +) +from llama_index.core.ingestion.transformations import ( + ConfigurableTransformations, +) from llama_index.core.node_parser import SentenceSplitter from llama_index.core.readers.base import ReaderConfig from llama_index.core.schema import ( + BaseComponent, BaseNode, Document, MetadataMode, @@ -31,6 +55,20 @@ from llama_index.core.vector_stores.types import BasePydanticVectorStore +def deserialize_transformation_component( + component_dict: dict, component_type: ConfigurableTransformationNames +) -> BaseComponent: + component_cls = ConfigurableTransformations[component_type].value.component_type + return component_cls.from_dict(component_dict) + + +def deserialize_source_component( + component_dict: dict, component_type: ConfigurableDataSourceNames +) -> BaseComponent: + component_cls = ConfigurableDataSources[component_type].value.component_type + return component_cls.from_dict(component_dict) + + def remove_unstable_values(s: str) -> str: """Remove unstable key/value pairs. @@ -164,12 +202,22 @@ class DocstoreStrategy(str, Enum): class IngestionPipeline(BaseModel): """An ingestion pipeline that can be applied to data.""" + name: str = Field( + default=DEFAULT_PIPELINE_NAME, + description="Unique name of the ingestion pipeline", + ) + project_name: str = Field( + default=DEFAULT_PROJECT_NAME, description="Unique name of the project" + ) + transformations: List[TransformComponent] = Field( description="Transformations to apply to the data" ) documents: Optional[Sequence[Document]] = Field(description="Documents to ingest") - reader: Optional[ReaderConfig] = Field(description="Reader to use to read the data") + readers: Optional[List[ReaderConfig]] = Field( + description="Reader to use to read the data" + ) vector_store: Optional[BasePydanticVectorStore] = Field( description="Vector store to use to store the data" ) @@ -186,34 +234,287 @@ class IngestionPipeline(BaseModel): ) disable_cache: bool = Field(default=False, description="Disable the cache") + base_url: str = Field( + default=DEFAULT_BASE_URL, description="Base URL for the LlamaCloud API" + ) + app_url: str = Field( + default=DEFAULT_APP_URL, description="Base URL for the LlamaCloud app" + ) + api_key: Optional[str] = Field(default=None, description="LlamaCloud API key") + class Config: arbitrary_types_allowed = True def __init__( self, + name: str = DEFAULT_PIPELINE_NAME, + project_name: str = DEFAULT_PROJECT_NAME, transformations: Optional[List[TransformComponent]] = None, - reader: Optional[ReaderConfig] = None, + readers: Optional[List[ReaderConfig]] = None, documents: Optional[Sequence[Document]] = None, vector_store: Optional[BasePydanticVectorStore] = None, cache: Optional[IngestionCache] = None, docstore: Optional[BaseDocumentStore] = None, docstore_strategy: DocstoreStrategy = DocstoreStrategy.UPSERTS, + base_url: Optional[str] = None, + app_url: Optional[str] = None, + api_key: Optional[str] = None, disable_cache: bool = False, ) -> None: if transformations is None: transformations = self._get_default_transformations() + api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) + super().__init__( + name=name, + project_name=project_name, transformations=transformations, - reader=reader, + readers=readers, documents=documents, vector_store=vector_store, cache=cache or IngestionCache(), docstore=docstore, docstore_strategy=docstore_strategy, + base_url=base_url or DEFAULT_BASE_URL, + app_url=app_url or DEFAULT_APP_URL, + api_key=api_key, + disable_cache=disable_cache, + ) + + @classmethod + def from_pipeline_name( + cls, + name: str, + project_name: str = DEFAULT_PROJECT_NAME, + base_url: Optional[str] = None, + cache: Optional[IngestionCache] = None, + api_key: Optional[str] = None, + app_url: Optional[str] = None, + vector_store: Optional[BasePydanticVectorStore] = None, + disable_cache: bool = False, + ) -> "IngestionPipeline": + base_url = base_url or os.environ.get("LLAMA_CLOUD_BASE_URL", DEFAULT_BASE_URL) + assert base_url is not None + + api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) + app_url = app_url or os.environ.get("LLAMA_CLOUD_APP_URL", None) + + client = PlatformApi(base_url=base_url, token=api_key) + + projects: List[Project] = client.project.list_projects( + project_name=project_name + ) + if len(projects) < 0: + raise ValueError(f"Project with name {project_name} not found") + + project = projects[0] + assert project.id is not None, "Project ID should not be None" + + pipelines: List[Pipeline] = client.pipeline.search_pipelines( + project_name=project_name, pipeline_name=name + ) + if len(pipelines) < 0: + raise ValueError(f"Pipeline with name {name} not found") + + pipeline = pipelines[0] + + transformations: List[TransformComponent] = [] + for configured_transformation in pipeline.configured_transformations: + component_dict = cast(dict, configured_transformation.component) + transformation_component_type = ( + configured_transformation.configurable_transformation_type + ) + transformation = deserialize_transformation_component( + component_dict, transformation_component_type + ) + transformations.append(transformation) + + documents = [] + readers = [] + for data_source in pipeline.data_sources: + component_dict = cast(dict, data_source.component) + source_component_type = data_source.source_type + + if data_source.source_type == ConfigurableDataSourceNames.READER: + source_component = deserialize_source_component( + component_dict, source_component_type + ) + readers.append(source_component) + elif data_source.source_type == ConfigurableDataSourceNames.DOCUMENT: + source_component = deserialize_source_component( + component_dict, source_component_type + ) + if ( + isinstance(source_component, BaseNode) + and source_component.get_content() + ): + documents.append(source_component) + + return cls( + name=name, + project_name=project_name, + transformations=transformations, + readers=readers, + documents=documents, + vector_store=vector_store, + base_url=base_url, + cache=cache, disable_cache=disable_cache, + api_key=api_key, + app_url=app_url, ) + @classmethod + def from_pipeline_name( + cls, + name: str, + project_name: str = DEFAULT_PROJECT_NAME, + base_url: Optional[str] = None, + cache: Optional[IngestionCache] = None, + api_key: Optional[str] = None, + app_url: Optional[str] = None, + vector_store: Optional[BasePydanticVectorStore] = None, + disable_cache: bool = False, + ) -> "IngestionPipeline": + base_url = base_url or os.environ.get("LLAMA_CLOUD_BASE_URL", DEFAULT_BASE_URL) + assert base_url is not None + + api_key = api_key or os.environ.get("LLAMA_CLOUD_API_KEY", None) + app_url = app_url or os.environ.get("LLAMA_CLOUD_APP_URL", None) + + client = PlatformApi(base_url=base_url, token=api_key) + + projects: List[Project] = client.project.list_projects( + project_name=project_name + ) + if len(projects) < 0: + raise ValueError(f"Project with name {project_name} not found") + + project = projects[0] + assert project.id is not None, "Project ID should not be None" + + pipelines: List[Pipeline] = client.pipeline.search_pipelines( + project_name=project_name, pipeline_name=name + ) + if len(pipelines) < 0: + raise ValueError(f"Pipeline with name {name} not found") + + pipeline = pipelines[0] + + transformations: List[TransformComponent] = [] + for configured_transformation in pipeline.configured_transformations: + component_dict = cast(dict, configured_transformation.component) + transformation_component_type = ( + configured_transformation.configurable_transformation_type + ) + transformation = deserialize_transformation_component( + component_dict, transformation_component_type + ) + transformations.append(transformation) + + documents = [] + readers = [] + for data_source in pipeline.data_sources: + component_dict = cast(dict, data_source.component) + source_component_type = data_source.source_type + + if data_source.source_type == ConfigurableDataSourceNames.READER: + source_component = deserialize_source_component( + component_dict, source_component_type + ) + readers.append(source_component) + elif data_source.source_type == ConfigurableDataSourceNames.DOCUMENT: + source_component = deserialize_source_component( + component_dict, source_component_type + ) + if ( + isinstance(source_component, BaseNode) + and source_component.get_content() + ): + documents.append(source_component) + + return cls( + name=name, + project_name=project_name, + transformations=transformations, + readers=readers, + documents=documents, + vector_store=vector_store, + base_url=base_url, + cache=cache, + disable_cache=disable_cache, + api_key=api_key, + app_url=app_url, + ) + + def register( + self, + verbose: bool = True, + documents: Optional[List[Document]] = None, + nodes: Optional[List[BaseNode]] = None, + ) -> str: + client = PlatformApi(base_url=self.base_url, token=self.api_key) + + input_nodes = self._prepare_inputs(documents, nodes) + + project = client.project.upsert_project( + request=ProjectCreate(name=self.project_name) + ) + assert project.id is not None, "Project ID should not be None" + + # avoid circular import + from llama_index.core.ingestion.api_utils import get_pipeline_create + + pipeline_create = get_pipeline_create( + self.name, + client, + PipelineType.PLAYGROUND, + project_name=self.project_name, + transformations=self.transformations, + input_nodes=input_nodes, + readers=self.readers, + ) + + # upload + pipeline = client.project.upsert_pipeline_for_project( + project.id, + request=pipeline_create, + ) + assert pipeline.id is not None, "Pipeline ID should not be None" + + # Print playground URL if not running remote + if verbose: + print( + f"Pipeline available at: {self.app_url}/project/{project.id}/playground/{pipeline.id}" + ) + + return pipeline.id + + def run_remote( + self, + documents: Optional[List[Document]] = None, + nodes: Optional[List[BaseNode]] = None, + ) -> str: + client = PlatformApi(base_url=self.base_url, token=self.api_key) + + pipeline_id = self.register(documents=documents, nodes=nodes, verbose=False) + + # start pipeline? + # the `PipeLineExecution` object should likely generate a URL at some point + pipeline_execution = client.pipeline.create_playground_job(pipeline_id) + + assert ( + pipeline_execution.id is not None + ), "Pipeline execution ID should not be None" + + print( + f"Find your remote results here: {self.app_url}/" + f"pipelines/execution?id={pipeline_execution.id}" + ) + + return pipeline_execution.id + def persist( self, persist_dir: str = "./pipeline_storage", @@ -278,8 +579,9 @@ def _prepare_inputs( if self.documents is not None: input_nodes += self.documents - if self.reader is not None: - input_nodes += self.reader.read() + if self.readers is not None: + for reader in self.readers: + input_nodes += reader.read() return input_nodes diff --git a/llama-index-core/llama_index/core/ingestion/transformations.py b/llama-index-core/llama_index/core/ingestion/transformations.py new file mode 100644 index 0000000000000..b9d21490ee122 --- /dev/null +++ b/llama-index-core/llama_index/core/ingestion/transformations.py @@ -0,0 +1,346 @@ +""" +This module maintains the list of transformations that are supported by the system. +""" + +from enum import Enum +from typing import Generic, Sequence, Type, TypeVar + +from llama_index.core.bridge.pydantic import BaseModel, Field, GenericModel +from llama_index.core.extractors import ( + KeywordExtractor, + QuestionsAnsweredExtractor, + SummaryExtractor, + TitleExtractor, +) +from llama_index.core.node_parser import ( + CodeSplitter, + HTMLNodeParser, + JSONNodeParser, + MarkdownNodeParser, + SentenceSplitter, + SimpleFileNodeParser, + TokenTextSplitter, +) +from llama_index.core.schema import BaseComponent, BaseNode, Document + + +# Transform Input/Output Types +class TransformationIOType(BaseModel): + name: str = Field(description="Name of the input/output type") + description: str = Field(description="Description of the input/output type") + python_type: str = Field(description="Python type of the input/output type") + + +class TransformationIOTypes(Enum): + DOCUMENTS = TransformationIOType( + name="Documents", + description="A sequence of Documents", + python_type=str(Sequence[Document]), + ) + NODES = TransformationIOType( + name="Nodes", + description="A sequence of Nodes from a sequence of Documents", + python_type=str(Sequence[BaseNode]), + ) + + +class TransformationCategory(BaseModel): + """A description for a category of transformation within a pipeline.""" + + name: str = Field(description="Unique name of the type of transformation") + description: str = Field(description="Description for the type of transformation") + input_type: TransformationIOType = Field( + description="Input type for the transformation type" + ) + output_type: TransformationIOType = Field( + description="Output type for the transformation type" + ) + + +class TransformationCategories(Enum): + """Supported transformation categories.""" + + METADATA_EXTRACTOR = TransformationCategory( + name="MetadataExtractor", + description="Applies a function to extract metadata from nodes", + input_type=TransformationIOTypes.NODES.value, + output_type=TransformationIOTypes.NODES.value, + ) + NODE_PARSER = TransformationCategory( + name="NodeParser", + description="Applies a function to parse nodes from documents", + input_type=TransformationIOTypes.DOCUMENTS.value, + output_type=TransformationIOTypes.NODES.value, + ) + EMBEDDING = TransformationCategory( + name="Embedding", + description="Applies a function to embed nodes", + input_type=TransformationIOTypes.NODES.value, + output_type=TransformationIOTypes.NODES.value, + ) + + +class ConfigurableTransformation(BaseModel): + """ + A class containing metadata for a type of transformation that can be in a pipeline. + """ + + name: str = Field( + description="Unique and human-readable name for the type of transformation" + ) + transformation_category: TransformationCategories = Field( + description="Type of transformation" + ) + component_type: Type[BaseComponent] = Field( + description="Type of component that implements the transformation" + ) + + +def build_configurable_transformation_enum(): + """ + Build an enum of configurable transformations. + But conditional on if the corresponding component is available. + """ + + class ConfigurableComponent(Enum): + @classmethod + def from_component( + cls, component: BaseComponent + ) -> "ConfigurableTransformations": + component_class = type(component) + for component_type in cls: + if component_type.value.component_type == component_class: + return component_type + raise ValueError( + f"Component {component} is not a supported transformation component." + ) + + def build_configured_transformation( + self, component: BaseComponent + ) -> "ConfiguredTransformation": + component_type = self.value.component_type + if not isinstance(component, component_type): + raise ValueError( + f"The enum value {self} is not compatible with component of " + f"type {type(component)}" + ) + return ConfiguredTransformation[component_type]( # type: ignore + component=component, name=self.value.name + ) + + enum_members = [] + + # Metadata extractors + enum_members.append( + ( + "KEYWORD_EXTRACTOR", + ConfigurableTransformation( + name="Keyword Extractor", + transformation_category=TransformationCategories.METADATA_EXTRACTOR, + component_type=KeywordExtractor, + ), + ) + ) + + enum_members.append( + ( + "TITLE_EXTRACTOR", + ConfigurableTransformation( + name="Title Extractor", + transformation_category=TransformationCategories.METADATA_EXTRACTOR, + component_type=TitleExtractor, + ), + ) + ) + + enum_members.append( + ( + "SUMMARY_EXTRACTOR", + ConfigurableTransformation( + name="Summary Extractor", + transformation_category=TransformationCategories.METADATA_EXTRACTOR, + component_type=SummaryExtractor, + ), + ) + ) + + enum_members.append( + ( + "QUESTIONS_ANSWERED_EXTRACTOR", + ConfigurableTransformation( + name="Questions Answered Extractor", + transformation_category=TransformationCategories.METADATA_EXTRACTOR, + component_type=QuestionsAnsweredExtractor, + ), + ) + ) + + # Node parsers + enum_members.append( + ( + "CODE_NODE_PARSER", + ConfigurableTransformation( + name="Code Splitter", + transformation_category=TransformationCategories.NODE_PARSER, + component_type=CodeSplitter, + ), + ) + ) + + enum_members.append( + ( + "SENTENCE_AWARE_NODE_PARSER", + ConfigurableTransformation( + name="Sentence Splitter", + transformation_category=TransformationCategories.NODE_PARSER, + component_type=SentenceSplitter, + ), + ) + ) + + enum_members.append( + ( + "TOKEN_AWARE_NODE_PARSER", + ConfigurableTransformation( + name="Token Text Splitter", + transformation_category=TransformationCategories.NODE_PARSER, + component_type=TokenTextSplitter, + ), + ) + ) + + enum_members.append( + ( + "HTML_NODE_PARSER", + ConfigurableTransformation( + name="HTML Node Parser", + transformation_category=TransformationCategories.NODE_PARSER, + component_type=HTMLNodeParser, + ), + ) + ) + + enum_members.append( + ( + "MARKDOWN_NODE_PARSER", + ConfigurableTransformation( + name="Markdown Node Parser", + transformation_category=TransformationCategories.NODE_PARSER, + component_type=MarkdownNodeParser, + ), + ) + ) + + enum_members.append( + ( + "JSON_NODE_PARSER", + ConfigurableTransformation( + name="JSON Node Parser", + transformation_category=TransformationCategories.NODE_PARSER, + component_type=JSONNodeParser, + ), + ) + ) + + enum_members.append( + ( + "SIMPLE_FILE_NODE_PARSER", + ConfigurableTransformation( + name="Simple File Node Parser", + transformation_category=TransformationCategories.NODE_PARSER, + component_type=SimpleFileNodeParser, + ), + ) + ) + + # Embeddings + try: + from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep + + enum_members.append( + ( + "OPENAI_EMBEDDING", + ConfigurableTransformation( + name="OpenAI Embedding", + transformation_category=TransformationCategories.EMBEDDING, + component_type=OpenAIEmbedding, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.embeddings.azure_openai import ( + AzureOpenAIEmbedding, + ) # pants: no-infer-dep + + enum_members.append( + ( + "AZURE_EMBEDDING", + ConfigurableTransformation( + name="Azure OpenAI Embedding", + transformation_category=TransformationCategories.EMBEDDING, + component_type=AzureOpenAIEmbedding, + ), + ) + ) + except ImportError: + pass + + try: + from llama_index.embeddings.huggingface import ( + HuggingFaceInferenceAPIEmbedding, + ) # pants: no-infer-dep + + enum_members.append( + ( + "HUGGINGFACE_API_EMBEDDING", + ConfigurableTransformation( + name="HuggingFace API Embedding", + transformation_category=TransformationCategories.EMBEDDING, + component_type=HuggingFaceInferenceAPIEmbedding, + ), + ) + ) + except ImportError: + pass + + return ConfigurableComponent("ConfigurableTransformations", enum_members) + + +ConfigurableTransformations = build_configurable_transformation_enum() + +T = TypeVar("T", bound=BaseComponent) + + +class ConfiguredTransformation(GenericModel, Generic[T]): + """ + A class containing metadata & implementation for a transformation in a pipeline. + """ + + name: str + component: T = Field(description="Component that implements the transformation") + + @classmethod + def from_component(cls, component: BaseComponent) -> "ConfiguredTransformation": + """ + Build a ConfiguredTransformation from a component. + + This should be the preferred way to build a ConfiguredTransformation + as it will ensure that the component is supported as indicated by having a + corresponding enum value in ConfigurableTransformations. + + This has the added bonus that you don't need to specify the generic type + like ConfiguredTransformation[SentenceSplitter]. The return value of + this ConfiguredTransformation.from_component(simple_node_parser) will be + ConfiguredTransformation[SentenceSplitter] if simple_node_parser is + a SentenceSplitter. + """ + return ConfigurableTransformations.from_component( + component + ).build_configured_transformation(component) + + @property + def configurable_transformation_type(self) -> ConfigurableTransformations: + return ConfigurableTransformations.from_component(self.component) diff --git a/llama-index-core/llama_index/core/llms/llm.py b/llama-index-core/llama_index/core/llms/llm.py index 2244b2bef285d..658cbb727c506 100644 --- a/llama-index-core/llama_index/core/llms/llm.py +++ b/llama-index-core/llama_index/core/llms/llm.py @@ -1,6 +1,7 @@ from collections import ChainMap from typing import ( Any, + Callable, Dict, List, Optional, @@ -25,7 +26,12 @@ StringableInput, validate_and_convert_stringable, ) -from llama_index.core.bridge.pydantic import BaseModel, Field, validator +from llama_index.core.bridge.pydantic import ( + BaseModel, + Field, + root_validator, + validator, +) from llama_index.core.callbacks import CBEventType, EventPayload from llama_index.core.llms.base import BaseLLM from llama_index.core.llms.generic_utils import ( @@ -112,14 +118,14 @@ class LLM(BaseLLM): system_prompt: Optional[str] = Field( default=None, description="System prompt for LLM calls." ) - messages_to_prompt: MessagesToPromptType = Field( + messages_to_prompt: Callable = Field( description="Function to convert a list of messages to an LLM prompt.", - default=generic_messages_to_prompt, + default=None, exclude=True, ) - completion_to_prompt: CompletionToPromptType = Field( + completion_to_prompt: Callable = Field( description="Function to convert a completion to an LLM prompt.", - default=default_completion_to_prompt, + default=None, exclude=True, ) output_parser: Optional[BaseOutputParser] = Field( @@ -148,6 +154,14 @@ def set_completion_to_prompt( ) -> CompletionToPromptType: return completion_to_prompt or default_completion_to_prompt + @root_validator + def check_prompts(cls, values: Dict[str, Any]) -> Dict[str, Any]: + if values.get("completion_to_prompt") is None: + values["completion_to_prompt"] = default_completion_to_prompt + if values.get("messages_to_prompt") is None: + values["messages_to_prompt"] = generic_messages_to_prompt + return values + def _log_template_data( self, prompt: BasePromptTemplate, **prompt_args: Any ) -> None: diff --git a/llama-index-core/llama_index/core/node_parser/interface.py b/llama-index-core/llama_index/core/node_parser/interface.py index bf450cb156e56..e7ca5b13aa24c 100644 --- a/llama-index-core/llama_index/core/node_parser/interface.py +++ b/llama-index-core/llama_index/core/node_parser/interface.py @@ -1,11 +1,10 @@ """Node parser interface.""" from abc import ABC, abstractmethod -from typing import Any, List, Sequence +from typing import Any, Callable, List, Sequence -from llama_index.core.bridge.pydantic import Field +from llama_index.core.bridge.pydantic import Field, validator from llama_index.core.callbacks import CallbackManager, CBEventType, EventPayload from llama_index.core.node_parser.node_utils import ( - IdFuncCallable, build_nodes_from_splits, default_id_func, ) @@ -31,14 +30,21 @@ class NodeParser(TransformComponent, ABC): callback_manager: CallbackManager = Field( default_factory=CallbackManager, exclude=True ) - id_func: IdFuncCallable = Field( - default=default_id_func, + id_func: Callable = Field( + default=None, description="Function to generate node IDs.", + exclude=True, ) class Config: arbitrary_types_allowed = True + @validator("id_func", pre=True) + def _validate_id_func(cls, v: Any) -> Any: + if v is None: + return default_id_func + return v + @abstractmethod def _parse_nodes( self, diff --git a/llama-index-core/llama_index/core/node_parser/node_utils.py b/llama-index-core/llama_index/core/node_parser/node_utils.py index b05d08223b6f5..a8c39cd49ec06 100644 --- a/llama-index-core/llama_index/core/node_parser/node_utils.py +++ b/llama-index-core/llama_index/core/node_parser/node_utils.py @@ -38,6 +38,10 @@ def build_nodes_from_splits( ref_doc = ref_doc or document id_func = id_func or default_id_func nodes: List[TextNode] = [] + """Calling as_related_node_info() on a document recomputes the hash for the whole text and metadata""" + """It is not that bad, when creating relationships between the nodes, but is terrible when adding a relationship""" + """between the node and a document, hence we create the relationship only once here and pass it to the nodes""" + relationships = {NodeRelationship.SOURCE: ref_doc.as_related_node_info()} for i, text_chunk in enumerate(text_splits): logger.debug(f"> Adding chunk: {truncate_text(text_chunk, 50)}") @@ -54,7 +58,7 @@ def build_nodes_from_splits( metadata_seperator=document.metadata_seperator, metadata_template=document.metadata_template, text_template=document.text_template, - relationships={NodeRelationship.SOURCE: ref_doc.as_related_node_info()}, + relationships=relationships, ) nodes.append(image_node) # type: ignore elif isinstance(document, Document): @@ -67,7 +71,7 @@ def build_nodes_from_splits( metadata_seperator=document.metadata_seperator, metadata_template=document.metadata_template, text_template=document.text_template, - relationships={NodeRelationship.SOURCE: ref_doc.as_related_node_info()}, + relationships=relationships, ) nodes.append(node) elif isinstance(document, TextNode): @@ -80,7 +84,7 @@ def build_nodes_from_splits( metadata_seperator=document.metadata_seperator, metadata_template=document.metadata_template, text_template=document.text_template, - relationships={NodeRelationship.SOURCE: ref_doc.as_related_node_info()}, + relationships=relationships, ) nodes.append(node) else: diff --git a/llama-index-core/llama_index/core/node_parser/text/utils.py b/llama-index-core/llama_index/core/node_parser/text/utils.py index 11e250177a87f..91ca6556f1355 100644 --- a/llama-index-core/llama_index/core/node_parser/text/utils.py +++ b/llama-index-core/llama_index/core/node_parser/text/utils.py @@ -5,8 +5,6 @@ logger = logging.getLogger(__name__) -logger = logging.getLogger(__name__) - def truncate_text(text: str, text_splitter: TextSplitter) -> str: """Truncate text to fit within the chunk size.""" diff --git a/llama-index-core/llama_index/core/program/utils.py b/llama-index-core/llama_index/core/program/utils.py index 01fdb348497de..fa60e5734b197 100644 --- a/llama-index-core/llama_index/core/program/utils.py +++ b/llama-index-core/llama_index/core/program/utils.py @@ -70,7 +70,9 @@ def get_program_for_llm( **kwargs, ) elif pydantic_program_mode == PydanticProgramMode.OPENAI: - from llama_index.core.program.openai_program import OpenAIPydanticProgram + from llama_index.program.openai import ( + OpenAIPydanticProgram, + ) # pants: no-infer-dep return OpenAIPydanticProgram.from_defaults( output_cls=output_cls, diff --git a/llama-index-core/llama_index/core/prompts/base.py b/llama-index-core/llama_index/core/prompts/base.py index c11c839b579a8..4c3089b0b4478 100644 --- a/llama-index-core/llama_index/core/prompts/base.py +++ b/llama-index-core/llama_index/core/prompts/base.py @@ -451,7 +451,7 @@ def partial_format(self, **kwargs: Any) -> "BasePromptTemplate": def format(self, llm: Optional[BaseLLM] = None, **kwargs: Any) -> str: """Format the prompt into a string.""" - from llama_index.core.llms.langchain import LangChainLLM + from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep if llm is not None: # if llamaindex LLM is provided, and we require a langchain LLM, @@ -474,8 +474,10 @@ def format_messages( self, llm: Optional[BaseLLM] = None, **kwargs: Any ) -> List[ChatMessage]: """Format the prompt into a list of chat messages.""" - from llama_index.core.llms.langchain import LangChainLLM - from llama_index.core.llms.langchain_utils import from_lc_messages + from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep + from llama_index.llms.langchain.utils import ( + from_lc_messages, + ) # pants: no-infer-dep if llm is not None: # if llamaindex LLM is provided, and we require a langchain LLM, @@ -497,7 +499,7 @@ def format_messages( return from_lc_messages(lc_messages) def get_template(self, llm: Optional[BaseLLM] = None) -> str: - from llama_index.core.llms.langchain import LangChainLLM + from llama_index.llms.langchain import LangChainLLM # pants: no-infer-dep if llm is not None: # if llamaindex LLM is provided, and we require a langchain LLM, diff --git a/llama-index-core/llama_index/core/query_pipeline/__init__.py b/llama-index-core/llama_index/core/query_pipeline/__init__.py index a82c409cfa499..109025f54058d 100644 --- a/llama-index-core/llama_index/core/query_pipeline/__init__.py +++ b/llama-index-core/llama_index/core/query_pipeline/__init__.py @@ -1,4 +1,5 @@ """Init file.""" + from llama_index.core.query_pipeline.components.agent import ( AgentFnComponent, AgentInputComponent, @@ -6,7 +7,10 @@ QueryComponent, ) from llama_index.core.query_pipeline.components.argpacks import ArgPackComponent -from llama_index.core.query_pipeline.components.function import FnComponent +from llama_index.core.query_pipeline.components.function import ( + FnComponent, + FunctionComponent, +) from llama_index.core.query_pipeline.components.input import InputComponent from llama_index.core.query_pipeline.components.router import RouterComponent from llama_index.core.query_pipeline.components.tool_runner import ToolRunnerComponent @@ -25,6 +29,7 @@ "AgentInputComponent", "ArgPackComponent", "FnComponent", + "FunctionComponent", "InputComponent", "RouterComponent", "ToolRunnerComponent", diff --git a/llama-index-core/llama_index/core/query_pipeline/components/function.py b/llama-index-core/llama_index/core/query_pipeline/components/function.py index bef5c377ab084..f6a180da0666c 100644 --- a/llama-index-core/llama_index/core/query_pipeline/components/function.py +++ b/llama-index-core/llama_index/core/query_pipeline/components/function.py @@ -114,3 +114,7 @@ def input_keys(self) -> InputKeys: def output_keys(self) -> OutputKeys: """Output keys.""" return OutputKeys.from_keys({self.output_key}) + + +# alias +FunctionComponent = FnComponent diff --git a/llama-index-core/llama_index/core/types.py b/llama-index-core/llama_index/core/types.py index 1394d23f0034f..cfa9a0fa54b9a 100644 --- a/llama-index-core/llama_index/core/types.py +++ b/llama-index-core/llama_index/core/types.py @@ -3,6 +3,7 @@ from typing import ( Any, AsyncGenerator, + Dict, Generator, Generic, List, @@ -29,6 +30,11 @@ class BaseOutputParser(Protocol): """Output parser class.""" + @classmethod + def __modify_schema__(cls, schema: Dict[str, Any]) -> None: + """Avoids serialization issues.""" + schema.update(type="object", default={}) + @abstractmethod def parse(self, output: str) -> Any: """Parse, validate, and correct errors programmatically.""" diff --git a/llama-index-core/poetry.lock b/llama-index-core/poetry.lock index 49e802aaaf517..83154184f2ef3 100644 --- a/llama-index-core/poetry.lock +++ b/llama-index-core/poetry.lock @@ -1,14 +1,14 @@ -# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. [[package]] name = "accelerate" -version = "0.27.0" +version = "0.27.2" description = "Accelerate" optional = true python-versions = ">=3.8.0" files = [ - {file = "accelerate-0.27.0-py3-none-any.whl", hash = "sha256:87eb754a1600a63d6fdbf8683603800388544456bfbdaaf622b89458005937de"}, - {file = "accelerate-0.27.0.tar.gz", hash = "sha256:8e8e34695dc34f6294d4209a617d5f934c9f88a3f26067aeee18e0568d3277a3"}, + {file = "accelerate-0.27.2-py3-none-any.whl", hash = "sha256:a818dd27b9ba24e9eb5030d1b285cf4cdd1b41bbfa675fb4eb2477ddfc097074"}, + {file = "accelerate-0.27.2.tar.gz", hash = "sha256:cc715fe9a8bc7a286259bfb6d65fb78363badd3371e7cbda4e4a4ef34a0010aa"}, ] [package.dependencies] @@ -1572,13 +1572,13 @@ protobuf = ">=3.19.5,<3.20.0 || >3.20.0,<3.20.1 || >3.20.1,<4.21.0 || >4.21.0,<4 [[package]] name = "google-api-core" -version = "2.17.0" +version = "2.17.1" description = "Google API client core library" optional = false python-versions = ">=3.7" files = [ - {file = "google-api-core-2.17.0.tar.gz", hash = "sha256:de7ef0450faec7c75e0aea313f29ac870fdc44cfaec9d6499a9a17305980ef66"}, - {file = "google_api_core-2.17.0-py3-none-any.whl", hash = "sha256:08ed79ed8e93e329de5e3e7452746b734e6bf8438d8d64dd3319d21d3164890c"}, + {file = "google-api-core-2.17.1.tar.gz", hash = "sha256:9df18a1f87ee0df0bc4eea2770ebc4228392d8cc4066655b320e2cfccb15db95"}, + {file = "google_api_core-2.17.1-py3-none-any.whl", hash = "sha256:610c5b90092c360736baccf17bd3efbcb30dd380e7a6dc28a71059edb8bd0d8e"}, ] [package.dependencies] @@ -1602,13 +1602,13 @@ grpcio-gcp = ["grpcio-gcp (>=0.2.2,<1.0.dev0)"] [[package]] name = "google-auth" -version = "2.27.0" +version = "2.28.0" description = "Google Authentication Library" optional = false python-versions = ">=3.7" files = [ - {file = "google-auth-2.27.0.tar.gz", hash = "sha256:e863a56ccc2d8efa83df7a80272601e43487fa9a728a376205c86c26aaefa821"}, - {file = "google_auth-2.27.0-py2.py3-none-any.whl", hash = "sha256:8e4bad367015430ff253fe49d500fdc3396c1a434db5740828c728e45bcce245"}, + {file = "google-auth-2.28.0.tar.gz", hash = "sha256:3cfc1b6e4e64797584fb53fc9bd0b7afa9b7c0dba2004fa7dcc9349e58cc3195"}, + {file = "google_auth-2.28.0-py2.py3-none-any.whl", hash = "sha256:7634d29dcd1e101f5226a23cbc4a0c6cda6394253bf80e281d9c5c6797869c53"}, ] [package.dependencies] @@ -1868,13 +1868,13 @@ files = [ [[package]] name = "httpcore" -version = "1.0.2" +version = "1.0.3" description = "A minimal low-level HTTP client." optional = false python-versions = ">=3.8" files = [ - {file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"}, - {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"}, + {file = "httpcore-1.0.3-py3-none-any.whl", hash = "sha256:9a6a501c3099307d9fd76ac244e08503427679b1e81ceb1d922485e2f2462ad2"}, + {file = "httpcore-1.0.3.tar.gz", hash = "sha256:5c0f9546ad17dac4d0772b0808856eb616eb8b48ce94f49ed819fd6982a8a544"}, ] [package.dependencies] @@ -1885,7 +1885,7 @@ h11 = ">=0.13,<0.15" asyncio = ["anyio (>=4.0,<5.0)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] -trio = ["trio (>=0.22.0,<0.23.0)"] +trio = ["trio (>=0.22.0,<0.24.0)"] [[package]] name = "httpx" @@ -2540,17 +2540,18 @@ test = ["jupyter-server (>=2.0.0)", "pytest (>=7.0)", "pytest-jupyter[server] (> [[package]] name = "jupyterlab" -version = "4.0.12" +version = "4.1.1" description = "JupyterLab computational environment" optional = false python-versions = ">=3.8" files = [ - {file = "jupyterlab-4.0.12-py3-none-any.whl", hash = "sha256:53f132480e5f6564f4e20d1b5ed4e8b7945952a2decd5bdfa43760b1b536c99d"}, - {file = "jupyterlab-4.0.12.tar.gz", hash = "sha256:965d92efa82a538ed70ccb3968d9aabba788840da882e13d7b061780cdedc3b7"}, + {file = "jupyterlab-4.1.1-py3-none-any.whl", hash = "sha256:fa3e8c18b804eac04e51ceebd9dd3dd396e08106816f0d09cc426799d7087632"}, + {file 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["guidance", "jsonpath-ng", "lm-format-enforcer", "rank-bm25", "sc [metadata] lock-version = "2.0" python-versions = ">=3.8.1,<4.0" -content-hash = "edf61c6e8fcdd425acd766dbdb489bd500f75be8631ca2240161b10325aa5cfa" +content-hash = "d5364f07fbd28636b7d4f998e45d2383505dfe8fb24eb10cb42f9b148a5d4a19" diff --git a/llama-index-core/pyproject.toml b/llama-index-core/pyproject.toml index a59ec960cbeee..8f089d12a8e52 100644 --- a/llama-index-core/pyproject.toml +++ b/llama-index-core/pyproject.toml @@ -42,7 +42,7 @@ name = "llama-index-core" packages = [{include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.10.3" +version = "0.10.5" [tool.poetry.dependencies] SQLAlchemy = {extras = ["asyncio"], version = ">=1.4.49"} @@ -82,6 +82,7 @@ dirtyjson = "^1.0.8" tqdm = "^4.66.1" pillow = ">=9.0.0" PyYAML = ">=6.0.1" +llamaindex-py-client = ">=0.1.12" [tool.poetry.extras] gradientai = [ diff --git a/llama-index-core/tests/embeddings/todo_hf_test_utils.py b/llama-index-core/tests/embeddings/todo_hf_test_utils.py new file mode 100644 index 0000000000000..b242941b7883a --- /dev/null +++ b/llama-index-core/tests/embeddings/todo_hf_test_utils.py @@ -0,0 +1,56 @@ +from typing import Any, Dict + +# pants: no-infer-dep +from llama_index.core.embeddings.mock_embed_model import MockEmbedding +from llama_index.core.embeddings.utils import resolve_embed_model +from llama_index.embeddings.huggingface import ( + HuggingFaceEmbedding, +) # pants: no-infer-dep +from llama_index.embeddings.openai import OpenAIEmbedding # pants: no-infer-dep +from pytest import MonkeyPatch + + +def mock_hf_embeddings(self: Any, *args: Any, **kwargs: Dict[str, Any]) -> Any: + """Mock HuggingFaceEmbeddings.""" + super(HuggingFaceEmbedding, self).__init__( + model_name="fake", + tokenizer_name="fake", + model="fake", + tokenizer="fake", + ) + return + + +def mock_openai_embeddings(self: Any, *args: Any, **kwargs: Dict[str, Any]) -> Any: + """Mock OpenAIEmbedding.""" + super(OpenAIEmbedding, self).__init__( + api_key="fake", api_base="fake", api_version="fake" + ) + return + + +def test_resolve_embed_model(monkeypatch: MonkeyPatch) -> None: + monkeypatch.setattr( + "llama_index.embeddings.huggingface.HuggingFaceEmbedding.__init__", + mock_hf_embeddings, + ) + monkeypatch.setattr( + "llama_index.embeddings.openai.OpenAIEmbedding.__init__", + mock_openai_embeddings, + ) + + # Test None + embed_model = resolve_embed_model(None) + assert isinstance(embed_model, MockEmbedding) + + # Test str + embed_model = resolve_embed_model("local") + assert isinstance(embed_model, HuggingFaceEmbedding) + + # Test LCEmbeddings + embed_model = resolve_embed_model(HuggingFaceEmbedding()) + assert isinstance(embed_model, HuggingFaceEmbedding) + + # Test BaseEmbedding + embed_model = resolve_embed_model(OpenAIEmbedding()) + assert isinstance(embed_model, OpenAIEmbedding) diff --git a/llama-index-core/tests/evaluation/test_platform_eval.py b/llama-index-core/tests/evaluation/test_platform_eval.py new file mode 100644 index 0000000000000..392d59ebf1255 --- /dev/null +++ b/llama-index-core/tests/evaluation/test_platform_eval.py @@ -0,0 +1,30 @@ +import os +import sys + +import pytest +from llama_index.core.evaluation.eval_utils import upload_eval_dataset +from llama_index_client.client import PlatformApi + +base_url = os.environ.get("LLAMA_CLOUD_BASE_URL", None) +api_key = os.environ.get("LLAMA_CLOUD_API_KEY", None) +python_version = sys.version + + +@pytest.mark.skipif( + not base_url or not api_key, reason="No platform base url or api keyset" +) +@pytest.mark.integration() +def test_upload_eval_dataset() -> None: + eval_dataset_id = upload_eval_dataset( + "test_dataset" + python_version, # avoid CI test clashes + project_name="test_project" + python_version, + questions=["foo", "bar"], + overwrite=True, + ) + + client = PlatformApi(base_url=base_url, token=api_key) + eval_dataset = client.eval.get_dataset(dataset_id=eval_dataset_id) + assert eval_dataset.name == "test_dataset" + python_version + + eval_questions = client.eval.get_questions(dataset_id=eval_dataset_id) + assert len(eval_questions) == 2 diff --git a/llama-index-core/tests/ingestion/test_data_sinks.py b/llama-index-core/tests/ingestion/test_data_sinks.py new file mode 100644 index 0000000000000..3c6eb252913f1 --- /dev/null +++ b/llama-index-core/tests/ingestion/test_data_sinks.py @@ -0,0 +1,76 @@ +import sys +from unittest.mock import MagicMock + +import pytest +from llama_index.core.ingestion.data_sinks import ( + ConfigurableDataSinks, + ConfiguredDataSink, +) + +try: + from llama_index.vector_store.weaviate import WeaviateVectorStore +except ImportError: + WeaviateVectorStore = None + + +@pytest.mark.parametrize("configurable_data_sink_type", ConfigurableDataSinks) +def test_can_generate_schema_for_data_sink_component_type( + configurable_data_sink_type: ConfigurableDataSinks, +) -> None: + schema = configurable_data_sink_type.value.schema() # type: ignore + assert schema is not None + assert len(schema) > 0 + + # also check that we can generate schemas for + # ConfiguredDataSink[component_type] + component_type = configurable_data_sink_type.value.component_type + configured_schema = ConfiguredDataSink[component_type].schema() # type: ignore + assert configured_schema is not None + assert len(configured_schema) > 0 + + +@pytest.mark.skipif(WeaviateVectorStore is None, reason="weaviate not installed") +def test_can_build_configured_data_sink_from_component() -> None: + sys.modules["weaviate"] = MagicMock() + weaviate_client = MagicMock() + batch_context_manager = MagicMock() + weaviate_client.batch.__enter__.return_value = batch_context_manager + + vector_store = WeaviateVectorStore(weaviate_client=weaviate_client) + configured_data_sink = ConfiguredDataSink.from_component(vector_store) + assert isinstance( + configured_data_sink, + ConfiguredDataSink[WeaviateVectorStore], # type: ignore + ) + assert ( + configured_data_sink.configurable_data_sink_type.value.component_type + == WeaviateVectorStore + ) + + +@pytest.mark.skipif(WeaviateVectorStore is None, reason="weaviate not installed") +def test_build_configured_data_sink() -> None: + sys.modules["weaviate"] = MagicMock() + weaviate_client = MagicMock() + batch_context_manager = MagicMock() + weaviate_client.batch.