From 40193e3b87252000e6b92f46956df59f70bb06ab Mon Sep 17 00:00:00 2001 From: PrashantDixit-dev Date: Thu, 8 Aug 2024 19:57:35 +0530 Subject: [PATCH 1/2] lancedb --- docs/train.mdx | 319 +- pandasai/agent/base.py | 13 + pandasai/ee/vectorstores/__init__.py | 3 +- pandasai/ee/vectorstores/lanceDB.py | 411 + poetry.lock | 6999 ++++++++++------- pyproject.toml | 4 + tests/unit_tests/vectorstores/test_lancedb.py | 137 + 7 files changed, 4728 insertions(+), 3158 deletions(-) create mode 100644 pandasai/ee/vectorstores/lanceDB.py create mode 100644 tests/unit_tests/vectorstores/test_lancedb.py diff --git a/docs/train.mdx b/docs/train.mdx index 343e561e7..a08a4812a 100644 --- a/docs/train.mdx +++ b/docs/train.mdx @@ -1,158 +1,161 @@ ---- -title: "Train PandasAI" ---- - -You can train PandasAI to understand your data better and to improve its performance. Training is as easy as calling the `train` method on the `Agent`. - -There are two kinds of training: - -- instructions training -- q/a training - - -
- -## Prerequisites - -Before you start training PandasAI, you need to set your PandasAI API key. You can generate your API key by signing up at [https://pandabi.ai](https://pandabi.ai). - -Then you can set your API key as an environment variable: - -```python -import os - -os.environ["PANDASAI_API_KEY"] = "YOUR_PANDASAIAPI_KEY" -``` - -It is important that you set the API key, or it will fail with the following error: `No vector store provided. Please provide a vector store to train the agent`. - -## Instructions training - -Instructions training is used to teach PandasAI how you expect it to respond to certain queries. You can provide generic instructions about how you expect the model to approach certain types of queries, and PandasAI will use these instructions to generate responses to similar queries. - -For example, you might want the LLM to be aware that your company's fiscal year starts in April, or about specific ways you want to handle missing data. Or you might want to teach it about specific business rules or data analysis best practices that are specific to your organization. - -To train PandasAI with instructions, you can use the `train` method on the `Agent`, as it follows: - -The training uses by default the `BambooVectorStore` to store the training data, and it's accessible with the API key. - -As an alternative, if you want to use a local vector store (enterprise only for production use cases), you can use the `ChromaDB`, `Qdrant` or `Pinecone` vector stores (see examples below). - -```python -from pandasai import Agent - -# Set your PandasAI API key (you can generate one signing up at https://pandabi.ai) -os.environ["PANDASAI_API_KEY"] = "YOUR_PANDASAI_API_KEY" - -agent = Agent("data.csv") -agent.train(docs="The fiscal year starts in April") - -response = agent.chat("What is the total sales for the fiscal year?") -print(response) -# The model will use the information provided in the training to generate a response -``` - -Your training data is persisted, so you only need to train the model once. - -## Q/A training - -Q/A training is used to teach PandasAI the desired process to answer specific questions, enhancing the model's performance and determinism. One of the biggest challenges with LLMs is that they are not deterministic, meaning that the same question can produce different answers at different times. Q/A training can help to mitigate this issue. - -To train PandasAI with Q/A, you can use the `train` method on the `Agent`, as it follows: - -```python -from pandasai import Agent - -agent = Agent("data.csv") - -# Train the model -query = "What is the total sales for the current fiscal year?" -response = """ -import pandas as pd - -df = dfs[0] - -# Calculate the total sales for the current fiscal year -total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum() -result = { "type": "number", "value": total_sales } -""" -agent.train(queries=[query], codes=[response]) - -response = agent.chat("What is the total sales for the last fiscal year?") -print(response) -# The model will use the information provided in the training to generate a response -``` - -Also in this case, your training data is persisted, so you only need to train the model once. - -## Training with local Vector stores - -If you want to train the model with a local vector store, you can use the local `ChromaDB`, `Qdrant` or `Pinecone` vector stores. Here's how to do it: -An enterprise license is required for using the vector stores locally, ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)). -If you plan to use it in production, [contact us](https://tally.so/r/wzZNWg). - -```python -from pandasai import