diff --git a/recipes/llamaindex/.gitignore b/recipes/llamaindex/.gitignore index 6d22ba0..e93bcc7 100644 --- a/recipes/llamaindex/.gitignore +++ b/recipes/llamaindex/.gitignore @@ -11,3 +11,6 @@ pyproject.local.toml __pycache__ data notebooks +.DS_Store +finetuning_examples +finetuning_assets diff --git a/recipes/llamaindex/Dockerfile b/recipes/llamaindex/Dockerfile deleted file mode 100644 index 8665c36..0000000 --- a/recipes/llamaindex/Dockerfile +++ /dev/null @@ -1,48 +0,0 @@ -FROM --platform=linux/amd64 python:3.10-slim as builder - -WORKDIR /app - -ENV POETRY_VERSION=1.7.1 - -# Install libraries for necessary python package builds -RUN apt-get update && apt-get --no-install-recommends install build-essential python3-dev libpq-dev -y && \ - pip install --no-cache-dir --upgrade pip && \ - pip install --no-cache-dir --upgrade poetry==${POETRY_VERSION} - -# Install ssh -RUN apt-get -yq update && apt-get -yqq install ssh - -# Configure Poetry -ENV POETRY_CACHE_DIR=/tmp/poetry_cache -ENV POETRY_NO_INTERACTION=1 -ENV POETRY_VIRTUALENVS_IN_PROJECT=true -ENV POETRY_VIRTUALENVS_CREATE=true - -# Install dependencies -COPY ./poetry.lock ./pyproject.toml ./ - -RUN mkdir -p -m 0600 ~/.ssh && ssh-keyscan github.com >> ~/.ssh/known_hosts - -RUN poetry install --no-cache --no-root - -FROM --platform=linux/amd64 python:3.10-slim as runtime - -# Install wget for healthcheck -RUN apt-get update && apt-get install -y wget - -RUN apt-get update -y && \ - apt-get install --no-install-recommends libpq5 -y && \ - rm -rf /var/lib/apt/lists/* # Install libpq for psycopg2 - -RUN groupadd -r appuser && useradd --no-create-home -g appuser -r appuser -USER appuser - -WORKDIR /app - -ENV VIRTUAL_ENV=/app/.venv -COPY --from=builder ${VIRTUAL_ENV} ${VIRTUAL_ENV} -ENV PATH="${VIRTUAL_ENV}/bin:${PATH}" - -# Copy source code -COPY ./logging.ini ./logging.ini -COPY ./llamaindex_cookbook ./llamaindex_cookbook diff --git a/recipes/llamaindex/README.md b/recipes/llamaindex/README.md index 4394935..618a05d 100644 --- a/recipes/llamaindex/README.md +++ b/recipes/llamaindex/README.md @@ -1,72 +1,179 @@ -# LlamaAgents Demo With Snowflake/Cybersyn Data Agents +# ARC Task (LLM) Solver With Human Input -image +The Abstraction and Reasoning Corpus ([ARC](https://github.com/fchollet/ARC-AGI)) for Artificial General Intelligence +benchmark aims to measure an AI system's ability to efficiently learn new skills. +Each task within the ARC benchmark contains a unique puzzle for which the systems +attempt to solve. Currently, the best AI systems achieve 34% solve rates, whereas +humans are able to achieve 85% ([source](https://www.kaggle.com/competitions/arc-prize-2024/overview/prizes)). -For this demo app, we have a multi-agent system comprised with the following -components: +

+ cover +

-- A data **Agent** that performs queries over [Cybersyn's Financial & Economic Essentials](https://app.snowflake.com/marketplace/listing/GZTSZAS2KF7/cybersyn-financial-economic-essentials?originTab=provider&providerName=Cybersyn&profileGlobalName=GZTSZAS2KCS) Dataset -- A data **Agent** that performs queries over [Cyberysyn' Government Essentials](https://app.snowflake.com/marketplace/listing/GZTSZAS2KGK/cybersyn-government-essentials?originTab=provider&providerName=Cybersyn&profileGlobalName=GZTSZAS2KCS) Dataset -- A general **Agent** that can answer all general queries -- A **Human (In the Loop) Service** that provides inputs to the two data agents -- A **ControlPlane** that features a router which routes tasks to the most appropriate orchestrator -- A **RabbitMQ MessageQueue** to broker communication between agents, human-in-the-loop, and control-plane +Motivated by this large disparity, we built this app with the goal of injecting +human-level reasoning on this benchmark to LLMs. Specifically, this app enables +the collaboration of LLMs and humans to solve an ARC task; and these collaborations +can then be used for fine-tuning the LLM. -For the frontend, we built a Streamlit App to interact with this multi-agent -system. +The Solver itself is a LlamaIndex `Workflow` that relies on successive runs for +which `Context` is maintained from previous runs. Doing so allows for an +effective implementation of the Human In the Loop Pattern. -## Pre-Requisites +

+ cover +