__enter__.return_value = batch_context_manager + + vector_store = WeaviateVectorStore(weaviate_client=weaviate_client) + configured_data_sink = ConfigurableDataSinks.WEAVIATE.build_configured_data_sink( + vector_store + ) + assert isinstance( + configured_data_sink, + ConfiguredDataSink[WeaviateVectorStore], # type: ignore + ) + + with pytest.raises(ValueError): + ConfigurableDataSinks.PINECONE.build_configured_data_sink(vector_store) + + +def test_unique_configurable_data_sink_names() -> None: + names = set() + for configurable_data_sink_type in ConfigurableDataSinks: + assert configurable_data_sink_type.value.name not in names + names.add(configurable_data_sink_type.value.name) + assert len(names) == len(ConfigurableDataSinks) diff --git a/llama-index-core/tests/ingestion/test_data_sources.py b/llama-index-core/tests/ingestion/test_data_sources.py new file mode 100644 index 0000000000000..e2cbb72e4df29 --- /dev/null +++ b/llama-index-core/tests/ingestion/test_data_sources.py @@ -0,0 +1,55 @@ +import pytest +from llama_index.core.ingestion.data_sources import ( + ConfigurableDataSources, + ConfiguredDataSource, +) +from llama_index.core.schema import Document + + +@pytest.mark.parametrize("configurable_data_source_type", ConfigurableDataSources) +def test_can_generate_schema_for_data_source_component_type( + configurable_data_source_type: ConfigurableDataSources, +) -> None: + schema = configurable_data_source_type.value.schema() # type: ignore + assert schema is not None + assert len(schema) > 0 + + # also check that we can generate schemas for + # ConfiguredDataSource[component_type] + component_type = configurable_data_source_type.value.component_type + configured_schema = ConfiguredDataSource[component_type].schema() # type: ignore + assert configured_schema is not None + assert len(configured_schema) > 0 + + +def test_can_build_configured_data_source_from_component() -> None: + document = Document.example() + configured_data_source = ConfiguredDataSource.from_component(document) + assert isinstance( + configured_data_source, + ConfiguredDataSource[Document], # type: ignore + ) + assert ( + configured_data_source.configurable_data_source_type.value.component_type + == Document + ) + + +def test_build_configured_data_source() -> None: + document = Document.example() + configured_data_source = ( + ConfigurableDataSources.DOCUMENT.build_configured_data_source(document) + ) + assert isinstance( + configured_data_source, + ConfiguredDataSource[Document], # type: ignore + ) + + +def test_unique_configurable_data_source_names() -> None: + names = set() + for configurable_data_source_type in ConfigurableDataSources: + assert configurable_data_source_type.value.name not in names + names.add(configurable_data_source_type.value.name) + assert len(names) > 0 + assert len(names) == len(ConfigurableDataSources) diff --git a/llama-index-core/tests/ingestion/test_pipeline.py b/llama-index-core/tests/ingestion/test_pipeline.py index f447932c7caba..eb0b65cf96a48 100644 --- a/llama-index-core/tests/ingestion/test_pipeline.py +++ b/llama-index-core/tests/ingestion/test_pipeline.py @@ -19,9 +19,12 @@ def teardown_function() -> None: def test_build_pipeline() -> None: pipeline = IngestionPipeline( - reader=ReaderConfig( - reader=StringIterableReader(), reader_kwargs={"texts": ["This is a test."]} - ), + readers=[ + ReaderConfig( + reader=StringIterableReader(), + reader_kwargs={"texts": ["This is a test."]}, + ) + ], documents=[Document.example()], transformations=[ SentenceSplitter(), @@ -35,9 +38,12 @@ def test_build_pipeline() -> None: def test_run_pipeline() -> None: pipeline = IngestionPipeline( - reader=ReaderConfig( - reader=StringIterableReader(), reader_kwargs={"texts": ["This is a test."]} - ), + readers=[ + ReaderConfig( + reader=StringIterableReader(), + reader_kwargs={"texts": ["This is a test."]}, + ) + ], documents=[Document.example()], transformations=[ SentenceSplitter(), diff --git a/llama-index-core/tests/ingestion/test_transformations.py b/llama-index-core/tests/ingestion/test_transformations.py new file mode 100644 index 0000000000000..f4e7fa9b17d39 --- /dev/null +++ b/llama-index-core/tests/ingestion/test_transformations.py @@ -0,0 +1,68 @@ +import pytest +from llama_index.core.ingestion.transformations import ( + ConfigurableTransformations, + ConfiguredTransformation, +) +from llama_index.core.node_parser.text import SentenceSplitter, TokenTextSplitter + + +@pytest.mark.parametrize( + "configurable_transformation_type", ConfigurableTransformations +) +def test_can_generate_schema_for_transformation_component_type( + configurable_transformation_type: ConfigurableTransformations, +) -> None: + schema = configurable_transformation_type.value.schema() # type: ignore + assert schema is not None + assert len(schema) > 0 + + # also check that we can generate schemas for + # ConfiguredTransformation[component_type] + component_type = configurable_transformation_type.value.component_type + configured_schema = ConfiguredTransformation[ + component_type # type: ignore + ].schema() + assert configured_schema is not None + assert len(configured_schema) > 0 + + +def test_can_build_configured_transform_from_component() -> None: + parser = SentenceSplitter() + configured_transformation = ConfiguredTransformation.from_component(parser) + assert isinstance( + configured_transformation, + ConfiguredTransformation[SentenceSplitter], # type: ignore + ) + assert not isinstance( + configured_transformation, + ConfiguredTransformation[TokenTextSplitter], # type: ignore + ) + assert ( + configured_transformation.configurable_transformation_type.value.component_type + == SentenceSplitter + ) + + +def test_build_configured_transformation() -> None: + parser = SentenceSplitter() + configured_transformation = ConfigurableTransformations.SENTENCE_AWARE_NODE_PARSER.build_configured_transformation( + parser + ) + assert isinstance( + configured_transformation, + ConfiguredTransformation[SentenceSplitter], # type: ignore + ) + + with pytest.raises(ValueError): + ConfigurableTransformations.TOKEN_AWARE_NODE_PARSER.build_configured_transformation( + parser + ) + + +def test_unique_configurable_transformations_names() -> None: + names = set() + for configurable_transformation_type in ConfigurableTransformations: + assert configurable_transformation_type.value.name not in names + names.add(configurable_transformation_type.value.name) + assert len(names) > 0 + assert len(names) == len(ConfigurableTransformations) diff --git a/llama-index-finetuning/llama_index/finetuning/embeddings/adapter.py b/llama-index-finetuning/llama_index/finetuning/embeddings/adapter.py index 1d1a394832ef3..b719ae619d56f 100644 --- a/llama-index-finetuning/llama_index/finetuning/embeddings/adapter.py +++ b/llama-index-finetuning/llama_index/finetuning/embeddings/adapter.py @@ -50,7 +50,7 @@ def __init__( ) -> None: """Init params.""" import torch - from llama_index.finetuning.embeddings.adapter_utils import ( + from llama_index.embeddings.adapter import ( BaseAdapter, LinearLayer, ) @@ -108,7 +108,7 @@ def from_model_path( **kwargs (Any): Additional kwargs (see __init__) """ - from llama_index.finetuning.embeddings.adapter_utils import LinearLayer + from llama_index.embeddings.adapter import LinearLayer model_cls = model_cls or LinearLayer model = model_cls.load(model_path) diff --git a/llama-index-finetuning/llama_index/finetuning/embeddings/adapter_utils.py b/llama-index-finetuning/llama_index/finetuning/embeddings/adapter_utils.py index 495f6689c9b02..5f51874f8dae3 100644 --- a/llama-index-finetuning/llama_index/finetuning/embeddings/adapter_utils.py +++ b/llama-index-finetuning/llama_index/finetuning/embeddings/adapter_utils.py @@ -6,7 +6,7 @@ import torch import transformers from llama_index.core.utils import print_text -from llama_index.embeddings.adapter.utils import BaseAdapter +from llama_index.embeddings.adapter import BaseAdapter from sentence_transformers.util import cos_sim from torch import Tensor, nn from torch.optim import Optimizer diff --git a/llama-index-finetuning/pyproject.toml b/llama-index-finetuning/pyproject.toml index b073a6e23459c..6c5b63661f15c 100644 --- a/llama-index-finetuning/pyproject.toml +++ b/llama-index-finetuning/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index finetuning" license = "MIT" name = "llama-index-finetuning" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" @@ -32,7 +32,7 @@ llama-index-core = "^0.10.1" llama-index-llms-openai = "^0.1.1" llama-index-llms-gradient = "^0.1.1" llama-index-postprocessor-cohere-rerank = "^0.1.1" -llama-index-embeddings-adapter = "^0.1.1" +llama-index-embeddings-adapter = "^0.1.2" sentence-transformers = "^2.3.0" [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/README.md b/llama-index-integrations/README.md index 9090ed7112a20..0c6b29f0f4e9c 100644 --- a/llama-index-integrations/README.md +++ b/llama-index-integrations/README.md @@ -1,4 +1,4 @@ # LlamaIndex Integrations Building LLM applications with LlamaIndex involves building with LlamaIndex -[core](https://github.com/run-llamae/llama_index/tree/main/llama-index-core) as well as with the LlamaIndex Integrations required for your application.Integrations are categorized by type and each are their own Python package. +[core](https://github.com/run-llama/llama_index/tree/main/llama-index-core) as well as with the LlamaIndex Integrations required for your application.Integrations are categorized by type and each are their own Python package. diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/poetry.lock b/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/poetry.lock index cdcc450e0e4f6..1908afb0c76f3 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/poetry.lock +++ b/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/poetry.lock @@ -1,98 +1,93 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. -[metadata] -content-hash = "6e160c7805d0284811eaf128438ad20db0024581b06fdfac788b67318dc3c468" -lock-version = "2.0" -python-versions = ">=3.8.1,<3.12" - [[package]] -description = "Async http client/server framework (asyncio)" -files = [ - {file = 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"pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<3.12" +content-hash = "e78f1dd735af2151cfb0ffc09257cfd6aa3af47dc14691c6c61c0cf1c910267e" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml index 1de9f3cd3b8b4..1210ae92cda10 100644 --- a/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml +++ b/llama-index-integrations/callbacks/llama-index-callbacks-arize-phoenix/pyproject.toml @@ -20,16 +20,20 @@ ignore_missing_imports = true python_version = "3.8" [tool.poetry] -authors = ["Your Name "] +authors = [ + "Arize AI ", + "OpenInference Authors ", +] description = "llama-index callbacks arize-phoenix integration" license = "MIT" name = "llama-index-callbacks-arize-phoenix" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" -arize-phoenix = "^2.5.0" +arize-phoenix = ">=3.0.3" +openinference-instrumentation-llama-index = ">=1.0.0" llama-index-core = "^0.10.1" [tool.poetry.group.dev.dependencies] diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-openinference/llama_index/callbacks/openinference/__pycache__/__init__.cpython-310.pyc b/llama-index-integrations/callbacks/llama-index-callbacks-openinference/llama_index/callbacks/openinference/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index fc29fa6bc1878..0000000000000 Binary files a/llama-index-integrations/callbacks/llama-index-callbacks-openinference/llama_index/callbacks/openinference/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-openinference/llama_index/callbacks/openinference/__pycache__/base.cpython-310.pyc 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a/llama-index-integrations/callbacks/llama-index-callbacks-promptlayer/tests/__pycache__/test_promptlayer_callback.cpython-310-pytest-7.2.1.pyc b/llama-index-integrations/callbacks/llama-index-callbacks-promptlayer/tests/__pycache__/test_promptlayer_callback.cpython-310-pytest-7.2.1.pyc deleted file mode 100644 index 38e5ff25f91ac..0000000000000 Binary files a/llama-index-integrations/callbacks/llama-index-callbacks-promptlayer/tests/__pycache__/test_promptlayer_callback.cpython-310-pytest-7.2.1.pyc and /dev/null differ diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/BUILD b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/Makefile b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/README.md b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/README.md new file mode 100644 index 0000000000000..f9c12773deaa9 --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/README.md @@ -0,0 +1,35 @@ +# LlamaIndex Callbacks Integration: UpTrain + +UpTrain is an open-source tool to evaluate and monitor the performance of language models. It provides a set of pre-built evaluations to assess the quality of responses generated by the model. Once you add UpTrainCallbackHandler to your existing LlamaIndex pipeline, it will take care of sending the generated responses to the UpTrain Managed Service for evaluations and display the results in the output. + +Three additional evaluations for Llamaindex have been introduced, complementing existing ones. These evaluations run automatically, with results displayed in the output. More details on UpTrain's evaluations can be found [here](https://github.com/uptrain-ai/uptrain?tab=readme-ov-file#pre-built-evaluations-we-offer-). + +Selected operators from the LlamaIndex pipeline are highlighted for demonstration: + +## 1. **RAG Query Engine Evaluations**: + +The RAG query engine plays a crucial role in retrieving context and generating responses. To ensure its performance and response quality, we conduct the following evaluations: + +- **Context Relevance**: Determines if the context extracted from the query is relevant to the response. +- **Factual Accuracy**: Assesses if the LLM is hallcuinating or providing incorrect information. +- **Response Completeness**: Checks if the response contains all the information requested by the query. + +## 2. **Sub-Question Query Generation Evaluation**: + +The SubQuestionQueryGeneration operator decomposes a question into sub-questions, generating responses for each using a RAG query engine. Given the complexity, we include the previous evaluations and add: + +- **Sub Query Completeness**: Assures that the sub-questions accurately and comprehensively cover the original query. + +## 3. **Re-Ranking Evaluations**: + +Re-ranking involves reordering nodes based on relevance to the query and choosing top n nodes. Different evaluations are performed based on the number of nodes returned after re-ranking. + +a. Same Number of Nodes + +- **Context Reranking**: Checks if the order of re-ranked nodes is more relevant to the query than the original order. + +b. Different Number of Nodes: + +- **Context Conciseness**: Examines whether the reduced number of nodes still provides all the required information. + +These evaluations collectively ensure the robustness and effectiveness of the RAG query engine, SubQuestionQueryGeneration operator, and the re-ranking process in the LlamaIndex pipeline. diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/BUILD b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/__init__.py b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/__init__.py new file mode 100644 index 0000000000000..438dd5f6c5fb3 --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/__init__.py @@ -0,0 +1,3 @@ +from llama_index.callbacks.uptrain.base import UpTrainCallbackHandler + +__all__ = ["UpTrainCallbackHandler"] diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/base.py b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/base.py new file mode 100644 index 0000000000000..69ab6de8e509b --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/llama_index/callbacks/uptrain/base.py @@ -0,0 +1,320 @@ +from collections import defaultdict +from typing import Any, DefaultDict, Dict, List, Literal, Optional, Set + +import nest_asyncio + +from llama_index.core.callbacks.base_handler import BaseCallbackHandler +from llama_index.core.callbacks.schema import ( + CBEvent, + CBEventType, +) + + +class UpTrainDataSchema: + """UpTrain data schema.""" + + def __init__(self, project_name_prefix: str) -> None: + """Initialize the UpTrain data schema.""" + # For tracking project name and results + self.project_name_prefix: str = project_name_prefix + self.uptrain_results: DefaultDict[str, Any] = defaultdict(list) + + # For tracking event types - reranking, sub_question + self.eval_types: Set[str] = set() + + ## SYNTHESIZE + self.question: str = "" + self.context: str = "" + self.response: str = "" + + ## RERANKING + self.old_context: List[str] = [] + self.new_context: List[str] = [] + + ## SUB_QUESTION + # Map of sub question ID to question, context, and response + self.sub_question_map: DefaultDict[str, dict] = defaultdict(dict) + # Parent ID of sub questions + self.sub_question_parent_id: str = "" + # Parent question + self.parent_question: str = "" + + +class UpTrainCallbackHandler(BaseCallbackHandler): + """ + UpTrain callback handler. + + This class is responsible for handling the UpTrain API and logging events to UpTrain. + + """ + + def __init__( + self, + api_key: str, + key_type: Literal["uptrain", "openai"], + project_name_prefix: str = "llama", + ) -> None: + """Initialize the UpTrain callback handler.""" + try: + from uptrain import APIClient, EvalLLM, Settings + except ImportError: + raise ImportError( + "UpTrainCallbackHandler requires the 'uptrain' package. " + "Please install it using 'pip install uptrain'." + ) + nest_asyncio.apply() + super().__init__( + event_starts_to_ignore=[], + event_ends_to_ignore=[], + ) + self.schema = UpTrainDataSchema(project_name_prefix=project_name_prefix) + self._event_pairs_by_id: Dict[str, List[CBEvent]] = defaultdict(list) + self._trace_map: Dict[str, List[str]] = defaultdict(list) + + # Based on whether the user enters an UpTrain API key or an OpenAI API key, the client is initialized + # If both are entered, the UpTrain API key is used + if key_type == "uptrain": + settings = Settings(uptrain_access_token=api_key) + self.uptrain_client = APIClient(settings=settings) + elif key_type == "openai": + settings = Settings(openai_api_key=api_key) + self.uptrain_client = EvalLLM(settings=settings) + else: + raise ValueError("Invalid key type: Must be 'uptrain' or 'openai'") + + def uptrain_evaluate( + self, + project_name: str, + data: List[Dict[str, str]], + checks: List[str], + ) -> None: + """Run an evaluation on the UpTrain server using UpTrain client.""" + if self.uptrain_client.__class__.__name__ == "APIClient": + uptrain_result = self.uptrain_client.log_and_evaluate( + project_name=project_name, + data=data, + checks=checks, + ) + else: + uptrain_result = self.uptrain_client.evaluate( + data=data, + checks=checks, + ) + self.schema.uptrain_results[project_name].append(uptrain_result) + + score_name_map = { + "score_context_relevance": "Context Relevance Score", + "score_factual_accuracy": "Factual Accuracy Score", + "score_response_completeness": "Response Completeness Score", + "score_sub_query_completeness": "Sub Query Completeness Score", + "score_context_reranking": "Context Reranking Score", + "score_context_conciseness": "Context Conciseness Score", + } + + # Print the results + for row in uptrain_result: + columns = list(row.keys()) + for column in columns: + if column == "question": + print(f"\nQuestion: {row[column]}") + elif column == "response": + print(f"Response: {row[column]}") + elif column.startswith("score"): + if column in score_name_map: + print(f"{score_name_map[column]}: {row[column]}") + else: + print(f"{column}: {row[column]}") + print() + + def on_event_start( + self, + event_type: CBEventType, + payload: Any = None, + event_id: str = "", + parent_id: str = "", + **kwargs: Any, + ) -> str: + """Run when an event starts and return id of event.""" + event = CBEvent(event_type, payload=payload, id_=event_id) + self._event_pairs_by_id[event.id_].append(event) + + if event_type is CBEventType.QUERY: + self.schema.question = payload["query_str"] + if event_type is CBEventType.TEMPLATING and "template_vars" in payload: + template_vars = payload["template_vars"] + self.schema.context = template_vars.get("context_str", "") + elif event_type is CBEventType.RERANKING and "nodes" in payload: + self.schema.eval_types.add("reranking") + # Store old context data + self.schema.old_context = [node.text for node in payload["nodes"]] + elif event_type is CBEventType.SUB_QUESTION: + # For the first sub question, store parent question and parent id + if "sub_question" not in self.schema.eval_types: + self.schema.parent_question = self.schema.question + self.schema.eval_types.add("sub_question") + # Store sub question data - question and parent id + self.schema.sub_question_parent_id = parent_id + return event_id + + def on_event_end( + self, + event_type: CBEventType, + payload: Any = None, + event_id: str = "", + **kwargs: Any, + ) -> None: + """Run when an event ends.""" + try: + from uptrain import Evals + except ImportError: + raise ImportError( + "UpTrainCallbackHandler requires the 'uptrain' package. " + "Please install it using 'pip install uptrain'." + ) + event = CBEvent(event_type, payload=payload, id_=event_id) + self._event_pairs_by_id[event.id_].append(event) + self._trace_map = defaultdict(list) + if event_id == self.schema.sub_question_parent_id: + # Perform individual evaluations for sub questions (but send all sub questions at once) + self.uptrain_evaluate( + project_name=f"{self.schema.project_name_prefix}_sub_question_answering", + data=list(self.schema.sub_question_map.values()), + checks=[ + Evals.CONTEXT_RELEVANCE, + Evals.FACTUAL_ACCURACY, + Evals.RESPONSE_COMPLETENESS, + ], + ) + # Perform evaluation for question and all sub questions (as a whole) + sub_questions = [ + sub_question["question"] + for sub_question in self.schema.sub_question_map.values() + ] + sub_questions_formatted = "\n".join( + [ + f"{index}. {string}" + for index, string in enumerate(sub_questions, start=1) + ] + ) + self.uptrain_evaluate( + project_name=f"{self.schema.project_name_prefix}_sub_query_completeness", + data=[ + { + "question": self.schema.parent_question, + "sub_questions": sub_questions_formatted, + } + ], + checks=[Evals.SUB_QUERY_COMPLETENESS], + ) + # Should not be called for sub questions + if ( + event_type is CBEventType.SYNTHESIZE + and "sub_question" not in self.schema.eval_types + ): + self.schema.response = payload["response"].response + # Perform evaluation for synthesization + self.uptrain_evaluate( + project_name=f"{self.schema.project_name_prefix}_question_answering", + data=[ + { + "question": self.schema.question, + "context": self.schema.context, + "response": self.schema.response, + } + ], + checks=[ + Evals.CONTEXT_RELEVANCE, + Evals.FACTUAL_ACCURACY, + Evals.RESPONSE_COMPLETENESS, + ], + ) + + elif event_type is CBEventType.RERANKING: + # Store new context data + self.schema.new_context = [node.text for node in payload["nodes"]] + if len(self.schema.old_context) == len(self.schema.new_context): + context = "\n".join( + [ + f"{index}. {string}" + for index, string in enumerate(self.schema.old_context, start=1) + ] + ) + reranked_context = "\n".join( + [ + f"{index}. {string}" + for index, string in enumerate(self.schema.new_context, start=1) + ] + ) + # Perform evaluation for reranking + self.uptrain_evaluate( + project_name=f"{self.schema.project_name_prefix}_context_reranking", + data=[ + { + "question": self.schema.question, + "context": context, + "reranked_context": reranked_context, + } + ], + checks=[ + Evals.CONTEXT_RERANKING, + ], + ) + else: + context = "\n".join(self.schema.old_context) + concise_context = "\n".join(self.schema.new_context) + # Perform evaluation for resizing + self.uptrain_evaluate( + project_name=f"{self.schema.project_name_prefix}_context_conciseness", + data=[ + { + "question": self.schema.question, + "context": context, + "concise_context": concise_context, + } + ], + checks=[ + Evals.CONTEXT_CONCISENESS, + ], + ) + elif event_type is CBEventType.SUB_QUESTION: + # Store sub question data + self.schema.sub_question_map[event_id]["question"] = payload[ + "sub_question" + ].sub_q.sub_question + self.schema.sub_question_map[event_id]["context"] = ( + payload["sub_question"].sources[0].node.text + ) + self.schema.sub_question_map[event_id]["response"] = payload[ + "sub_question" + ].answer + + def start_trace(self, trace_id: Optional[str] = None) -> None: + self._trace_map = defaultdict(list) + return super().start_trace(trace_id) + + def end_trace( + self, + trace_id: Optional[str] = None, + trace_map: Optional[Dict[str, List[str]]] = None, + ) -> None: + self._trace_map = trace_map or defaultdict(list) + return super().end_trace(trace_id, trace_map) + + def build_trace_map( + self, + cur_event_id: str, + trace_map: Any, + ) -> Dict[str, Any]: + event_pair = self._event_pairs_by_id[cur_event_id] + if event_pair: + event_data = { + "event_type": event_pair[0].event_type, + "event_id": event_pair[0].id_, + "children": {}, + } + trace_map[cur_event_id] = event_data + + child_event_ids = self._trace_map[cur_event_id] + for child_event_id in child_event_ids: + self.build_trace_map(child_event_id, event_data["children"]) + return trace_map diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/poetry.lock b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/poetry.lock new file mode 100644 index 0000000000000..64f1cf4b053cc --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/poetry.lock @@ -0,0 +1,4595 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiohttp" +version = "3.9.3" +description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" +files = [ + {file = "aiohttp-3.9.3-cp310-cp310-macosx_10_9_universal2.whl", hash = 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(>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<3.12" +content-hash = "1abf8c8fe8824de42816fc0bab833f23e98f29fb2b8a550e067f6d85cb1fcbaa" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/pyproject.toml b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/pyproject.toml new file mode 100644 index 0000000000000..5cd3d191cc538 --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/pyproject.toml @@ -0,0 +1,55 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Your Name "] +description = "llama-index callbacks uptrain integration" +license = "MIT" +name = "llama-index-callbacks-uptrain" +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +llama-index-core = "0.10.0" +uptrain = ">=0.5.0" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "1.9.1" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/BUILD b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/__init__.py b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/test_uptrain_callback.py b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/test_uptrain_callback.py new file mode 100644 index 0000000000000..7abf9a195d59c --- /dev/null +++ b/llama-index-integrations/callbacks/llama-index-callbacks-uptrain/tests/test_uptrain_callback.py @@ -0,0 +1,7 @@ +from llama_index.callbacks.uptrain.base import UpTrainCallbackHandler +from llama_index.core.callbacks.base_handler import BaseCallbackHandler + + +def test_handler_callable(): + names_of_base_classes = [b.__name__ for b in UpTrainCallbackHandler.__mro__] + assert BaseCallbackHandler.__name__ in names_of_base_classes diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/llama_index/callbacks/wandb/__pycache__/__init__.cpython-310.pyc b/llama-index-integrations/callbacks/llama-index-callbacks-wandb/llama_index/callbacks/wandb/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 52772321900f9..0000000000000 Binary files a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/llama_index/callbacks/wandb/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/llama_index/callbacks/wandb/__pycache__/base.cpython-310.pyc b/llama-index-integrations/callbacks/llama-index-callbacks-wandb/llama_index/callbacks/wandb/__pycache__/base.cpython-310.pyc deleted file mode 100644 index b90ae214e7876..0000000000000 Binary files a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/llama_index/callbacks/wandb/__pycache__/base.cpython-310.pyc and /dev/null differ diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/tests/__pycache__/__init__.cpython-310.pyc b/llama-index-integrations/callbacks/llama-index-callbacks-wandb/tests/__pycache__/__init__.cpython-310.pyc deleted file mode 100644 index 06d5695628c0a..0000000000000 Binary files a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/tests/__pycache__/__init__.cpython-310.pyc and /dev/null differ diff --git a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/tests/__pycache__/test_wandb_callback.cpython-310-pytest-7.2.1.pyc b/llama-index-integrations/callbacks/llama-index-callbacks-wandb/tests/__pycache__/test_wandb_callback.cpython-310-pytest-7.2.1.pyc deleted file mode 100644 index 2f6c5321b3dfb..0000000000000 Binary files a/llama-index-integrations/callbacks/llama-index-callbacks-wandb/tests/__pycache__/test_wandb_callback.cpython-310-pytest-7.2.1.pyc and /dev/null differ diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/__init__.py index 2376cd580b2d7..bbdfb36e5536a 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/__init__.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/llama_index/embeddings/adapter/__init__.py @@ -1,3 +1,12 @@ -from llama_index.embeddings.adapter.base import AdapterEmbeddingModel +from llama_index.embeddings.adapter.base import ( + AdapterEmbeddingModel, + LinearAdapterEmbeddingModel, +) +from llama_index.embeddings.adapter.utils import BaseAdapter, LinearLayer -__all__ = ["AdapterEmbeddingModel"] +__all__ = [ + "AdapterEmbeddingModel", + "LinearAdapterEmbeddingModel", + "BaseAdapter", + "LinearLayer", +] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml index 9a8c719a27aa8..c3c2fc05ac5a7 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-adapter/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index embeddings adapter integration" license = "MIT" name = "llama-index-embeddings-adapter" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py index f44631b931570..fc699fee9fbfe 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/llama_index/embeddings/azure_openai/base.py @@ -22,6 +22,11 @@ class AzureOpenAIEmbedding(OpenAIEmbedding): default=None, description="The Azure deployment to use." ) + api_base: str = Field(default="", description="The base URL for Azure deployment.") + api_version: str = Field( + default="", description="The version for Azure OpenAI API." + ) + _client: AzureOpenAI = PrivateAttr() _aclient: AsyncAzureOpenAI = PrivateAttr() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml index 74238ea32c373..e29d141428fa5 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-azure-openai/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index embeddings azure openai integration" license = "MIT" name = "llama-index-embeddings-azure-openai" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-clip/CHANGELOG.md b/llama-index-integrations/embeddings/llama-index-embeddings-clip/CHANGELOG.md new file mode 100644 index 0000000000000..3bce19e3fb289 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-clip/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-2-15 + +- Remove incorrect one; resort back to lazy load of clip/torch diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py index 8bcf0b849213f..6bc6da46c37fc 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-clip/llama_index/embeddings/clip/base.py @@ -1,7 +1,6 @@ import logging from typing import Any, List -import torch from llama_index.core.base.embeddings.base import Embedding from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.constants import DEFAULT_EMBED_BATCH_SIZE @@ -9,8 +8,6 @@ from llama_index.core.schema import ImageType from PIL import Image -import clip - logger = logging.getLogger(__name__) @@ -75,6 +72,14 @@ def __init__( if embed_batch_size <= 0: raise ValueError(f"Embed batch size {embed_batch_size} must be > 0.") + try: + import clip + import torch + except ImportError: + raise ImportError( + "ClipEmbedding requires `pip install git+https://github.com/openai/CLIP.git` and torch." + ) + super().__init__( embed_batch_size=embed_batch_size, model_name=model_name, **kwargs ) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-clip/poetry.lock b/llama-index-integrations/embeddings/llama-index-embeddings-clip/poetry.lock index 7d8c9642d88bf..c4387b1b22937 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-clip/poetry.lock +++ b/llama-index-integrations/embeddings/llama-index-embeddings-clip/poetry.lock @@ -1,12 +1,11 @@ # This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. -[metadata] -content-hash = "6ae708f07026a838fa4b1568bad4200675cd4f7252f529f6f16b7c7cff8e7b56" -lock-version = "2.0" -python-versions = ">=3.8.1,<3.12" - [[package]] +name = "aiohttp" +version = "3.9.1" description = "Async http client/server framework (asyncio)" +optional = false +python-versions = ">=3.8" files = [ {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:e1f80197f8b0b846a8d5cf7b7ec6084493950d0882cc5537fb7b96a69e3c8590"}, {file = "aiohttp-3.9.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c72444d17777865734aa1a4d167794c34b63e5883abb90356a0364a28904e6c0"}, @@ -85,14 +84,10 @@ files = [ {file = "aiohttp-3.9.1-cp39-cp39-win_amd64.whl", hash = "sha256:9b05d33ff8e6b269e30a7957bd3244ffbce2a7a35a81b81c382629b80af1a8bf"}, {file = "aiohttp-3.9.1.tar.gz", hash = "sha256:8fc49a87ac269d4529da45871e2ffb6874e87779c3d0e2ccd813c0899221239d"}, ] -name = "aiohttp" -optional = false -python-versions = ">=3.8" -version = "3.9.1" [package.dependencies] aiosignal = ">=1.1.2" -async-timeout = {markers = "python_version < \"3.11\"", version = ">=4.0,<5.0"} +async-timeout = {version = ">=4.0,<5.0", markers = "python_version < \"3.11\""} attrs = ">=17.3.0" frozenlist = ">=1.1.1" multidict = ">=4.5,<7.0" @@ -102,49 +97,49 @@ yarl = ">=1.0,<2.0" speedups = ["Brotli", "aiodns", "brotlicffi"] [[package]] +name = "aiosignal" +version = "1.3.1" description = "aiosignal: a list of registered asynchronous callbacks" +optional = false +python-versions = ">=3.7" files = [ {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"}, {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"}, ] -name = "aiosignal" -optional = false -python-versions = ">=3.7" -version = "1.3.1" [package.dependencies] frozenlist = ">=1.1.0" [[package]] +name = "annotated-types" +version = "0.6.0" description = "Reusable constraint types to use with typing.Annotated" +optional = false +python-versions = ">=3.8" files = [ {file = "annotated_types-0.6.0-py3-none-any.whl", hash = "sha256:0641064de18ba7a25dee8f96403ebc39113d0cb953a01429249d5c7564666a43"}, {file = "annotated_types-0.6.0.tar.gz", hash = "sha256:563339e807e53ffd9c267e99fc6d9ea23eb8443c08f112651963e24e22f84a5d"}, ] -name = "annotated-types" -optional = false -python-versions = ">=3.8" -version = "0.6.0" [package.dependencies] -typing-extensions = {markers = "python_version < \"3.9\"", version = ">=4.0.0"} +typing-extensions = {version = ">=4.0.0", markers = "python_version < \"3.9\""} [[package]] +name = "anyio" +version = "4.2.0" description = "High level compatibility layer for multiple asynchronous event loop implementations" +optional = false +python-versions = ">=3.8" files = [ {file = "anyio-4.2.0-py3-none-any.whl", hash = "sha256:745843b39e829e108e518c489b31dc757de7d2131d53fac32bd8df268227bfee"}, {file = "anyio-4.2.0.tar.gz", hash = "sha256:e1875bb4b4e2de1669f4bc7869b6d3f54231cdced71605e6e64c9be77e3be50f"}, ] -name = "anyio" -optional = false -python-versions = ">=3.8" -version = "4.2.0" [package.dependencies] -exceptiongroup = {markers = "python_version < \"3.11\"", version = ">=1.0.2"} +exceptiongroup = {version = ">=1.0.2", markers = "python_version < \"3.11\""} idna = ">=2.8" sniffio = ">=1.1" -typing-extensions = {markers = "python_version < \"3.11\"", version = ">=4.1"} +typing-extensions = {version = ">=4.1", markers = "python_version < \"3.11\""} [package.extras] doc = ["Sphinx (>=7)", "packaging", "sphinx-autodoc-typehints (>=1.2.0)", "sphinx-rtd-theme"] @@ -152,26 +147,26 @@ test = ["anyio[trio]", "coverage[toml] (>=7)", "exceptiongroup (>=1.2.0)", "hypo trio = ["trio (>=0.23)"] [[package]] +name = "appnope" +version = "0.1.3" description = "Disable App Nap on macOS >= 10.9" +optional = false +python-versions = "*" files = [ {file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"}, {file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"}, ] -name = "appnope" -optional = false -python-versions = "*" -version = "0.1.3" [[package]] +name = "argon2-cffi" +version = "23.1.0" description = "Argon2 for Python" +optional = false +python-versions = ">=3.7" files = [ {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"}, {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"}, ] -name = "argon2-cffi" -optional = false -python-versions = ">=3.7" -version = "23.1.0" [package.dependencies] argon2-cffi-bindings = "*" @@ -183,7 +178,11 @@ tests = ["hypothesis", "pytest"] typing = ["mypy"] [[package]] +name = "argon2-cffi-bindings" +version = "21.2.0" description = "Low-level CFFI bindings for Argon2" +optional = false +python-versions = ">=3.6" files = [ {file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = 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files = [ {file = "arrow-1.3.0-py3-none-any.whl", hash = "sha256:c728b120ebc00eb84e01882a6f5e7927a53960aa990ce7dd2b10f39005a67f80"}, {file = "arrow-1.3.0.tar.gz", hash = "sha256:d4540617648cb5f895730f1ad8c82a65f2dad0166f57b75f3ca54759c4d67a85"}, ] -name = "arrow" -optional = false -python-versions = ">=3.8" -version = "1.3.0" [package.dependencies] python-dateutil = ">=2.7.0" @@ -239,34 +234,34 @@ doc = ["doc8", "sphinx (>=7.0.0)", "sphinx-autobuild", "sphinx-autodoc-typehints test = ["dateparser (==1.*)", "pre-commit", "pytest", "pytest-cov", "pytest-mock", "pytz (==2021.1)", "simplejson (==3.*)"] [[package]] +name = "astroid" +version = "2.13.5" description = "An abstract syntax tree for Python with inference support." +optional = false +python-versions = ">=3.7.2" files = [ {file = "astroid-2.13.5-py3-none-any.whl", hash = "sha256:6891f444625b6edb2ac798829b689e95297e100ddf89dbed5a8c610e34901501"}, {file = "astroid-2.13.5.tar.gz", hash = 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parsing" +optional = false +python-versions = ">=3.7" files = [ {file = "iniconfig-2.0.0-py3-none-any.whl", hash = "sha256:b6a85871a79d2e3b22d2d1b94ac2824226a63c6b741c88f7ae975f18b6778374"}, {file = "iniconfig-2.0.0.tar.gz", hash = "sha256:2d91e135bf72d31a410b17c16da610a82cb55f6b0477d1a902134b24a455b8b3"}, ] -name = "iniconfig" -optional = false -python-versions = ">=3.7" -version = "2.0.0" [[package]] +name = "ipykernel" +version = "6.29.0" description = "IPython Kernel for Jupyter" +optional = false +python-versions = ">=3.8" files = [ {file = "ipykernel-6.29.0-py3-none-any.whl", hash = "sha256:076663ca68492576f051e4af7720d33f34383e655f2be0d544c8b1c9de915b2f"}, {file = "ipykernel-6.29.0.tar.gz", hash = "sha256:b5dd3013cab7b330df712891c96cd1ab868c27a7159e606f762015e9bf8ceb3f"}, ] -name = "ipykernel" -optional = false -python-versions = ">=3.8" -version = "6.29.0" [package.dependencies] -appnope = {markers = "platform_system == \"Darwin\"", version = "*"} +appnope = {version = "*", markers = "platform_system == \"Darwin\""} comm = ">=0.1.1" debugpy = ">=1.6.5" ipython = ">=7.23.1" @@ -1336,24 +1296,24 @@ pyside6 = ["pyside6"] test = ["flaky", "ipyparallel", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (==0.23.2)", "pytest-cov", "pytest-timeout"] [[package]] +name = "ipython" +version = "8.10.0" description = "IPython: Productive Interactive Computing" +optional = false +python-versions = ">=3.8" files = [ {file = "ipython-8.10.0-py3-none-any.whl", hash = "sha256:b38c31e8fc7eff642fc7c597061fff462537cf2314e3225a19c906b7b0d8a345"}, {file = "ipython-8.10.0.tar.gz", hash = "sha256:b13a1d6c1f5818bd388db53b7107d17454129a70de2b87481d555daede5eb49e"}, ] -name = "ipython" -optional = false -python-versions = ">=3.8" -version = "8.10.0" [package.dependencies] -appnope = {markers = "sys_platform == \"darwin\"", version = "*"} +appnope = {version = "*", markers = "sys_platform == \"darwin\""} backcall = "*" -colorama = {markers = "sys_platform == \"win32\"", version = "*"} +colorama = {version = "*", markers = "sys_platform == \"win32\""} decorator = "*" jedi = ">=0.16" matplotlib-inline = "*" -pexpect = {markers = "sys_platform != \"win32\"", version = ">4.3"} +pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""} pickleshare = "*" prompt-toolkit = ">=3.0.30,<3.1.0" pygments = ">=2.4.0" @@ -1361,7 +1321,7 @@ stack-data = "*" traitlets = ">=5" [package.extras] -all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib (!=3.2.0)", "matplotlib", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"] +all = ["black", "curio", "docrepr", "ipykernel", "ipyparallel", "ipywidgets", "matplotlib", "matplotlib (!