Agent -from pandasai.ee.vectorstores import ChromaDB -from pandasai.ee.vectorstores import Qdrant -from pandasai.ee.vectorstores import Pinecone - -# Instantiate the vector store -vector_store = ChromaDB() -# or with Qdrant -# vector_store = Qdrant() -# or with Pinecone -# vector_store = Pinecone( -# api_key="*****", -# embedding_function=embedding_function, -# dimensions=384, # dimension of your embedding model -# ) - -# Instantiate the agent with the custom vector store -agent = Agent("data.csv", vectorstore=vector_store) - -# Train the model -query = "What is the total sales for the current fiscal year?" -response = """ -import pandas as pd - -df = dfs[0] - -# Calculate the total sales for the current fiscal year -total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum() -result = { "type": "number", "value": total_sales } -""" -agent.train(queries=[query], codes=[response]) - -response = agent.chat("What is the total sales for the last fiscal year?") -print(response) -# The model will use the information provided in the training to generate a response -``` - -## Troubleshooting - -In some cases, you might get an error like this: `No vector store provided. Please provide a vector store to train the agent`. It means no API key has been generated to use the `BambooVectorStore`. - -Here's how to fix it: - -First of all, you'll need to generated an API key (check the prerequisites paragraph above). -Once you have generated the API key, you have 2 options: - -1. Override the env variable (`os.environ["PANDASAI_API_KEY"] = "YOUR_PANDASAI_API_KEY"`) -2. Instantiate the vector store and pass the API key: - -```python -# Instantiate the vector store with the API keys -vector_store = BambooVectorStor(api_key="YOUR_PANDASAI_API_KEY") - -# Instantiate the agent with the custom vector store -agent = Agent(connector, config={...} vectorstore=vector_store) -``` +--- +title: "Train PandasAI" +--- + +You can train PandasAI to understand your data better and to improve its performance. Training is as easy as calling the `train` method on the `Agent`. + +There are two kinds of training: + +- instructions training +- q/a training + + +
+ +## Prerequisites + +Before you start training PandasAI, you need to set your PandasAI API key. You can generate your API key by signing up at [https://pandabi.ai](https://pandabi.ai). + +Then you can set your API key as an environment variable: + +```python +import os + +os.environ["PANDASAI_API_KEY"] = "YOUR_PANDASAIAPI_KEY" +``` + +It is important that you set the API key, or it will fail with the following error: `No vector store provided. Please provide a vector store to train the agent`. + +## Instructions training + +Instructions training is used to teach PandasAI how you expect it to respond to certain queries. You can provide generic instructions about how you expect the model to approach certain types of queries, and PandasAI will use these instructions to generate responses to similar queries. + +For example, you might want the LLM to be aware that your company's fiscal year starts in April, or about specific ways you want to handle missing data. Or you might want to teach it about specific business rules or data analysis best practices that are specific to your organization. + +To train PandasAI with instructions, you can use the `train` method on the `Agent`, as it follows: + +The training uses by default the `BambooVectorStore` to store the training data, and it's accessible with the API key. + +As an alternative, if you want to use a local vector store (enterprise only for production use cases), you can use the `ChromaDB`, `Qdrant` or `Pinecone` vector stores (see examples below). + +```python +from pandasai import Agent + +# Set your PandasAI API key (you can generate one signing up at https://pandabi.ai) +os.environ["PANDASAI_API_KEY"] = "YOUR_PANDASAI_API_KEY" + +agent = Agent("data.csv") +agent.train(docs="The fiscal year starts in April") + +response = agent.chat("What is the total sales for the fiscal year?") +print(response) +# The model will use the information provided in the training to generate a response +``` + +Your training data is persisted, so you only need to train the model once. + +## Q/A training + +Q/A training is used to teach PandasAI the desired process to answer specific questions, enhancing the model's performance and determinism. One of the biggest challenges with LLMs is that they are not deterministic, meaning that the same question can produce different answers at different times. Q/A training can help to mitigate this issue. + +To train PandasAI with Q/A, you can use the `train` method on the `Agent`, as it follows: + +```python +from pandasai import Agent + +agent = Agent("data.csv") + +# Train the model +query = "What is the total sales for the current fiscal year?" +response = """ +import pandas as pd + +df = dfs[0] + +# Calculate the total sales for the current fiscal year +total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum() +result = { "type": "number", "value": total_sales } +""" +agent.train(queries=[query], codes=[response]) + +response = agent.chat("What is the total sales for the last fiscal year?") +print(response) +# The model will use the information provided in the training to generate a response +``` + +Also in this case, your training data is persisted, so you only need to train the model once. + +## Training with local Vector stores + +If you want to train the model with a local vector store, you can use the local `ChromaDB`, `Qdrant` or `Pinecone` vector stores. Here's how to do it: +An enterprise license is required for using the vector stores locally, ([check it out](https://github.com/Sinaptik-AI/pandas-ai/blob/master/pandasai/ee/LICENSE)). +If you plan to use it in production, [contact us](https://forms.gle/JEUqkwuTqFZjhP7h8). + +```python +from pandasai import Agent +from pandasai.ee.vectorstores import ChromaDB +from pandasai.ee.vectorstores import Qdrant +from pandasai.ee.vectorstores import Pinecone +from pandasai.ee.vector_stores import LanceDB + +# Instantiate the vector store +vector_store = ChromaDB() +# or with Qdrant +# vector_store = Qdrant() +# or with LanceDB +vector_store = LanceDB() +# or with Pinecone +# vector_store = Pinecone( +# api_key="*****", +# embedding_function=embedding_function, +# dimensions=384, # dimension of your embedding model +# ) + +# Instantiate the agent with the custom vector store +agent = Agent("data.csv", vectorstore=vector_store) + +# Train the model +query = "What is the total sales for the current fiscal year?" +response = """ +import pandas as pd + +df = dfs[0] + +# Calculate the total sales for the current fiscal year +total_sales = df[df['date'] >= pd.to_datetime('today').replace(month=4, day=1)]['sales'].sum() +result = { "type": "number", "value": total_sales } +""" +agent.train(queries=[query], codes=[response]) + +response = agent.chat("What is the total sales for the last fiscal year?") +print(response) +# The model will use the information provided in the training to generate a response +``` + +## Troubleshooting + +In some cases, you might get an error like this: `No vector store provided. Please provide a vector store to train the agent`. It means no API key has been generated to use the `BambooVectorStore`. + +Here's how to fix it: + +First of all, you'll need to generated an API key (check the prerequisites paragraph above). +Once you have generated the API key, you have 2 options: + +1. Override the env variable (`os.environ["PANDASAI_API_KEY"] = "YOUR_PANDASAI_API_KEY"`) +2. Instantiate the vector store and pass the API key: + +```python +# Instantiate the vector store with the API keys +vector_store = BambooVectorStor(api_key="YOUR_PANDASAI_API_KEY") + +# Instantiate the agent with the custom vector store +agent = Agent(connector, config={...} vectorstore=vector_store) +``` diff --git a/pandasai/agent/base.py b/pandasai/agent/base.py index 28204afbe..24d46b98d 100644 --- a/pandasai/agent/base.py +++ b/pandasai/agent/base.py @@ -98,6 +98,19 @@ def __init__( self._vectorstore = BambooVectorStore(logger=self.logger) self.context.vectorstore = self._vectorstore + elif self._vectorstore == "lancedb" and os.environ.get("PANDASAI_API_KEY"): + try: + from pandasai.vectorstores.lancedb_vectorstore import LanceDBVectorStore + except ImportError as e: + raise ImportError( + "Could not import LanceDBVectorStore. Please install the required dependencies." + ) from e + + self._vectorstore = LanceDBVectorStore( + logger=self.logger, uri="/tmp/lancedb" + ) + self.context.vectorstore = self._vectorstore + self._callbacks = Callbacks(self) self.configure() diff --git a/pandasai/ee/vectorstores/__init__.py b/pandasai/ee/vectorstores/__init__.py index 5818115bc..694d4ce42 100644 --- a/pandasai/ee/vectorstores/__init__.py +++ b/pandasai/ee/vectorstores/__init__.py @@ -3,6 +3,7 @@ """ from .chroma import ChromaDB +from .lanceDB import LanceDB from .qdrant import Qdrant -__all__ = ["ChromaDB", "Qdrant"] +__all__ = ["ChromaDB", "Qdrant", "LanceDB"] diff --git a/pandasai/ee/vectorstores/lanceDB.py b/pandasai/ee/vectorstores/lanceDB.py new file mode 100644 index 000000000..e59590f29 --- /dev/null +++ b/pandasai/ee/vectorstores/lanceDB.py @@ -0,0 +1,411 @@ +import uuid +from typing import Callable, Iterable, List, Optional, Union + +import lancedb +import pandas as pd +from lancedb.embeddings import get_registry +from lancedb.pydantic import LanceModel, Vector +from sentence_transformers import SentenceTransformer + +from pandasai.helpers.logger import Logger +from pandasai.vectorstores.vectorstore import VectorStore + + +class LanceDB(VectorStore): + """ + Implementation of LanceDB vector store + """ + + _logger: Logger + + def __init__( + self, + table_name: str = "pandasai", + embedding_function: Optional[Callable[[List[str]], List[float]]] = None, + persist_path: Optional[str] = "/tmp/lancedb", + max_samples: int = 1, + similary_threshold: int = 1.5, + logger: Optional[Logger] = None, + ) -> None: + self._logger = logger or Logger() + self._max_samples = max_samples + self._similarity_threshold = similary_threshold + self._persist_directory = persist_path + + # Initialize LanceDB database + self._db = lancedb.connect(self._persist_directory) + # Embedding function + self._embedding_function = embedding_function + + if self._embedding_function is None: + embedding_function = ( + get_registry() + .get("sentence-transformers") + .create(name="BAAI/bge-small-en-v1.5", device="cpu") + ) + + # schema for qa-pairs + class QA_pairs(LanceModel): + id: str + qa: str = embedding_function.SourceField() + metadata: str + vector: Vector( + embedding_function.ndims() + ) = embedding_function.VectorField() + + # schema for docs + class Docs(LanceModel): + id: str + doc: str = embedding_function.SourceField() + metadata: str + vector: Vector( + embedding_function.ndims() + ) = embedding_function.VectorField() + + else: + # schema for qa-pairs + class QA_pairs(LanceModel): + id: str + qa: str + metadata: str + vector: Vector + + # schema for docs + class Docs(LanceModel): + id: str + doc: str + metadata: str + vector: Vector + + self._logger.log(f"Persisting Agent Training data in {self._persist_directory}") + + # table for qa pairs + if f"{table_name}-qa" not in self._db.table_names(): + self._qa_table = self._db.create_table(f"{table_name}-qa", schema=QA_pairs) + else: + self._qa_table = self._db.open_table(f"{table_name}-qa") + + # table for docs + if f"{table_name}-docs" not in self._db.table_names(): + self._docs_table = self._db.create_table(f"{table_name}-docs", schema=Docs) + else: + self._docs_table = self._db.open_table(f"{table_name}-docs") + + self._logger.log(f"Successfully initialized collection {table_name}") + + def add_question_answer( + self, + queries: Iterable[str], + codes: Iterable[str], + ids: Optional[Iterable[str]] = None, + metadatas: Optional[List[dict]] = None, + ) -> List[str]: + """ + Add question and answer(code) to the training set + Args: + query: string of question + code: str + ids: Optional Iterable of ids associated with the texts. + metadatas: Optional list of metadatas associated with the texts. + kwargs: vectorstore specific parameters + Returns: + List of ids from adding the texts into the vectorstore. + """ + if len(queries) != len(codes): + raise ValueError( + f"Queries and codes dimension doesn't match {len(queries)} != {len(codes)}" + ) + + if ids is None: + ids = [f"{str(uuid.uuid4())}-qa" for _ in queries] + qa_str = [self._format_qa(query, code) for query, code in zip(queries, codes)] + + if metadatas is not None and len(metadatas): + metadatas = [str(data) for data in metadatas] + else: + metadatas = ["None" for _ in range(len(ids))] + + if self._embedding_function is not None: + embeddings = self._embedding_function(qa_str) + data = { + "id": ids, + "qa": qa_str, + "metadata": metadatas, + "vector": embeddings, + } + else: + data = {"id": ids, "qa": qa_str, "metadata": metadatas} + + print("data: ", data) + df = pd.DataFrame(data) + print("df: ", df) + self._qa_table.add(df) + print("Len of table: ", self._qa_table.head()) + + return ids + + def add_docs( + self, + docs: Iterable[str], + ids: Optional[Iterable[str]] = None, + metadatas: Optional[List[dict]] = None, + ) -> List[str]: + """ + Add docs to the training set + Args: + docs: Iterable of strings to add to the vectorstore. + ids: Optional Iterable of ids associated with the texts. + metadatas: Optional list of metadatas associated with the texts. + kwargs: vectorstore specific parameters + + Returns: + List of ids from adding the texts into the vectorstore. + """ + if ids is None: + ids = [f"{str(uuid.uuid4())}-docs" for _ in docs] + + if metadatas is not None and len(metadatas): + metadatas = [str(data) for data in metadatas] + else: + metadatas = ["None" for _ in range(len(ids))] + + if self._embedding_function is not None: + embeddings = self._embedding_function(docs) + data = { + "id": ids, + "doc": docs, + "metadata": metadatas, + "vector": embeddings, + } + else: + data = {"id": ids, "doc": docs, "metadata": metadatas} + + df = pd.DataFrame(data) + self._docs_table.add(df) + + return ids + + def get_embeddings(self, text): + if self._embedding_function is not None: + return self._embedding_function([text]) + + model = SentenceTransformer("BAAI/bge-large-zh-v1.5") + embedding_function = model.encode(text, normalize_embeddings=True) + return embedding_function(text) + + def update_question_answer( + self, + ids: Iterable[str], + queries: Iterable[str], + codes: Iterable[str], + metadatas: Optional[List[dict]] = None, + ) -> List[str]: + """ + Update question and answer(code) to the training set + Args: + ids: Iterable of ids associated with the texts. + queries: string of question + codes: str + metadatas: Optional list of metadatas associated with the texts. + kwargs: vectorstore specific parameters + Returns: + List of ids from updating the texts into the vectorstore. + """ + if len(queries) != len(codes): + raise ValueError( + f"Queries and codes dimension doesn't match {len(queries)} != {len(codes)}" + ) + + qa_str = [self._format_qa(query, code) for query, code in zip(queries, codes)] + if metadatas is not None and len(metadatas): + metadatas = [str(data) for data in metadatas] + else: + metadatas = ["None" for _ in range(len(ids))] + + for i in range(len(ids)): + updated_values = { + "qa": str(qa_str[i]), + "metadata": metadatas[i], + } + print("updated values: ", updated_values, ids[i]) + self._qa_table.update(values=updated_values, where=f"id = '{ids[i]}'") + + return ids + + def update_docs( + self, + ids: Iterable[str], + docs: Iterable[str], + metadatas: Optional[List[dict]] = None, + ) -> List[str]: + """ + Update docs to the training set + Args: + ids: Iterable of ids associated with the texts. + docs: Iterable of strings to update to the vectorstore. + metadatas: Optional list of metadatas associated with the texts. + kwargs: vectorstore specific parameters + + Returns: + List of ids from adding the texts into the vectorstore. + """ + if metadatas is not None and len(metadatas): + metadatas = [str(data) for data in metadatas] + else: + metadatas = ["None" for _ in range(len(ids))] + + for i in range(len(ids)): + updated_values = { + "doc": str(docs[i]), + "metadata": metadatas[i], + } + self._docs_table.update(values=updated_values, where=f"id = '{ids[i]}'") + return ids + + def delete_question_and_answers( + self, ids: Optional[List[str]] = None + ) -> Optional[bool]: + """ + Delete by vector ID to delete question and answers + Args: + ids: List of ids to delete + + Returns: + Optional[bool]: True if deletion is successful, + False otherwise + """ + for id in ids: + self._qa_table.delete(f"id = '{id}'") + return True + + def delete_docs(self, ids: Optional[List[str]] = None) -> Optional[bool]: + """ + Delete by vector ID to delete docs + Args: + ids: List of ids to delete + + Returns: + Optional[bool]: True if deletion is successful, + False otherwise + """ + for id in ids: + self._docs_table.delete(f"id = '{id}'") + return True + + def get_relevant_question_answers( + self, question: str, k: Union[int, None] = None + ) -> List[dict]: + """ + Returns relevant question answers based on search + """ + k = k or self._max_samples + + if self._embedding_function is None: + relevant_data = self._qa_table.search(query=question).limit(k).to_list() + else: + question_embeddings = self._embedding_function([question]) + relevant_data = ( + self._qa_table.search(question_embeddings).limit(k).to_list() + ) + + return self._filter_docs_based_on_distance( + relevant_data, self._similarity_threshold + ) + + def get_relevant_docs(self, question: str, k: int = None) -> List[dict]: + """ + Returns relevant documents based search + """ + k = k or self._max_samples + + if self._embedding_function is None: + relevant_data = self._docs_table.search(query=question).limit(k).to_list() + else: + question_embeddings = self._embedding_function([question]) + relevant_data = ( + self._docs_table.search(question_embeddings).limit(k).to_list() + ) + + return self._filter_docs_based_on_distance( + relevant_data, self._similarity_threshold + ) + + def get_relevant_question_answers_by_id(self, ids: Iterable[str]) -> List[dict]: + """ + Returns relevant question answers based on ids + """ + # to_search = ', ' .join([str(id) for id in ids]) + results = [] + for qa_id in ids: + relevant_data = ( + self._qa_table.search() + .limit(len(self._qa_table)) + .where(f"id = '{qa_id}'") + .select(["metadata", "qa"]) + .to_list() + ) + results.append(relevant_data) + return results + + def get_relevant_docs_by_id(self, ids: Iterable[str]) -> List[dict]: + """ + Returns relevant question answers based on ids + """ + # to_search = ', '.join([str(id) for id in ids]) + results = [] + for doc_id in ids: + relevant_data = ( + self._docs_table.search() + .limit(len(self._docs_table)) + .where(f"id = '{doc_id}'") + .select(["metadata", "doc"]) + .to_list() + ) + results.append(relevant_data) + return results + + def get_relevant_qa_documents(self, question: str, k: int = None) -> List[str]: + """ + Returns relevant question answers documents only + Args: + question (_type_): list of documents + """ + return self.get_relevant_question_answers(question, k)["documents"][0] + + def get_relevant_docs_documents(self, question: str, k: int = None) -> List[str]: + """ + Returns relevant question answers documents only + Args: + question (_type_): list of documents + """ + return self.get_relevant_docs(question, k)["documents"][0] + + def _filter_docs_based_on_distance( + self, documents: list, threshold: int + ) -> List[str]: + """ + Filter documents based on threshold + Args: + documents (List[str]): list of documents in string + threshold (int): similarity threshold + + Returns: + _type_: _description_ + """ + relevant_column = list( + documents[0].keys() - {"id", "vector", "metadata", "_distance"} + ) + + filtered_data = [ + ( + document[relevant_column[0]], + document["metadata"], + ) + for document in documents + if document["_distance"] < threshold + ] + + return { + key: [[data[i] for data in filtered_data]] + for i, key in enumerate(["documents", "metadatas"]) + } diff --git a/poetry.lock b/poetry.lock index 0845e4eab..e27e814e4 100644 --- a/poetry.lock +++ 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"yfinance" -version = "0.2.31" +version = "0.2.41" description = "Download market data from Yahoo! Finance API" optional = true python-versions = "*" files = [ - {file = "yfinance-0.2.31-py2.py3-none-any.whl", hash = "sha256:a2367c8f07fbeca95fc9e5ec46c2c8d0c41ab06b2c91f60d4a04c06aa01c7fba"}, - {file = "yfinance-0.2.31.tar.gz", hash = "sha256:4e09959a6b838c9ade9fdfe054d2663d5543c8b3ec8b463d17ce4789a3a0b3c4"}, + {file = "yfinance-0.2.41-py2.py3-none-any.whl", hash = "sha256:2ed7b453cb8568773eb2dbb4d87cc37ff02e5d133f7723ec3e219ab0b86b56d8"}, + {file = "yfinance-0.2.41.tar.gz", hash = "sha256:f94409a1ed4d596b9da8d2dbb498faaabfcf593d5870e1412e17669a212bb345"}, ] [package.dependencies] -appdirs = ">=1.4.4" beautifulsoup4 = ">=4.11.1" frozendict = ">=2.3.4" html5lib = ">=1.1" @@ -7345,23 +8339,28 @@ multitasking = ">=0.0.7" numpy = ">=1.16.5" pandas = ">=1.3.0" peewee = ">=3.16.2" +platformdirs = ">=2.0.0" pytz = ">=2022.5" requests = ">=2.31" +[package.extras] +nospam = ["requests-cache (>=1.0)", "requests-ratelimiter (>=0.3.1)"] +repair = ["scipy (>=1.6.3)"] + [[package]] name = "zipp" -version = "3.17.0" +version = "3.19.2" description = "Backport of 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"}, + {file = "zipp-3.19.2-py3-none-any.whl", hash = "sha256:f091755f667055f2d02b32c53771a7a6c8b47e1fdbc4b72a8b9072b3eef8015c"}, + {file = "zipp-3.19.2.tar.gz", hash = "sha256:bf1dcf6450f873a13e952a29504887c89e6de7506209e5b1bcc3460135d4de19"}, ] [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"] +doc = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"] +test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more-itertools", "pytest (>=6,!=8.1.*)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-ignore-flaky", "pytest-mypy", "pytest-ruff (>=0.2.1)"] [extras] bedrock = ["boto3"] @@ -7373,6 +8372,7 @@ ggplot = ["ggplot"] google-ai = ["google-cloud-aiplatform", "google-generativeai"] google-sheets = ["beautifulsoup4"] ibm-watsonx-ai = ["ibm-watsonx-ai"] +lancedb = ["lancedb"] langchain = ["langchain"] modin = ["modin"] numpy = ["numpy"] @@ -7382,6 +8382,7 @@ polars = ["polars"] qdrant = ["qdrant-client"] scikit-learn = ["scikit-learn"] seaborn = ["seaborn"] +sentencec-transformers = ["sentence-transformers"] statsmodels = ["statsmodels"] streamlit = ["streamlit"] text-generation = ["text-generation"] @@ -7390,4 +8391,4 @@ yfinance = ["yfinance"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.9.7 || >3.9.7,<4.0" -content-hash = "b4e9db29ccd4fb3bba58fb8b6933363e911acd48c2369311ee6bbca8f065b6f3" +content-hash = "4daebd82c22d04c8b8e65eaa66fa7e896ed31e69a92ba54ad9a3941f145e2a2d" diff --git a/pyproject.toml b/pyproject.toml index 605156822..8d67b0789 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -52,6 +52,8 @@ qdrant-client = {version = "^1.8.0", extras = ["fastembed"], optional = true } ibm-watsonx-ai = { version = "^0.2.3", optional = true, markers = "python_version >= '3.10'"} cx-Oracle = { version = "^8.3.0", optional = true } pinecone-client = { version = "^4.1.0", optional = true, markers = "python_version >= '3.10'"} +lancedb = {version = "^0.11.0", optional = true} +sentence-transformers = {version = "^2.3.0", optional = true} sqlglot = {extras = ["rs"], version = "^25.0.3"} [tool.poetry.group.dev] @@ -88,6 +90,8 @@ text-generation = ["fsspec", "huggingface-hub", "text-generation"] yfinance = ["yfinance"] modin = ["modin", "ray"] chromadb = ["chromadb"] +lancedb = ["lancedb"] +sentencec-transformers = ["sentence-transformers"] bedrock = ["boto3"] flask = ["flask"] qdrant = ["qdrant-client"] diff --git a/tests/unit_tests/vectorstores/test_lancedb.py b/tests/unit_tests/vectorstores/test_lancedb.py new file mode 100644 index 000000000..faa2ec6be --- /dev/null +++ b/tests/unit_tests/vectorstores/test_lancedb.py @@ -0,0 +1,137 @@ +import unittest +from unittest.mock import MagicMock + +from pandasai.ee.vectorstores import LanceDB +from pandasai.helpers.logger import Logger + + +class TestLanceDB(unittest.TestCase): + def setUp(self): + # Mock the LanceDB class within the setUp method + self.vector_store = LanceDB() + self.vector_store._format_qa = MagicMock( + side_effect=lambda q, c: f"Q: {q}\nA: {c}" + ) + # self.vector_store._embedding_function = MagicMock( + # return_value=[[1.0, 2.0, 3.0]] * 2 + # ) + self.vector_store._qa_table + self.vector_store._docs_table + + def test_constructor_default_parameters(self): + self.assertEqual(self.vector_store._max_samples, 1) + self.assertEqual(self.vector_store._similarity_threshold, 1.5) + self.assertIsInstance(self.vector_store._logger, Logger) + assert "pandasai-qa" in self.vector_store._db.table_names() + assert "pandasai-docs" in self.vector_store._db.table_names() + + def test_constructor_with_custom_logger(self): + custom_logger = Logger() + self.vector_store._logger = custom_logger + self.assertIs(self.vector_store._logger, custom_logger) + + def test_constructor_creates_table_if_not_exists(self): + index_name = "pandasai" + exists = f"{index_name}-qa" in self.vector_store._db.table_names() + self.assertEqual(exists, True) + + def test_add_question_answer(self): + inserted_ids = self.vector_store.add_question_answer( + ["What is LanceDB?", "How does it work?"], + ["print('Hello')", "for i in range(10): print(i)"], + ) + assert len(inserted_ids) == 2 + + def test_add_question_answer_with_ids(self): + inserted_ids = self.vector_store.add_question_answer( + ["What is LanceDB?", "How does it work?"], + ["print('Hello')", "for i in range(10): print(i)"], + ["test_id_1", "test_id_2"], + ) + assert inserted_ids == ["test_id_1", "test_id_2"] + + def test_add_question_answer_different_dimensions(self): + with self.assertRaises(ValueError): + self.vector_store.add_question_answer( + ["What is LanceDB?", "How does it work?"], + ["print('Hello')"], + ) + + def test_update_question_answer(self): + updated_ids = self.vector_store.update_question_answer( + ["test_id"], + ["What is LanceDB?"], + ["print(Hello)"], + ) + self.assertEqual(updated_ids, ["test_id"]) + + def test_update_question_answer_different_dimensions(self): + with self.assertRaises(ValueError): + self.vector_store.update_question_answer( + ["test_id"], + ["What is LanceDB?", "How does it work?"], + ["print('Hello')"], + ) + + def test_add_docs(self): + inserted_ids = self.vector_store.add_docs(["Document 1", "Document 2"]) + self.assertEqual(len(inserted_ids), 2) + + def test_add_docs_with_ids(self): + inserted_ids = self.vector_store.add_docs( + ["Document 1", "Document 2"], ["test_id_1", "test_id_2"] + ) + self.assertEqual(inserted_ids, ["test_id_1", "test_id_2"]) + + def test_delete_question_and_answers(self): + deleted_qa = self.vector_store.delete_question_and_answers(["id1", "id2"]) + self.assertEqual(deleted_qa, True) + + def test_delete_docs(self): + deleted_docs = self.vector_store.delete_docs(["id1", "id2"]) + self.assertEqual(deleted_docs, True) + + def test_get_relevant_question_answers(self): + result = self.vector_store.get_relevant_question_answers( + "What is LanceDB?", k=2 + ) + print("result: ", result) + self.assertEqual( + result, + { + "documents": [ + [ + "Q: What is LanceDB?\nA: print('Hello')", + "Q: What is LanceDB?\nA: print('Hello')", + ] + ], + "metadatas": [["None", "None"]], + }, + ) + + def test_get_relevant_question_answers_by_ids(self): + result = self.vector_store.get_relevant_question_answers_by_id(["test_id_1"]) + self.assertEqual( + result, + [[{"metadata": "None", "qa": "Q: What is LanceDB?