-### Poetry +## Running The App -For this app, we use [Poetry](https://python-poetry.org/) as the package's -dependency manager. The `poetry` cli tool is what we'll need to install the package's -virtual environment in order to run our streamlit app. +Before running the streamlit app, we first must download the ARC dataset. The +below command will download the dataset and store it in a directory named `data/`: -### Docker +```sh +wget https://github.com/fchollet/ARC-AGI/archive/refs/heads/master.zip -O ./master.zip +unzip ./master.zip -d ./ +mv ARC-AGI-master/data ./ +rm -rf ARC-AGI-master +rm master.zip +``` -To run this demo, we make use of `Docker`, specifically `docker-compose`. For this -demo, all of the necessary services (with the exception of the message queue) -are packaged in one common Docker image and can be instantianted through their -respective commands (i.e., see `docker-compose.yml`.) +Next, we must install the app's dependencies. To do so, we can use `poetry`: -### Credentials +```sh +poetry shell +poetry install +``` -For this app, we use OpenAI as the LLM provider and so an `OPENAI_API_KEY` will -need to be supplied. Moreover, the Cybersyn data is pulled from Snowflake and so -various Snowflake params are also required. See the section "# FILL-IN" in the -`template.env.docker` file. Once, you've filled in the necessary environment -variables, rename the file to `.env.docker`. +Finally, to run the streamlit app: -Similarly, you need to provide credentials in the `template.env.local`. Once -filled in, rename the file to `.env.local`. +```sh +export OPENAI_API_KEY= && streamlit run arc_finetuning_st/streamlit/app.py +``` -## Running The App +## How To Use The App + +In the next two sections, we discuss how to use the app in order to solve a given +ARC task. + +

+ cover +

+ +### Solving an ARC Task + +Each ARC task consists of training examples, each of which consist of input and +output pairs. There exists a common pattern between these input and output pairs, +and the problem is solved by uncovering this pattern, which can be verified by +the included test examples. + +To solve the task, we cycle through the following three steps: + +1. Prediction (of test output grid) +2. Evaluation +3. Critique (human in the loop) + +(Under the hood a LlamaIndex `Workflow` implements these three `steps`.) + +Step 1. makes use of an LLM to produce the Prediction whereas Step 2. is +deterministic and is a mere comparison between the ground truth test output and +the Prediction. If the Prediction doesn't match the ground truth grid, then Step 3. +is performed. Similar to step 1. an LLM is prompted to generate a Critique on the +Prediction as to why it may not match the pattern underlying the train input and +output pairs. However, we also allow for a human in the loop to override this +LLM generated Critique. + +The Critique is carried on from a previous cycle onto the next in order to +generate an improved and hopefully correct next Prediction. + +To begin, click the `Start` button found in the top-right corner. If the +prediction is incorrect, you can view the Critique produced by the LLM in the +designated text area. You can choose to use this Critique or supply your own by +overwriting the text and applying the change. Once ready to produce the next +prediction, hit the `Continue` button. + +### Saving solutions for fine-tuning + +Any collaboration session involving the LLM and human can be saved and used to +finetune an LLM. In this app, we use OpenAI LLMs, and so the finetuning examples +adhere to the [OpenAI fine-tuning API](https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset). +Click the `fine-tuning example` button during a session to see the current +example that can be used for fine-tuning. -### The backend (multi-agent system) +

+ cover +

-To start the multi-agent system, use the following command while in the root of -the project directory: +## Fine-tuning (with `arc-finetuning-cli`) + +After you've created your finetuning examples (you'll need at least 10 of them), +you can submit a job to OpenAI to finetune an LLM on them. To do so, we have a +convenient command line tool, that is powered by LlamaIndex plugins such as +`llama-index-finetuning`. ```sh -docker-compose up --build +arc finetuning cli tool. + +options: + -h, --help show this help message and exit + +commands: + {evaluate,finetune,job-status} + evaluate Evaluation of ARC Task predictions with LLM and ARCTaskSolverWorkflow. + finetune Finetune OpenAI LLM on ARC Task Solver examples. + job-status Check the status of finetuning job. ``` -### Streamlit App +### Submitting a fine-tuning job -Once the services are all running, you can then run the streamlit app. First, -ensure that you have the package's virtual environment active and the environment -variables set. Again, while in the root directory of this project, run the commands -found below: +To submit a fine-tuning job, use any of the following three `finetune` command: ```sh -poetry shell -poetry install -set -a && source .env.local +# submit a new finetune job using the specified llm +arc-finetuning-cli finetune --llm gpt-4o-2024-08-06 + +# submit a new finetune job that continues from previously finetuned model +arc-finetuning-cli finetune --llm gpt-4o-2024-08-06 --start-job-id ftjob-TqJd5Nfe3GIiScyTTJH56l61 + +# submit a new finetune job that continues from the most recent finetuned model +arc-finetuning-cli finetune --continue-latest ``` -Next, run the streamlit app: +The commands above will take care of compiling all of the single finetuning json +examples (i.e. stored in `finetuning_examples/`) into a single `jsonl` file that +is then passed to OpenAI finetuning API. + +### Checking the status of a fine-tuning job + +After submitting a job, you can check its status using the below cli commands: ```sh -streamlit run llamaindex_cookbook/apps/streamlit.py +arc-finetuning-cli job-status -j ftjob-WYySY3iGYpfiTbSDeKDZO0YL -m gpt-4o-2024-08-06 + +# or check status of the latest job submission +arc-finetuning-cli job-status --latest ``` + +## Evaluation + +You can evaluate the `ARCTaskSolverWorkflow` and a specified LLM on the ARC test +dataset. You can even supply a fine-tuned LLM here. + +```sh +# evaluate ARCTaskSolverWorkflow single attempt with gpt-4o +arc-finetuning-cli evaluate --llm gpt-4o-2024-08-06 + +# evaluate ARCTaskSolverWorkflow single attempt with a previously fine-tuned gpt-4o +arc-finetuning-cli evaluate --llm gpt-4o-2024-08-06 --start-job-id ftjob-TqJd5Nfe3GIiScyTTJH56l61 +``` + +You can also specify certain parameters to control the speed of the execution so +as to not run into `RateLimitError`'s from OpenAI. + +```sh +arc-finetuning-cli evaluate --llm gpt-4o-2024-08-06 --batch-size 5 --num-workers 3 --sleep 10 +``` + +In the above command, `batch-size` refers to the number of test cases handled in +single batch. In total, there are 400 test cases. Moreover, `num-workers` is the +maximum number of async calls allowed to be made to OpenAI API at any given moment. +Finally, `sleep` is the amount of time in seconds the execution halts before moving +onto the next batch of test cases. diff --git a/recipes/llamaindex/arc-finetuning/placeholder.md b/recipes/llamaindex/arc-finetuning/placeholder.md deleted file mode 100644 index 8b13789..0000000 --- a/recipes/llamaindex/arc-finetuning/placeholder.md +++ /dev/null @@ -1 +0,0 @@ - diff --git a/recipes/llamaindex/llamaindex_cookbook/__init__.py b/recipes/llamaindex/arc_finetuning_st/__init__.py similarity index 100% rename from recipes/llamaindex/llamaindex_cookbook/__init__.py rename to recipes/llamaindex/arc_finetuning_st/__init__.py diff --git a/recipes/llamaindex/llamaindex_cookbook/additional_services/__init__.py b/recipes/llamaindex/arc_finetuning_st/cli/__init__.py similarity index 100% rename from recipes/llamaindex/llamaindex_cookbook/additional_services/__init__.py rename to recipes/llamaindex/arc_finetuning_st/cli/__init__.py diff --git a/recipes/llamaindex/arc_finetuning_st/cli/command_line.py b/recipes/llamaindex/arc_finetuning_st/cli/command_line.py new file mode 100644 index 0000000..01ff87b --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/cli/command_line.py @@ -0,0 +1,213 @@ +import argparse +import asyncio +import json +from os import listdir +from pathlib import Path +from typing import Any, List, Optional, cast + +from llama_index.llms.openai import OpenAI + +from arc_finetuning_st.cli.evaluation import batch_runner +from arc_finetuning_st.cli.finetune import ( + FINETUNE_JOBS_FILENAME, + check_job_status, + prepare_finetuning_jsonl_file, + submit_finetune_job, +) +from arc_finetuning_st.workflows.arc_task_solver import ( + ARCTaskSolverWorkflow, + WorkflowOutput, +) + +SINGLE_EXAMPLE_JSON_PATH = Path( + Path(__file__).parents[2].absolute(), "finetuning_examples" +) +FINETUNING_ASSETS_PATH = Path( + Path(__file__).parents[2].absolute(), "finetuning_assets" +) + + +def handle_evaluate( + llm: str, + batch_size: int, + num_workers: int, + verbose: bool, + sleep: int, + **kwargs: Any, +) -> None: + data_path = Path( + Path(__file__).parents[2].absolute(), "data", "evaluation" + ) + task_paths = [data_path / t for t in listdir(data_path)] + llm = OpenAI(llm) + w = ARCTaskSolverWorkflow(llm=llm, timeout=None) + results = asyncio.run( + batch_runner( + w, + task_paths[:10], + verbose=verbose, + batch_size=batch_size, + num_workers=num_workers, + sleep=sleep, + ) + ) + results = cast(List[WorkflowOutput], results) + num_solved = sum(el.passing for el in results) + print( + f"Solved: {num_solved}\nTotal Tasks:{len(results)}\nAverage Solve Rate: {float(num_solved) / len(results)}" + ) + + +def handle_finetune_job_submit( + llm: str, + start_job_id: Optional[str], + continue_latest: bool = False, + **kwargs: Any, +) -> None: + prepare_finetuning_jsonl_file( + json_path=SINGLE_EXAMPLE_JSON_PATH, assets_path=FINETUNING_ASSETS_PATH + ) + if continue_latest: + try: + with open(FINETUNING_ASSETS_PATH / FINETUNE_JOBS_FILENAME) as f: + lines = f.read().splitlines() + metadata_str = lines[-1] + metadata = json.loads(metadata_str) + start_job_id = metadata["start_job_id"] + llm = metadata["model"] + except FileNotFoundError: + # no previous finetune model + raise ValueError( + "Missing `finetuning_jobs.jsonl` file. Have you submitted a prior job?" + ) + + submit_finetune_job( + llm=llm, + start_job_id=start_job_id, + assets_path=FINETUNING_ASSETS_PATH, + ) + + +def handle_check_finetune_job( + start_job_id: Optional[str], + llm: Optional[str], + latest: bool, + **kwargs: Any, +) -> None: + if latest: + try: + with open(FINETUNING_ASSETS_PATH / FINETUNE_JOBS_FILENAME) as f: + lines = f.read().splitlines() + metadata_str = lines[-1] + metadata = json.loads(metadata_str) + start_job_id = metadata["start_job_id"] + llm = metadata["model"] + except FileNotFoundError: + raise ValueError( + "No finetuning_jobs.json file exists. You likely haven't submitted a job yet." + ) + if not latest and (start_job_id is None or llm is None): + raise ValueError( + "If not `use_latest` then must provide `start_job_id` and `llm`." + ) + + # make type checking happy + if start_job_id and llm: + check_job_status( + start_job_id=start_job_id, + llm=llm, + assets_path=FINETUNING_ASSETS_PATH, + ) + + +def main() -> None: + parser = argparse.ArgumentParser(description="arc-finetuning cli tool.") + + # Subparsers + subparsers = parser.add_subparsers( + title="commands", dest="command", required=True + ) + + # evaluate command + evaluate_parser = subparsers.add_parser( + "evaluate", + help="Evaluation of ARC Task predictions with LLM and ARCTaskSolverWorkflow.", + ) + evaluate_parser.add_argument( + "-m", + "--llm", + type=str, + default="gpt-4o", + help="The OpenAI LLM model to use with the Workflow.", + ) + evaluate_parser.add_argument("-b", "--batch-size", type=int, default=5) + evaluate_parser.add_argument("-w", "--num-workers", type=int, default=3) + evaluate_parser.add_argument( + "-v", "--verbose", action=argparse.BooleanOptionalAction + ) + evaluate_parser.add_argument("-s", "--sleep", type=int, default=10) + evaluate_parser.set_defaults( + func=lambda args: handle_evaluate(**vars(args)) + ) + + # finetune command + finetune_parser = subparsers.add_parser( + "finetune", help="Finetune OpenAI LLM on ARC Task Solver examples." + ) + finetune_parser.add_argument( + "-m", + "--llm", + type=str, + default="gpt-4o-2024-08-06", + help="The OpenAI LLM model to finetune.", + ) + finetune_parser.add_argument( + "-j", + "--start-job-id", + type=str, + default=None, + help="Previously started job id, to continue finetuning.", + ) + finetune_parser.add_argument( + "--continue-latest", action=argparse.BooleanOptionalAction + ) + finetune_parser.set_defaults( + func=lambda args: handle_finetune_job_submit(**vars(args)) + ) + + # job status command + job_status_parser = subparsers.add_parser( + "job-status", help="Check the status of finetuning job." + ) + job_status_parser.add_argument( + "-j", + "--start-job-id", + type=str, + default=None, + help="Previously started job id, to continue finetuning.", + ) + job_status_parser.add_argument( + "-m", + "--llm", + type=str, + default="gpt-4o-2024-08-06", + help="The OpenAI LLM model to finetune.", + ) + job_status_parser.add_argument( + "--latest", + action=argparse.BooleanOptionalAction, + help="If set, checks the status of the last submitted job.", + ) + job_status_parser.set_defaults( + func=lambda args: handle_check_finetune_job(**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/recipes/llamaindex/arc_finetuning_st/cli/evaluation.py b/recipes/llamaindex/arc_finetuning_st/cli/evaluation.py new file mode 100644 index 0000000..db3ade5 --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/cli/evaluation.py @@ -0,0 +1,58 @@ +import asyncio +from os import listdir +from pathlib import Path +from typing import Any, List, cast + +from llama_index.core.async_utils import chunks +from llama_index.llms.openai import OpenAI + +from arc_finetuning_st.workflows.arc_task_solver import ( + ARCTaskSolverWorkflow, + WorkflowOutput, +) + +DATA_PATH = Path(Path(__file__).parents[1].absolute(), "data", "evaluation") + + +async def batch_runner( + workflow: ARCTaskSolverWorkflow, + task_paths: List[Path], + batch_size: int = 5, + verbose: bool = False, + sleep: int = 10, + num_workers: int = 3, +) -> List[Any]: + output: List[Any] = [] + sem = asyncio.Semaphore(num_workers) + for task_chunk in chunks(task_paths, batch_size): + task_chunk = ( + workflow.load_and_run_task(task_path=task_path, sem=sem) + for task_path in task_chunk + if task_path is not None + ) + output_chunk = await asyncio.gather(*task_chunk) + output.extend(output_chunk) + if verbose: + print( + f"Completed {len(output)} out of {len(task_paths)} tasks", + flush=True, + ) + await asyncio.sleep(sleep) + return output + + +async def main() -> None: + task_paths = [DATA_PATH / t for t in listdir(DATA_PATH)] + w = ARCTaskSolverWorkflow( + timeout=None, verbose=False, llm=OpenAI("gpt-4o") + ) + results = await batch_runner(w, task_paths[:10], verbose=True) + results = cast(List[WorkflowOutput], results) + num_solved = sum(el.passing for el in results) + print( + f"Solved: {num_solved}\nTotal Tasks:{len(results)}\nAverage Solve Rate: {float(num_solved) / len(results)}" + ) + + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/recipes/llamaindex/arc_finetuning_st/cli/finetune.py b/recipes/llamaindex/arc_finetuning_st/cli/finetune.py new file mode 100644 index 0000000..ad38033 --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/cli/finetune.py @@ -0,0 +1,72 @@ +import json +from os import listdir +from pathlib import Path +from typing import Optional + +from llama_index.finetuning import OpenAIFinetuneEngine + +SINGLE_EXAMPLE_JSON_PATH = Path( + Path(__file__).parents[1].absolute(), "finetuning_examples" +) + +FINETUNING_ASSETS_PATH = Path( + Path(__file__).parents[1].absolute(), "finetuning_assets" +) + +FINETUNE_JSONL_FILENAME = "finetuning.jsonl" +FINETUNE_JOBS_FILENAME = "finetuning_jobs.jsonl" + + +def prepare_finetuning_jsonl_file( + json_path: Path = SINGLE_EXAMPLE_JSON_PATH, + assets_path: Path = FINETUNING_ASSETS_PATH, +) -> None: + """Read all json files from data path and write a jsonl file.""" + with open(assets_path / FINETUNE_JSONL_FILENAME, "w") as jsonl_out: + for json_name in listdir(json_path): + with open(json_path / json_name) as f: + for line in f: + jsonl_out.write(line) + jsonl_out.write("\n") + + +def submit_finetune_job( + llm: str = "gpt-4o-2024-08-06", + start_job_id: Optional[str] = None, + assets_path: Path = FINETUNING_ASSETS_PATH, +) -> None: + """Submit finetuning job.""" + finetune_engine = OpenAIFinetuneEngine( + llm, + (assets_path / FINETUNE_JSONL_FILENAME).as_posix(), + start_job_id=start_job_id, + validate_json=False, + ) + finetune_engine.finetune() + + with open(assets_path / FINETUNE_JOBS_FILENAME, "a+") as f: + metadata = { + "model": llm, + "start_job_id": finetune_engine._start_job.id, + } + json.dump(metadata, f) + f.write("\n") + + print(finetune_engine.get_current_job()) + + +def check_job_status( + start_job_id: str, + llm: str = "gpt-4o-2024-08-06", + assets_path: Path = FINETUNING_ASSETS_PATH, +) -> None: + """Check on status of most recent submitted finetuning job.""" + + finetune_engine = OpenAIFinetuneEngine( + llm, + (assets_path / FINETUNE_JSONL_FILENAME).as_posix(), + start_job_id=start_job_id, + validate_json=False, + ) + + print(finetune_engine.get_current_job()) diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/__init__.py b/recipes/llamaindex/arc_finetuning_st/finetuning/__init__.py similarity index 100% rename from recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/__init__.py rename to recipes/llamaindex/arc_finetuning_st/finetuning/__init__.py diff --git a/recipes/llamaindex/arc_finetuning_st/finetuning/finetuning_example.py b/recipes/llamaindex/arc_finetuning_st/finetuning/finetuning_example.py new file mode 100644 index 0000000..4e90301 --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/finetuning/finetuning_example.py @@ -0,0 +1,91 @@ +import json +from pathlib import Path +from typing import Annotated, Any, Callable, List + +from llama_index.core.base.llms.types import ChatMessage, MessageRole +from llama_index.core.bridge.pydantic import BaseModel, Field, WrapSerializer + +from arc_finetuning_st.finetuning.templates import ( + ASSISTANT_TEMPLATE, + SYSTEM_MESSAGE, + USER_CRITIQUE_TEMPLATE, + USER_TASK_TEMPLATE, +) +from arc_finetuning_st.workflows.models import Attempt + + +def remove_additional_kwargs(value: Any, handler: Callable, info: Any) -> Any: + partial_result = handler(value, info) + del partial_result["additional_kwargs"] + return partial_result + + +class FineTuningExample(BaseModel): + messages: List[ + Annotated[ChatMessage, WrapSerializer(remove_additional_kwargs)] + ] + task_name: str = Field(exclude=True) + + @classmethod + def from_attempts( + cls, + task_name: str, + examples: str, + test_input: str, + attempts: List[Attempt], + system_message: str = SYSTEM_MESSAGE, + user_task_template: str = USER_TASK_TEMPLATE, + user_critique_template: str = USER_CRITIQUE_TEMPLATE, + assistant_template: str = ASSISTANT_TEMPLATE, + ) -> "FineTuningExample": + messages = [ + ChatMessage(role=MessageRole.SYSTEM, content=system_message), + ChatMessage( + role=MessageRole.USER, + content=user_task_template.format( + examples=examples, test_input=test_input + ), + ), + ] + for a in attempts: + messages.extend( + [ + ChatMessage( + role=MessageRole.ASSISTANT, + content=assistant_template.format( + predicted_output=str(a.prediction), + rationale=a.prediction.rationale, + ), + ), + ChatMessage( + role=MessageRole.USER, + content=user_critique_template.format( + critique=str(a.critique) + ), + ), + ] + ) + + # always