=3.2.0)", "nbconvert", "nbformat", "notebook", "numpy (>=1.21)", "pandas", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "qtconsole", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "trio", "typing-extensions"] black = ["black"] doc = ["docrepr", "ipykernel", "matplotlib", "pytest (<7)", "pytest (<7.1)", "pytest-asyncio", "setuptools (>=18.5)", "sphinx (>=1.3)", "sphinx-rtd-theme", "stack-data", "testpath", "typing-extensions"] kernel = ["ipykernel"] @@ -1374,15 +1334,15 @@ test = ["pytest (<7.1)", "pytest-asyncio", "testpath"] test-extra = ["curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.21)", "pandas", "pytest (<7.1)", "pytest-asyncio", "testpath", "trio"] [[package]] +name = "ipywidgets" +version = "8.1.1" description = "Jupyter interactive widgets" +optional = false +python-versions = ">=3.7" files = [ {file = "ipywidgets-8.1.1-py3-none-any.whl", hash = "sha256:2b88d728656aea3bbfd05d32c747cfd0078f9d7e159cf982433b58ad717eed7f"}, {file = "ipywidgets-8.1.1.tar.gz", hash = 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imports." +optional = false +python-versions = ">=3.8.0" files = [ {file = "isort-5.13.2-py3-none-any.whl", hash = "sha256:8ca5e72a8d85860d5a3fa69b8745237f2939afe12dbf656afbcb47fe72d947a6"}, {file = "isort-5.13.2.tar.gz", hash = "sha256:48fdfcb9face5d58a4f6dde2e72a1fb8dcaf8ab26f95ab49fab84c2ddefb0109"}, ] -name = "isort" -optional = false -python-versions = ">=3.8.0" -version = "5.13.2" [package.extras] colors = ["colorama (>=0.4.6)"] [[package]] +name = "jedi" +version = "0.19.1" description = "An autocompletion tool for Python that can be used for text editors." +optional = false +python-versions = ">=3.6" files = [ {file = "jedi-0.19.1-py2.py3-none-any.whl", hash = "sha256:e983c654fe5c02867aef4cdfce5a2fbb4a50adc0af145f70504238f18ef5e7e0"}, {file = "jedi-0.19.1.tar.gz", hash = "sha256:cf0496f3651bc65d7174ac1b7d043eff454892c708a87d1b683e57b569927ffd"}, ] -name = "jedi" -optional = false -python-versions = ">=3.6" -version = "0.19.1" [package.dependencies] parso = ">=0.8.3,<0.9.0" @@ -1442,15 +1402,15 @@ qa = ["flake8 (==5.0.4)", "mypy (==0.971)", "types-setuptools (==67.2.0.1)"] testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"] [[package]] +name = "jinja2" +version = "3.1.3" description = "A very fast and expressive template engine." +optional = false +python-versions = ">=3.7" files = [ {file = "Jinja2-3.1.3-py3-none-any.whl", hash = "sha256:7d6d50dd97d52cbc355597bd845fabfbac3f551e1f99619e39a35ce8c370b5fa"}, {file = "Jinja2-3.1.3.tar.gz", hash = "sha256:ac8bd6544d4bb2c9792bf3a159e80bba8fda7f07e81bc3aed565432d5925ba90"}, ] -name = "jinja2" -optional = false -python-versions = ">=3.7" -version = "3.1.3" [package.dependencies] MarkupSafe = ">=2.0" @@ -1459,98 +1419,98 @@ MarkupSafe = ">=2.0" i18n = ["Babel (>=2.7)"] [[package]] +name = "joblib" +version = "1.3.2" description = "Lightweight pipelining with Python functions" +optional = false +python-versions = ">=3.7" files = [ {file = "joblib-1.3.2-py3-none-any.whl", hash = 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{version = ">=1.3.10", markers = "python_version < \"3.9\""} referencing = ">=0.28.4" -rfc3339-validator = {markers = "extra == \"format-nongpl\"", optional = true, version = "*"} -rfc3986-validator = {markers = "extra == \"format-nongpl\"", optional = true, version = ">0.1.0"} +rfc3339-validator = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +rfc3986-validator = {version = ">0.1.0", optional = true, markers = "extra == \"format-nongpl\""} rpds-py = ">=0.7.1" -uri-template = {markers = "extra == \"format-nongpl\"", optional = true, version = "*"} -webcolors = {markers = "extra == \"format-nongpl\"", optional = true, version = ">=1.11"} +uri-template = {version = "*", optional = true, markers = "extra == \"format-nongpl\""} +webcolors = {version = ">=1.11", optional = true, markers = "extra == \"format-nongpl\""} [package.extras] format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"] format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"] [[package]] +name = "jsonschema-specifications" +version = "2023.12.1" description = "The JSON Schema meta-schemas and vocabularies, exposed as a Registry" +optional = false +python-versions = ">=3.8" files = [ {file = "jsonschema_specifications-2023.12.1-py3-none-any.whl", hash = "sha256:87e4fdf3a94858b8a2ba2778d9ba57d8a9cafca7c7489c46ba0d30a8bc6a9c3c"}, {file = "jsonschema_specifications-2023.12.1.tar.gz", hash = "sha256:48a76787b3e70f5ed53f1160d2b81f586e4ca6d1548c5de7085d1682674764cc"}, ] -name = "jsonschema-specifications" -optional = false -python-versions = ">=3.8" -version = "2023.12.1" [package.dependencies] -importlib-resources = {markers = "python_version < \"3.9\"", version = ">=1.4.0"} +importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""} referencing = ">=0.31.0" [[package]] +name = "jupyter" +version = "1.0.0" description = "Jupyter metapackage. Install all the Jupyter components in one go." +optional = false +python-versions = "*" files = [ {file = "jupyter-1.0.0-py2.py3-none-any.whl", hash = "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78"}, {file = "jupyter-1.0.0.tar.gz", hash = "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"}, {file = "jupyter-1.0.0.zip", hash = "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"}, ] -name = "jupyter" -optional = false -python-versions = "*" -version = "1.0.0" [package.dependencies] ipykernel = "*" @@ -1561,18 +1521,18 @@ notebook = "*" qtconsole = "*" [[package]] +name = "jupyter-client" +version = "8.6.0" description = "Jupyter protocol implementation and client libraries" +optional = false +python-versions = ">=3.8" files = [ {file = "jupyter_client-8.6.0-py3-none-any.whl", hash = "sha256:909c474dbe62582ae62b758bca86d6518c85234bdee2d908c778db6d72f39d99"}, {file = "jupyter_client-8.6.0.tar.gz", hash = "sha256:0642244bb83b4764ae60d07e010e15f0e2d275ec4e918a8f7b80fbbef3ca60c7"}, ] -name = "jupyter-client" -optional = false -python-versions = ">=3.8" -version = "8.6.0" [package.dependencies] -importlib-metadata = {markers = "python_version < \"3.10\"", version = ">=4.8.3"} +importlib-metadata = {version = ">=4.8.3", markers = "python_version < \"3.10\""} jupyter-core = ">=4.12,<5.0.dev0 || >=5.1.dev0" python-dateutil = ">=2.8.2" pyzmq = ">=23.0" @@ -1584,15 +1544,15 @@ docs = ["ipykernel", "myst-parser", "pydata-sphinx-theme", "sphinx (>=4)", "sphi test = ["coverage", "ipykernel (>=6.14)", "mypy", "paramiko", "pre-commit", "pytest", "pytest-cov", "pytest-jupyter[client] (>=0.4.1)", "pytest-timeout"] [[package]] +name = "jupyter-console" +version = "6.6.3" description = "Jupyter terminal console" +optional = false +python-versions = ">=3.7" files = [ {file = "jupyter_console-6.6.3-py3-none-any.whl", hash = "sha256:309d33409fcc92ffdad25f0bcdf9a4a9daa61b6f341177570fdac03de5352485"}, {file = "jupyter_console-6.6.3.tar.gz", hash = "sha256:566a4bf31c87adbfadf22cdf846e3069b59a71ed5da71d6ba4d8aaad14a53539"}, ] -name = "jupyter-console" -optional = false -python-versions = ">=3.7" -version = "6.6.3" [package.dependencies] ipykernel = ">=6.14" @@ -1608,19 +1568,19 @@ traitlets = ">=5.4" test = ["flaky", "pexpect", "pytest"] [[package]] +name = "jupyter-core" +version = "5.7.1" description = "Jupyter core package. A base package on which Jupyter projects rely." +optional = false +python-versions = ">=3.8" files = [ {file = "jupyter_core-5.7.1-py3-none-any.whl", hash = "sha256:c65c82126453a723a2804aa52409930434598fd9d35091d63dfb919d2b765bb7"}, {file = "jupyter_core-5.7.1.tar.gz", hash = "sha256:de61a9d7fc71240f688b2fb5ab659fbb56979458dc66a71decd098e03c79e218"}, ] -name = "jupyter-core" -optional = false -python-versions = ">=3.8" -version = "5.7.1" [package.dependencies] platformdirs = ">=2.5" -pywin32 = {markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\"", version = ">=300"} +pywin32 = {version = ">=300", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""} traitlets = ">=5.3" [package.extras] @@ -1628,18 +1588,18 @@ docs = ["myst-parser", "pydata-sphinx-theme", "sphinx-autodoc-typehints", "sphin test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"] [[package]] +name = "jupyter-events" +version = "0.9.0" description = "Jupyter Event System library" +optional = false +python-versions = ">=3.8" files = [ {file = "jupyter_events-0.9.0-py3-none-any.whl", hash = "sha256:d853b3c10273ff9bc8bb8b30076d65e2c9685579db736873de6c2232dde148bf"}, {file = "jupyter_events-0.9.0.tar.gz", hash = "sha256:81ad2e4bc710881ec274d31c6c50669d71bbaa5dd9d01e600b56faa85700d399"}, ] -name = "jupyter-events" -optional = false -python-versions = ">=3.8" -version = "0.9.0" [package.dependencies] -jsonschema = {extras = ["format-nongpl"], version = ">=4.18.0"} +jsonschema = {version = ">=4.18.0", extras = ["format-nongpl"]} python-json-logger = ">=2.0.4" pyyaml = ">=5.3" referencing = "*" @@ -1653,30 +1613,30 @@ docs = ["jupyterlite-sphinx", "myst-parser", "pydata-sphinx-theme", "sphinxcontr test = ["click", "pre-commit", "pytest (>=7.0)", "pytest-asyncio (>=0.19.0)", "pytest-console-scripts", "rich"] [[package]] +name = "jupyter-lsp" +version = "2.2.2" description = "Multi-Language Server WebSocket proxy for Jupyter 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"e2bd79c371ca89fac9c2d487015226085d5b67eef7e86941360f8f7b809dec2c" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml index 76bc6a1227dcb..9a1359112fd90 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-clip/pyproject.toml @@ -24,14 +24,15 @@ description = "llama-index embeddings clip integration" license = "MIT" name = "llama-index-embeddings-clip" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" llama-index-core = "^0.10.1" -clip = "^0.2.0" torch = "^2.1.2" pillow = "^10.2.0" +torchvision = "^0.17.0" +ftfy = "^6.1.3" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/.gitignore b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/Makefile b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/README.md b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/README.md new file mode 100644 index 0000000000000..31b97d6460804 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/README.md @@ -0,0 +1 @@ +# LlamaIndex Embeddings Integration: Huggingface Optimum Intel diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/__init__.py new file mode 100644 index 0000000000000..0be24545adf9f --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/__init__.py @@ -0,0 +1,3 @@ +from llama_index.embeddings.huggingface_optimum_intel.base import IntelEmbedding + +__all__ = ["IntelEmbedding"] diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py new file mode 100644 index 0000000000000..719a559e5a870 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/llama_index/embeddings/huggingface_optimum_intel/base.py @@ -0,0 +1,156 @@ +from typing import Any, List, Optional + +from llama_index.core.base.embeddings.base import ( + DEFAULT_EMBED_BATCH_SIZE, + BaseEmbedding, +) +from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.core.callbacks import CallbackManager +from llama_index.core.utils import infer_torch_device +from llama_index.embeddings.huggingface.utils import format_query, format_text +from transformers import AutoTokenizer + + +class IntelEmbedding(BaseEmbedding): + folder_name: str = Field(description="Folder name to load from.") + max_length: int = Field(description="Maximum length of input.") + pooling: str = Field(description="Pooling strategy. One of ['cls', 'mean'].") + normalize: str = Field(default=True, description="Normalize embeddings or not.") + query_instruction: Optional[str] = Field( + description="Instruction to prepend to query text." + ) + text_instruction: Optional[str] = Field( + description="Instruction to prepend to text." + ) + cache_folder: Optional[str] = Field( + description="Cache folder for huggingface files." + ) + + _model: Any = PrivateAttr() + _tokenizer: Any = PrivateAttr() + _device: Any = PrivateAttr() + + def __init__( + self, + folder_name: str, + pooling: str = "cls", + max_length: Optional[int] = None, + normalize: bool = True, + query_instruction: Optional[str] = None, + text_instruction: Optional[str] = None, + model: Optional[Any] = None, + tokenizer: Optional[Any] = None, + embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE, + callback_manager: Optional[CallbackManager] = None, + device: Optional[str] = None, + ): + try: + from optimum.intel import INCModel + except ImportError: + raise ImportError( + "Optimum-Intel requires the following dependencies; please install with " + "`pip install optimum[exporters] optimum-intel neural-compressor`." + ) + + self._model = model or INCModel.from_pretrained(folder_name) + self._tokenizer = tokenizer or AutoTokenizer.from_pretrained(folder_name) + self._device = device or infer_torch_device() + + if max_length is None: + try: + max_length = int(self._model.config.max_position_embeddings) + except Exception: + raise ValueError( + "Unable to find max_length from model config. " + "Please provide max_length." + ) + + if pooling not in ["cls", "mean"]: + raise ValueError(f"Pooling {pooling} not supported.") + + super().__init__( + embed_batch_size=embed_batch_size, + callback_manager=callback_manager, + folder_name=folder_name, + max_length=max_length, + pooling=pooling, + normalize=normalize, + query_instruction=query_instruction, + text_instruction=text_instruction, + ) + + @classmethod + def class_name(cls) -> str: + return "IntelEmbedding" + + def _mean_pooling(self, model_output: Any, attention_mask: Any) -> Any: + """Mean Pooling - Take attention mask into account for correct averaging.""" + import torch + + # First element of model_output contains all token embeddings + token_embeddings = model_output[0] + input_mask_expanded = ( + attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float() + ) + return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp( + input_mask_expanded.sum(1), min=1e-9 + ) + + def _cls_pooling(self, model_output: list) -> Any: + """Use the CLS token as the pooling token.""" + if isinstance(model_output, dict): + token_embeddings = model_output["last_hidden_state"] + else: + token_embeddings = model_output[0] + return token_embeddings[:, 0] + + def _embed(self, sentences: List[str]) -> List[List[float]]: + """Embed sentences.""" + encoded_input = self._tokenizer( + sentences, + padding=True, + max_length=self.max_length, + truncation=True, + return_tensors="pt", + ) + import torch + + with torch.inference_mode(), torch.cpu.amp.autocast(): + model_output = self._model(**encoded_input) + + if self.pooling == "cls": + embeddings = self._cls_pooling(model_output) + else: + embeddings = self._mean_pooling( + model_output, encoded_input["attention_mask"].to(self._device) + ) + + if self.normalize: + embeddings = torch.nn.functional.normalize(embeddings, p=2, dim=1) + + return embeddings.tolist() + + def _get_query_embedding(self, query: str) -> List[float]: + """Get query embedding.""" + query = format_query(query, self.model_name, self.query_instruction) + return self._embed([query])[0] + + async def _aget_query_embedding(self, query: str) -> List[float]: + """Get query embedding async.""" + return self._get_query_embedding(query) + + async def _aget_text_embedding(self, text: str) -> List[float]: + """Get text embedding async.""" + return self._get_text_embedding(text) + + def _get_text_embedding(self, text: str) -> List[float]: + """Get text embedding.""" + text = format_text(text, self.model_name, self.text_instruction) + return self._embed([text])[0] + + def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: + """Get text embeddings.""" + texts = [ + format_text(text, self.model_name, self.text_instruction) for text in texts + ] + return self._embed(texts) diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/poetry.lock 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"pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<3.12" +content-hash = "8c0c752ef88d7c324a94b52d4f6d58190c8df8a1ad45582a6face8855ed05c59" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml new file mode 100644 index 0000000000000..18a5b560ff4cc --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/pyproject.toml @@ -0,0 +1,66 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +classes = ["IntelEmbedding"] +contains_example = false +import_path = "llama_index.embeddings.huggingface_optimum_intel" + +[tool.mypy] +disallow_untyped_defs = true +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Your Name "] +description = "llama-index embeddings Optimum Intel integration" +license = "MIT" +name = "llama-index-embeddings-huggingface-optimum-intel" +readme = "README.md" +version = "0.1.1" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +llama-index-core = "^0.10.1" +llama-index-embeddings-huggingface = "^0.1.1" +optimum-intel = "^1.14.0" +neural-compressor = "^2.4.1" + +[tool.poetry.dependencies.optimum] +extras = ["exporters"] +version = "^1.16.2" + +[tool.poetry.group.dev.dependencies] +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" +types-setuptools = "67.1.0.0" + +[tool.poetry.group.dev.dependencies.black] +extras = ["jupyter"] +version = "<=23.9.1,>=23.7.0" + +[tool.poetry.group.dev.dependencies.codespell] +extras = ["toml"] +version = ">=v2.2.6" + +[[tool.poetry.packages]] +include = "llama_index/" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/BUILD b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/__init__.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/test_embeddings_huggingface_optimum.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/test_embeddings_huggingface_optimum.py new file mode 100644 index 0000000000000..636665470e2cf --- /dev/null +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface-optimum-intel/tests/test_embeddings_huggingface_optimum.py @@ -0,0 +1,7 @@ +from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.embeddings.huggingface_optimum_intel import IntelEmbedding + + +def test_optimum_intel_embedding_class(): + names_of_base_classes = [b.__name__ for b in IntelEmbedding.__mro__] + assert BaseEmbedding.__name__ in names_of_base_classes diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py index 3c401fce3cffe..6fd28ae8f4e95 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/llama_index/embeddings/huggingface/base.py @@ -212,37 +212,19 @@ class HuggingFaceInferenceAPIEmbedding(BaseEmbedding): # type: ignore[misc] pooling: Optional[Pooling] = Field( default=Pooling.CLS, - description=( - "Optional pooling technique to use with embeddings capability, if" - " the model's raw output needs pooling." - ), + description="Pooling strategy. If None, the model's default pooling is used.", ) query_instruction: Optional[str] = Field( - default=None, - description=( - "Instruction to prepend during query embedding." - " Use of None means infer the instruction based on the model." - " Use of empty string will defeat instruction prepending entirely." - ), + default=None, description="Instruction to prepend during query embedding." ) text_instruction: Optional[str] = Field( - default=None, - description=( - "Instruction to prepend during text embedding." - " Use of None means infer the instruction based on the model." - " Use of empty string will defeat instruction prepending entirely." - ), + default=None, description="Instruction to prepend during text embedding." ) # Corresponds with huggingface_hub.InferenceClient model_name: Optional[str] = Field( default=None, - description=( - "The model to run inference with. Can be a model id hosted on the Hugging" - " Face Hub, e.g. bigcode/starcoder or a URL to a deployed Inference" - " Endpoint. Defaults to None, in which case a recommended model is" - " automatically selected for the task (see Field below)." - ), + description="Hugging Face model name. If None, the task will be used.", ) token: Union[str, bool, None] = Field( default=None, diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml index d1bfd014a981e..8c007e8729df6 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-huggingface/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index embeddings huggingface integration" license = "MIT" name = "llama-index-embeddings-huggingface" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py index 1a38b9f1a8bd1..1a2fb5c38653d 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/llama_index/embeddings/nomic/base.py @@ -1,10 +1,23 @@ from enum import Enum -from typing import Any, List, Optional +from typing import Any, List, Optional, Union -from llama_index.core.base.embeddings.base import BaseEmbedding +from llama_index.core.base.embeddings.base import ( + BaseEmbedding, + DEFAULT_EMBED_BATCH_SIZE, +) from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.callbacks import CallbackManager +from llama_index.embeddings.huggingface import HuggingFaceEmbedding + +from llama_index.core.bridge.pydantic import Field, PrivateAttr +from llama_index.embeddings.huggingface.pooling import Pooling +import torch +import logging + +DEFAULT_HUGGINGFACE_LENGTH = 512 +logger = logging.getLogger(__name__) + class NomicAITaskType(str, Enum): SEARCH_QUERY = "search_query" @@ -27,6 +40,7 @@ class NomicEmbedding(BaseEmbedding): # Instance variables initialized via Pydantic's mechanism query_task_type: Optional[str] = Field(description="Query Embedding prefix") document_task_type: Optional[str] = Field(description="Document Embedding prefix") + dimensionality: Optional[int] = Field(description="Dimension of the Embedding") model_name: str = Field(description="Embedding model name") _model: Any = PrivateAttr() @@ -38,6 +52,7 @@ def __init__( callback_manager: Optional[CallbackManager] = None, query_task_type: Optional[str] = "search_query", document_task_type: Optional[str] = "search_document", + dimensionality: Optional[int] = 768, **kwargs: Any, ) -> None: if query_task_type not in TASK_TYPES or document_task_type not in TASK_TYPES: @@ -63,12 +78,14 @@ def __init__( _model=embed, query_task_type=query_task_type, document_task_type=document_task_type, + dimensionality=dimensionality, **kwargs, ) self._model = embed self.model_name = model_name self.query_task_type = query_task_type self.document_task_type = document_task_type + self.dimensionality = dimensionality @classmethod def class_name(cls) -> str: @@ -78,7 +95,12 @@ def _embed( self, texts: List[str], task_type: Optional[str] = None ) -> List[List[float]]: """Embed sentences using NomicAI.""" - result = self._model.text(texts, model=self.model_name, task_type=task_type) + result = self._model.text( + texts, + model=self.model_name, + task_type=task_type, + dimensionality=self.dimensionality, + ) return result["embeddings"] def _get_query_embedding(self, query: str) -> List[float]: @@ -100,3 +122,106 @@ async def _aget_text_embedding(self, text: str) -> List[float]: def _get_text_embeddings(self, texts: List[str]) -> List[List[float]]: """Get text embeddings.""" return self._embed(texts, task_type=self.document_task_type) + + +class NomicHFEmbedding(HuggingFaceEmbedding): + tokenizer_name: str = Field(description="Tokenizer name from HuggingFace.") + max_length: int = Field( + default=DEFAULT_HUGGINGFACE_LENGTH, description="Maximum length of input.", gt=0 + ) + pooling: Pooling = Field(default=Pooling.MEAN, description="Pooling strategy.") + normalize: bool = Field(default=True, description="Normalize embeddings or not.") + query_instruction: Optional[str] = Field( + description="Instruction to prepend to query text." + ) + text_instruction: Optional[str] = Field( + description="Instruction to prepend to text." + ) + cache_folder: Optional[str] = Field( + description="Cache folder for huggingface files." + ) + dimensionality: Optional[int] = Field(description="Dimensionality of embedding") + + _model: Any = PrivateAttr() + _tokenizer: Any = PrivateAttr() + _device: str = PrivateAttr() + + def __init__( + self, + model_name: Optional[str] = None, + tokenizer_name: Optional[str] = None, + pooling: Union[str, Pooling] = "cls", + max_length: Optional[int] = None, + query_instruction: Optional[str] = None, + text_instruction: Optional[str] = None, + normalize: bool = True, + model: Optional[Any] = None, + tokenizer: Optional[Any] = None, + embed_batch_size: int = DEFAULT_EMBED_BATCH_SIZE, + cache_folder: Optional[str] = None, + trust_remote_code: bool = False, + device: Optional[str] = None, + callback_manager: Optional[CallbackManager] = None, + dimensionality: int = 768, + ): + super().__init__( + model_name=model_name, + tokenizer_name=tokenizer_name, + pooling=pooling, + max_length=max_length, + query_instruction=query_instruction, + text_instruction=text_instruction, + normalize=normalize, + model=model, + tokenizer=tokenizer, + embed_batch_size=embed_batch_size, + cache_folder=cache_folder, + trust_remote_code=trust_remote_code, + device=device, + callback_manager=callback_manager, + ) + self.dimensionality = dimensionality + self._model.eval() + + def _embed(self, sentences: List[str]) -> List[List[float]]: + """Embed sentences.""" + encoded_input = self._tokenizer( + sentences, + padding=True, + max_length=self.max_length, + truncation=True, + return_tensors="pt", + ) + + # pop token_type_ids + encoded_input.pop("token_type_ids", None) + + # move tokenizer inputs to device + encoded_input = { + key: val.to(self._device) for key, val in encoded_input.items() + } + + with torch.no_grad(): + model_output = self._model(**encoded_input) + + if self.pooling == Pooling.CLS: + context_layer: "torch.Tensor" = model_output[0] + embeddings = self.pooling.cls_pooling(context_layer) + else: + embeddings = self._mean_pooling( + token_embeddings=model_output[0], + attention_mask=encoded_input["attention_mask"], + ) + + if self.normalize: + import torch.nn.functional as F + + if self.model_name == "nomic-ai/nomic-embed-text-v1.5": + emb_ln = F.layer_norm( + embeddings, normalized_shape=(embeddings.shape[1],) + ) + embeddings = emb_ln[:, : self.dimensionality] + + embeddings = F.normalize(embeddings, p=2, dim=1) + + return embeddings.tolist() diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml index 470a87befd2b3..e6e3b0c19139a 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-nomic/pyproject.toml @@ -8,7 +8,7 @@ check-hidden = true skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" [tool.llamahub] -classes = ["NomicEmbedding"] +classes = ["NomicEmbedding", "NomicHFEmbedding"] contains_example = false import_path = "llama_index.embeddings.nomic" @@ -24,11 +24,14 @@ description = "llama-index embeddings nomic integration" license = "MIT" name = "llama-index-embeddings-nomic" readme = "README.md" -version = "0.1.1" +version = "0.1.3" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" llama-index-core = "^0.10.1" +llama-index-embeddings-huggingface = "^0.1.0" +einops = "^0.7.0" +nomic = "^3.0.12" [tool.poetry.group.dev.dependencies] ipython = "8.10.0" diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py b/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py index 8dee0c003e084..e83fe3f7bb385 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py +++ b/llama-index-integrations/embeddings/llama-index-embeddings-openai/llama_index/embeddings/openai/base.py @@ -8,6 +8,8 @@ from llama_index.core.bridge.pydantic import Field, PrivateAttr from llama_index.core.callbacks.base import CallbackManager from llama_index.embeddings.openai.utils import ( + DEFAULT_OPENAI_API_BASE, + DEFAULT_OPENAI_API_VERSION, create_retry_decorator, resolve_openai_credentials, ) @@ -244,8 +246,12 @@ class OpenAIEmbedding(BaseEmbedding): ) api_key: str = Field(description="The OpenAI API key.") - api_base: str = Field(description="The base URL for OpenAI API.") - api_version: str = Field(description="The version for OpenAI API.") + api_base: str = Field( + default=DEFAULT_OPENAI_API_BASE, description="The base URL for OpenAI API." + ) + api_version: str = Field( + default=DEFAULT_OPENAI_API_VERSION, description="The version for OpenAI API." + ) max_retries: int = Field( default=10, description="Maximum number of retries.", gte=0 diff --git a/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml b/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml index a3d6626d28e14..d67c45c670430 100644 --- a/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml +++ b/llama-index-integrations/embeddings/llama-index-embeddings-openai/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index embeddings openai integration" license = "MIT" name = "llama-index-embeddings-openai" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/.gitignore b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/BUILD b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/BUILD new file mode 100644 index 0000000000000..94c4d19852dde --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/BUILD @@ -0,0 +1,6 @@ +python_sources() + +poetry_requirements( + name="poetry", + module_mapping={"llamaindex-py-client": ["llama_index_client"]}, +) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/Makefile b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/README.md b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/README.md new file mode 100644 index 0000000000000..f979055e18cb9 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/README.md @@ -0,0 +1 @@ +# LlamaIndex Indices Integration: Llama-Cloud diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/BUILD b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/__init__.py b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/__init__.py new file mode 100644 index 0000000000000..2b754e097c1e9 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/__init__.py @@ -0,0 +1,7 @@ +from llama_index.indices.managed.llama_cloud.base import LlamaCloudIndex +from llama_index.indices.managed.llama_cloud.retriever import LlamaCloudRetriever + +__all__ = [ + "LlamaCloudIndex", + "LlamaCloudRetriever", +] diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/__pycache__/__init__.cpython-311.pyc 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a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/__pycache__/retriever.cpython-311.pyc b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/__pycache__/retriever.cpython-311.pyc new file mode 100644 index 0000000000000..e694d11789e60 Binary files /dev/null and b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/__pycache__/retriever.cpython-311.pyc differ diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/base.py b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/base.py new file mode 100644 index 0000000000000..25ecd1f31893c --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/base.py @@ -0,0 +1,224 @@ +"""Managed index. + +A managed Index - where the index is accessible via some API that +interfaces a managed service. + +""" +import os +import time +from typing import Any, List, Optional, Sequence, Type + +from llama_index_client import PipelineType, ProjectCreate, StatusEnum + +from llama_index.core.base.base_query_engine import BaseQueryEngine +from llama_index.core.base.base_retriever import BaseRetriever +from llama_index.core.constants import DEFAULT_APP_URL, DEFAULT_PROJECT_NAME +from llama_index.core.indices.managed.base import BaseManagedIndex +from llama_index.core.ingestion.api_utils import ( + default_transformations, + get_aclient, + get_client, + get_pipeline_create, +) +from llama_index.core.schema import BaseNode, Document, TransformComponent + + +class LlamaCloudIndex(BaseManagedIndex): + """LlamaIndex Platform Index.""" + + def __init__( + self, + name: str, + nodes: Optional[List[BaseNode]] = None, + transformations: Optional[List[TransformComponent]] = None, + timeout: int = 60, + project_name: str = DEFAULT_PROJECT_NAME, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + app_url: Optional[str] = None, + show_progress: bool = False, + **kwargs: Any, + ) -> None: + """Initialize the Platform Index.""" + self.name = name + self.project_name = project_name + self.transformations = transformations or [] + + if nodes is not None: + # TODO: How to handle uploading nodes without running transforms on them? + raise ValueError("LlamaCloudIndex does not support nodes on initialization") + + self._client = get_client(api_key, base_url, app_url, timeout) + self._aclient = get_aclient(api_key, base_url, app_url, timeout) + + self._api_key = api_key + self._base_url = base_url + self._app_url = app_url + self._timeout = timeout + self._show_progress = show_progress + + @classmethod + def from_documents( # type: ignore + cls: Type["LlamaCloudIndex"], + documents: List[Document], + name: str, + transformations: Optional[List[TransformComponent]] = None, + project_name: str = DEFAULT_PROJECT_NAME, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + app_url: Optional[str] = None, + timeout: int = 60, + verbose: bool = False, + **kwargs: Any, + ) -> "LlamaCloudIndex": + """Build a Vectara index from a sequence of documents.""" + app_url = app_url or os.environ.get("LLAMA_CLOUD_APP_URL", DEFAULT_APP_URL) + client = get_client(api_key, base_url, app_url, timeout) + + pipeline_create = get_pipeline_create( + name, + client, + PipelineType.MANAGED, + project_name=project_name, + transformations=transformations or default_transformations(), + input_nodes=documents, + ) + + project = client.project.upsert_project( + request=ProjectCreate(name=project_name) + ) + if project.id is None: + raise ValueError(f"Failed to create/get project {project_name}") + + if verbose: + print(f"Created project {project.id} with name {project.name}") + + pipeline = client.project.upsert_pipeline_for_project( + project_id=project.id, request=pipeline_create + ) + if pipeline.id is None: + raise ValueError(f"Failed to create/get pipeline {name}") + + if verbose: + print(f"Created pipeline {pipeline.id} with name {pipeline.name}") + + # TODO: remove when sourabh's PR is merged + # kick off data source execution + execution_ids = [] + data_source_ids = [data_source.id for data_source in pipeline.data_sources] + for data_source in pipeline.data_sources: + execution = client.data_source.create_data_source_execution( + data_source_id=data_source.id + ) + execution_ids.append(execution.id) + + if verbose: + print("Loading data: ", end="") + + is_done = False + while not is_done: + statuses = [] + for data_source_id, execution_id in zip(data_source_ids, execution_ids): + execution = client.data_source.get_data_source_execution( + data_source_id=data_source_id, + data_source_load_execution_id=execution_id, + ) + statuses.append(execution.status) + + if all(status == StatusEnum.SUCCESS for status in statuses): + is_done = True + if verbose: + print("Done!") + elif any( + status in [StatusEnum.ERROR, StatusEnum.CANCELED] for status in statuses + ): + raise ValueError( + f"Data source execution failed with statuses {statuses}!" + ) + else: + if verbose: + print(".", end="") + time.sleep(0.5) + + # kick off ingestion + execution = client.pipeline.run_managed_pipeline_ingestion( + pipeline_id=pipeline.id + ) + ingestion_id = execution.id + + if verbose: + print("Running ingestion: ", end="") + + is_done = False + while not is_done: + ingestion = client.pipeline.get_managed_ingestion_execution( + pipeline_id=pipeline.id, managed_pipeline_ingestion_id=ingestion_id + ) + + if ingestion.status == StatusEnum.SUCCESS: + is_done = True + if verbose: + print("Done!") + elif ingestion.status in [StatusEnum.ERROR, StatusEnum.CANCELED]: + raise ValueError(f"Ingestion failed with status {ingestion.status}") + else: + if verbose: + print(".", end="") + time.sleep(0.5) + + print(f"Find your index at {app_url}/project/{project.id}/deploy/{pipeline.id}") + + return cls( + name, + transformations=transformations, + project_name=project_name, + api_key=api_key, + base_url=base_url, + app_url=app_url, + timeout=timeout, + **kwargs, + ) + + def as_retriever(self, **kwargs: Any) -> BaseRetriever: + """Return a Retriever for this managed index.""" + from llama_index.indices.managed.llama_cloud.retriever import ( + LlamaCloudRetriever, + ) + + similarity_top_k = kwargs.pop("similarity_top_k", None) + dense_similarity_top_k = kwargs.pop("dense_similarity_top_k", None) + if similarity_top_k is not None: + dense_similarity_top_k = similarity_top_k + + return LlamaCloudRetriever( + self.name, + project_name=self.project_name, + api_key=self._api_key, + base_url=self._base_url, + app_url=self._app_url, + timeout=self._timeout, + dense_similarity_top_k=dense_similarity_top_k, + **kwargs, + ) + + def as_query_engine(self, **kwargs: Any) -> BaseQueryEngine: + from llama_index.core.query_engine.retriever_query_engine import ( + RetrieverQueryEngine, + ) + + kwargs["retriever"] = self.as_retriever(**kwargs) + return RetrieverQueryEngine.from_args(**kwargs) + + def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: + """Insert a set of documents (each a node).""" + raise NotImplementedError("_insert not implemented for LlamaCloudIndex.") + + def delete_ref_doc( + self, ref_doc_id: str, delete_from_docstore: bool = False, **delete_kwargs: Any + ) -> None: + """Delete a document and it's nodes by using ref_doc_id.""" + raise NotImplementedError("delete_ref_doc not implemented for LlamaCloudIndex.") + + def update_ref_doc(self, document: Document, **update_kwargs: Any) -> None: + """Update a document and it's corresponding nodes.""" + raise NotImplementedError("update_ref_doc not implemented for LlamaCloudIndex.") diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/retriever.py b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/retriever.py new file mode 100644 index 0000000000000..28fa4f1fcbb99 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/llama_index/indices/managed/llama_cloud/retriever.py @@ -0,0 +1,116 @@ +from typing import Any, Dict, List, Optional + +from llama_index_client import TextNodeWithScore +from llama_index_client.resources.pipeline.client import OMIT + +from llama_index.core.base.base_retriever import BaseRetriever +from llama_index.core.constants import DEFAULT_PROJECT_NAME +from llama_index.core.ingestion.api_utils import get_aclient, get_client +from llama_index.core.schema import NodeWithScore, QueryBundle, TextNode + + +class LlamaCloudRetriever(BaseRetriever): + def __init__( + self, + name: str, + project_name: str = DEFAULT_PROJECT_NAME, + dense_similarity_top_k: Optional[int] = None, + sparse_similarity_top_k: Optional[int] = None, + enable_reranking: Optional[bool] = None, + rerank_top_n: Optional[int] = None, + alpha: Optional[float] = None, + search_filters: Optional[Dict[str, List[Any]]] = None, + api_key: Optional[str] = None, + base_url: Optional[str] = None, + app_url: Optional[str] = None, + timeout: int = 60, + **kwargs: Any, + ) -> None: + """Initialize the Platform Retriever.""" + self.name = name + self.project_name = project_name + self._client = get_client(api_key, base_url, app_url, timeout) + self._aclient = get_aclient(api_key, base_url, app_url, timeout) + + projects = self._client.project.list_projects(project_name=project_name) + if len(projects) == 0: + raise ValueError(f"No project found with name {project_name}") + + self._dense_similarity_top_k = dense_similarity_top_k or OMIT + self._sparse_similarity_top_k = sparse_similarity_top_k or OMIT + self._enable_reranking = enable_reranking or OMIT + self._rerank_top_n = rerank_top_n or OMIT + self._alpha = alpha or OMIT + self._search_filters = search_filters or OMIT + + super().__init__(**kwargs) + + def _result_nodes_to_node_with_score( + self, result_nodes: List[TextNodeWithScore] + ) -> List[NodeWithScore]: + nodes = [] + for res in result_nodes: + text_node = TextNode.parse_obj(res.node.dict()) + nodes.append(NodeWithScore(node=text_node, score=res.score)) + + return nodes + + def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: + """Retrieve from the platform.""" + pipelines = self._client.pipeline.search_pipelines( + project_name=self.project_name, pipeline_name=self.name + ) + if len(pipelines) != 1: + raise ValueError( + f"Unknown index name {self.name}. Please confirm a " + "managed index with this name exists." + ) + pipeline = pipelines[0] + + if pipeline.id is None: + raise ValueError( + f"No pipeline found with name {self.name} in project {self.project_name}" + ) + + results = self._client.pipeline.run_search( + query=query_bundle.query_str, + pipeline_id=pipeline.id, + dense_similarity_top_k=self._dense_similarity_top_k, + sparse_similarity_top_k=self._sparse_similarity_top_k, + enable_reranking=self._enable_reranking, + rerank_top_n=self._rerank_top_n, + alpha=self._alpha, + search_filters=self._search_filters, + ) + + result_nodes = results.retrieval_nodes + + return self._result_nodes_to_node_with_score(result_nodes) + + async def _aretrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: + """Asynchronously retrieve from the platform.""" + pipelines = await self._aclient.pipeline.search_pipelines( + project_name=self.project_name, pipeline_name=self.name + ) + assert len(pipelines) == 1 + pipeline = pipelines[0] + + if pipeline.id is None: + raise ValueError( + f"No pipeline found with name {self.name} in project {self.project_name}" + ) + + results = await self._aclient.pipeline.run_search( + query=query_bundle.query_str, + pipeline_id=pipeline.id, + dense_similarity_top_k=self._dense_similarity_top_k, + sparse_similarity_top_k=self._sparse_similarity_top_k, + enable_reranking=self._enable_reranking, + rerank_top_n=self._rerank_top_n, + alpha=self._alpha, + search_filters=self._search_filters, + ) + + result_nodes = results.retrieval_nodes + + return self._result_nodes_to_node_with_score(result_nodes) diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/poetry.lock b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/poetry.lock new file mode 100644 index 0000000000000..2ffa4d4436172 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/poetry.lock @@ -0,0 +1,4474 @@ +# This file is automatically 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(>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<3.12" +content-hash = "7ab69eb4dda231058ab17bf5c4462218f11b773266c08490f1c81341ad344314" diff --git a/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml new file mode 100644 index 0000000000000..ee2ab768920a3 --- /dev/null +++ b/llama-index-integrations/indices/llama-index-indices-managed-llama-cloud/pyproject.toml @@ -0,0 +1,50 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Logan Markewich "] +description = "llama-index indices llama-cloud integration" +license = "MIT" +name = "llama-index-indices-llama-cloud" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +llama-index-core = "^0.10.0" +llamaindex-py-client = "^0.1.9" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml index a76c0adfc1576..8620cd2ef1fee 100644 --- a/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-nvidia-tensorrt/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index llms nvidia tensorrt integration" license = "MIT" name = "llama-index-llms-nvidia-tensorrt" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py b/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py index 7face5bc26418..ed76fec77c1fa 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py +++ b/llama-index-integrations/llms/llama-index-llms-openai-like/tests/test_openai_like.py @@ -140,7 +140,6 @@ def test_serialization() -> None: serialized = llm.to_dict() # Check OpenAI base class specifics - assert "api_key" not in serialized assert serialized["max_tokens"] == 42 # Check OpenAILike subclass specifics assert serialized["context_window"] == 43 diff --git a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py index 6ac3fc7232aa8..1a8da01a3312e 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py +++ b/llama-index-integrations/llms/llama-index-llms-openai/llama_index/llms/openai/base.py @@ -111,7 +111,7 @@ class OpenAI(LLM): ), ) - api_key: str = Field(default=None, description="The OpenAI API key.", exclude=True) + api_key: str = Field(default=None, description="The OpenAI API key.") api_base: str = Field(description="The base URL for OpenAI API.") api_version: str = Field(description="The API version for OpenAI API.") diff --git a/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml index 7360eec55d510..a73ec5a7109c9 100644 --- a/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-openai/pyproject.toml @@ -8,7 +8,7 @@ check-hidden = true skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" [tool.llamahub] -classes = ["AsyncOpenAI", "OpenAI", "SyncOpenAI", "Tokenizer"] +classes = ["OpenAI"] contains_example = false import_path = "llama_index.llms.openai" @@ -24,7 +24,7 @@ description = "llama-index llms openai integration" license = "MIT" name = "llama-index-llms-openai" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/__init__.py b/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/__init__.py index 102b56c6fdf77..0e07870cd04eb 100644 --- a/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/__init__.py +++ b/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/__init__.py @@ -1,3 +1,3 @@ -from llama_index.llms.vllm.base import Vllm +from llama_index.llms.vllm.base import Vllm, VllmServer -__all__ = ["Vllm"] +__all__ = ["Vllm", "VllmServer"] diff --git a/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py b/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py index 5fdd577c7dedb..93c795871b23f 100644 --- a/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py +++ b/llama-index-integrations/llms/llama-index-llms-vllm/llama_index/llms/vllm/base.py @@ -212,6 +212,17 @@ def _model_kwargs(self) -> Dict[str, Any]: } return {**base_kwargs} + def __del__(self) -> None: + import torch + from vllm.model_executor.parallel_utils.parallel_state import ( + destroy_model_parallel, + ) + + destroy_model_parallel() + del self._client + if torch.cuda.is_available(): + torch.cuda.synchronize() + def _get_all_kwargs(self, **kwargs: Any) -> Dict[str, Any]: return { **self._model_kwargs, @@ -262,7 +273,8 @@ async def achat( async def acomplete( self, prompt: str, formatted: bool = False, **kwargs: Any ) -> CompletionResponse: - raise (ValueError("Not Implemented")) + kwargs = kwargs if kwargs else {} + return self.complete(prompt, **kwargs) @llm_chat_callback() async def astream_chat( diff --git a/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml b/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml index bd5499519609c..3e08676380b09 100644 --- a/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml +++ b/llama-index-integrations/llms/llama-index-llms-vllm/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index llms vllm integration" license = "MIT" name = "llama-index-llms-vllm" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/.gitignore b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/BUILD b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/Makefile b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/README.md b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/README.md new file mode 100644 index 0000000000000..42199e8503e77 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/README.md @@ -0,0 +1 @@ +# LlamaIndex Postprocessor Integration: Presidio diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/BUILD b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/__init__.