\nA: print('Hello')"}]], + ) + + def test_get_relevant_docs(self): + result = self.vector_store.get_relevant_docs("What is LanceDB?", k=3) + print("result:", result) + self.assertEqual( + result, + { + "documents": [["Document 1", "Document 1", "Document 2"]], + "metadatas": [["None", "None", "None"]], + }, + ) + + def test_get_relevant_docs_by_ids(self): + result = self.vector_store.get_relevant_docs_by_id(["test_id_1"]) + print("Result docs ids: ", result) + self.assertEqual(result, [[{"doc": "Document 1", "metadata": "None"}]]) + + +if __name__ == "__main__": + unittest.main() From 83cb95483f41d060764511437bac13086045f239 Mon Sep 17 00:00:00 2001 From: PrashantDixit-dev Date: Wed, 14 Aug 2024 13:48:09 +0530 Subject: [PATCH 2/2] global schema redefinition --- pandasai/agent/base.py | 13 ---- pandasai/ee/vectorstores/lanceDB.py | 95 +++++++++++++++++------------ 2 files changed, 56 insertions(+), 52 deletions(-) diff --git a/pandasai/agent/base.py b/pandasai/agent/base.py index 24d46b98d..28204afbe 100644 --- a/pandasai/agent/base.py +++ b/pandasai/agent/base.py @@ -98,19 +98,6 @@ def __init__( self._vectorstore = BambooVectorStore(logger=self.logger) self.context.vectorstore = self._vectorstore - elif self._vectorstore == "lancedb" and os.environ.get("PANDASAI_API_KEY"): - try: - from pandasai.vectorstores.lancedb_vectorstore import LanceDBVectorStore - except ImportError as e: - raise ImportError( - "Could not import LanceDBVectorStore. Please install the required dependencies." - ) from e - - self._vectorstore = LanceDBVectorStore( - logger=self.logger, uri="/tmp/lancedb" - ) - self.context.vectorstore = self._vectorstore - self._callbacks = Callbacks(self) self.configure() diff --git a/pandasai/ee/vectorstores/lanceDB.py b/pandasai/ee/vectorstores/lanceDB.py index e59590f29..daf79b812 100644 --- a/pandasai/ee/vectorstores/lanceDB.py +++ b/pandasai/ee/vectorstores/lanceDB.py @@ -3,7 +3,9 @@ import lancedb import pandas as pd -from lancedb.embeddings import get_registry +from lancedb.embeddings import EmbeddingFunctionRegistry, get_registry +from lancedb.embeddings.base import TextEmbeddingFunction +from lancedb.embeddings.registry import register from lancedb.pydantic import LanceModel, Vector from sentence_transformers import SentenceTransformer @@ -11,6 +13,54 @@ from pandasai.vectorstores.vectorstore import VectorStore +@register("embedding_function") +class EmbeddingFunction(TextEmbeddingFunction): + def __init__(self, model, **kwargs): + super().__init__(**kwargs) + self._ndims = None + self._model = model + + def generate_embeddings(self, texts): + return self._model(list(texts)) + + def ndims(self): + if self._ndims is None: + self._ndims = len(self.generate_embeddings(texts=["foo"])[0]) + return self._ndims + + +class Schema: + def __init__(self, custom_embedding_function, model=None): + if custom_embedding_function: + self._embed = ( + EmbeddingFunctionRegistry.get_instance(model) + .get("embedding_function") + .create() + ) + else: + self._embed = ( + get_registry() + .get("sentence-transformers") + .create(name="BAAI/bge-small-en-v1.5", device="cpu") + ) + + def _create_schema(self): + class QA_pairs(LanceModel): + id: str + qa: str = self._embed.SourceField() + metadata: str + vector: Vector(self._embed.ndims()) = self._embed.VectorField() + + # schema for docs + class Docs(LanceModel): + id: str + doc: str = self._embed.SourceField() + metadata: str + vector: Vector(self._embed.ndims()) = self._embed.VectorField() + + return QA_pairs, Docs + + class LanceDB(VectorStore): """ Implementation of LanceDB vector store @@ -34,48 +84,15 @@ def __init__( # Initialize LanceDB database self._db = lancedb.connect(self._persist_directory) + # Embedding function self._embedding_function = embedding_function - if self._embedding_function is None: - embedding_function = ( - get_registry() - .get("sentence-transformers") - .create(name="BAAI/bge-small-en-v1.5", device="cpu") - ) - - # schema for qa-pairs - class QA_pairs(LanceModel): - id: str - qa: str = embedding_function.SourceField() - metadata: str - vector: Vector( - embedding_function.ndims() - ) = embedding_function.VectorField() - - # schema for docs - class Docs(LanceModel): - id: str - doc: str = embedding_function.SourceField() - metadata: str - vector: Vector( - embedding_function.ndims() - ) = embedding_function.VectorField() - + QA_pairs, Docs = Schema(custom_embedding_function=False)._create_schema() else: - # schema for qa-pairs - class QA_pairs(LanceModel): - id: str - qa: str - metadata: str - vector: Vector - - # schema for docs - class Docs(LanceModel): - id: str - doc: str - metadata: str - vector: Vector + QA_pairs, Docs = Schema( + custom_embedding_function=True, model=self._embedding_function + )._create_schema() self._logger.log(f"Persisting Agent Training data in {self._persist_directory}")