end with an asst message or else openai finetuning job will failt + if a.critique == "This predicted output is correct.": + final_asst_message = ChatMessage( + role=MessageRole.ASSISTANT, + content="Glad, we were able to solve the puzzle!", + ) + else: + final_asst_message = ChatMessage( + role=MessageRole.ASSISTANT, + content="Thanks for the feedback. I'll incorporate this into my next prediction.", + ) + + messages.append(final_asst_message) + return cls(messages=messages, task_name=task_name) + + def to_json(self) -> str: + data = self.model_dump() + return json.dumps(data, indent=4) + + def write_json(self, dirpath: Path) -> None: + data = self.model_dump() + with open(Path(dirpath, self.task_name), "w") as f: + json.dump(data, f) diff --git a/recipes/llamaindex/arc_finetuning_st/finetuning/templates.py b/recipes/llamaindex/arc_finetuning_st/finetuning/templates.py new file mode 100644 index 0000000..9a6a77d --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/finetuning/templates.py @@ -0,0 +1,28 @@ +SYSTEM_MESSAGE = """You are a bot that is very good at solving puzzles. You will work with the user who will present you a new puzzle to solve it. +The puzzle consists of a list of EXAMPLES, each containing an INPUT/OUTPUT pair describing a pattern which is shared amongst all examples. +The user will also provide a TEST INPUT for which you are to produce a predicted OUTPUT that follows the common pattern of all the examples. + +Your task is collaborate with the user in order to solve the problem. +""" + +USER_TASK_TEMPLATE = """Here is a new task to solve: +EXAMPLES: +{examples} + +TEST INPUT: +{test_input} +""" + +# attempt.prediction +ASSISTANT_TEMPLATE = """ +PREDICTED OUTPUT: +{predicted_output} + +RATIONALE: +{rationale} +""" + +# attempt.critique +USER_CRITIQUE_TEMPLATE = """ +{critique} +""" diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/__init__.py b/recipes/llamaindex/arc_finetuning_st/streamlit/__init__.py similarity index 100% rename from recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/__init__.py rename to recipes/llamaindex/arc_finetuning_st/streamlit/__init__.py diff --git a/recipes/llamaindex/arc_finetuning_st/streamlit/app.py b/recipes/llamaindex/arc_finetuning_st/streamlit/app.py new file mode 100644 index 0000000..58bb776 --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/streamlit/app.py @@ -0,0 +1,203 @@ +from typing import Tuple + +import streamlit as st +from llama_index.core.tools.function_tool import async_to_sync + +from arc_finetuning_st.streamlit.controller import Controller + +# startup +st.set_page_config(layout="wide") + + +@st.cache_resource +def startup() -> Tuple[Controller,]: + controller = Controller() + return (controller,) + + +(controller,) = startup() + +# states +if "show_finetuning_preview_dialog" not in st.session_state: + st.session_state["show_finetuning_preview_dialog"] = True +if "disable_continue_button" not in st.session_state: + st.session_state["disable_continue_button"] = True +if "disable_start_button" not in st.session_state: + st.session_state["disable_start_button"] = False +if "disable_abort_button" not in st.session_state: + st.session_state["disable_abort_button"] = True +if "disable_preview_button" not in st.session_state: + st.session_state["disable_preview_button"] = True +if "metric_value" not in st.session_state: + st.session_state["metric_value"] = "N/A" + +logo = '[](https://github.com/run-llama/llama-agents "Check out the llama-agents Github repo!")' +st.title("ARC Task Solver with Human Input") +st.markdown( + f"{logo}   _Powered with LlamaIndex Worfklows_", + unsafe_allow_html=True, +) + +# sidebar +with st.sidebar: + task_selection = st.radio( + label="Tasks", + options=controller.task_file_names, + index=0, + on_change=controller.selectbox_selection_change_handler, + key="selected_task", + format_func=controller.radio_format_task_name, + ) + +train_col, test_col = st.columns( + [1, 1], vertical_alignment="top", gap="medium" +) + +with train_col: + st.subheader("Train Examples") + with st.container(): + selected_task = st.session_state.selected_task + if selected_task: + task = controller.load_task(selected_task) + num_examples = len(task["train"]) + tabs = st.tabs( + [f"Example {ix}" for ix in range(1, num_examples + 1)] + ) + for ix, tab in enumerate(tabs): + with tab: + left, right = st.columns( + [1, 1], vertical_alignment="top", gap="medium" + ) + with left: + ex = task["train"][ix] + grid = ex["input"] + fig = Controller.plot_grid(grid, kind="input") + st.plotly_chart(fig, use_container_width=True) + + with right: + ex = task["train"][ix] + grid = ex["output"] + fig = Controller.plot_grid(grid, kind="output") + st.plotly_chart(fig, use_container_width=True) + + +with test_col: + header_col, start_col, preview_col = st.columns( + [4, 1, 2], vertical_alignment="bottom", gap="small" + ) + with header_col: + st.subheader("Test") + with start_col: + st.button( + "start", + on_click=async_to_sync(controller.handle_prediction_click), + use_container_width=True, + type="primary", + disabled=st.session_state.get("disable_start_button"), + ) + with preview_col: + st.button( + "fine-tuning example", + on_click=async_to_sync(controller.handle_finetuning_preview_click), + use_container_width=True, + disabled=st.session_state.get("disable_preview_button"), + key="preview_button", + ) + + with st.container(): + selected_task = st.session_state.selected_task + if selected_task: + task = controller.load_task(selected_task) + num_cases = len(task["test"]) + tabs = st.tabs( + [f"Test Case {ix}" for ix in range(1, num_cases + 1)] + ) + for ix, tab in enumerate(tabs): + with tab: + left, right = st.columns( + [1, 1], vertical_alignment="top", gap="medium" + ) + with left: + ex = task["test"][ix] + grid = ex["input"] + fig = Controller.plot_grid(grid, kind="input") + st.plotly_chart(fig, use_container_width=True) + + with right: + prediction_fig = st.session_state.get( + "prediction", None + ) + if prediction_fig: + st.plotly_chart( + prediction_fig, + use_container_width=True, + key="prediction", + ) + + # metrics and past attempts + with st.container(): + metric_col, critique_col = st.columns( + [1, 7], vertical_alignment="top" + ) + with metric_col: + metric_value = st.session_state.get("metric_value") + st.markdown(body="Passing") + st.markdown(body=f"# {metric_value}") + + with critique_col: + st.markdown(body="Critique of Attempt") + st.text_area( + label="This critique is passed to the LLM to generate a new prediction.", + key="critique", + help=( + "An LLM was prompted to critique the prediction on why it might not fit the pattern. " + "This critique is passed in the PROMPT in the next prediction attempt. " + "Feel free to make edits to the critique or use your own." + ), + ) + + with st.expander("Past Attempts"): + st.dataframe( + controller.attempts_history_df, + hide_index=True, + selection_mode="single-row", + on_select=controller.handle_workflow_run_selection, + column_order=( + "attempt #", + "passing", + "critique", + "rationale", + ), + key="attempts_history_df", + use_container_width=True, + ) + + with st.container(): + continue_col, abort_col = st.columns([3, 1]) + with continue_col: + st.button( + "continue", + on_click=async_to_sync(controller.handle_prediction_click), + use_container_width=True, + disabled=st.session_state.get("disable_continue_button"), + key="continue_button", + type="primary", + ) + + with abort_col: + + @st.dialog("Are you sure you want to abort the session?") + def abort_solving() -> None: + st.write( + "Confirm that you want to abort the session by clicking 'confirm' button below." + ) + if st.button("Confirm"): + controller.reset() + st.rerun() + + st.button( + "abort", + on_click=abort_solving, + use_container_width=True, + disabled=st.session_state.get("disable_abort_button"), + ) diff --git a/recipes/llamaindex/arc_finetuning_st/streamlit/controller.py b/recipes/llamaindex/arc_finetuning_st/streamlit/controller.py new file mode 100644 index 0000000..f7eb07b --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/streamlit/controller.py @@ -0,0 +1,288 @@ +import asyncio +import json +import logging +from os import listdir +from pathlib import Path +from typing import Any, List, Literal, Optional, cast + +import pandas as pd +import plotly.express as px +import streamlit as st +from llama_index.core.workflow.handler import WorkflowHandler +from llama_index.llms.openai import OpenAI + +from arc_finetuning_st.finetuning.finetuning_example import FineTuningExample +from arc_finetuning_st.workflows.arc_task_solver import ( + ARCTaskSolverWorkflow, + WorkflowOutput, +) +from arc_finetuning_st.workflows.models import Attempt, Prediction + +logger = logging.getLogger(__name__) + + +class Controller: + def __init__(self) -> None: + self._handler: Optional[WorkflowHandler] = None + self._attempts: List[Attempt] = [] + self._passing_results: List[bool] = [] + self._data_path = Path( + Path(__file__).parents[2].absolute(), "data", "training" + ) + self._finetuning_examples_path = Path( + Path(__file__).parents[2].absolute(), "finetuning_examples" + ) + self._finetuning_examples_path.mkdir(exist_ok=True, parents=True) + + def reset(self) -> None: + # clear prediction + st.session_state.prediction = None + st.session_state.disable_continue_button = True + st.session_state.disable_abort_button = True + st.session_state.disable_preview_button = True + st.session_state.disable_start_button = False + st.session_state.critique = None + st.session_state.metric_value = "N/A" + + self._handler = None + self._attempts = [] + self._passing_results = [] + + def selectbox_selection_change_handler(self) -> None: + # only reset states + # loading of task is delegated to relevant calls made with each + # streamlit element + self.reset() + + @staticmethod + def plot_grid( + grid: List[List[int]], + kind: Literal["input", "output", "prediction", "latest prediction"], + ) -> Any: + m = len(grid) + n = len(grid[0]) + fig = px.imshow( + grid, + text_auto=True, + labels={"x": f"{kind.title()}