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/__init__.py new file mode 100644 index 0000000000000..371d26cc63f2f --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/__init__.py @@ -0,0 +1,4 @@ +from llama_index.postprocessor.presidio.base import PresidioPIINodePostprocessor + + +__all__ = ["PresidioPIINodePostprocessor"] diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/base.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/base.py new file mode 100644 index 0000000000000..ed0beae404593 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/llama_index/postprocessor/presidio/base.py @@ -0,0 +1,110 @@ +from typing import Any, Dict, List, Optional, Tuple +from copy import deepcopy + +from presidio_anonymizer.operators import Operator, OperatorType +from llama_index.core.postprocessor.types import BaseNodePostprocessor +from llama_index.core.schema import MetadataMode, NodeWithScore, QueryBundle + +from presidio_analyzer import AnalyzerEngine +from presidio_anonymizer import AnonymizerEngine +from presidio_anonymizer.entities import OperatorConfig + + +class EntityTypeCountAnonymizer(Operator): + """ + Anonymizer which replaces the entity value + with an type counter per entity. + """ + + REPLACING_FORMAT = "<{entity_type}_{index}>" + + def operate(self, text: str, params: Dict[str, Any]) -> str: + """Anonymize the input text.""" + entity_type: str = params["entity_type"] + entity_mapping: Dict[str, Dict] = params["entity_mapping"] + deanonymize_mapping: Dict[str, str] = params["deanonymize_mapping"] + + entity_mapping_for_type = entity_mapping.get(entity_type) + if not entity_mapping_for_type: + entity_mapping_for_type = entity_mapping[entity_type] = {} + + if text in entity_mapping_for_type: + return entity_mapping_for_type[text] + + new_text = self.REPLACING_FORMAT.format( + entity_type=entity_type, index=len(entity_mapping_for_type) + 1 + ) + entity_mapping[entity_type][text] = new_text + deanonymize_mapping[new_text] = text + return new_text + + def validate(self, params: Dict[str, Any]) -> None: + """Validate operator parameters.""" + if "entity_mapping" not in params: + raise ValueError("An input Dict called `entity_mapping` is required.") + if "entity_type" not in params: + raise ValueError("An entity_type param is required.") + if "deanonymize_mapping" not in params: + raise ValueError("A deanonymize_mapping param is required.") + + def operator_name(self) -> str: + return self.__class__.__name__ + + def operator_type(self) -> OperatorType: + return OperatorType.Anonymize + + +class PresidioPIINodePostprocessor(BaseNodePostprocessor): + """presidio PII Node processor. + Uses a presidio to analyse PIIs. + """ + + pii_node_info_key: str = "__pii_node_info__" + entity_mapping: Dict[str, Dict] = {} + mapping: Dict[str, str] = {} + + @classmethod + def class_name(cls) -> str: + return "PresidioPIINodePostprocessor" + + def mask_pii(self, text: str) -> Tuple[str, Dict]: + analyzer = AnalyzerEngine() + results = analyzer.analyze(text=text, language="en") + engine = AnonymizerEngine() + engine.add_anonymizer(EntityTypeCountAnonymizer) + + new_text = engine.anonymize( + text=text, + analyzer_results=results, + operators={ + "DEFAULT": OperatorConfig( + "EntityTypeCountAnonymizer", + { + "entity_mapping": self.entity_mapping, + "deanonymize_mapping": self.mapping, + }, + ) + }, + ) + + return new_text.text + + def _postprocess_nodes( + self, + nodes: List[NodeWithScore], + query_bundle: Optional[QueryBundle] = None, + ) -> List[NodeWithScore]: + """Postprocess nodes.""" + # swap out text from nodes, with the original node mappings + new_nodes = [] + for node_with_score in nodes: + node = node_with_score.node + new_text = self.mask_pii(node.get_content(metadata_mode=MetadataMode.LLM)) + new_node = deepcopy(node) + new_node.excluded_embed_metadata_keys.append(self.pii_node_info_key) + new_node.excluded_llm_metadata_keys.append(self.pii_node_info_key) + new_node.metadata[self.pii_node_info_key] = self.mapping + new_node.set_content(new_text) + new_nodes.append(NodeWithScore(node=new_node, score=node_with_score.score)) + + return new_nodes diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/poetry.lock 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pathlib-compatible object wrapper for zip files" +optional = false +python-versions = ">=3.8" +files = [ + {file = "zipp-3.17.0-py3-none-any.whl", hash = "sha256:0e923e726174922dce09c53c59ad483ff7bbb8e572e00c7f7c46b88556409f31"}, + {file = "zipp-3.17.0.tar.gz", hash = "sha256:84e64a1c28cf7e91ed2078bb8cc8c259cb19b76942096c8d7b84947690cabaf0"}, +] + +[package.extras] +docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (<7.2.5)", "sphinx (>=3.5)", "sphinx-lint"] +testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy (>=0.9.1)", "pytest-ruff"] + +[metadata] +lock-version = "2.0" +python-versions = ">=3.8.1,<3.12" +content-hash = "b5cc255899a05b1859623cf0956b143155e41d56fc67910b2f3e962d90150d4e" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/pyproject.toml b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/pyproject.toml new file mode 100644 index 0000000000000..84bfeef1b47f1 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/pyproject.toml @@ -0,0 +1,57 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +classes = ["PresidioPIINodePostprocessor"] +contains_example = false +import_path = "llama_index.postprocessor.presidio" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Your Name "] +description = "llama-index postprocessor presidio integration" +license = "MIT" +maintainers = ["roeybc"] +name = "llama-index-postprocessor-presidio" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +llama-index-core = "^0.10.0" +presidio-analyzer = "^2.2.353" +presidio-anonymizer = "^2.2.353" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/BUILD b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/__init__.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/test_postprocessor_presidio.py b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/test_postprocessor_presidio.py new file mode 100644 index 0000000000000..1f5c992c79c23 --- /dev/null +++ b/llama-index-integrations/postprocessor/llama-index-postprocessor-presidio/tests/test_postprocessor_presidio.py @@ -0,0 +1,7 @@ +from llama_index.core.postprocessor.types import BaseNodePostprocessor +from llama_index.postprocessor.presidio import PresidioPIINodePostprocessor + + +def test_class(): + names_of_base_classes = [b.__name__ for b in PresidioPIINodePostprocessor.__mro__] + assert BaseNodePostprocessor.__name__ in names_of_base_classes diff --git a/llama-index-integrations/readers/llama-index-readers-agent-search/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-agent-search/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-agent-search/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml index c8b68fe2a9c69..c08dcf013bca3 100644 --- a/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-agent-search/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers agent_search integration" license = "MIT" +maintainers = ["emrgnt-cmplxty"] name = "llama-index-readers-agent-search" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml index 1f482558988db..361516e9bdf44 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-cdk/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_cdk integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-cdk" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-gong/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-gong/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-gong/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml index 3aa938101528b..13314029175e8 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-gong/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_gong integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-gong" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml index cf725b9def2e6..3f43f53c3b56f 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-hubspot/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_hubspot integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-hubspot" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml index 3f3e5440f9bd9..c5c53ddee8fa3 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-salesforce/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_salesforce integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-salesforce" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml index 6d109f20a579e..462ed15183604 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-shopify/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_shopify integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-shopify" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml index 2c3c773bd3477..c7b04369ba1ae 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-stripe/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_stripe integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-stripe" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml index d9eb85b6de6ff..37ecf03b1f064 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-typeform/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_typeform integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-typeform" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml index 7a8ba2b338f24..4dc51fdab8724 100644 --- a/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airbyte-zendesk-support/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airbyte_zendesk_support integration" license = "MIT" +maintainers = ["flash1293"] name = "llama-index-readers-airbyte-zendesk-support" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-airtable/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-airtable/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-airtable/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml index 33bc09ff2f122..d0940815ba6b3 100644 --- a/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-airtable/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers airtable integration" license = "MIT" +maintainers = ["smyja"] name = "llama-index-readers-airtable" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-apify/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-apify/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-apify/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml index 85ac0ef71fcb1..12369aeede177 100644 --- a/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-apify/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers apify integration" +keywords = ["apify", "crawler", "scraper", "scraping"] license = "MIT" +maintainers = ["drobnikj"] name = "llama-index-readers-apify" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-arango-db/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-arango-db/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-arango-db/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml index ba26d095e11d6..6b010182872b3 100644 --- a/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-arango-db/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers arango db integration" license = "MIT" +maintainers = ["mmaatouk"] name = "llama-index-readers-arango-db" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-asana/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-asana/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-asana/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml index 47c783aca407c..1f2c58a8ed8b6 100644 --- a/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-asana/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers asana integration" license = "MIT" +maintainers = ["daveey"] name = "llama-index-readers-asana" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-assemblyai/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-assemblyai/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-assemblyai/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml index 2cce51c245fd8..ca48b874ee0c8 100644 --- a/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-assemblyai/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers assemblyai integration" license = "MIT" +maintainers = ["patrickloeber"] name = "llama-index-readers-assemblyai" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-astra-db/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-astra-db/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-astra-db/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml index d05aa30fe5cde..3245e15b865f2 100644 --- a/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-astra-db/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers astra_db integration" license = "MIT" +maintainers = ["erichare"] name = "llama-index-readers-astra-db" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-athena/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-athena/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-athena/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml index 4a6220d0cc479..744430405146e 100644 --- a/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-athena/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers athena integration" +keywords = ["aws athena", "datalake", "sql"] license = "MIT" +maintainers = ["mattick27"] name = "llama-index-readers-athena" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-azcognitive-search/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-azcognitive-search/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-azcognitive-search/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml index 5b9218305a925..4b05532a029a6 100644 --- a/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-azcognitive-search/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers azcognitive_search integration" license = "MIT" +maintainers = ["mrcabellom"] name = "llama-index-readers-azcognitive-search" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-azstorage-blob/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-azstorage-blob/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-azstorage-blob/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml index 3c49a03e78956..d212f8c49dc71 100644 --- a/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-azstorage-blob/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers azstorage_blob integration" +keywords = ["azure storage", "azure", "blob", "container"] license = "MIT" +maintainers = ["rivms"] name = "llama-index-readers-azstorage-blob" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-bagel/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-bagel/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-bagel/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml index 3b0adb92ad8f4..d3364c997758b 100644 --- a/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-bagel/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers bagel integration" +keywords = ["bagelDB", "database", "storage", "vector"] license = "MIT" +maintainers = ["asif"] name = "llama-index-readers-bagel" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-bilibili/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-bilibili/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-bilibili/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml index 830e327bbe113..8d09147a74c96 100644 --- a/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-bilibili/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers bilibili integration" license = "MIT" +maintainers = ["alexzhangji"] name = "llama-index-readers-bilibili" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-bitbucket/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-bitbucket/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-bitbucket/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml index 3321d7e1941ff..6cf644c74ba4f 100644 --- a/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-bitbucket/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers bitbucket integration" +keywords = ["bitbucket", "project", "repository"] license = "MIT" +maintainers = ["lejdiprifti"] name = "llama-index-readers-bitbucket" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-boarddocs/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-boarddocs/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-boarddocs/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml index 3d411f995415a..a65f13937d98d 100644 --- a/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-boarddocs/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers boarddocs integration" +keywords = ["board", "boarddocs"] license = "MIT" +maintainers = ["dweekly"] name = "llama-index-readers-boarddocs" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml index 19e0fc0c84019..3f2ec978bcad2 100644 --- a/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-chatgpt-plugin/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers chatgpt plugin integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-chatgpt-plugin" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-chroma/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-chroma/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-chroma/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml index d0addb6323ee5..51868f3cbf29f 100644 --- a/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers chroma integration" license = "MIT" +maintainers = ["atroyn"] name = "llama-index-readers-chroma" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-confluence/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-confluence/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-confluence/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml index 83250919a2806..82469f45a9dc0 100644 --- a/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-confluence/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers confluence integration" license = "MIT" +maintainers = ["zywilliamli"] name = "llama-index-readers-confluence" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-couchbase/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-couchbase/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-couchbase/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml index 1e03fcd4c37cd..3520d7fa273a5 100644 --- a/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-couchbase/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers couchbase integration" +keywords = ["Capella", "Couchbase", "NoSQL"] license = "MIT" +maintainers = ["nithishr"] name = "llama-index-readers-couchbase" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-couchdb/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-couchdb/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-couchdb/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml index a2af44ea7d8ec..495c38820b7c9 100644 --- a/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-couchdb/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers couchdb integration" license = "MIT" +maintainers = ["technosophy"] name = "llama-index-readers-couchdb" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-dad-jokes/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-dad-jokes/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-dad-jokes/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml index 0b6cdc41bd45a..2c560b46eacf2 100644 --- a/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-dad-jokes/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers dad_jokes integration" +keywords = ["dad jokes", "jokes"] license = "MIT" +maintainers = ["sidu"] name = "llama-index-readers-dad-jokes" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-database/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-database/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-database/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml index a7acf541612dd..a5fdda273fdc6 100644 --- a/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-database/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers database integration" +keywords = ["aws rds", "postgres", "snowflake", "sql"] license = "MIT" +maintainers = ["kevinqz"] name = "llama-index-readers-database" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-deeplake/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-deeplake/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-deeplake/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml index 5f93be60e4f17..2dbf69dadafee 100644 --- a/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-deeplake/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers deeplake integration" +keywords = ["deeplake"] license = "MIT" +maintainers = ["adolkhan"] name = "llama-index-readers-deeplake" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-discord/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-discord/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-discord/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml index 2725935de6378..e21d12ae88e07 100644 --- a/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-discord/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers discord integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-discord" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-docstring-walker/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-docstring-walker/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-docstring-walker/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml index 00ead8f7366fe..d209241ac2780 100644 --- a/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-docstring-walker/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers docstring_walker integration" +keywords = ["code", "docstring", "python", "source code"] license = "MIT" +maintainers = ["Filip Wojcik"] name = "llama-index-readers-docstring-walker" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-docugami/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-docugami/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-docugami/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml index eb2ce13f78c51..6cff99c705c6c 100644 --- a/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-docugami/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers docugami integration" +keywords = ["doc", "docugami", "docx", "pdf", "xml"] license = "MIT" +maintainers = ["tjaffri"] name = "llama-index-readers-docugami" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml index 2bb3398c0e167..9cfa60e17dfc4 100644 --- a/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-earnings-call-transcript/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers earnings_call_transcript integration" +keywords = ["Earning calls", "Finance", "Investor"] license = "MIT" +maintainers = ["Athe-kunal"] name = "llama-index-readers-earnings-call-transcript" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-elasticsearch/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-elasticsearch/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-elasticsearch/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml index c018ddbb1fa8a..8ecf97eb58661 100644 --- a/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-elasticsearch/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers elasticsearch integration" license = "MIT" +maintainers = ["jaylmiller"] name = "llama-index-readers-elasticsearch" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-faiss/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-faiss/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-faiss/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml index ad43faf177dd1..a1947d76e7cb6 100644 --- a/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-faiss/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers faiss integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-faiss" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-feedly-rss/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-feedly-rss/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-feedly-rss/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml index b0425b4d9b3fb..9b8144ccd152b 100644 --- a/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-feedly-rss/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers feedly_rss integration" +keywords = ["feedly", "rss"] license = "MIT" +maintainers = ["kychanbp"] name = "llama-index-readers-feedly-rss" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-feishu-docs/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-feishu-docs/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-feishu-docs/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml index a775a21de08a6..baabbdb88871f 100644 --- a/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-feishu-docs/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers feishu_docs integration" license = "MIT" +maintainers = ["ma-chengcheng"] name = "llama-index-readers-feishu-docs" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-file/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-file/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-file/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml index 18e3075b13585..ef841d2af0d35 100644 --- a/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-file/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers file integration" +keywords = ["10k", "10q", "chart", "eml", "figure", "html", "hwp", "image", "invoice", "ipynb", "jupyter", "notebook", "pdf", "pymupdf", "receipt", "sec", "spreadsheet", "tabular", "unstructured.io", "yaml", "yml"] license = "MIT" +maintainers = ["FarisHijazi", "Haowjy", "ephe-meral", "hursh-desai", "iamarunbrahma", "jon-chuang", "mmaatouk", "ravi03071991", "sangwongenip", "thejessezhang"] name = "llama-index-readers-file" readme = "README.md" -version = "0.1.2" +version = "0.1.3" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml index 07327ed2f5fe4..03bdabae9e48f 100644 --- a/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-firebase-realtimedb/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers firebase_realtimedb integration" +keywords = ["database", "firebase", "realtimedb"] license = "MIT" +maintainers = ["ajay"] name = "llama-index-readers-firebase-realtimedb" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-firestore/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-firestore/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-firestore/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml index 779814c5592d3..24742450322fd 100644 --- a/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-firestore/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers firestore integration" +keywords = ["datastore", "firestore"] license = "MIT" +maintainers = ["rayzhudev"] name = "llama-index-readers-firestore" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-github/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-github/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-github/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml index 285c39aee3d67..e60346bb3751d 100644 --- a/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-github/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers github integration" +keywords = ["code", "collaborators", "git", "github", "issues", "placeholder", "repository", "source code"] license = "MIT" +maintainers = ["ahmetkca", "moncho", "rwood-97"] name = "llama-index-readers-github" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-google/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-google/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-google/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml index 2d9e1728df143..5dbc71eabb839 100644 --- a/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-google/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers google integration" +keywords = ["email", "gmail", "google keep", "google notes"] license = "MIT" +maintainers = ["bbornsztein", "jerryjliu", "ong", "piroz", "pycui", "ravi03071991"] name = "llama-index-readers-google" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.10,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-gpt-repo/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-gpt-repo/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-gpt-repo/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml index f5591ada0685d..c3cde1866a5a8 100644 --- a/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-gpt-repo/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers gpt_repo integration" license = "MIT" +maintainers = ["mpoon"] name = "llama-index-readers-gpt-repo" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml index 1aa7c96ed60d9..c048ae75bbfe6 100644 --- a/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-graphdb-cypher/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers graphdb_cypher integration" +keywords = ["cypher", "graph", "neo4j"] license = "MIT" +maintainers = ["jexp"] name = "llama-index-readers-graphdb-cypher" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-graphql/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-graphql/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-graphql/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml index c071f14ac0df1..d03ea52962672 100644 --- a/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-graphql/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers graphql integration" +keywords = ["apollo", "gql", "graphql"] license = "MIT" +maintainers = ["jexp"] name = "llama-index-readers-graphql" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-guru/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-guru/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-guru/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml index 724c8b2cc30d3..9e3c159042564 100644 --- a/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-guru/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers guru integration" +keywords = ["getguru", "guru", "knowledge base"] license = "MIT" +maintainers = ["mcclain-thiel"] name = "llama-index-readers-guru" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-hatena-blog/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-hatena-blog/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-hatena-blog/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml index 86e6c4244c6c8..d52c538c24efb 100644 --- a/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-hatena-blog/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers hatena_blog integration" +keywords = ["blog", "hatena"] license = "MIT" +maintainers = ["Shoya SHIRAKI"] name = "llama-index-readers-hatena-blog" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-hive/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-hive/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-hive/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml index 52083c0abd46d..2b5e659e8b62e 100644 --- a/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-hive/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers hive integration" +keywords = ["HDFS", "Hadoop", "Hive"] license = "MIT" +maintainers = ["kasen"] name = "llama-index-readers-hive" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-hubspot/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-hubspot/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-hubspot/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml index a233ab12162f8..6ea3c82e30bd4 100644 --- a/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-hubspot/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers hubspot integration" +keywords = ["hubspot"] license = "MIT" +maintainers = ["ykhli"] name = "llama-index-readers-hubspot" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-huggingface-fs/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-huggingface-fs/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-huggingface-fs/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml index f0b9f46620780..35ece98233420 100644 --- a/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-huggingface-fs/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers huggingface fs integration" +keywords = ["face", "filesystem", "fs", "hugging", "huggingface"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-huggingface-fs" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-imdb-review/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-imdb-review/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-imdb-review/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml index 84464aee2e5a8..a4205a5d3ae35 100644 --- a/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-imdb-review/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers imdb_review integration" +keywords = ["IMDB", "movies", "reviews"] license = "MIT" +maintainers = ["Athe-kunal"] name = "llama-index-readers-imdb-review" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-intercom/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-intercom/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-intercom/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml index c5b9da709d3aa..fed0451e75763 100644 --- a/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-intercom/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers intercom integration" +keywords = ["help center", "intercom", "knowledge base"] license = "MIT" +maintainers = ["bbornsztein"] name = "llama-index-readers-intercom" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-jira/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-jira/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-jira/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml index 070ad1e8b55a6..df89b882eb3d9 100644 --- a/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-jira/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers jira integration" +keywords = ["jira"] license = "MIT" +maintainers = ["bearguy"] name = "llama-index-readers-jira" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-joplin/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-joplin/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-joplin/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml index da4155b075a5a..599f6481ea2ab 100644 --- a/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-joplin/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers joplin integration" license = "MIT" +maintainers = ["alondmnt"] name = "llama-index-readers-joplin" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-json/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-json/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-json/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml index 69d3825cccbca..3a478f8a939f5 100644 --- a/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-json/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers json integration" license = "MIT" +maintainers = ["yisding"] name = "llama-index-readers-json" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-kaltura/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-kaltura/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-kaltura/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml index edb9e8fd1bf33..167778265f426 100644 --- a/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-kaltura/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers kaltura e-search integration" +keywords = ["audio", "events", "image", "kaltura", "library", "media", "portal", "search", "video"] license = "MIT" +maintainers = ["kaltura"] name = "llama-index-readers-kaltura-esearch" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-kibela/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-kibela/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-kibela/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml index d6d20aac3e77c..af4316de53fdf 100644 --- a/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-kibela/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers kibela integration" license = "MIT" +maintainers = ["higebu"] name = "llama-index-readers-kibela" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-lilac/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-lilac/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-lilac/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml index 9cba45892e517..fa86299471ea1 100644 --- a/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-lilac/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers lilac integration" license = "MIT" +maintainers = ["nsthorat"] name = "llama-index-readers-lilac" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-linear/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-linear/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-linear/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml index 3fceea78bcd55..baaaf6e1ddf29 100644 --- a/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-linear/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers linear integration" +keywords = ["linear"] license = "MIT" +maintainers = ["Sushmithamallesh"] name = "llama-index-readers-linear" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml index 8ee6143ff5eab..7fbae388d842c 100644 --- a/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-macrometa-gdn/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers macrometa_gdn integration" +keywords = ["macrometa"] license = "MIT" +maintainers = ["Dain Im"] name = "llama-index-readers-macrometa-gdn" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-make-com/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-make-com/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-make-com/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml index 1fa836b765fb2..287e2faa7c788 100644 --- a/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-make-com/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index readers make-com integration" license = "MIT" name = "llama-index-readers-make-com" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-mangadex/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-mangadex/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-mangadex/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml index 0da35346cb7fc..1f411b783687f 100644 --- a/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mangadex/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers mangadex integration" +keywords = ["anime", "manga"] license = "MIT" +maintainers = ["choombaa"] name = "llama-index-readers-mangadex" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml index e6c262b5a312f..60c2516f03f21 100644 --- a/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mangoapps-guides/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers mangoapps_guides integration" +keywords = ["mangoapps"] license = "MIT" +maintainers = ["mangoapps"] name = "llama-index-readers-mangoapps-guides" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-maps/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-maps/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-maps/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml index 434c58b7dc06c..18ce6ec7292b6 100644 --- a/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-maps/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers maps integration" +keywords = ["geo", "maps", "open maps", "open street maps", "overpass api"] license = "MIT" +maintainers = ["carrotpy"] name = "llama-index-readers-maps" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-mbox/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-mbox/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-mbox/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml index edc2c0ba50e66..1eb299749571c 100644 --- a/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mbox/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers mbox integration" license = "MIT" +maintainers = ["minosvasilias"] name = "llama-index-readers-mbox" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-memos/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-memos/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-memos/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml index 938f4807ccc74..d27dc9139743e 100644 --- a/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-memos/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers memos integration" +keywords = ["memos", "note"] license = "MIT" +maintainers = ["bubu"] name = "llama-index-readers-memos" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-metal/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-metal/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-metal/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml index ad46b945025cd..888b7c98e0526 100644 --- a/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-metal/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers metal integration" +keywords = ["metal", "retriever", "storage"] license = "MIT" +maintainers = ["getmetal"] name = "llama-index-readers-metal" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml index d830f6becf6bc..11d582b27dd2a 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-onedrive/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers microsoft_onedrive integration" +keywords = ["microsoft 365", "microsoft onedrive", "microsoft365", "onedrive for business", "onedrive personal", "onedrive"] license = "MIT" +maintainers = ["godwin3737"] name = "llama-index-readers-microsoft-onedrive" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml index 0f6d67405013a..6e1ac455a3257 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-outlook/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers microsoft outlook integration" +keywords = ["calendar", "outlook"] license = "MIT" +maintainers = ["tevslin"] name = "llama-index-readers-microsoft-outlook" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml index 64596ebba7a03..9cd8f6287369b 100644 --- a/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-microsoft-sharepoint/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers microsoft_sharepoint integration" +keywords = ["microsoft 365", "microsoft365", "sharepoint"] license = "MIT" +maintainers = ["arun-soliton"] name = "llama-index-readers-microsoft-sharepoint" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-milvus/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-milvus/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-milvus/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml index 7dac376eccf16..80f7c1564ee86 100644 --- a/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-milvus/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers milvus integration" license = "MIT" +maintainers = ["filip-halt"] name = "llama-index-readers-milvus" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-minio/README.md b/llama-index-integrations/readers/llama-index-readers-minio/README.md new file mode 100644 index 0000000000000..d0bb5d20dd2d1 --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-minio/README.md @@ -0,0 +1,9 @@ +# Minio Reader + +## Install + +`pip install llama-index-readers-minio` + +## Import + +`from llama_index.readers.minio import MinioReader, BotoMinioReader` diff --git a/llama-index-integrations/readers/llama-index-readers-minio/llama_index/README.md b/llama-index-integrations/readers/llama-index-readers-minio/llama_index/README.md index 35c3feeedb50a..e17e81f4d6bcc 100644 --- a/llama-index-integrations/readers/llama-index-readers-minio/llama_index/README.md +++ b/llama-index-integrations/readers/llama-index-readers-minio/llama_index/README.md @@ -37,6 +37,8 @@ To use this loader, you need to pass in the name of your Minio Bucket. After tha Otherwise, you may specify a prefix if you only want to parse certain files in the Bucket, or a subdirectory. +You can now use the client with a TLS-secured MinIO instance (`minio_secure=True`), even if server's certificate isn't trusted (`minio_cert_check=False`). + ```python from llama_index import download_loader @@ -44,7 +46,8 @@ MinioReader = download_loader("MinioReader") loader = MinioReader( bucket="documents", minio_endpoint="localhost:9000", - minio_secure=False, + minio_secure=True, + minio_cert_check=False, minio_access_key="minio_access_key", minio_secret_key="minio_secret_key", ) diff --git a/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py b/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py index 35ec79858f3ac..3c4db2ec95129 100644 --- a/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py +++ b/llama-index-integrations/readers/llama-index-readers-minio/llama_index/readers/minio/minio_client/base.py @@ -29,6 +29,7 @@ def __init__( file_metadata: Optional[Callable[[str], Dict]] = None, minio_endpoint: Optional[str] = None, minio_secure: bool = False, + minio_cert_check: bool = True, minio_access_key: Optional[str] = None, minio_secret_key: Optional[str] = None, minio_session_token: Optional[str] = None, @@ -59,6 +60,8 @@ def __init__( minio_access_key (Optional[str]): The Minio access key. Default is None. minio_secret_key (Optional[str]): The Minio secret key. Default is None. minio_session_token (Optional[str]): The Minio session token. + minio_secure: MinIO server runs in TLS mode + minio_cert_check: allows the usage of a self-signed cert for MinIO server """ super().__init__(*args, **kwargs) @@ -74,6 +77,7 @@ def __init__( self.minio_endpoint = minio_endpoint self.minio_secure = minio_secure + self.minio_cert_check = minio_cert_check self.minio_access_key = minio_access_key self.minio_secret_key = minio_secret_key self.minio_session_token = minio_session_token @@ -85,6 +89,7 @@ def load_data(self) -> List[Document]: minio_client = Minio( self.minio_endpoint, secure=self.minio_secure, + cert_check=self.minio_cert_check, access_key=self.minio_access_key, secret_key=self.minio_secret_key, session_token=self.minio_session_token, diff --git a/llama-index-integrations/readers/llama-index-readers-mondaydotcom/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-mondaydotcom/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-mondaydotcom/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml index 3ff82234410cc..906a708066873 100644 --- a/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mondaydotcom/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers mondaydotcom integration" +keywords = ["monday", "mondaydotcom"] license = "MIT" +maintainers = ["nadavgr"] name = "llama-index-readers-mondaydotcom" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-mongodb/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-mongodb/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-mongodb/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml index 9b942579aa21d..3486732d7ddac 100644 --- a/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-mongodb/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers mongodb integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-mongodb" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-notion/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-notion/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-notion/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-notion/llama_index/readers/notion/base.py b/llama-index-integrations/readers/llama-index-readers-notion/llama_index/readers/notion/base.py index d8519301174a6..57b3314fbc09c 100644 --- a/llama-index-integrations/readers/llama-index-readers-notion/llama_index/readers/notion/base.py +++ b/llama-index-integrations/readers/llama-index-readers-notion/llama_index/readers/notion/base.py @@ -4,7 +4,7 @@ from typing import Any, Dict, List, Optional import requests # type: ignore -from llama_index.core.readers.base import BaseReader +from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document INTEGRATION_TOKEN_NAME = "NOTION_INTEGRATION_TOKEN" @@ -14,7 +14,7 @@ # TODO: Notion DB reader coming soon! -class NotionPageReader(BaseReader): +class NotionPageReader(BasePydanticReader): """Notion Page reader. Reads a set of Notion pages. @@ -24,6 +24,10 @@ class NotionPageReader(BaseReader): """ + is_remote: bool = True + token: str + headers: Dict[str, str] + def __init__(self, integration_token: Optional[str] = None) -> None: """Initialize with parameters.""" if integration_token is None: @@ -33,13 +37,16 @@ def __init__(self, integration_token: Optional[str] = None) -> None: "Must specify `integration_token` or set environment " "variable `NOTION_INTEGRATION_TOKEN`." ) - self.token = integration_token - self.headers = { + + token = integration_token + headers = { "Authorization": "Bearer " + self.token, "Content-Type": "application/json", "Notion-Version": "2022-06-28", } + super().__init__(token=token, headers=headers) + def _read_block(self, block_id: str, num_tabs: int = 0) -> str: """Read a block.""" done = False diff --git a/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml index 3d91e442f7394..45f645174d1c1 100644 --- a/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-notion/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers notion integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-notion" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-nougat-ocr/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-nougat-ocr/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-nougat-ocr/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml index bb4cb5f25b327..80bfd5ca21f26 100644 --- a/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-nougat-ocr/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers nougat_ocr integration" +keywords = ["academic papers", "ocr", "pdf"] license = "MIT" +maintainers = ["mdarshad1000"] name = "llama-index-readers-nougat-ocr" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-obsidian/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-obsidian/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-obsidian/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml index f475249adc30e..e49529a7e5c1e 100644 --- a/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-obsidian/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers obsidian integration" license = "MIT" +maintainers = ["hursh-desai"] name = "llama-index-readers-obsidian" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-openalex/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-openalex/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-openalex/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml index b95295dfd3c1e..5a6a1a9b0faff 100644 --- a/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-openalex/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers openalex integration" +keywords = ["academic papers", "open access", "openalex", "scientific papers"] license = "MIT" +maintainers = ["shauryr"] name = "llama-index-readers-openalex" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-opendal/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-opendal/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-opendal/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml index 103fdb8395f5b..fc7a542060058 100644 --- a/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-opendal/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers opendal_reader integration" +keywords = ["azblob", "gcs", "s3", "storage"] license = "MIT" +maintainers = ["OpenDAL Contributors"] name = "llama-index-readers-opendal-reader" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-opensearch/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-opensearch/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-opensearch/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml index 929ca4060e942..f82edcae712e8 100644 --- a/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-opensearch/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers opensearch integration" license = "MIT" +maintainers = ["chnsagitchen"] name = "llama-index-readers-opensearch" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-pandas-ai/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-pandas-ai/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pandas-ai/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml index 1727764313903..97ab32367a980 100644 --- a/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pandas-ai/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers pandas_ai integration" +keywords = ["ai", "pandas"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-pandas-ai" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-papers/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-papers/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-papers/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml index 5e9bc9211d58c..22cee84d4ab9f 100644 --- a/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-papers/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers papers integration" license = "MIT" +maintainers = ["thejessezhang"] name = "llama-index-readers-papers" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-patentsview/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-patentsview/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-patentsview/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml index f62f07b77afcf..0d77c27b6a99f 100644 --- a/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-patentsview/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers patentsview integration" +keywords = ["patent"] license = "MIT" +maintainers = ["shao-shuai"] name = "llama-index-readers-patentsview" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-pdb/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-pdb/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pdb/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml index 110ae22d3934f..ec06937b8ffc4 100644 --- a/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pdb/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers pdb integration" +keywords = ["Protein Data Bank", "academic papers", "pdb", "proteins"] license = "MIT" +maintainers = ["joshuakto"] name = "llama-index-readers-pdb" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-pdf-table/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-pdf-table/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pdf-table/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml index d6d2a3aac91a9..2c06b0b8abe06 100644 --- a/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pdf-table/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers pdf_table integration" +keywords = ["pdf table", "pdf", "table"] license = "MIT" +maintainers = ["yy0867"] name = "llama-index-readers-pdf-table" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-pinecone/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-pinecone/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-pinecone/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-pinecone/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-pinecone/pyproject.toml index 75ea073ac14ae..453cd44fa4b3a 100644 --- a/llama-index-integrations/readers/llama-index-readers-pinecone/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-pinecone/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers pinecone integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-pinecone" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-preprocess/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-preprocess/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-preprocess/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml index f38fcc913c929..01c60fec8530d 100644 --- a/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-preprocess/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers preprocess integration" +keywords = ["chunk", "chunking", "documents", "preprocess"] license = "MIT" +maintainers = ["preprocess"] name = "llama-index-readers-preprocess" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-qdrant/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-qdrant/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-qdrant/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml index 1df77c5c231b4..5fa835e55e911 100644 --- a/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-qdrant/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers qdrant integration" license = "MIT" +maintainers = ["kacperlukawski"] name = "llama-index-readers-qdrant" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-rayyan/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-rayyan/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-rayyan/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml index 3eb60d547eb23..17596e4afe7ba 100644 --- a/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-rayyan/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers rayyan integration" +keywords = ["rayyan", "systematic review"] license = "MIT" +maintainers = ["hammady"] name = "llama-index-readers-rayyan" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-readwise/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-readwise/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-readwise/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml index e25c6bcb50554..d5936a44a8c5d 100644 --- a/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-readwise/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers readwise integration" +keywords = ["highlights", "pkm", "reading", "readwise"] license = "MIT" +maintainers = ["alexbowe"] name = "llama-index-readers-readwise" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-reddit/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-reddit/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-reddit/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml index 41929382fb08f..02475b4e4f901 100644 --- a/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-reddit/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers reddit integration" +keywords = ["comments", "reddit", "search", "subreddit"] license = "MIT" +maintainers = ["vanessahlyan"] name = "llama-index-readers-reddit" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-remote-depth/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-remote-depth/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-remote-depth/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml index ab41e1461f3bb..3ec2b73139d14 100644 --- a/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-remote-depth/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers remote_depth integration" +keywords = ["hosted", "multiple", "url"] license = "MIT" +maintainers = ["simonMoisselin"] name = "llama-index-readers-remote-depth" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-remote/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-remote/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-remote/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml index 9be6153e59d89..d284c93bc2ab4 100644 --- a/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-remote/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers remote integration" +keywords = ["gutenberg", "hosted", "url"] license = "MIT" +maintainers = ["thejessezhang"] name = "llama-index-readers-remote" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-s3/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-s3/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-s3/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml index faa293098115e..45e0a8006e88a 100644 --- a/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-s3/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers s3 integration" +keywords = ["amazon web services", "aws s3", "bucket"] license = "MIT" +maintainers = ["thejessezhang"] name = "llama-index-readers-s3" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-sec-filings/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-sec-filings/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-sec-filings/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml index 640cbcd4c955d..6a358c84fb8ea 100644 --- a/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-sec-filings/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers sec_filings integration" +keywords = ["10-K", "10-Q", "SEC Filings", "finance"] license = "MIT" +maintainers = ["Athe-kunal"] name = "llama-index-readers-sec-filings" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-semanticscholar/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-semanticscholar/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-semanticscholar/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml index d90c772e1b61b..7704ffefcc096 100644 --- a/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-semanticscholar/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers semanticscholar integration" +keywords = ["paper", "research", "scholar", "semantic"] license = "MIT" +maintainers = ["shauryr"] name = "llama-index-readers-semanticscholar" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-singlestore/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-singlestore/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-singlestore/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml index dc996a1810ec1..fabb37e55f100 100644 --- a/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-singlestore/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers singlestore integration" +keywords = ["memsql", "singlestore"] license = "MIT" +maintainers = ["singlestore"] name = "llama-index-readers-singlestore" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-slack/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-slack/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-slack/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml index 63d2eabc05a67..6f9cd9b11868c 100644 --- a/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-slack/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers slack integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-slack" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml index fbcd08e79ed62..4b7882813650f 100644 --- a/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-smart-pdf-loader/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers smart_pdf_loader integration" +keywords = ["pdf layout", "pdf table", "pdf"] license = "MIT" +maintainers = ["ansukla"] name = "llama-index-readers-smart-pdf-loader" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-snowflake/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-snowflake/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-snowflake/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml index 15b7243646ca9..1a596dbc0b534 100644 --- a/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-snowflake/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers snowflake integration" +keywords = ["data warehouse", "database", "snowflake", "warehouse"] license = "MIT" +maintainers = ["godwin3737"] name = "llama-index-readers-snowflake" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml index ada61bf842f64..5a4d41d851bd7 100644 --- a/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-snscrape-twitter/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers snscrape_twitter integration" license = "MIT" +maintainers = ["smyja"] name = "llama-index-readers-snscrape-twitter" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-spotify/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-spotify/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-spotify/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml index c4a017406174b..8dca1aff4b39a 100644 --- a/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-spotify/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers spotify integration" +keywords = ["music", "spotify"] license = "MIT" +maintainers = ["ong"] name = "llama-index-readers-spotify" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-stackoverflow/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-stackoverflow/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-stackoverflow/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml index f9df6c55eec81..d2c1f0d6de526 100644 --- a/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-stackoverflow/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers stackoverflow integration" +keywords = ["answers", "posts", "questions"] license = "MIT" +maintainers = ["allen-munsch"] name = "llama-index-readers-stackoverflow" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-steamship/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-steamship/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-steamship/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml index 8fc6cb9c3ec50..1c38870bd52eb 100644 --- a/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-steamship/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers steamship integration" +keywords = ["steamship"] license = "MIT" +maintainers = ["douglas-reid"] name = "llama-index-readers-steamship" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-stripe-docs/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-stripe-docs/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-stripe-docs/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml index a147f68c6e054..14feb2ff501e4 100644 --- a/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-stripe-docs/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers stripe_docs integration" +keywords = ["documentation", "stripe"] license = "MIT" +maintainers = ["amorriscode"] name = "llama-index-readers-stripe-docs" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-telegram/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-telegram/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-telegram/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml index c295cfe5d36e9..28ab06298b949 100644 --- a/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-telegram/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Dias Kalkamanov "] description = "llama-index readers telegram integration" license = "MIT" +maintainers = ["diicell"] name = "llama-index-readers-telegram" readme = "README.md" -version = "0.1.2" +version = "0.1.3" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-trello/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-trello/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-trello/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml index 89bb44ee06736..711f942e626ed 100644 --- a/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-trello/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers trello integration" +keywords = ["trello"] license = "MIT" +maintainers = ["bluzir"] name = "llama-index-readers-trello" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-twitter/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-twitter/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-twitter/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-twitter/llama_index/readers/twitter/base.py b/llama-index-integrations/readers/llama-index-readers-twitter/llama_index/readers/twitter/base.py index f2cc92f451d46..80e25f11b28ca 100644 --- a/llama-index-integrations/readers/llama-index-readers-twitter/llama_index/readers/twitter/base.py +++ b/llama-index-integrations/readers/llama-index-readers-twitter/llama_index/readers/twitter/base.py @@ -69,5 +69,5 @@ def load_data( response = " " for tweet in tweets.data: response = response + tweet.text + "\n" - results.append(Document(text=response)) + results.append(Document(text=response, id_=username)) return results diff --git a/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml index 5dce05c495a65..df7b8a165d18c 100644 --- a/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-twitter/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers twitter integration" license = "MIT" +maintainers = ["ravi03071991"] name = "llama-index-readers-twitter" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-weather/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-weather/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-weather/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml index 74f65946dd7fe..a7a92eb30f7b8 100644 --- a/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-weather/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers weather integration" +keywords = ["openweather", "weather"] license = "MIT" +maintainers = ["iamadhee"] name = "llama-index-readers-weather" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-weaviate/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-weaviate/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-weaviate/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml index 5590b6caa04f7..703e61b83a973 100644 --- a/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-weaviate/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers weaviate integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-weaviate" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-web/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-web/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-web/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py index 4a9d6fe1ebb24..1758752c69d3d 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/beautiful_soup_web/base.py @@ -4,7 +4,7 @@ from typing import Any, Callable, Dict, List, Optional, Tuple from urllib.parse import urljoin -from llama_index.core.readers.base import BaseReader +from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document logger = logging.getLogger(__name__) @@ -131,7 +131,7 @@ def _gitbook_reader( } -class BeautifulSoupWebReader(BaseReader): +class BeautifulSoupWebReader(BasePydanticReader): """BeautifulSoup web page reader. Reads pages from the web. @@ -143,12 +143,8 @@ class BeautifulSoupWebReader(BaseReader): extract text from the BeautifulSoup obj. See DEFAULT_WEBSITE_EXTRACTOR. """ - def __init__( - self, - website_extractor: Optional[Dict[str, Callable]] = None, - ) -> None: - """Initialize with parameters.""" - self.website_extractor = website_extractor or DEFAULT_WEBSITE_EXTRACTOR + is_remote: bool = True + website_extractor: Dict[str, Callable] = DEFAULT_WEBSITE_EXTRACTOR def load_data( self, @@ -195,6 +191,6 @@ def load_data( else: data = soup.getText() - documents.append(Document(text=data, extra_info=extra_info)) + documents.append(Document(text=data, id_=url, extra_info=extra_info)) return documents diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss/base.py index 5238de4c1150c..db419d4a656e0 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss/base.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/rss/base.py @@ -2,40 +2,23 @@ from typing import List -from llama_index.core.readers.base import BaseReader +from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document -class RssReader(BaseReader): +class RssReader(BasePydanticReader): """RSS reader. Reads content from an RSS feed. """ - def __init__(self, html_to_text: bool = False) -> None: - """Initialize with parameters. + is_remote: bool = True + html_to_text: bool = False - Args: - html_to_text (bool): Whether to convert HTML to text. - Requires `html2text` package. - - """ - try: - import feedparser # noqa: F401 - except ImportError: - raise ValueError( - "`feedparser` package not found, please run `pip install feedparser`" - ) - - if html_to_text: - try: - import html2text # noqa: F401 - except ImportError: - raise ValueError( - "`html2text` package not found, please run `pip install html2text`" - ) - self._html_to_text = html_to_text + @classmethod + def class_name(cls) -> str: + return "RssReader" def load_data(self, urls: List[str]) -> List[Document]: """Load data from RSS feeds. @@ -57,17 +40,18 @@ def load_data(self, urls: List[str]) -> List[Document]: for url in urls: parsed = feedparser.parse(url) for entry in parsed.entries: + doc_id = entry.id or entry.link if "content" in entry: data = entry.content[0].value else: data = entry.description or entry.summary - if self._html_to_text: + if self.html_to_text: import html2text data = html2text.html2text(data) extra_info = {"title": entry.title, "link": entry.link} - documents.append(Document(text=data, extra_info=extra_info)) + documents.append(Document(text=data, id_=doc_id, extra_info=extra_info)) return documents diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py index ccd2d0c46ca55..e19e485ce15a8 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/simple_web/base.py @@ -1,12 +1,14 @@ """Simple Web scraper.""" -from typing import List +from typing import List, Optional, Dict, Callable import requests -from llama_index.core.readers.base import BaseReader + +from llama_index.core.bridge.pydantic import PrivateAttr +from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document -class SimpleWebPageReader(BaseReader): +class SimpleWebPageReader(BasePydanticReader): """Simple web page reader. Reads pages from the web. @@ -14,12 +16,34 @@ class SimpleWebPageReader(BaseReader): Args: html_to_text (bool): Whether to convert HTML to text. Requires `html2text` package. - + metadata_fn (Optional[Callable[[str], Dict]]): A function that takes in + a URL and returns a dictionary of metadata. + Default is None. """ - def __init__(self, html_to_text: bool = False) -> None: + is_remote: bool = True + html_to_text: bool + + _metadata_fn: Optional[Callable[[str], Dict]] = PrivateAttr() + + def __init__( + self, + html_to_text: bool = False, + metadata_fn: Optional[Callable[[str], Dict]] = None, + ) -> None: """Initialize with parameters.""" - self._html_to_text = html_to_text + try: + import html2text # noqa + except ImportError: + raise ImportError( + "`html2text` package not found, please run `pip install html2text`" + ) + self._metadata_fn = metadata_fn + super().__init__(html_to_text=html_to_text) + + @classmethod + def class_name(cls) -> str: + return "SimpleWebPageReader" def load_data(self, urls: List[str]) -> List[Document]: """Load data from the input directory. @@ -33,15 +57,18 @@ def load_data(self, urls: List[str]) -> List[Document]: """ if not isinstance(urls, list): raise ValueError("urls must be a list of strings.") - documents = [] for url in urls: - response = requests.get(url).text - if self._html_to_text: + response = requests.get(url, headers=None).text + if self.html_to_text: import html2text response = html2text.html2text(response) - documents.append(Document(text=response)) + metadata: Optional[Dict] = None + if self._metadata_fn is not None: + metadata = self._metadata_fn(url) + + documents.append(Document(text=response, id_=url, metadata=metadata or {})) return documents diff --git a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/trafilatura_web/base.py b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/trafilatura_web/base.py index 1ccaf4ec5ef08..0e64d6b8e4cfe 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/trafilatura_web/base.py +++ b/llama-index-integrations/readers/llama-index-readers-web/llama_index/readers/web/trafilatura_web/base.py @@ -1,11 +1,10 @@ -from importlib.util import find_spec from typing import List -from llama_index.core.readers.base import BaseReader +from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document -class TrafilaturaWebReader(BaseReader): +class TrafilaturaWebReader(BasePydanticReader): """Trafilatura web page reader. Reads pages from the web. @@ -13,12 +12,7 @@ class TrafilaturaWebReader(BaseReader): """ - def __init__(self) -> None: - if find_spec("trafilatura") is None: - raise ImportError( - "Missing package: trafilatura.\n" - "Please `pip install trafilatura` to use this Reader" - ) + is_remote: bool = True def load_data( self, @@ -61,6 +55,6 @@ def load_data( include_formatting=include_formatting, include_links=include_links, ) - documents.append(Document(text=response)) + documents.append(Document(text=response, id_=url)) return documents diff --git a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml index 17da6a0e5076e..150bf685072c8 100644 --- a/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-web/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers web integration" +keywords = ["BFS", "article", "atom", "documentation", "feed", "main content extractor", "news", "readthedocs", "rss", "scraper", "selenium", "seo", "sitemap", "substack", "trafilatura", "unstructured.io", "url", "web reader", "web", "website"] license = "MIT" +maintainers = ["HawkClaws", "Hironsan", "NA", "an-bluecat", "bborn", "jasonwcfan", "kravetsmic", "pandazki", "ruze00", "selamanse", "thejessezhang"] name = "llama-index-readers-web" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-whatsapp/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-whatsapp/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-whatsapp/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml index 8989bd0997d83..779675dacfc4d 100644 --- a/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-whatsapp/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers whatsapp integration" +keywords = ["chat", "whatsapp"] license = "MIT" +maintainers = ["batmanscode"] name = "llama-index-readers-whatsapp" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-wikipedia/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-wikipedia/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-wikipedia/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml index 1867d76f7589a..896b9fc277f02 100644 --- a/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-wikipedia/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers wikipedia integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-readers-wikipedia" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-wordlift/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-wordlift/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-wordlift/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml index af304482adff2..365118486aab1 100644 --- a/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-wordlift/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers wordlift integration" +keywords = ["graphql", "knowledge graph", "seo", "structured data", "wordlift"] license = "MIT" +maintainers = ["msftwarelab"] name = "llama-index-readers-wordlift" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-wordpress/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-wordpress/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-wordpress/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml index c31468c5f89ad..b8e8458713d55 100644 --- a/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-wordpress/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers wordpress integration" +keywords = ["blog", "wordpress"] license = "MIT" +maintainers = ["bbornsztein"] name = "llama-index-readers-wordpress" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-youtube-transcript/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-youtube-transcript/llama_index/readers/youtube_transcript/base.py b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/llama_index/readers/youtube_transcript/base.py index 02a1b1c5ed40c..235c5c66a67fd 100644 --- a/llama-index-integrations/readers/llama-index-readers-youtube-transcript/llama_index/readers/youtube_transcript/base.py +++ b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/llama_index/readers/youtube_transcript/base.py @@ -1,23 +1,18 @@ """Simple Reader that reads transcript of youtube video.""" import re -from importlib.util import find_spec from typing import Any, List, Optional -from llama_index.core.readers.base import BaseReader +from youtube_transcript_api import YouTubeTranscriptApi + +from llama_index.core.readers.base import BasePydanticReader from llama_index.core.schema import Document from llama_index.readers.youtube_transcript.utils import YOUTUBE_URL_PATTERNS -class YoutubeTranscriptReader(BaseReader): +class YoutubeTranscriptReader(BasePydanticReader): """Youtube Transcript reader.""" - def __init__(self) -> None: - if find_spec("youtube_transcript_api") is None: - raise ImportError( - "Missing package: youtube_transcript_api.\n" - "Please `pip install youtube_transcript_api` to use this Reader" - ) - super().__init__() + is_remote: bool = True def load_data( self, @@ -32,8 +27,6 @@ def load_data( for which transcripts are to be read. """ - from youtube_transcript_api import YouTubeTranscriptApi - results = [] for link in ytlinks: video_id = self._extract_video_id(link) @@ -52,7 +45,11 @@ def load_data( ) chunk_text = [chunk["text"] for chunk in transcript_chunks] transcript = "\n".join(chunk_text) - results.append(Document(text=transcript, extra_info={"video_id": video_id})) + results.append( + Document( + text=transcript, id_=video_id, extra_info={"video_id": video_id} + ) + ) return results @staticmethod diff --git a/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml index bffc6c0823437..ef05aeb641149 100644 --- a/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-youtube-transcript/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers youtube transcript integration" +keywords = ["video"] license = "MIT" +maintainers = ["ravi03071991"] name = "llama-index-readers-youtube-transcript" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-zendesk/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-zendesk/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-zendesk/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml index 6b068099e8353..ced8df1a16ca2 100644 --- a/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-zendesk/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers zendesk integration" +keywords = ["help center", "knowledge base", "zendesk"] license = "MIT" +maintainers = ["bbornsztein"] name = "llama-index-readers-zendesk" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-zep/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-zep/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-zep/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml index 9c05cc02e0563..9b31ea1f16b83 100644 --- a/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-zep/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index readers zep integration" +keywords = ["memory", "retriever", "storage", "zep"] license = "MIT" +maintainers = ["zep"] name = "llama-index-readers-zep" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/readers/llama-index-readers-zulip/CHANGELOG.md b/llama-index-integrations/readers/llama-index-readers-zulip/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/readers/llama-index-readers-zulip/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml b/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml index d9f647077b0d7..18c7579f9a4aa 100644 --- a/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml +++ b/llama-index-integrations/readers/llama-index-readers-zulip/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index readers zulip integration" license = "MIT" +maintainers = ["plurigrid"] name = "llama-index-readers-zulip" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-arxiv/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-arxiv/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-arxiv/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml index e0f37fb906cdc..f17f24a3ec7ac 100644 --- a/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-arxiv/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools arxiv integration" +keywords = ["math", "research", "science"] license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-arxiv" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-azure-cv/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-azure-cv/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-azure-cv/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml index d381abba941b3..7d4aaea01c11f 100644 --- a/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-azure-cv/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools azure_cv integration" +keywords = ["cv", "image", "vision"] license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-azure-cv" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-azure-speech/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-azure-speech/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-azure-speech/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml index 9fe40f393ead0..6502d7231aea4 100644 --- a/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-azure-speech/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools azure_speech integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-azure-speech" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-azure-translate/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-azure-translate/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-azure-translate/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml index 98da8d988aacd..b9f202005d213 100644 --- a/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-azure-translate/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools azure_translate integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-azure-translate" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-bing-search/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-bing-search/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-bing-search/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml index e17a82e4f4704..1a1f4d09cb411 100644 --- a/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-bing-search/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools bing_search integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-bing-search" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml index ba2c1a10ba2ef..26aa11645c62b 100644 --- a/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-chatgpt-plugin/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools chatgpt_plugin integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-chatgpt-plugin" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-code-interpreter/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-code-interpreter/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-code-interpreter/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml index cc75cf163c038..163c8ca879e73 100644 --- a/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-code-interpreter/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools code_interpreter integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-code-interpreter" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-cogniswitch/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-cogniswitch/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-cogniswitch/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml index 0954cf915e847..33c2baf429cfc 100644 --- a/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-cogniswitch/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools cogniswitch integration" +keywords = ["embedding", "graph", "knowledge graph", "neural", "symbolic"] license = "MIT" +maintainers = ["cogniswitch"] name = "llama-index-tools-cogniswitch" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-database/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-database/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-database/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml index d41d57df19505..ee4f13ef2771c 100644 --- a/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-database/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools database integration" +keywords = ["aws rds", "postgres", "snowflake", "sql"] license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-database" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-exa/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-exa/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-exa/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml index eaa7139f2c13b..30ec02b8e8266 100644 --- a/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-exa/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools exa integration" license = "MIT" +maintainers = ["jeffzwang"] name = "llama-index-tools-exa" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-google/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-google/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-google/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml index 47a451c61d856..b5a7c511aa9fd 100644 --- a/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-google/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools google integration" +keywords = ["email", "gmail"] license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-google" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-graphql/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-graphql/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-graphql/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml index 1133e94f290f1..bf2b2622b490f 100644 --- a/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-graphql/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools graphql integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-graphql" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-ionic-shopping/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-ionic-shopping/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-ionic-shopping/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml index 7de4a02f025d2..2841043b51b4a 100644 --- a/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-ionic-shopping/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools ionic shopping integration" license = "MIT" +maintainers = ["stewartjarod"] name = "llama-index-tools-ionic-shopping" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-metaphor/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-metaphor/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-metaphor/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml index 0491502180f9e..ab1cfb5a393aa 100644 --- a/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-metaphor/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools metaphor integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-metaphor" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-multion/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-multion/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-multion/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml index c5271a8391aea..583514512cb9e 100644 --- a/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-multion/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools multion integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-multion" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-neo4j/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-neo4j/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-neo4j/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml index 138c4be9ea706..820796d60a159 100644 --- a/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-neo4j/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools neo4j integration" +keywords = ["cypher", "graph", "neo4j"] license = "MIT" +maintainers = ["shahafp"] name = "llama-index-tools-neo4j" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-notion/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-notion/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-notion/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml index 0c2eb37c739c3..671df4d15cfeb 100644 --- a/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-notion/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools notion integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-tools-notion" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-openai/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-openai/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-openai/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml index 7dd427b3038ca..578d90c9dd217 100644 --- a/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-openai/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools openai_image_generation integration" +keywords = ["cv", "gpt-3", "image", "openai", "vision"] license = "MIT" +maintainers = ["manelferreira_"] name = "llama-index-tools-openai-image-generation" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-openapi/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-openapi/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-openapi/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml index 0cd8475993f5b..1ce1469fc8a45 100644 --- a/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-openapi/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools openapi integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-openapi" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-playgrounds/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-playgrounds/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-playgrounds/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml index a759ed9aa987c..abc58adb42c50 100644 --- a/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-playgrounds/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools playgrounds integration" +keywords = ["blockchain", "decentralized", "graphql", "playgroundsapi", "subgraph", "thegraph"] license = "MIT" +maintainers = ["tachi"] name = "llama-index-tools-playgrounds" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-python-file/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-python-file/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-python-file/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml index 0640824f7bc24..f33fed7edafe8 100644 --- a/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-python-file/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools python_file integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-python-file" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-requests/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-requests/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-requests/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml index 4b3cfe8d81940..abd7098925a85 100644 --- a/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-requests/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools requests integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-requests" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-salesforce/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-salesforce/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-salesforce/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml index ded2270807bc4..b5dca1445d5fc 100644 --- a/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-salesforce/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools salesforce integration" +keywords = ["salesforce"] license = "MIT" +maintainers = ["chrispangg"] name = "llama-index-tools-salesforce" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-shopify/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-shopify/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-shopify/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml index d5dbb82ba4a07..8ff003b4f16c8 100644 --- a/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-shopify/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools shopify integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-shopify" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-slack/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-slack/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-slack/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml index 649325f651787..024f3b6715a8d 100644 --- a/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-slack/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools slack integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-tools-slack" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-tavily-research/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-tavily-research/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-tavily-research/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml index 5f0a20b766cfa..b647d965ee2af 100644 --- a/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-tavily-research/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools tavily_research integration" license = "MIT" +maintainers = ["rotemweiss57"] name = "llama-index-tools-tavily-research" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-text-to-image/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-text-to-image/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-text-to-image/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml index 199ff8ceb6895..b939a09c2e2eb 100644 --- a/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-text-to-image/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools text_to_image integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-text-to-image" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-vector-db/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-vector-db/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-vector-db/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml index eb1e763cfe51c..deb5f0a90e102 100644 --- a/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-vector-db/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools vector_db integration" license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-tools-vector-db" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-waii/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-waii/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-waii/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml index c5296321a6bf2..1cfe59ab14385 100644 --- a/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-waii/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools waii integration" license = "MIT" +maintainers = ["wangdatan"] name = "llama-index-tools-waii" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-weather/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-weather/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-weather/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml index 7a0a72efc0124..746fff567add3 100644 --- a/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-weather/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools weather integration" license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-tools-weather" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-wikipedia/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-wikipedia/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-wikipedia/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml index ddd974f0fc460..090acb1d7f16a 100644 --- a/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-wikipedia/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools wikipedia integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-wikipedia" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml index 23a31c36bfb33..059e51c748be1 100644 --- a/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-wolfram-alpha/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index tools wolfram_alpha integration" +keywords = ["math"] license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-wolfram-alpha" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/.gitignore b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/BUILD b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/BUILD new file mode 100644 index 0000000000000..0896ca890d8bf --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/BUILD @@ -0,0 +1,3 @@ +poetry_requirements( + name="poetry", +) diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/Makefile b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/README.md b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/README.md new file mode 100644 index 0000000000000..21779bb953c08 --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/README.md @@ -0,0 +1,34 @@ +# Yahoo Finance Tool + +This tool connects to Yahoo Finance and allows an Agent to access stock, news, and financial data of a company. + +## Usage + +Here's an example of how to use this tool: + +```python +from llama_index.tools.yahoo_finance import YahooFinanceToolSpec +from llama_index.agent import OpenAIAgent + +tool_spec = YahooFinanceToolSpec() +agent = OpenAIAgent.from_tools(tool_spec.to_tool_list()) + +agent.chat("What is the price of Apple stock?") +agent.chat("What is the latest news about Apple?") +``` + +The tools available are: + +`balance_sheet`: A tool that returns the balance sheet of a company. + +`income_statement`: A tool that returns the income statement of a company. + +`cash_flow`: A tool that returns the cash flow of a company. + +`stock_news`: A tool that returns the latest news about a company. + +`stock_basic_info`: A tool that returns basic information about a company including price. + +`stock_analyst_recommendations`: A tool that returns analyst recommendations for a company. + +This loader is designed to be used as a way to load data as a Tool in a Agent. See [here](https://github.com/emptycrown/llama-hub/tree/main) for examples. diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/examples/yahoo_finance.ipynb b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/examples/yahoo_finance.ipynb new file mode 100644 index 0000000000000..34cf59c1bf7ae --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/examples/yahoo_finance.ipynb @@ -0,0 +1,230 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ba49c2998ed32020", + "metadata": {}, + "source": [ + "## Building a Yahoo Finance Agent\n", + "\n", + "This tutorial walks you through the process of building a Yahoo Finance Agent using the `yahoo_finance` tool. The agent will be able to retrieve stock data, financial statements, and other financial information from Yahoo Finance." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "initial_id", + "metadata": {}, + "outputs": [], + "source": [ + "!pip install llama-index yfinance" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b095b103c429e4cb", + "metadata": {}, + "outputs": [], + "source": [ + "import openai\n", + "from llama_index.agent import OpenAIAgent" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1e91dc5fcc2f4cf4", + "metadata": {}, + "outputs": [], + "source": [ + "openai.api_key = \"sk-...\"" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5b0877f51a351135", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "balance_sheet\n", + "income_statement\n", + "cash_flow\n", + "stock_basic_info\n", + "stock_analyst_recommendations\n", + "stock_news\n" + ] + } + ], + "source": [ + "from llama_index.tools.yahoo_finance.base import YahooFinanceToolSpec\n", + "\n", + "finance_tool = YahooFinanceToolSpec()\n", + "\n", + "finance_tool_list = finance_tool.to_tool_list()\n", + "for tool in finance_tool_list:\n", + " print(tool.metadata.name)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2d1e7865b0301f9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Balance Sheet: \n", + " 2023-09-30 2022-09-30 2021-09-30 2020-09-30\n", + "Treasury Shares Number 0.0 NaN NaN NaN\n", + "Ordinary Shares Number 15550061000.0 15943425000.0 16426786000.0 16976763000.0\n", + "Share Issued 15550061000.0 15943425000.0 16426786000.0 16976763000.0\n", + "Net Debt 81123000000.0 96423000000.0 89779000000.0 74420000000.0\n", + "Total Debt 111088000000.0 120069000000.0 124719000000.0 112436000000.0\n", + "Tangible Book Value 62146000000.0 50672000000.0 63090000000.0 65339000000.0\n", + "Invested Capital 173234000000.0 170741000000.0 187809000000.0 177775000000.0\n", + "Working Capital -1742000000.0 -18577000000.0 9355000000.0 38321000000.0\n", + "Net Tangible Assets 62146000000.0 50672000000.0 63090000000.0 65339000000.0\n", + "Common Stock Equity 62146000000.0 50672000000.0 63090000000.0 65339000000.0\n", + "Total Capitalization 157427000000.0 149631000000.0 172196000000.0 164006000000.0\n", + "Total Equity Gross Minority Interest 62146000000.0 50672000000.0 63090000000.0 65339000000.0\n", + "Stockholders Equity 62146000000.0 50672000000.0 63090000000.0 65339000000.0\n", + "Gains Losses Not Affecting Retained Earnings -11452000000.0 -11109000000.0 163000000.0 -406000000.0\n", + "Retained Earnings -214000000.0 -3068000000.0 5562000000.0 14966000000.0\n", + "Capital Stock 73812000000.0 64849000000.0 57365000000.0 50779000000.0\n", + "Common Stock 73812000000.0 64849000000.0 57365000000.0 50779000000.0\n", + "Total Liabilities Net Minority Interest 290437000000.0 302083000000.0 287912000000.0 258549000000.0\n", + "Total Non Current Liabilities Net Minority Interest 145129000000.0 148101000000.0 162431000000.0 153157000000.0\n", + "Other Non Current Liabilities 34391000000.0 32485000000.0 28636000000.0 26320000000.0\n", + "Tradeand Other Payables Non Current 15457000000.0 16657000000.0 24689000000.0 28170000000.0\n", + "Long Term Debt And Capital Lease Obligation 95281000000.0 98959000000.0 109106000000.0 98667000000.0\n", + "Long Term Debt 95281000000.0 98959000000.0 109106000000.0 98667000000.0\n", + "Current Liabilities 145308000000.0 153982000000.0 125481000000.0 105392000000.0\n", + "Other Current Liabilities 58829000000.0 60845000000.0 47493000000.0 42684000000.0\n", + "Current Deferred Liabilities 8061000000.0 7912000000.0 7612000000.0 6643000000.0\n", + "Current Deferred Revenue 8061000000.0 7912000000.0 7612000000.0 6643000000.0\n", + "Current Debt And Capital Lease Obligation 15807000000.0 21110000000.0 15613000000.0 13769000000.0\n", 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"code", + "execution_count": null, + "id": "ba290fe8f1621839", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The analyst recommendations for AAPL (Apple Inc.) are as follows:\n", + "\n", + "- Current period: \n", + " - Strong Buy: 11\n", + " - Buy: 21\n", + " - Hold: 6\n", + " - Sell: 0\n", + " - Strong Sell: 0\n", + "\n", + "- 1 month ago: \n", + " - Strong Buy: 10\n", + " - Buy: 20\n", + " - Hold: 12\n", + " - Sell: 1\n", + " - Strong Sell: 0\n", + "\n", + "- 2 months ago: \n", + " - Strong Buy: 10\n", + " - Buy: 21\n", + " - Hold: 12\n", + " - Sell: 1\n", + " - Strong Sell: 0\n", + "\n", + "- 3 months ago: \n", + " - Strong Buy: 10\n", + " - Buy: 24\n", + " - Hold: 7\n", + " - Sell: 1\n", + " - Strong Sell: 0\n" + ] + } + ], + "source": [ + "print(agent.chat(\"What are the analyst recommendations for AAPL?\"))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" 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/dev/null +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/llama_index/tools/yahoo_finance/__init__.py @@ -0,0 +1,6 @@ +## init +from llama_index.tools.yahoo_finance.base import ( + YahooFinanceToolSpec, +) + +__all__ = ["YahooFinanceToolSpec"] diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/llama_index/tools/yahoo_finance/base.py b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/llama_index/tools/yahoo_finance/base.py new file mode 100644 index 0000000000000..6df628cbad6bc --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/llama_index/tools/yahoo_finance/base.py @@ -0,0 +1,92 @@ +from llama_index.core.tools.tool_spec.base import BaseToolSpec +import yfinance as yf +import pandas as pd + + +class YahooFinanceToolSpec(BaseToolSpec): + """Yahoo Finance tool spec.""" + + spec_functions = [ + "balance_sheet", + "income_statement", + "cash_flow", + "stock_basic_info", + "stock_analyst_recommendations", + "stock_news", + ] + + def __init__(self) -> None: + """Initialize the Yahoo Finance tool spec.""" + + def balance_sheet(self, ticker: str) -> str: + """ + Return the balance sheet of the stock. + + Args: + ticker (str): the stock ticker to be given to yfinance + + """ + stock = yf.Ticker(ticker) + balance_sheet = pd.DataFrame(stock.balance_sheet) + return "Balance Sheet: \n" + balance_sheet.to_string() + + def income_statement(self, ticker: str) -> str: + """ + Return the income statement of the stock. + + Args: + ticker (str): the stock ticker to be given to yfinance + + """ + stock = yf.Ticker(ticker) + income_statement = pd.DataFrame(stock.income_stmt) + return "Income Statement: \n" + income_statement.to_string() + + def cash_flow(self, ticker: str) -> str: + """ + Return the cash flow of the stock. + + Args: + ticker (str): the stock ticker to be given to yfinance + + """ + stock = yf.Ticker(ticker) + cash_flow = pd.DataFrame(stock.cashflow) + return "Cash Flow: \n" + cash_flow.to_string() + + def stock_basic_info(self, ticker: str) -> str: + """ + Return the basic info of the stock. Ex: price, description, name. + + Args: + ticker (str): the stock ticker to be given to yfinance + + """ + stock = yf.Ticker(ticker) + return "Info: \n" + str(stock.info) + + def stock_analyst_recommendations(self, ticker: str) -> str: + """ + Get the analyst recommendations for a stock. + + Args: + ticker (str): the stock ticker to be given to yfinance + + """ + stock = yf.Ticker(ticker) + return "Recommendations: \n" + str(stock.recommendations) + + def stock_news(self, ticker: str) -> str: + """ + Get the most recent news titles of a stock. + + Args: + ticker (str): the stock ticker to be given to yfinance + + """ + stock = yf.Ticker(ticker) + news = stock.news + out = "News: \n" + for i in news: + out += i["title"] + "\n" + return out diff --git a/llama-index-integrations/tools/llama-index-tools-yahoo-finance/poetry.lock b/llama-index-integrations/tools/llama-index-tools-yahoo-finance/poetry.lock new file mode 100644 index 0000000000000..b37c1712c6f50 --- /dev/null +++ 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2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml index 47630933604c2..68958994b172a 100644 --- a/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-yelp/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools yelp integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-yelp" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/tools/llama-index-tools-zapier/CHANGELOG.md b/llama-index-integrations/tools/llama-index-tools-zapier/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-integrations/tools/llama-index-tools-zapier/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml b/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml index 7dc17d5c7c4bb..dc6fe869686fa 100644 --- a/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml +++ b/llama-index-integrations/tools/llama-index-tools-zapier/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index tools zapier integration" license = "MIT" +maintainers = ["ajhofmann"] name = "llama-index-tools-zapier" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py index 16a51c6532452..b81e5e5512761 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/llama_index/vector_stores/azureaisearch/base.py @@ -143,14 +143,14 @@ def _create_index(self, index_name: Optional[str]) -> None: SearchableField( name=self._field_mapping["chunk"], type="Edm.String", - analyzer_name="en.microsoft", + analyzer_name=self.language_analyzer, ), SearchField( name=self._field_mapping["embedding"], type=SearchFieldDataType.Collection(SearchFieldDataType.Single), searchable=True, vector_search_dimensions=self.embedding_dimensionality, - vector_search_profile_name="default", + vector_search_profile_name=self.vector_profile_name, ), SimpleField(name=self._field_mapping["metadata"], type="Edm.String"), SimpleField( @@ -243,6 +243,10 @@ def __init__( ] = None, index_management: IndexManagement = IndexManagement.NO_VALIDATION, embedding_dimensionality: int = 1536, + vector_algorithm_type: str = "exhaustiveKnn", + # If we have content in other languages, it is better to enable the language analyzer to be adjusted in searchable fields. + # https://learn.microsoft.com/en-us/azure/search/index-add-language-analyzers + language_analyzer: str = "en.lucene", **kwargs: Any, ) -> None: # ruff: noqa: E501 @@ -306,6 +310,16 @@ def __init__( self._search_client: SearchClient = cast(SearchClient, None) self.embedding_dimensionality = embedding_dimensionality + if vector_algorithm_type == "exhaustiveKnn": + self.vector_profile_name = "myExhaustiveKnnProfile" + elif vector_algorithm_type == "hnsw": + self.vector_profile_name = "myHnswProfile" + else: + raise ValueError( + "Only 'exhaustiveKnn' and 'hnsw' are supported for vector_algorithm_type" + ) + + self.language_analyzer = language_analyzer # Validate search_or_index_client if search_or_index_client is not None: if isinstance(search_or_index_client, SearchIndexClient): diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml index 5dc3d8f03f296..db2698e58d185 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-azureaisearch/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index vector_stores azureaisearch integration" license = "MIT" name = "llama-index-vector-stores-azureaisearch" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/tests/test_chromadb.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/tests/test_chromadb.py index d0d718dda9ba8..ccf10b02eacc3 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/tests/test_chromadb.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-chroma/tests/test_chromadb.py @@ -1,16 +1,12 @@ import os -from typing import Dict, List +from typing import Dict, List, Generator import pytest from llama_index.core.schema import NodeRelationship, RelatedNodeInfo, TextNode -from llama_index.core.vector_stores.types import VectorStoreQuery from llama_index.vector_stores.chroma import ChromaVectorStore +from llama_index.core.vector_stores.types import VectorStoreQuery ## -# Start chromadb locally -# cd tests -# docker-compose up -# # Run tests # cd tests/vector_stores # pytest test_chromadb.py @@ -29,20 +25,19 @@ conn__ = chromadb.HttpClient(**PARAMS) # type: ignore conn__.get_or_create_collection(COLLECTION_NAME) - chromadb_not_available = False + http_client_chromadb_mode = True except (ImportError, Exception): - chromadb_not_available = True + http_client_chromadb_mode = False -@pytest.mark.skipif(chromadb_not_available, reason="chromadb is not available") -def test_instance_creation_from_collection() -> None: - connection = chromadb.HttpClient(**PARAMS) - collection = connection.get_collection(COLLECTION_NAME) - store = ChromaVectorStore.from_collection(collection) - assert isinstance(store, ChromaVectorStore) - - -@pytest.mark.skipif(chromadb_not_available, reason="chromadb is not available") +# To test chromadb http-client functionality do: +# cd tests +# docker-compose up +# +@pytest.mark.skipif( + http_client_chromadb_mode is False, + reason="chromadb is not running in http client mode", +) def test_instance_creation_from_http_params() -> None: store = ChromaVectorStore.from_params( host=PARAMS["host"], @@ -53,7 +48,13 @@ def test_instance_creation_from_http_params() -> None: assert isinstance(store, ChromaVectorStore) -@pytest.mark.skipif(chromadb_not_available, reason="chromadb is not available") +def test_instance_creation_from_collection() -> None: + chroma_client = chromadb.Client() + collection = chroma_client.get_or_create_collection(COLLECTION_NAME) + store = ChromaVectorStore.from_collection(collection) + assert isinstance(store, ChromaVectorStore) + + def test_instance_creation_from_persist_dir() -> None: store = ChromaVectorStore.from_params( persist_dir="./data", @@ -64,10 +65,11 @@ def test_instance_creation_from_persist_dir() -> None: @pytest.fixture() -def vector_store() -> ChromaVectorStore: - connection = chromadb.HttpClient(**PARAMS) - collection = connection.get_collection(COLLECTION_NAME) - return ChromaVectorStore(chroma_collection=collection) +def vector_store() -> Generator[ChromaVectorStore, None, None]: + chroma_client = chromadb.Client() + collection = chroma_client.get_or_create_collection(COLLECTION_NAME) + yield ChromaVectorStore(chroma_collection=collection) + chroma_client.delete_collection(name=COLLECTION_NAME) @pytest.fixture(scope="session") @@ -106,7 +108,7 @@ def node_embeddings() -> List[TextNode]: text="I was taught that the way of progress was neither swift nor easy.", id_="0b31ae71-b797-4e88-8495-031371a7752e", relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-3")}, - metadate={ + metadata={ "author": "Marie Curie", }, embedding=[0.0, 0.0, 0.9], @@ -118,7 +120,7 @@ def node_embeddings() -> List[TextNode]: ), id_="bd2e080b-159a-4030-acc3-d98afd2ba49b", relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-4")}, - metadate={ + metadata={ "author": "Albert Einstein", }, embedding=[0.0, 0.0, 0.5], @@ -130,7 +132,7 @@ def node_embeddings() -> List[TextNode]: ), id_="f658de3b-8cef-4d1c-8bed-9a263c907251", relationships={NodeRelationship.SOURCE: RelatedNodeInfo(node_id="text-5")}, - metadate={ + metadata={ "author": "Charlotte Bronte", }, embedding=[0.0, 0.0, 0.3], @@ -138,7 +140,6 @@ def node_embeddings() -> List[TextNode]: ] -@pytest.mark.skipif(chromadb_not_available, reason="chromadb is not available") @pytest.mark.asyncio() @pytest.mark.parametrize("use_async", [True, False]) async def test_add_to_chromadb_and_query( diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py index 4a6d96b19c332..734da12f31b61 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-deeplake/llama_index/vector_stores/deeplake/base.py @@ -69,37 +69,24 @@ def __init__( ) -> None: """ Args: - dataset_path (str): Path to the deeplake dataset, where data will be - stored. Defaults to "llama_index". - overwrite (bool, optional): Whether to overwrite existing dataset with same - name. Defaults to False. - token (str, optional): the deeplake token that allows you to access the - dataset with proper access. Defaults to None. - read_only (bool, optional): Whether to open the dataset with read only mode. - ingestion_batch_size (int): used for controlling batched data - ingestion to deeplake dataset. Defaults to 1024. + dataset_path (str): The full path for storing to the Deep Lake Vector Store. It can be: + - a Deep Lake cloud path of the form ``hub://org_id/dataset_name``. Requires registration with Deep Lake. + - an s3 path of the form ``s3://bucketname/path/to/dataset``. Credentials are required in either the environment or passed to the creds argument. + - a local file system path of the form ``./path/to/dataset`` or ``~/path/to/dataset`` or ``path/to/dataset``. + - a memory path of the form ``mem://path/to/dataset`` which doesn't save the dataset but keeps it in memory instead. Should be used only for testing as it does not persist. + Defaults to "llama_index". + overwrite (bool, optional): If set to True this overwrites the Vector Store if it already exists. Defaults to False. + token (str, optional): Activeloop token, used for fetching user credentials. This is Optional, tokens are normally autogenerated. Defaults to None. + read_only (bool, optional): Opens dataset in read-only mode if True. Defaults to False. + ingestion_batch_size (int): During data ingestion, data is divided + into batches. Batch size is the size of each batch. Defaults to 1024. ingestion_num_workers (int): number of workers to use during data ingestion. Defaults to 4. - overwrite (bool): Whether to overwrite existing dataset with the - new dataset with the same name. - exec_option (str): Default method for search execution. It could be either - It could be either ``"python"``, ``"compute_engine"`` or - ``"tensor_db"``. Defaults to ``"python"``. - - ``python`` - Pure-python implementation that runs on the client and - can be used for data stored anywhere. WARNING: using this option - with big datasets is discouraged because it can lead to memory - issues. - - ``compute_engine`` - Performant C++ implementation of the Deep Lake - Compute Engine that runs on the client and can be used for any data - stored in or connected to Deep Lake. It cannot be used with - in-memory or local datasets. - - ``tensor_db`` - Performant and fully-hosted Managed Tensor Database - that is responsible for storage and query execution. Only available - for data stored in the Deep Lake Managed Database. Store datasets in - this database by specifying runtime = {"tensor_db": True} during - dataset creation. - verbose (bool): Specify if verbose output is enabled. Default is True. - **kwargs (Any): Additional keyword arguments. + exec_option (str): Default method for search execution. It could be either ``"auto"``, ``"python"``, ``"compute_engine"`` or ``"tensor_db"``. Defaults to ``"auto"``. If None, it's set to "auto". + - ``auto``- Selects the best execution method based on the storage location of the Vector Store. It is the default option. + - ``python`` - Pure-python implementation that runs on the client and can be used for data stored anywhere. WARNING: using this option with big datasets is discouraged because it can lead to memory issues. + - ``compute_engine`` - Performant C++ implementation of the Deep Lake Compute Engine that runs on the client and can be used for any data stored in or connected to Deep Lake. It cannot be used with in-memory or local datasets. + - ``tensor_db`` - Performant and fully-hosted Managed Tensor Database that is responsible for storage and query execution. Only available for data stored in the Deep Lake Managed Database. Store datasets in this database by specifying runtime = {"tensor_db": True} during dataset creation. Raises: ImportError: Unable to import `deeplake`. diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py index abd3776fecbaa..c883a3e39ae68 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/llama_index/vector_stores/mongodb/base.py @@ -9,10 +9,11 @@ from importlib.metadata import version from typing import Any, Dict, List, Optional, cast +from llama_index.core.bridge.pydantic import PrivateAttr from llama_index.core.schema import BaseNode, MetadataMode, TextNode from llama_index.core.vector_stores.types import ( MetadataFilters, - VectorStore, + BasePydanticVectorStore, VectorStoreQuery, VectorStoreQueryResult, ) @@ -35,7 +36,7 @@ def _to_mongodb_filter(standard_filters: MetadataFilters) -> Dict: return filters -class MongoDBAtlasVectorSearch(VectorStore): +class MongoDBAtlasVectorSearch(BasePydanticVectorStore): """MongoDB Atlas Vector Store. To use, you should have both: @@ -48,6 +49,15 @@ class MongoDBAtlasVectorSearch(VectorStore): stores_text: bool = True flat_metadata: bool = True + _mongodb_client: Any = PrivateAttr() + _collection: Any = PrivateAttr() + _index_name: str = PrivateAttr() + _embedding_key: str = PrivateAttr() + _id_key: str = PrivateAttr() + _text_key: str = PrivateAttr() + _metadata_key: str = PrivateAttr() + _insert_kwargs: Dict = PrivateAttr() + def __init__( self, mongodb_client: Optional[Any] = None, @@ -97,6 +107,8 @@ def __init__( self._metadata_key = metadata_key self._insert_kwargs = insert_kwargs or {} + super().__init__() + def add( self, nodes: List[BaseNode], diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml index 1fd00c14843fe..de01ba5aae5e6 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/pyproject.toml @@ -24,7 +24,7 @@ description = "llama-index vector_stores mongodb integration" license = "MIT" name = "llama-index-vector-stores-mongodb" readme = "README.md" -version = "0.1.1" +version = "0.1.3" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/tests/test_vector_stores_mongodb.py b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/tests/test_vector_stores_mongodb.py index 88017acc967ff..4066dae44415f 100644 --- a/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/tests/test_vector_stores_mongodb.py +++ b/llama-index-integrations/vector_stores/llama-index-vector-stores-mongodb/tests/test_vector_stores_mongodb.py @@ -1,7 +1,7 @@ -from llama_index.core.vector_stores.types import VectorStore +from llama_index.core.vector_stores.types import BasePydanticVectorStore from llama_index.vector_stores.mongodb import MongoDBAtlasVectorSearch def test_class(): names_of_base_classes = [b.__name__ for b in MongoDBAtlasVectorSearch.__mro__] - assert VectorStore.__name__ in names_of_base_classes + assert BasePydanticVectorStore.__name__ in names_of_base_classes diff --git a/llama-index-legacy/llama_index/legacy/callbacks/__init__.py b/llama-index-legacy/llama_index/legacy/callbacks/__init__.py index 097353e3bd341..d419baa947969 100644 --- a/llama-index-legacy/llama_index/legacy/callbacks/__init__.py +++ b/llama-index-legacy/llama_index/legacy/callbacks/__init__.py @@ -5,6 +5,7 @@ from .open_inference_callback import OpenInferenceCallbackHandler from .schema import CBEvent, CBEventType, EventPayload from .token_counting import TokenCountingHandler +from .uptrain_callback import UpTrainCallbackHandler from .utils import trace_method from .wandb_callback import WandbCallbackHandler @@ -21,4 +22,5 @@ "OpenAIFineTuningHandler", "GradientAIFineTuningHandler", "trace_method", + "UpTrainCallbackHandler", ] diff --git a/llama-index-legacy/llama_index/legacy/callbacks/global_handlers.py b/llama-index-legacy/llama_index/legacy/callbacks/global_handlers.py index f191de2181bda..c52ed3ed354f1 100644 --- a/llama-index-legacy/llama_index/legacy/callbacks/global_handlers.py +++ b/llama-index-legacy/llama_index/legacy/callbacks/global_handlers.py @@ -14,6 +14,7 @@ ) from llama_index.legacy.callbacks.promptlayer_handler import PromptLayerHandler from llama_index.legacy.callbacks.simple_llm_handler import SimpleLLMHandler +from llama_index.legacy.callbacks.uptrain_callback import UpTrainCallbackHandler from llama_index.legacy.callbacks.wandb_callback import WandbCallbackHandler @@ -40,6 +41,8 @@ def create_global_handler(eval_mode: str, **eval_params: Any) -> BaseCallbackHan handler = deepeval_callback_handler(**eval_params) elif eval_mode == "simple": handler = SimpleLLMHandler(**eval_params) + elif eval_mode == "uptrain": + handler = UpTrainCallbackHandler(**eval_params) elif eval_mode == "argilla": handler = argilla_callback_handler(**eval_params) else: diff --git a/llama-index-legacy/llama_index/legacy/indices/knowledge_graph/base.py b/llama-index-legacy/llama_index/legacy/indices/knowledge_graph/base.py index a09fd5b937789..6d94aad25ee13 100644 --- a/llama-index-legacy/llama_index/legacy/indices/knowledge_graph/base.py +++ b/llama-index-legacy/llama_index/legacy/indices/knowledge_graph/base.py @@ -217,17 +217,25 @@ def _insert(self, nodes: Sequence[BaseNode], **insert_kwargs: Any) -> None: ) self._index_struct.add_to_embedding_dict(triplet_str, rel_embedding) - def upsert_triplet(self, triplet: Tuple[str, str, str]) -> None: - """Insert triplets. + def upsert_triplet( + self, triplet: Tuple[str, str, str], include_embeddings: bool = False + ) -> None: + """Insert triplets and optionally embeddings. Used for manual insertion of KG triplets (in the form of (subject, relationship, object)). Args: - triplet (str): Knowledge triplet - + triplet (tuple): Knowledge triplet + embedding (Any, optional): Embedding option for the triplet. Defaults to None. """ self._graph_store.upsert_triplet(*triplet) + triplet_str = str(triplet) + if include_embeddings: + set_embedding = self._service_context.embed_model.get_text_embedding( + triplet_str + ) + self._index_struct.add_to_embedding_dict(str(triplet), set_embedding) def add_node(self, keywords: List[str], node: BaseNode) -> None: """Add node. @@ -243,7 +251,10 @@ def add_node(self, keywords: List[str], node: BaseNode) -> None: self._docstore.add_documents([node], allow_update=True) def upsert_triplet_and_node( - self, triplet: Tuple[str, str, str], node: BaseNode + self, + triplet: Tuple[str, str, str], + node: BaseNode, + include_embeddings: bool = False, ) -> None: """Upsert KG triplet and node. @@ -254,11 +265,18 @@ def upsert_triplet_and_node( Args: keywords (List[str]): Keywords to index the node. node (Node): Node to be indexed. + include_embeddings (bool): Option to add embeddings for triplets. Defaults to False """ subj, _, obj = triplet self.upsert_triplet(triplet) self.add_node([subj, obj], node) + triplet_str = str(triplet) + if include_embeddings: + set_embedding = self._service_context.embed_model.get_text_embedding( + triplet_str + ) + self._index_struct.add_to_embedding_dict(str(triplet), set_embedding) def _delete_node(self, node_id: str, **delete_kwargs: Any) -> None: """Delete a node.""" diff --git a/llama-index-legacy/tests/agent/custom/BUILD b/llama-index-legacy/tests/agent/custom/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/agent/custom/BUILD +++ b/llama-index-legacy/tests/agent/custom/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/agent/openai/BUILD b/llama-index-legacy/tests/agent/openai/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/agent/openai/BUILD +++ b/llama-index-legacy/tests/agent/openai/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/agent/react/BUILD b/llama-index-legacy/tests/agent/react/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/agent/react/BUILD +++ b/llama-index-legacy/tests/agent/react/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/agent/runner/BUILD b/llama-index-legacy/tests/agent/runner/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/agent/runner/BUILD +++ b/llama-index-legacy/tests/agent/runner/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/callbacks/BUILD b/llama-index-legacy/tests/callbacks/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/callbacks/BUILD +++ b/llama-index-legacy/tests/callbacks/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/chat_engine/BUILD b/llama-index-legacy/tests/chat_engine/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/chat_engine/BUILD +++ b/llama-index-legacy/tests/chat_engine/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/embeddings/BUILD b/llama-index-legacy/tests/embeddings/BUILD index c961ee65a4ae1..4fd9889d4d4b4 100644 --- a/llama-index-legacy/tests/embeddings/BUILD +++ b/llama-index-legacy/tests/embeddings/BUILD @@ -2,6 +2,7 @@ python_sources() python_tests( name="tests", + skip_tests=True, dependencies=[ "!!llama-index-core:poetry", "!!llama-index-core/pyproject.toml:poetry", diff --git a/llama-index-legacy/tests/evaluation/BUILD b/llama-index-legacy/tests/evaluation/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/evaluation/BUILD +++ b/llama-index-legacy/tests/evaluation/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/extractors/BUILD b/llama-index-legacy/tests/extractors/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/extractors/BUILD +++ b/llama-index-legacy/tests/extractors/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/finetuning/BUILD b/llama-index-legacy/tests/finetuning/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/finetuning/BUILD +++ b/llama-index-legacy/tests/finetuning/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/BUILD b/llama-index-legacy/tests/indices/BUILD index f48e68990b6ae..829c31b3437a6 100644 --- a/llama-index-legacy/tests/indices/BUILD +++ b/llama-index-legacy/tests/indices/BUILD @@ -6,4 +6,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/composability/BUILD b/llama-index-legacy/tests/indices/composability/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/indices/composability/BUILD +++ b/llama-index-legacy/tests/indices/composability/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/document_summary/BUILD b/llama-index-legacy/tests/indices/document_summary/BUILD index f48e68990b6ae..829c31b3437a6 100644 --- a/llama-index-legacy/tests/indices/document_summary/BUILD +++ b/llama-index-legacy/tests/indices/document_summary/BUILD @@ -6,4 +6,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/empty/BUILD b/llama-index-legacy/tests/indices/empty/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/indices/empty/BUILD +++ b/llama-index-legacy/tests/indices/empty/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/keyword_table/BUILD b/llama-index-legacy/tests/indices/keyword_table/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/indices/keyword_table/BUILD +++ b/llama-index-legacy/tests/indices/keyword_table/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/knowledge_graph/BUILD b/llama-index-legacy/tests/indices/knowledge_graph/BUILD index f48e68990b6ae..829c31b3437a6 100644 --- a/llama-index-legacy/tests/indices/knowledge_graph/BUILD +++ b/llama-index-legacy/tests/indices/knowledge_graph/BUILD @@ -6,4 +6,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/list/BUILD b/llama-index-legacy/tests/indices/list/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/indices/list/BUILD +++ b/llama-index-legacy/tests/indices/list/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/managed/BUILD b/llama-index-legacy/tests/indices/managed/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/indices/managed/BUILD +++ b/llama-index-legacy/tests/indices/managed/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/query/BUILD b/llama-index-legacy/tests/indices/query/BUILD index f48e68990b6ae..829c31b3437a6 100644 --- a/llama-index-legacy/tests/indices/query/BUILD +++ b/llama-index-legacy/tests/indices/query/BUILD @@ -6,4 +6,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/query/query_transform/BUILD b/llama-index-legacy/tests/indices/query/query_transform/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/indices/query/query_transform/BUILD +++ b/llama-index-legacy/tests/indices/query/query_transform/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/response/BUILD b/llama-index-legacy/tests/indices/response/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/indices/response/BUILD +++ b/llama-index-legacy/tests/indices/response/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/struct_store/BUILD b/llama-index-legacy/tests/indices/struct_store/BUILD index f48e68990b6ae..829c31b3437a6 100644 --- a/llama-index-legacy/tests/indices/struct_store/BUILD +++ b/llama-index-legacy/tests/indices/struct_store/BUILD @@ -6,4 +6,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/indices/tree/BUILD b/llama-index-legacy/tests/indices/tree/BUILD index 40ac166288421..7107a6517aef4 100644 --- a/llama-index-legacy/tests/indices/tree/BUILD +++ b/llama-index-legacy/tests/indices/tree/BUILD @@ -4,6 +4,7 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) python_sources() diff --git a/llama-index-legacy/tests/indices/vector_store/BUILD b/llama-index-legacy/tests/indices/vector_store/BUILD index de9a3569f120d..a88e9ef158b72 100644 --- a/llama-index-legacy/tests/indices/vector_store/BUILD +++ b/llama-index-legacy/tests/indices/vector_store/BUILD @@ -4,6 +4,7 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, dependencies=[ "!!llama-index-core:poetry", "!!llama-index-core/pyproject.toml:poetry", diff --git a/llama-index-legacy/tests/indices/vector_store/auto_retriever/BUILD b/llama-index-legacy/tests/indices/vector_store/auto_retriever/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/indices/vector_store/auto_retriever/BUILD +++ b/llama-index-legacy/tests/indices/vector_store/auto_retriever/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/ingestion/BUILD b/llama-index-legacy/tests/ingestion/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/ingestion/BUILD +++ b/llama-index-legacy/tests/ingestion/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/ingestion/test_pipeline.py b/llama-index-legacy/tests/ingestion/test_pipeline.py index 3a7302c8949f0..a41e00254979a 100644 --- a/llama-index-legacy/tests/ingestion/test_pipeline.py +++ b/llama-index-legacy/tests/ingestion/test_pipeline.py @@ -1,162 +1,45 @@ -from multiprocessing import cpu_count +from typing import Any, Dict -from llama_index.legacy.embeddings import OpenAIEmbedding -from llama_index.legacy.extractors import KeywordExtractor -from llama_index.legacy.ingestion.pipeline import IngestionPipeline -from llama_index.legacy.llms import MockLLM -from llama_index.legacy.node_parser import SentenceSplitter -from llama_index.legacy.readers import ReaderConfig, StringIterableReader -from llama_index.legacy.schema import Document -from llama_index.legacy.storage.docstore import SimpleDocumentStore +from llama_index.legacy.embeddings import ( + HuggingFaceEmbedding, + OpenAIEmbedding, +) +from llama_index.legacy.embeddings.utils import resolve_embed_model +from llama_index.legacy.token_counter.mock_embed_model import MockEmbedding +from pytest import MonkeyPatch -# clean up folders after tests -def teardown_function() -> None: - import shutil +def mock_hf_embeddings(*args: Any, **kwargs: Dict[str, Any]) -> Any: + """Mock HuggingFaceEmbeddings.""" + return - shutil.rmtree("./test_pipeline", ignore_errors=True) - -def test_build_pipeline() -> None: - pipeline = IngestionPipeline( - reader=ReaderConfig( - reader=StringIterableReader(), reader_kwargs={"texts": ["This is a test."]} - ), - documents=[Document.example()], - transformations=[ - SentenceSplitter(), - KeywordExtractor(llm=MockLLM()), - OpenAIEmbedding(api_key="fake"), - ], - ) - - assert len(pipeline.transformations) == 3 - - -def test_run_pipeline() -> None: - pipeline = IngestionPipeline( - reader=ReaderConfig( - reader=StringIterableReader(), reader_kwargs={"texts": ["This is a test."]} - ), - documents=[Document.example()], - transformations=[ - SentenceSplitter(), - KeywordExtractor(llm=MockLLM()), - ], - ) - - nodes = pipeline.run() - - assert len(nodes) == 2 - assert len(nodes[0].metadata) > 0 - - -def test_save_load_pipeline() -> None: - documents = [ - Document(text="one", doc_id="1"), - Document(text="two", doc_id="2"), - Document(text="one", doc_id="1"), - ] - - pipeline = IngestionPipeline( - transformations=[ - SentenceSplitter(chunk_size=25, chunk_overlap=0), - ], - docstore=SimpleDocumentStore(), - ) - - nodes = pipeline.run(documents=documents) - assert len(nodes) == 2 - assert pipeline.docstore is not None - assert len(pipeline.docstore.docs) == 2 - - # dedup will catch the last node - nodes = pipeline.run(documents=[documents[-1]]) - assert len(nodes) == 0 - assert pipeline.docstore is not None - assert len(pipeline.docstore.docs) == 2 - - # test save/load - pipeline.persist("./test_pipeline") - - pipeline2 = IngestionPipeline( - transformations=[ - SentenceSplitter(chunk_size=25, chunk_overlap=0), - ], - ) - - pipeline2.load("./test_pipeline") - - # dedup will catch the last node - nodes = pipeline.run(documents=[documents[-1]]) - assert len(nodes) == 0 - assert pipeline.docstore is not None - assert len(pipeline.docstore.docs) == 2 +def mock_openai_embeddings(*args: Any, **kwargs: Dict[str, Any]) -> Any: + """Mock OpenAIEmbedding.""" + return -def test_pipeline_update() -> None: - document1 = Document.example() - document1.id_ = "1" - - pipeline = IngestionPipeline( - transformations=[ - SentenceSplitter(chunk_size=25, chunk_overlap=0), - ], - docstore=SimpleDocumentStore(), +def test_resolve_embed_model(monkeypatch: MonkeyPatch) -> None: + monkeypatch.setattr( + "llama_index.legacy.embeddings.huggingface.HuggingFaceEmbedding.__init__", + mock_hf_embeddings, ) - - nodes = pipeline.run(documents=[document1]) - assert len(nodes) == 19 - assert pipeline.docstore is not None - assert len(pipeline.docstore.docs) == 1 - - # adjust document content - document1 = Document(text="test", doc_id="1") - - # run pipeline again - nodes = pipeline.run(documents=[document1]) - - assert len(nodes) == 1 - assert pipeline.docstore is not None - assert len(pipeline.docstore.docs) == 1 - assert next(iter(pipeline.docstore.docs.values())).text == "test" # type: ignore - - -def test_pipeline_dedup_duplicates_only() -> None: - documents = [ - Document(text="one", doc_id="1"), - Document(text="two", doc_id="2"), - Document(text="three", doc_id="3"), - ] - - pipeline = IngestionPipeline( - transformations=[ - SentenceSplitter(chunk_size=25, chunk_overlap=0), - ], - docstore=SimpleDocumentStore(), + monkeypatch.setattr( + "llama_index.legacy.embeddings.OpenAIEmbedding.__init__", mock_openai_embeddings ) - nodes = pipeline.run(documents=documents) - assert len(nodes) == 3 + # Test None + embed_model = resolve_embed_model(None) + assert isinstance(embed_model, MockEmbedding) - nodes = pipeline.run(documents=documents) - assert len(nodes) == 0 + # Test str + embed_model = resolve_embed_model("local") + assert isinstance(embed_model, HuggingFaceEmbedding) + # Test LCEmbeddings + embed_model = resolve_embed_model(HuggingFaceEmbedding()) + assert isinstance(embed_model, HuggingFaceEmbedding) -def test_pipeline_parallel() -> None: - document1 = Document.example() - document1.id_ = "1" - document2 = Document(text="One\n\n\nTwo\n\n\nThree.", doc_id="2") - - pipeline = IngestionPipeline( - transformations=[ - SentenceSplitter(chunk_size=25, chunk_overlap=0), - ], - docstore=SimpleDocumentStore(), - ) - - num_workers = min(2, cpu_count()) - nodes = pipeline.run(documents=[document1, document2], num_workers=num_workers) - assert len(nodes) == 20 - assert pipeline.docstore is not None - assert len(pipeline.docstore.docs) == 2 + # Test BaseEmbedding + embed_model = resolve_embed_model(OpenAIEmbedding()) + assert isinstance(embed_model, OpenAIEmbedding) diff --git a/llama-index-legacy/tests/llm_predictor/BUILD b/llama-index-legacy/tests/llm_predictor/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/llm_predictor/BUILD +++ b/llama-index-legacy/tests/llm_predictor/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/llm_predictor/vellum/BUILD b/llama-index-legacy/tests/llm_predictor/vellum/BUILD index d20ffc39f7022..26312c34480e5 100644 --- a/llama-index-legacy/tests/llm_predictor/vellum/BUILD +++ b/llama-index-legacy/tests/llm_predictor/vellum/BUILD @@ -4,4 +4,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/llms/BUILD b/llama-index-legacy/tests/llms/BUILD index b8583f391e3e8..3cf92e7132765 100644 --- a/llama-index-legacy/tests/llms/BUILD +++ b/llama-index-legacy/tests/llms/BUILD @@ -1,5 +1,6 @@ python_tests( name="tests", + skip_tests=True, dependencies=[ "!!llama-index-core:poetry", "!!llama-index-core/pyproject.toml:poetry", diff --git a/llama-index-legacy/tests/logger/BUILD b/llama-index-legacy/tests/logger/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/logger/BUILD +++ b/llama-index-legacy/tests/logger/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/memory/BUILD b/llama-index-legacy/tests/memory/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/memory/BUILD +++ b/llama-index-legacy/tests/memory/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/multi_modal_llms/BUILD b/llama-index-legacy/tests/multi_modal_llms/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/multi_modal_llms/BUILD +++ b/llama-index-legacy/tests/multi_modal_llms/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/node_parser/BUILD b/llama-index-legacy/tests/node_parser/BUILD index c961ee65a4ae1..4fd9889d4d4b4 100644 --- a/llama-index-legacy/tests/node_parser/BUILD +++ b/llama-index-legacy/tests/node_parser/BUILD @@ -2,6 +2,7 @@ python_sources() python_tests( name="tests", + skip_tests=True, dependencies=[ "!!llama-index-core:poetry", "!!llama-index-core/pyproject.toml:poetry", diff --git a/llama-index-legacy/tests/objects/BUILD b/llama-index-legacy/tests/objects/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/objects/BUILD +++ b/llama-index-legacy/tests/objects/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/output_parsers/BUILD b/llama-index-legacy/tests/output_parsers/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/output_parsers/BUILD +++ b/llama-index-legacy/tests/output_parsers/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/param_tuner/BUILD b/llama-index-legacy/tests/param_tuner/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/param_tuner/BUILD +++ b/llama-index-legacy/tests/param_tuner/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/playground/BUILD b/llama-index-legacy/tests/playground/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/playground/BUILD +++ b/llama-index-legacy/tests/playground/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/postprocessor/BUILD b/llama-index-legacy/tests/postprocessor/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/postprocessor/BUILD +++ b/llama-index-legacy/tests/postprocessor/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/program/BUILD b/llama-index-legacy/tests/program/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/program/BUILD +++ b/llama-index-legacy/tests/program/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/prompts/BUILD b/llama-index-legacy/tests/prompts/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/prompts/BUILD +++ b/llama-index-legacy/tests/prompts/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/query_engine/BUILD b/llama-index-legacy/tests/query_engine/BUILD index b8583f391e3e8..3cf92e7132765 100644 --- a/llama-index-legacy/tests/query_engine/BUILD +++ b/llama-index-legacy/tests/query_engine/BUILD @@ -1,5 +1,6 @@ python_tests( name="tests", + skip_tests=True, dependencies=[ "!!llama-index-core:poetry", "!!llama-index-core/pyproject.toml:poetry", diff --git a/llama-index-legacy/tests/query_pipeline/BUILD b/llama-index-legacy/tests/query_pipeline/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/query_pipeline/BUILD +++ b/llama-index-legacy/tests/query_pipeline/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/query_pipeline/components/BUILD b/llama-index-legacy/tests/query_pipeline/components/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/query_pipeline/components/BUILD +++ b/llama-index-legacy/tests/query_pipeline/components/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/question_gen/BUILD b/llama-index-legacy/tests/question_gen/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/question_gen/BUILD +++ b/llama-index-legacy/tests/question_gen/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/readers/BUILD b/llama-index-legacy/tests/readers/BUILD index c961ee65a4ae1..4fd9889d4d4b4 100644 --- a/llama-index-legacy/tests/readers/BUILD +++ b/llama-index-legacy/tests/readers/BUILD @@ -2,6 +2,7 @@ python_sources() python_tests( name="tests", + skip_tests=True, dependencies=[ "!!llama-index-core:poetry", "!!llama-index-core/pyproject.toml:poetry", diff --git a/llama-index-legacy/tests/response_synthesizers/BUILD b/llama-index-legacy/tests/response_synthesizers/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/response_synthesizers/BUILD +++ b/llama-index-legacy/tests/response_synthesizers/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/retrievers/BUILD b/llama-index-legacy/tests/retrievers/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/retrievers/BUILD +++ b/llama-index-legacy/tests/retrievers/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/selectors/BUILD b/llama-index-legacy/tests/selectors/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/selectors/BUILD +++ b/llama-index-legacy/tests/selectors/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/storage/BUILD b/llama-index-legacy/tests/storage/BUILD index d20ffc39f7022..26312c34480e5 100644 --- a/llama-index-legacy/tests/storage/BUILD +++ b/llama-index-legacy/tests/storage/BUILD @@ -4,4 +4,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/storage/chat_store/BUILD b/llama-index-legacy/tests/storage/chat_store/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/storage/chat_store/BUILD +++ b/llama-index-legacy/tests/storage/chat_store/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/storage/docstore/BUILD b/llama-index-legacy/tests/storage/docstore/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/storage/docstore/BUILD +++ b/llama-index-legacy/tests/storage/docstore/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/storage/index_store/BUILD b/llama-index-legacy/tests/storage/index_store/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/storage/index_store/BUILD +++ b/llama-index-legacy/tests/storage/index_store/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/storage/kvstore/BUILD b/llama-index-legacy/tests/storage/kvstore/BUILD index 0eea8b1cf1a32..1d58cc63c8f14 100644 --- a/llama-index-legacy/tests/storage/kvstore/BUILD +++ b/llama-index-legacy/tests/storage/kvstore/BUILD @@ -2,4 +2,5 @@ python_sources() python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/text_splitter/BUILD b/llama-index-legacy/tests/text_splitter/BUILD index d20ffc39f7022..26312c34480e5 100644 --- a/llama-index-legacy/tests/text_splitter/BUILD +++ b/llama-index-legacy/tests/text_splitter/BUILD @@ -4,4 +4,5 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/token_predictor/BUILD b/llama-index-legacy/tests/token_predictor/BUILD index 00e59b30170b1..134d0f2fdba31 100644 --- a/llama-index-legacy/tests/token_predictor/BUILD +++ b/llama-index-legacy/tests/token_predictor/BUILD @@ -1,5 +1,6 @@ python_tests( name="tests", + skip_tests=True, ) python_sources() diff --git a/llama-index-legacy/tests/tools/BUILD b/llama-index-legacy/tests/tools/BUILD index 40ac166288421..7107a6517aef4 100644 --- a/llama-index-legacy/tests/tools/BUILD +++ b/llama-index-legacy/tests/tools/BUILD @@ -4,6 +4,7 @@ python_test_utils( python_tests( name="tests", + skip_tests=True, ) python_sources() diff --git a/llama-index-legacy/tests/tools/tool_spec/BUILD b/llama-index-legacy/tests/tools/tool_spec/BUILD index 00e59b30170b1..134d0f2fdba31 100644 --- a/llama-index-legacy/tests/tools/tool_spec/BUILD +++ b/llama-index-legacy/tests/tools/tool_spec/BUILD @@ -1,5 +1,6 @@ python_tests( name="tests", + skip_tests=True, ) python_sources() diff --git a/llama-index-legacy/tests/utilities/BUILD b/llama-index-legacy/tests/utilities/BUILD index 57341b1358b56..03cf00dcf3579 100644 --- a/llama-index-legacy/tests/utilities/BUILD +++ b/llama-index-legacy/tests/utilities/BUILD @@ -1,3 +1,4 @@ python_tests( name="tests", + skip_tests=True, ) diff --git a/llama-index-legacy/tests/vector_stores/BUILD b/llama-index-legacy/tests/vector_stores/BUILD index b8583f391e3e8..3cf92e7132765 100644 --- a/llama-index-legacy/tests/vector_stores/BUILD +++ b/llama-index-legacy/tests/vector_stores/BUILD @@ -1,5 +1,6 @@ python_tests( name="tests", + skip_tests=True, dependencies=[ "!!llama-index-core:poetry", "!!llama-index-core/pyproject.toml:poetry", diff --git a/llama-index-packs/llama-index-packs-agent-search-retriever/CHANGELOG.md b/llama-index-packs/llama-index-packs-agent-search-retriever/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-agent-search-retriever/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml index 0ae1107511c87..abca133f0a77f 100644 --- a/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-agent-search-retriever/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs agent_search_retriever integration" +keywords = ["agent", "retriever", "search"] license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-packs-agent-search-retriever" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-packs/llama-index-packs-agents-llm-compiler/CHANGELOG.md b/llama-index-packs/llama-index-packs-agents-llm-compiler/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-agents-llm-compiler/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml b/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml index 50284e73130ea..dfb4c9051e0f7 100644 --- a/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml +++ b/llama-index-packs/llama-index-packs-agents-llm-compiler/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs agents llm compiler" +keywords = ["agent", "compiler", "llm"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-agents-llm-compiler" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-amazon-product-extraction/CHANGELOG.md b/llama-index-packs/llama-index-packs-amazon-product-extraction/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-amazon-product-extraction/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml b/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml index 2c59063594c9b..f5aefa2aa7514 100644 --- a/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml +++ b/llama-index-packs/llama-index-packs-amazon-product-extraction/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs amazon_product_extraction integration" +keywords = ["amazon", "extraction", "product"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-amazon-product-extraction" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/CHANGELOG.md b/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml index df4018e47666d..0b021cedbc93c 100644 --- a/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-arize-phoenix-query-engine/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs arize_phoenix_query_engine integration" +keywords = ["arize", "engine", "index", "phoenix", "query"] license = "MIT" +maintainers = ["axiomofjoy"] name = "llama-index-packs-arize-phoenix-query-engine" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-auto-merging-retriever/CHANGELOG.md b/llama-index-packs/llama-index-packs-auto-merging-retriever/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-auto-merging-retriever/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml index ea37093e849d9..5ed7a2dbd2b9b 100644 --- a/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-auto-merging-retriever/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs auto_merging_retriever integration" +keywords = ["auto", "automerging", "index", "merging", "retriever"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-auto-merging-retriever" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-chroma-autoretrieval/CHANGELOG.md b/llama-index-packs/llama-index-packs-chroma-autoretrieval/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-chroma-autoretrieval/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml b/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml index 9691432d9cf7f..3d00dd3d031b1 100644 --- a/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-chroma-autoretrieval/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs chroma_autoretrieval integration" +keywords = ["chroma", "retrieval", "vector"] license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-packs-chroma-autoretrieval" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-cogniswitch-agent/CHANGELOG.md b/llama-index-packs/llama-index-packs-cogniswitch-agent/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-cogniswitch-agent/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml b/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml index 409ca98282ada..6393b705035cb 100644 --- a/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml +++ b/llama-index-packs/llama-index-packs-cogniswitch-agent/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs cogniswitch_agent integration" +keywords = ["embedding", "graph", "knowledge graph", "neural", "symbolic"] license = "MIT" +maintainers = ["cogniswitch"] name = "llama-index-packs-cogniswitch-agent" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-corrective-rag/.gitignore b/llama-index-packs/llama-index-packs-corrective-rag/.gitignore new file mode 100644 index 0000000000000..990c18de22908 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/.gitignore @@ -0,0 +1,153 @@ +llama_index/_static +.DS_Store +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +bin/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +etc/ +include/ +lib/ +lib64/ +parts/ +sdist/ +share/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +.ruff_cache + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints +notebooks/ + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ +pyvenv.cfg + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# Jetbrains +.idea +modules/ +*.swp + +# VsCode +.vscode + +# pipenv +Pipfile +Pipfile.lock + +# pyright +pyrightconfig.json diff --git a/llama-index-packs/llama-index-packs-corrective-rag/BUILD b/llama-index-packs/llama-index-packs-corrective-rag/BUILD new file mode 100644 index 0000000000000..0b67818b466ca --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/BUILD @@ -0,0 +1,5 @@ +python_sources() + +poetry_requirements( + name="poetry", +) diff --git a/llama-index-packs/llama-index-packs-corrective-rag/Makefile b/llama-index-packs/llama-index-packs-corrective-rag/Makefile new file mode 100644 index 0000000000000..b9eab05aa3706 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/Makefile @@ -0,0 +1,17 @@ +GIT_ROOT ?= $(shell git rev-parse --show-toplevel) + +help: ## Show all Makefile targets. + @grep -E '^[a-zA-Z_-]+:.*?## .*$$' $(MAKEFILE_LIST) | awk 'BEGIN {FS = ":.*?## "}; {printf "\033[33m%-30s\033[0m %s\n", $$1, $$2}' + +format: ## Run code autoformatters (black). + pre-commit install + git ls-files | xargs pre-commit run black --files + +lint: ## Run linters: pre-commit (black, ruff, codespell) and mypy + pre-commit install && git ls-files | xargs pre-commit run --show-diff-on-failure --files + +test: ## Run tests via pytest. + pytest tests + +watch-docs: ## Build and watch documentation. + sphinx-autobuild docs/ docs/_build/html --open-browser --watch $(GIT_ROOT)/llama_index/ diff --git a/llama-index-packs/llama-index-packs-corrective-rag/README.md b/llama-index-packs/llama-index-packs-corrective-rag/README.md new file mode 100644 index 0000000000000..223cc85fb8a95 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/README.md @@ -0,0 +1,47 @@ +# Corrective Retrieval Augmented Generation Llama Pack + +This LlamaPack implements the Corrective Retrieval Augmented Generation (CRAG) [paper](https://arxiv.org/pdf/2401.15884.pdf) + +Corrective Retrieval Augmented Generation (CRAG) is a method designed to enhance the robustness of language model generation by evaluating and augmenting the relevance of retrieved documents through a an evaluator and large-scale web searches, ensuring more accurate and reliable information is used in generation. + +This LlamaPack uses [Tavily AI](https://app.tavily.com/home) API for web-searches. So, we recommend you to get the api-key before proceeding further. + +### Installation + +```bash +pip install llama-index llama-index-tools-tavily-research +``` + +## CLI Usage + +You can download llamapacks directly using `llamaindex-cli`, which comes installed with the `llama-index` python package: + +```bash +llamaindex-cli download-llamapack CorrectiveRAGPack --download-dir ./corrective_rag_pack +``` + +You can then inspect the files at `./corrective_rag_pack` and use them as a template for your own project. + +## Code Usage + +You can download the pack to a the `./corrective_rag_pack` directory: + +```python +from llama_index.core.llama_pack import download_llama_pack + +# download and install dependencies +CorrectiveRAGPack = download_llama_pack( + "CorrectiveRAGPack", "./corrective_rag_pack" +) + +# You can use any llama-hub loader to get documents! +corrective_rag = CorrectiveRAGPack(documents, tavily_ai_api_key) +``` + +From here, you can use the pack, or inspect and modify the pack in `./corrective_rag_pack`. + +The `run()` function contains around logic behind Corrective Retrieval Augmented Generation - [CRAG](https://arxiv.org/pdf/2401.15884.pdf) paper. + +```python +response = corrective_rag.run("", similarity_top_k=2) +``` diff --git a/llama-index-packs/llama-index-packs-corrective-rag/examples/BUILD b/llama-index-packs/llama-index-packs-corrective-rag/examples/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/examples/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-packs/llama-index-packs-corrective-rag/examples/corrective_rag.ipynb b/llama-index-packs/llama-index-packs-corrective-rag/examples/corrective_rag.ipynb new file mode 100644 index 0000000000000..162725c9e7c51 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/examples/corrective_rag.ipynb @@ -0,0 +1,198 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Corrective RAG Pack\n", + "\n", + "This notebook walks through using the `CorrectiveRAGPack` based on the paper [Corrective Retrieval Augmented Generation](https://arxiv.org/abs/2401.15884).\n", + "\n", + "A brief understanding of the paper:\n", + "\n", + "\n", + "Corrective Retrieval Augmented Generation (CRAG) is a method designed to enhance the robustness of language model generation by evaluating and augmenting the relevance of retrieved documents through a an evaluator and large-scale web searches, ensuring more accurate and reliable information is used in generation.\n", + "\n", + "We use `GPT-4` as a relevancy evaluator and `Tavily AI` for web searches. So, we recommend getting `OPENAI_API_KEY` and `tavily_ai_api_key` before proceeding further." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "%pip install llama-index-llms-openai llama-index-tools-tavily-research llama-index-embeddings-openai\n", + "%pip install llama-index-packs-corrective-rag\n", + "%pip install llama-index-readers-file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"YOUR OPENAI API KEY\"\n", + "\n", + "tavily_ai_api_key = \"\"\n", + "\n", + "import nest_asyncio\n", + "\n", + "nest_asyncio.apply()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Download `Llama2` paper." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " % Total % Received % Xferd Average Speed Time Time Time Current\n", + " Dload Upload Total Spent Left Speed\n", + "100 13.0M 100 13.0M 0 0 7397k 0 0:00:01 0:00:01 --:--:-- 7415k\n" + ] + } + ], + "source": [ + "!mkdir -p 'data/'\n", + "!curl 'https://arxiv.org/pdf/2307.09288.pdf' -o 'data/llama2.pdf'" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core import SimpleDirectoryReader\n", + "\n", + "documents = SimpleDirectoryReader(\"data\").load_data()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run the CorrectiveRAGPack" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.packs.corrective_rag import CorrectiveRAGPack\n", + "\n", + "corrective_rag_pack = CorrectiveRAGPack(documents, tavily_ai_apikey=tavily_ai_api_key)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Test Queries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "Llama2 was pretrained using an optimized auto-regressive transformer. Several changes were made to improve performance, including more robust data cleaning, updated data mixes, training on 40% more total tokens, doubling the context length, and using grouped-query attention (GQA) to improve inference scalability for larger models." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Markdown, display\n", + "\n", + "response = corrective_rag_pack.run(\"How was Llama2 pretrained?\")\n", + "display(Markdown(str(response)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "text/markdown": [ + "The latest ChatGPT memory update allows the chatbot to carry what it learns between chats, enabling it to provide more relevant responses. Users can ask ChatGPT to remember specific information or let it pick up details itself. This memory feature allows ChatGPT to remember and forget things based on user input, enhancing user interaction with personalized continuity and emotional intelligence. Users can toggle this function on or off within the ChatGPT settings menu, and when disabled, ChatGPT will neither generate nor access memories." + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from IPython.display import Markdown, display\n", + "\n", + "response = corrective_rag_pack.run(\n", + " \"What is the functionality of latest ChatGPT memory.\"\n", + ")\n", + "display(Markdown(str(response)))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "corrective-rag", + "language": "python", + "name": "corrective-rag" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3" + }, + "vscode": { + "interpreter": { + "hash": "b1d2a638b53f4d7129cb7686d8e3b97ae1d80a593a1618479f60cef5591ea888" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/llama-index-packs/llama-index-packs-corrective-rag/examples/example.py b/llama-index-packs/llama-index-packs-corrective-rag/examples/example.py new file mode 100644 index 0000000000000..932b1ae6f6046 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/examples/example.py @@ -0,0 +1,17 @@ +# Required Environment Variables: OPENAI_API_KEY +# Required TavilyAI API KEY for web searches - https://tavily.com/ +from llama_index.core import SimpleDirectoryReader +from llama_index.core.llama_pack import download_llama_pack + +# download and install dependencies +CorrectiveRAGPack = download_llama_pack("CorrectiveRAGPack", "./corrective_rag_pack") + +# load documents +documents = SimpleDirectoryReader("./data").load_data() + +# uses the LLM to extract propositions from every document/node! +corrective_rag = CorrectiveRAGPack(documents, tavily_ai_apikey="") + +# run the pack +response = corrective_rag.run("") +print(response) diff --git a/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/BUILD b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/BUILD new file mode 100644 index 0000000000000..db46e8d6c978c --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/BUILD @@ -0,0 +1 @@ +python_sources() diff --git a/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/__init__.py b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/__init__.py new file mode 100644 index 0000000000000..1f4a64458bbb7 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/__init__.py @@ -0,0 +1,3 @@ +from llama_index.packs.corrective_rag.base import CorrectiveRAGPack + +__all__ = ["CorrectiveRAGPack"] diff --git a/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/base.py b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/base.py new file mode 100644 index 0000000000000..4217c6c0b34f5 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/llama_index/packs/corrective_rag/base.py @@ -0,0 +1,132 @@ +"""Corrective RAG LlamaPack class.""" + +from typing import Any, Dict, List + +from llama_index.core import VectorStoreIndex, SummaryIndex +from llama_index.core.llama_pack.base import BaseLlamaPack +from llama_index.llms.openai import OpenAI +from llama_index.core.schema import Document, NodeWithScore +from llama_index.core.query_pipeline.query import QueryPipeline +from llama_index.tools.tavily_research.base import TavilyToolSpec +from llama_index.core.prompts import PromptTemplate + +DEFAULT_RELEVANCY_PROMPT_TEMPLATE = PromptTemplate( + template="""As a grader, your task is to evaluate the relevance of a document retrieved in response to a user's question. + + Retrieved Document: + ------------------- + {context_str} + + User Question: + -------------- + {query_str} + + Evaluation Criteria: + - Consider whether the document contains keywords or topics related to the user's question. + - The evaluation should not be overly stringent; the primary objective is to identify and filter out clearly irrelevant retrievals. + + Decision: + - Assign a binary score to indicate the document's relevance. + - Use 'yes' if the document is relevant to the question, or 'no' if it is not. + + Please provide your binary score ('yes' or 'no') below to indicate the document's relevance to the user question.""" +) + +DEFAULT_TRANSFORM_QUERY_TEMPLATE = PromptTemplate( + template="""Your task is to refine a query to ensure it is highly effective for retrieving relevant search results. \n + Analyze the given input to grasp the core semantic intent or meaning. \n + Original Query: + \n ------- \n + {query_str} + \n ------- \n + Your goal is to rephrase or enhance this query to improve its search performance. Ensure the revised query is concise and directly aligned with the intended search objective. \n + Respond with the optimized query only:""" +) + + +class CorrectiveRAGPack(BaseLlamaPack): + def __init__(self, documents: List[Document], tavily_ai_apikey: str) -> None: + """Init params.""" + llm = OpenAI(model="gpt-4") + self.relevancy_pipeline = QueryPipeline( + chain=[DEFAULT_RELEVANCY_PROMPT_TEMPLATE, llm] + ) + self.transform_query_pipeline = QueryPipeline( + chain=[DEFAULT_TRANSFORM_QUERY_TEMPLATE, llm] + ) + + self.llm = llm + self.index = VectorStoreIndex.from_documents(documents) + self.tavily_tool = TavilyToolSpec(api_key=tavily_ai_apikey) + + def get_modules(self) -> Dict[str, Any]: + """Get modules.""" + return {"llm": self.llm, "index": self.index} + + def retrieve_nodes(self, query_str: str, **kwargs: Any) -> List[NodeWithScore]: + """Retrieve the relevant nodes for the query.""" + retriever = self.index.as_retriever(**kwargs) + return retriever.retrieve(query_str) + + def evaluate_relevancy( + self, retrieved_nodes: List[Document], query_str: str + ) -> List[str]: + """Evaluate relevancy of retrieved documents with the query.""" + relevancy_results = [] + for node in retrieved_nodes: + relevancy = self.relevancy_pipeline.run( + context_str=node.text, query_str=query_str + ) + relevancy_results.append(relevancy.message.content.lower().strip()) + return relevancy_results + + def extract_relevant_texts( + self, retrieved_nodes: List[NodeWithScore], relevancy_results: List[str] + ) -> str: + """Extract relevant texts from retrieved documents.""" + relevant_texts = [ + retrieved_nodes[i].text + for i, result in enumerate(relevancy_results) + if result == "yes" + ] + return "\n".join(relevant_texts) + + def search_with_transformed_query(self, query_str: str) -> str: + """Search the transformed query with Tavily API.""" + search_results = self.tavily_tool.search(query_str, max_results=5) + return "\n".join([result.text for result in search_results]) + + def get_result(self, relevant_text: str, search_text: str, query_str: str) -> Any: + """Get result with relevant text.""" + documents = [Document(text=relevant_text + "\n" + search_text)] + index = SummaryIndex.from_documents(documents) + query_engine = index.as_query_engine() + return query_engine.query(query_str) + + def run(self, query_str: str, **kwargs: Any) -> Any: + """Run the pipeline.""" + # Retrieve nodes based on the input query string. + retrieved_nodes = self.retrieve_nodes(query_str, **kwargs) + + # Evaluate the relevancy of each retrieved document in relation to the query string. + relevancy_results = self.evaluate_relevancy(retrieved_nodes, query_str) + # Extract texts from documents that are deemed relevant based on the evaluation. + relevant_text = self.extract_relevant_texts(retrieved_nodes, relevancy_results) + + # Initialize search_text variable to handle cases where it might not get defined. + search_text = "" + + # If any document is found irrelevant, transform the query string for better search results. + if "no" in relevancy_results: + transformed_query_str = self.transform_query_pipeline.run( + query_str=query_str + ).message.content + # Conduct a search with the transformed query string and collect the results. + search_text = self.search_with_transformed_query(transformed_query_str) + + # Compile the final result. 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b/llama-index-packs/llama-index-packs-corrective-rag/pyproject.toml new file mode 100644 index 0000000000000..da27bb775c0bf --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/pyproject.toml @@ -0,0 +1,57 @@ +[build-system] +build-backend = "poetry.core.masonry.api" +requires = ["poetry-core"] + +[tool.codespell] +check-filenames = true +check-hidden = true +# Feel free to un-skip examples, and experimental, you will just need to +# work through many typos (--write-changes and --interactive will help) +skip = "*.csv,*.html,*.json,*.jsonl,*.pdf,*.txt,*.ipynb" + +[tool.llamahub] +classes = ["CorrectiveRAGPack"] +contains_example = false +import_path = "llama_index.packs.corrective_rag" + +[tool.mypy] +disallow_untyped_defs = true +# Remove venv skip when integrated with pre-commit +exclude = ["_static", "build", "examples", "notebooks", "venv"] +ignore_missing_imports = true +python_version = "3.8" + +[tool.poetry] +authors = ["Ravi Theja "] +description = "llama-index packs corrective_rag paper implementation" +keywords = ["corrective", "corrective_rag", "crag", "rag", "retrieve"] +license = "MIT" +maintainers = ["ravi-theja"] +name = "llama-index-packs-corrective-rag" +packages = [{include = "llama_index/"}] +readme = "README.md" +version = "0.1.0" + +[tool.poetry.dependencies] +python = ">=3.8.1,<3.12" +llama-index-core = "^0.10.0" +tavily-python = "^0.3.1" + +[tool.poetry.group.dev.dependencies] +black = {extras = ["jupyter"], version = "<=23.9.1,>=23.7.0"} +codespell = {extras = ["toml"], version = ">=v2.2.6"} +ipython = "8.10.0" +jupyter = "^1.0.0" +mypy = "0.991" +pre-commit = "3.2.0" +pylint = "2.15.10" +pytest = "7.2.1" +pytest-mock = "3.11.1" +ruff = "0.0.292" +tree-sitter-languages = "^1.8.0" +types-Deprecated = ">=0.1.0" +types-PyYAML = "^6.0.12.12" +types-protobuf = "^4.24.0.4" +types-redis = "4.5.5.0" +types-requests = "2.28.11.8" # TODO: unpin when mypy>0.991 +types-setuptools = "67.1.0.0" diff --git a/llama-index-packs/llama-index-packs-corrective-rag/tests/BUILD b/llama-index-packs/llama-index-packs-corrective-rag/tests/BUILD new file mode 100644 index 0000000000000..dabf212d7e716 --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/tests/BUILD @@ -0,0 +1 @@ +python_tests() diff --git a/llama-index-packs/llama-index-packs-corrective-rag/tests/__init__.py b/llama-index-packs/llama-index-packs-corrective-rag/tests/__init__.py new file mode 100644 index 0000000000000..e69de29bb2d1d diff --git a/llama-index-packs/llama-index-packs-corrective-rag/tests/test_packs_corrective_rag.py b/llama-index-packs/llama-index-packs-corrective-rag/tests/test_packs_corrective_rag.py new file mode 100644 index 0000000000000..ecf0b6cf1898e --- /dev/null +++ b/llama-index-packs/llama-index-packs-corrective-rag/tests/test_packs_corrective_rag.py @@ -0,0 +1,7 @@ +from llama_index.core.llama_pack import BaseLlamaPack +from llama_index.packs.corrective_rag import CorrectiveRAGPack + + +def test_class(): + names_of_base_classes = [b.__name__ for b in CorrectiveRAGPack.__mro__] + assert BaseLlamaPack.__name__ in names_of_base_classes diff --git a/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/CHANGELOG.md b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml index d02f95bb8e7dd..6d6eb870e3a27 100644 --- a/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-deeplake-deepmemory-retriever/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs deeplake_deepmemory_retriever integration" +keywords = ["deeplake", "deepmemory", "retriever"] license = "MIT" +maintainers = ["AdkSarsen"] name = "llama-index-packs-deeplake-deepmemory-retriever" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/CHANGELOG.md b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml index 5410ce6bb81df..9cb4e5612f001 100644 --- a/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-deeplake-multimodal-retrieval/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs deeplake_multimodal_retrieval integration" +keywords = ["deeplake", "multimodal", "retriever"] license = "MIT" +maintainers = ["AdkSarsen"] name = "llama-index-packs-deeplake-multimodal-retrieval" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-dense-x-retrieval/CHANGELOG.md b/llama-index-packs/llama-index-packs-dense-x-retrieval/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-dense-x-retrieval/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml b/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml index 7b061be17570e..55aa02097b72a 100644 --- a/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-dense-x-retrieval/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index packs dense_x_retrieval integration" license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-packs-dense-x-retrieval" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-evaluator-benchmarker/CHANGELOG.md b/llama-index-packs/llama-index-packs-evaluator-benchmarker/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-evaluator-benchmarker/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml b/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml index d68cec2ce5d23..151637e8ba747 100644 --- a/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml +++ b/llama-index-packs/llama-index-packs-evaluator-benchmarker/pyproject.toml @@ -22,9 +22,10 @@ python_version = "3.8" authors = ["Your Name "] description = "llama-index packs evaluator_benchmarker integration" license = "MIT" +maintainers = ["nerdai"] name = "llama-index-packs-evaluator-benchmarker" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-fusion-retriever/CHANGELOG.md b/llama-index-packs/llama-index-packs-fusion-retriever/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-fusion-retriever/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml index be94c38e519e3..c8ae962c1a503 100644 --- a/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-fusion-retriever/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs fusion_retriever integration" +keywords = ["fusion", "hybrid", "query", "retriever", "rewriting"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-fusion-retriever" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-fuzzy-citation/CHANGELOG.md b/llama-index-packs/llama-index-packs-fuzzy-citation/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-fuzzy-citation/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml b/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml index c24c7491f25b8..990430951098d 100644 --- a/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml +++ b/llama-index-packs/llama-index-packs-fuzzy-citation/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs fuzzy_citation integration" +keywords = ["citation", "cite", "engine", "fuzzy", "index", "query"] license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-packs-fuzzy-citation" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-gmail-openai-agent/CHANGELOG.md b/llama-index-packs/llama-index-packs-gmail-openai-agent/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-gmail-openai-agent/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml b/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml index 2c19d720d4d38..4b4c5ed5c7442 100644 --- a/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml +++ b/llama-index-packs/llama-index-packs-gmail-openai-agent/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs gmail_openai_agent integration" +keywords = ["math", "research", "science"] license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-packs-gmail-openai-agent" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-gradio-agent-chat/CHANGELOG.md b/llama-index-packs/llama-index-packs-gradio-agent-chat/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-gradio-agent-chat/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml b/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml index 44178ee25cfdb..fd06c1df43f61 100644 --- a/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml +++ b/llama-index-packs/llama-index-packs-gradio-agent-chat/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs gradio_agent_chat integration" +keywords = ["agent", "chatbot", "gradio", "tools"] license = "MIT" +maintainers = ["nerdai"] name = "llama-index-packs-gradio-agent-chat" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/CHANGELOG.md b/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml b/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml index d986cf9a80a7b..7b91155c07803 100644 --- a/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml +++ b/llama-index-packs/llama-index-packs-gradio-react-agent-chatbot/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs gradio_react_agent_chatbot integration" +keywords = ["chatbot", "gradio", "react-agent", "tools"] license = "MIT" +maintainers = ["nerdai"] name = "llama-index-packs-gradio-react-agent-chatbot" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-infer-retrieve-rerank/CHANGELOG.md b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml index e03461dd11e61..c0bc1e68f148a 100644 --- a/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml +++ b/llama-index-packs/llama-index-packs-infer-retrieve-rerank/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs infer retrieve rerank integration" +keywords = ["infer", "rag", "rerank", "retrieve", "retriever"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-infer-retrieve-rerank" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-llama-dataset-metadata/CHANGELOG.md b/llama-index-packs/llama-index-packs-llama-dataset-metadata/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-llama-dataset-metadata/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-llama-dataset-metadata/llama_index/packs/llama_dataset_metadata/base.py b/llama-index-packs/llama-index-packs-llama-dataset-metadata/llama_index/packs/llama_dataset_metadata/base.py index 21284e2868907..bdefa413cf393 100644 --- a/llama-index-packs/llama-index-packs-llama-dataset-metadata/llama_index/packs/llama_dataset_metadata/base.py +++ b/llama-index-packs/llama-index-packs-llama-dataset-metadata/llama_index/packs/llama_dataset_metadata/base.py @@ -1,14 +1,16 @@ import json -from typing import List, Optional +from typing import List, Optional, TYPE_CHECKING import pandas as pd from llama_index.core.bridge.pydantic import BaseModel from llama_index.core.download.module import LLAMA_HUB_URL from llama_index.core.download.utils import get_file_content from llama_index.core.indices.base import BaseIndex -from llama_index.core.llama_dataset import LabelledRagDataset from llama_index.core.llama_pack.base import BaseLlamaPack +if TYPE_CHECKING: + from llama_index.core.llama_dataset import LabelledRagDataset + class Readme(BaseModel): """A simple class for creating a README.md string.""" @@ -104,7 +106,7 @@ def from_rag_evaluation( cls, index: BaseIndex, benchmark_df: pd.DataFrame, - rag_dataset: LabelledRagDataset, + rag_dataset: "LabelledRagDataset", name: str, baseline_name: str, description: str, @@ -203,7 +205,7 @@ def run( self, index: BaseIndex, benchmark_df: pd.DataFrame, - rag_dataset: LabelledRagDataset, + rag_dataset: "LabelledRagDataset", name: str, description: str, baseline_name: str, diff --git a/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml b/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml index d7f1059b5b07c..49bc31ad456ab 100644 --- a/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml +++ b/llama-index-packs/llama-index-packs-llama-dataset-metadata/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs llama_dataset_metadata integration" +keywords = ["evaluation", "llamadataset", "rag", "submission"] license = "MIT" +maintainers = ["nerdai"] name = "llama-index-packs-llama-dataset-metadata" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-llama-guard-moderator/CHANGELOG.md b/llama-index-packs/llama-index-packs-llama-guard-moderator/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-llama-guard-moderator/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml b/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml index 179aaa79a5a10..1c5935710115e 100644 --- a/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml +++ b/llama-index-packs/llama-index-packs-llama-guard-moderator/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs llama_guard_moderator integration" +keywords = ["llama guard", "llm security", "prompt injection", "purple llama"] license = "MIT" +maintainers = ["wenqiglantz"] name = "llama-index-packs-llama-guard-moderator" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-llava-completion/CHANGELOG.md b/llama-index-packs/llama-index-packs-llava-completion/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-llava-completion/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml b/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml index 512154b1fe7b9..caaec55e7cb2e 100644 --- a/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml +++ b/llama-index-packs/llama-index-packs-llava-completion/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs llava_completion integration" +keywords = ["image", "llava", "multimodal"] license = "MIT" +maintainers = ["wenqiglantz"] name = "llama-index-packs-llava-completion" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-multi-document-agents/CHANGELOG.md b/llama-index-packs/llama-index-packs-multi-document-agents/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-multi-document-agents/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml b/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml index 1343eb0765b21..88c2d8faaeb2b 100644 --- a/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml +++ b/llama-index-packs/llama-index-packs-multi-document-agents/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs multi_document_agents integration" +keywords = ["agents", "document", "multi"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-multi-document-agents" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-multi-tenancy-rag/CHANGELOG.md b/llama-index-packs/llama-index-packs-multi-tenancy-rag/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-multi-tenancy-rag/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml b/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml index 2ba336951583a..1aa280776a66d 100644 --- a/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-multi-tenancy-rag/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs multi_tenancy_rag integration" +keywords = ["multi", "multi-tenancy", "rag", "tenancy"] license = "MIT" +maintainers = ["ravi03071991"] name = "llama-index-packs-multi-tenancy-rag" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-multidoc-autoretrieval/CHANGELOG.md b/llama-index-packs/llama-index-packs-multidoc-autoretrieval/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-multidoc-autoretrieval/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml b/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml index 55672a9b1679e..80060133b34e7 100644 --- a/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-multidoc-autoretrieval/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs multidoc_autoretrieval integration" +keywords = ["autoretrieval", "document", "multi", "multidoc", "retrieval"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-multidoc-autoretrieval" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-nebulagraph-query-engine/CHANGELOG.md b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml index 887bab2233da1..59c0db4158e5d 100644 --- a/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-nebulagraph-query-engine/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs nebulagraph_query_engine integration" +keywords = ["knowledge graph", "nebulagraph", "query engine"] license = "MIT" +maintainers = ["wenqiglantz"] name = "llama-index-packs-nebulagraph-query-engine" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-neo4j-query-engine/CHANGELOG.md b/llama-index-packs/llama-index-packs-neo4j-query-engine/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-neo4j-query-engine/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml index 8a428e79aba5d..15de137809271 100644 --- a/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-neo4j-query-engine/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs neo4j_query_engine integration" +keywords = ["knowledge graph", "neo4j", "query engine"] license = "MIT" +maintainers = ["wenqiglantz"] name = "llama-index-packs-neo4j-query-engine" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/CHANGELOG.md b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml index 2c4a45149889f..3ad2b69e2ee9d 100644 --- a/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml +++ b/llama-index-packs/llama-index-packs-node-parser-semantic-chunking/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs node_parser integration" +keywords = ["chunk", "chunking", "embedding", "node", "parser", "semantic"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-node-parser" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-ollama-query-engine/CHANGELOG.md b/llama-index-packs/llama-index-packs-ollama-query-engine/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-ollama-query-engine/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml index 69e8d189725ff..3f9da7981fc8a 100644 --- a/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-ollama-query-engine/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs ollama_query_engine integration" +keywords = ["engine", "index", "local", "ollama", "query"] license = "MIT" +maintainers = ["chnsagitchen"] name = "llama-index-packs-ollama-query-engine" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-panel-chatbot/CHANGELOG.md b/llama-index-packs/llama-index-packs-panel-chatbot/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-panel-chatbot/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml b/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml index 3c97659022146..94d089c211795 100644 --- a/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml +++ b/llama-index-packs/llama-index-packs-panel-chatbot/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs panel_chatbot integration" +keywords = ["chatbot", "github", "index", "openai", "panel"] license = "MIT" +maintainers = ["MarcSkovMadsen"] name = "llama-index-packs-panel-chatbot" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-packs/llama-index-packs-rag-cli-local/CHANGELOG.md b/llama-index-packs/llama-index-packs-rag-cli-local/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-rag-cli-local/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml b/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml index e7f4d86d962d7..0ea07f6ee79ab 100644 --- a/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml +++ b/llama-index-packs/llama-index-packs-rag-cli-local/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs rag cli local integration" +keywords = ["cli", "local", "rag"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-rag-cli-local" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-rag-evaluator/CHANGELOG.md b/llama-index-packs/llama-index-packs-rag-evaluator/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-rag-evaluator/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml b/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml index 40e4ec591f076..552cb200302a4 100644 --- a/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml +++ b/llama-index-packs/llama-index-packs-rag-evaluator/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs rag_evaluator integration" +keywords = ["benchmarks", "evaluation", "rag"] license = "MIT" +maintainers = ["nerdai"] name = "llama-index-packs-rag-evaluator" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/CHANGELOG.md b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml index 323af0e27e793..24ae39abdc4cd 100644 --- a/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml +++ b/llama-index-packs/llama-index-packs-rag-fusion-query-pipeline/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs query integration" +keywords = ["fusion", "pipeline", "query", "rag"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-query" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-ragatouille-retriever/CHANGELOG.md b/llama-index-packs/llama-index-packs-ragatouille-retriever/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-ragatouille-retriever/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml index db9599c5f55e9..4fe2e41129f8a 100644 --- a/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-ragatouille-retriever/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs ragatouille_retriever integration" +keywords = ["rag", "ragatouille", "retriever"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-ragatouille-retriever" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-packs/llama-index-packs-recursive-retriever/CHANGELOG.md b/llama-index-packs/llama-index-packs-recursive-retriever/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-recursive-retriever/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml index 038bd98d606a1..e3ce570b7e967 100644 --- a/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs recursive_retriever integration" +keywords = ["big", "embedded", "recursive", "retriever", "small", "tables", "unstructured"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-recursive-retriever" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/CHANGELOG.md b/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml b/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml index 76e84c2c4dc7c..7e84d85431985 100644 --- a/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml +++ b/llama-index-packs/llama-index-packs-redis-ingestion-pipeline/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs redis_ingestion_pipeline integration" +keywords = ["index", "ingestion", "pipeline", "redis"] license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-packs-redis-ingestion-pipeline" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-resume-screener/CHANGELOG.md b/llama-index-packs/llama-index-packs-resume-screener/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-resume-screener/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml b/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml index 9ab6f9e46df67..c93738145ff6d 100644 --- a/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml +++ b/llama-index-packs/llama-index-packs-resume-screener/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs resume_screener integration" +keywords = ["document", "pdf", "resume", "structured output"] license = "MIT" +maintainers = ["Disiok"] name = "llama-index-packs-resume-screener" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-retry-engine-weaviate/CHANGELOG.md b/llama-index-packs/llama-index-packs-retry-engine-weaviate/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-retry-engine-weaviate/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml b/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml index 0c20e03811117..41a3a60373070 100644 --- a/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml +++ b/llama-index-packs/llama-index-packs-retry-engine-weaviate/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs retry_engine_weaviate integration" +keywords = ["engine", "retry", "weaviate"] license = "MIT" +maintainers = ["erika-cardenas"] name = "llama-index-packs-retry-engine-weaviate" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-self-rag/CHANGELOG.md b/llama-index-packs/llama-index-packs-self-rag/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-self-rag/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-self-rag/pyproject.toml b/llama-index-packs/llama-index-packs-self-rag/pyproject.toml index 128e27c5888f1..cdcba219cf424 100644 --- a/llama-index-packs/llama-index-packs-self-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-self-rag/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs self rag integration" +keywords = ["llm", "self-RAG", "smart-retreiver"] license = "MIT" +maintainers = ["mmaatouk"] name = "llama-index-packs-self-rag" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-sentence-window-retriever/CHANGELOG.md b/llama-index-packs/llama-index-packs-sentence-window-retriever/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-sentence-window-retriever/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml b/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml index 6471c45a1be4f..8356a1c9de17c 100644 --- a/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml +++ b/llama-index-packs/llama-index-packs-sentence-window-retriever/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs sentence_window_retriever integration" +keywords = ["retriever", "sentence", "window"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-sentence-window-retriever" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-snowflake-query-engine/CHANGELOG.md b/llama-index-packs/llama-index-packs-snowflake-query-engine/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-snowflake-query-engine/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml index 5d1b579fcba4c..5b3bfe8781b55 100644 --- a/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-snowflake-query-engine/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs snowflake_query_engine integration" +keywords = ["engine", "query", "snowflake"] license = "MIT" +maintainers = ["wenqiglantz"] name = "llama-index-packs-snowflake-query-engine" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-stock-market-data-query-engine/CHANGELOG.md b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml index 0fd6b7bbf0914..dbebe455898aa 100644 --- a/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-stock-market-data-query-engine/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs stock_market_data_query_engine integration" +keywords = ["data", "engine", "finance", "market", "query", "stock", "yahoo finance", "yahoo"] license = "MIT" +maintainers = ["anoopshrma"] name = "llama-index-packs-stock-market-data-query-engine" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-streamlit-chatbot/CHANGELOG.md b/llama-index-packs/llama-index-packs-streamlit-chatbot/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-streamlit-chatbot/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml b/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml index 0aae46c96d684..785665cd4d7be 100644 --- a/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml +++ b/llama-index-packs/llama-index-packs-streamlit-chatbot/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs streamlit_chatbot integration" +keywords = ["chatbot", "snowflake", "streamlit", "wikipedia"] license = "MIT" +maintainers = ["carolinedlu"] name = "llama-index-packs-streamlit-chatbot" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">3.9.7,<3.12" diff --git a/llama-index-packs/llama-index-packs-sub-question-weaviate/CHANGELOG.md b/llama-index-packs/llama-index-packs-sub-question-weaviate/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-sub-question-weaviate/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml b/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml index afa85e2429281..05af56e63294f 100644 --- a/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml +++ b/llama-index-packs/llama-index-packs-sub-question-weaviate/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs sub_question_weaviate integration" +keywords = ["index", "query", "weaviate"] license = "MIT" +maintainers = ["erika-cardenas"] name = "llama-index-packs-sub-question-weaviate" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-tables/CHANGELOG.md b/llama-index-packs/llama-index-packs-tables/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-tables/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-tables/pyproject.toml b/llama-index-packs/llama-index-packs-tables/pyproject.toml index 6239d4e22ad03..6cffb85b0ec15 100644 --- a/llama-index-packs/llama-index-packs-tables/pyproject.toml +++ b/llama-index-packs/llama-index-packs-tables/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs tables integration" +keywords = ["chain", "dataframe", "pandas", "table", "tables"] license = "MIT" +maintainers = ["Disiok", "jerryjliu"] name = "llama-index-packs-tables" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/CHANGELOG.md b/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml b/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml index a4bb97836f846..43307e1f835bb 100644 --- a/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml +++ b/llama-index-packs/llama-index-packs-timescale-vector-autoretrieval/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs timescale_vector_autoretrieval integration" +keywords = ["autoretrieval", "index", "timescale", "vector"] license = "MIT" +maintainers = ["cevian"] name = "llama-index-packs-timescale-vector-autoretrieval" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-trulens-eval-packs/CHANGELOG.md b/llama-index-packs/llama-index-packs-trulens-eval-packs/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-trulens-eval-packs/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml b/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml index b1113a65f583e..dac0e675d75ea 100644 --- a/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml +++ b/llama-index-packs/llama-index-packs-trulens-eval-packs/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs trulens_eval_packs integration" +keywords = ["eval", "harmless", "helpful", "rag", "triad", "trulens"] license = "MIT" +maintainers = ["joshreini1"] name = "llama-index-packs-trulens-eval-packs" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">3.9.7,<3.12" diff --git a/llama-index-packs/llama-index-packs-vanna/CHANGELOG.md b/llama-index-packs/llama-index-packs-vanna/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-vanna/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-vanna/pyproject.toml b/llama-index-packs/llama-index-packs-vanna/pyproject.toml index 8e702e7ee4743..ac49cb9f44df6 100644 --- a/llama-index-packs/llama-index-packs-vanna/pyproject.toml +++ b/llama-index-packs/llama-index-packs-vanna/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs vanna integration" +keywords = ["ai", "sql", "text-to-sql", "vanna"] license = "MIT" +maintainers = ["jerryjliu"] name = "llama-index-packs-vanna" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.9,<3.12" diff --git a/llama-index-packs/llama-index-packs-vectara-rag/CHANGELOG.md b/llama-index-packs/llama-index-packs-vectara-rag/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-vectara-rag/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml b/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml index f5d41d3ce1db0..5e25f1a6b633f 100644 --- a/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml +++ b/llama-index-packs/llama-index-packs-vectara-rag/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs vectara_rag integration" +keywords = ["embeddings", "rag", "retrieval", "vectara"] license = "MIT" +maintainers = ["ofermend"] name = "llama-index-packs-vectara-rag" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-voyage-query-engine/CHANGELOG.md b/llama-index-packs/llama-index-packs-voyage-query-engine/CHANGELOG.md new file mode 100644 index 0000000000000..36bff877abcbe --- /dev/null +++ b/llama-index-packs/llama-index-packs-voyage-query-engine/CHANGELOG.md @@ -0,0 +1,5 @@ +# CHANGELOG + +## [0.1.2] - 2024-02-13 + +- Add maintainers and keywords from library.json (llamahub) diff --git a/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml index f24a197fb8812..d8f59b79a783c 100644 --- a/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-voyage-query-engine/pyproject.toml @@ -21,10 +21,12 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs voyage_query_engine integration" +keywords = ["embeddings", "query", "retrieval", "voyage"] license = "MIT" +maintainers = ["Liuhong99"] name = "llama-index-packs-voyage-query-engine" readme = "README.md" -version = "0.1.1" +version = "0.1.2" [tool.poetry.dependencies] python = ">=3.8.1,<3.12" diff --git a/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml b/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml index 5470ff453a8dc..e942c4ab337b3 100644 --- a/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml +++ b/llama-index-packs/llama-index-packs-zephyr-query-engine/pyproject.toml @@ -21,7 +21,9 @@ python_version = "3.8" [tool.poetry] authors = ["Your Name "] description = "llama-index packs zephyr_query_engine integration" +keywords = ["engine", "huggingface", "index", "local", "query", "zephyr"] license = "MIT" +maintainers = ["logan-markewich"] name = "llama-index-packs-zephyr-query-engine" readme = "README.md" version = "0.1.1" diff --git a/poetry.lock b/poetry.lock index 4a03033fd43e8..58e4c5f0ecd45 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1067,40 +1067,39 @@ files = [ [[package]] name = "httpcore" -version = "1.0.2" +version = "0.17.3" description = "A minimal low-level HTTP client." optional = false -python-versions = ">=3.8" +python-versions = ">=3.7" files = [ - {file = "httpcore-1.0.2-py3-none-any.whl", hash = "sha256:096cc05bca73b8e459a1fc3dcf585148f63e534eae4339559c9b8a8d6399acc7"}, - {file = "httpcore-1.0.2.tar.gz", hash = "sha256:9fc092e4799b26174648e54b74ed5f683132a464e95643b226e00c2ed2fa6535"}, + {file = "httpcore-0.17.3-py3-none-any.whl", hash = "sha256:c2789b767ddddfa2a5782e3199b2b7f6894540b17b16ec26b2c4d8e103510b87"}, + {file = "httpcore-0.17.3.tar.gz", hash = "sha256:a6f30213335e34c1ade7be6ec7c47f19f50c56db36abef1a9dfa3815b1cb3888"}, ] [package.dependencies] +anyio = ">=3.0,<5.0" certifi = "*" h11 = ">=0.13,<0.15" +sniffio = "==1.*" [package.extras] -asyncio = ["anyio (>=4.0,<5.0)"] http2 = ["h2 (>=3,<5)"] socks = ["socksio (==1.*)"] -trio = ["trio (>=0.22.0,<0.23.0)"] [[package]] name = "httpx" -version = "0.26.0" +version = "0.24.1" description = "The next generation HTTP client." optional = false -python-versions = ">=3.8" +python-versions = ">=3.7" files = [ - {file = "httpx-0.26.0-py3-none-any.whl", hash = "sha256:8915f5a3627c4d47b73e8202457cb28f1266982d1159bd5779d86a80c0eab1cd"}, - {file = "httpx-0.26.0.tar.gz", hash = "sha256:451b55c30d5185ea6b23c2c793abf9bb237d2a7dfb901ced6ff69ad37ec1dfaf"}, + {file = "httpx-0.24.1-py3-none-any.whl", hash = "sha256:06781eb9ac53cde990577af654bd990a4949de37a28bdb4a230d434f3a30b9bd"}, + {file = "httpx-0.24.1.tar.gz", hash = "sha256:5853a43053df830c20f8110c5e69fe44d035d850b2dfe795e196f00fdb774bdd"}, ] [package.dependencies] -anyio = "*" certifi = "*" -httpcore = "==1.*" +httpcore = ">=0.15.0,<0.18.0" idna = "*" sniffio = "*" @@ -2817,7 +2816,6 @@ files = [ {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:69b023b2b4daa7548bcfbd4aa3da05b3a74b772db9e23b982788168117739938"}, {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:81e0b275a9ecc9c0c0c07b4b90ba548307583c125f54d5b6946cfee6360c733d"}, {file = "PyYAML-6.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba336e390cd8e4d1739f42dfe9bb83a3cc2e80f567d8805e11b46f4a943f5515"}, - {file = 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disallow_untyped_defs = true # Remove venv skip when integrated with pre-commit -exclude = ["_static", "build", "examples", "notebooks", "venv"] +exclude = ["_static", "build", "examples", "llama_index/ingestion/client", "notebooks", "venv"] ignore_missing_imports = true python_version = "3.8" @@ -41,10 +41,10 @@ name = "llama-index" packages = [{from = "_llama-index", include = "llama_index"}] readme = "README.md" repository = "https://github.com/run-llama/llama_index" -version = "0.10.3" +version = "0.10.5" [tool.poetry.dependencies] -python = ">=3.8.1,<3.12" +python = ">=3.8.1,<4.0" llama-index-legacy = "^0.9.48" llama-index-llms-openai = "^0.1.0" llama-index-embeddings-openai = "^0.1.0" @@ -99,6 +99,7 @@ url = "https://pypi.nvidia.com" exclude = [ "_static", "examples", + "llama_index/ingestion/client", "notebooks", ] ignore = [