{m}x{n}"}, + ) + fig.update_coloraxes(showscale=False) + fig.update_layout( + yaxis={"visible": False}, + xaxis={"visible": True, "showticklabels": False}, + margin=dict( + l=20, + r=20, + b=20, + t=20, + ), + ) + return fig + + async def show_progress_bar(self, handler: WorkflowHandler) -> None: + progress_text_template = "{event} completed. Next step in progress." + my_bar = st.progress(0, text="Workflow run in progress. Please wait.") + num_steps = 5.0 + current_step = 1 + async for ev in handler.stream_events(): + my_bar.progress( + current_step / num_steps, + text=progress_text_template.format(event=type(ev).__name__), + ) + current_step += 1 + my_bar.empty() + + def handle_abort_click(self) -> None: + self.reset() + + async def handle_prediction_click(self) -> None: + """Run workflow to generate prediction.""" + selected_task = st.session_state.selected_task + if selected_task: + task = self.load_task(selected_task) + w = ARCTaskSolverWorkflow( + timeout=None, verbose=False, llm=OpenAI("gpt-4o") + ) + + if not self._handler: # start a new solver + handler = w.run(task=task) + + else: # continuing from past Workflow execution + # need to reset this queue otherwise will use nested event loops + self._handler.ctx._streaming_queue = asyncio.Queue() + + # use the critique and prediction str from streamlit + critique = st.session_state.get("critique") + self._attempts[-1].critique = critique + await self._handler.ctx.set("attempts", self._attempts) + + # run Workflow + handler = w.run(ctx=self._handler.ctx, task=task) + + # progress bar + _ = asyncio.create_task(self.show_progress_bar(handler)) + + res: WorkflowOutput = await handler + + handler = cast(WorkflowHandler, handler) + self._handler = handler + self._passing_results.append(res.passing) + self._attempts = res.attempts + + # update streamlit states + grid = Prediction.prediction_str_to_int_array( + prediction=str(res.attempts[-1].prediction) + ) + prediction_fig = Controller.plot_grid( + grid, kind="latest prediction" + ) + st.session_state.prediction = prediction_fig + st.session_state.critique = str(res.attempts[-1].critique) + st.session_state.disable_continue_button = False + st.session_state.disable_abort_button = False + st.session_state.disable_preview_button = False + st.session_state.disable_start_button = True + metric_value = "✅" if res.passing else "❌" + st.session_state.metric_value = metric_value + + @property + def saved_finetuning_examples(self) -> List[str]: + return listdir(self._finetuning_examples_path) + + @property + def task_file_names(self) -> List[str]: + return listdir(self._data_path) + + def radio_format_task_name(self, selected_task: str) -> str: + if selected_task in self.saved_finetuning_examples: + return f"{selected_task} ✅" + return selected_task + + def load_task(self, selected_task: str) -> Any: + task_path = Path(self._data_path, selected_task) + + with open(task_path) as f: + task = json.load(f) + return task + + @property + def passing(self) -> Optional[bool]: + if self._passing_results: + return self._passing_results[-1] + return None + + @property + def attempts_history_df( + self, + ) -> pd.DataFrame: + if self._attempts: + attempt_number_list: List[int] = [] + passings: List[str] = [] + rationales: List[str] = [] + critiques: List[str] = [] + predictions: List[str] = [] + for ix, a in enumerate(self._attempts): + passings = ["✅" if a.passing else "❌"] + passings + rationales = [a.prediction.rationale] + rationales + predictions = [str(a.prediction)] + predictions + critiques = [str(a.critique)] + critiques + attempt_number_list = [ix + 1] + attempt_number_list + return pd.DataFrame( + { + "attempt #": attempt_number_list, + "passing": passings, + "rationale": rationales, + "critique": critiques, + # hidden from UI + "prediction": predictions, + } + ) + return pd.DataFrame( + { + "attempt #": [], + "passing": [], + "rationale": [], + "critique": [], + # hidden from UI + "prediction": [], + } + ) + + def handle_workflow_run_selection(self) -> None: + @st.dialog("Past Attempt") + def _display_attempt( + fig: Any, rationale: str, critique: str, passing: bool + ) -> None: + st.plotly_chart( + fig, + use_container_width=True, + key="prediction", + ) + st.markdown(body=f"### Passing\n{passing}") + st.markdown(body=f"### Rationale\n{rationale}") + st.markdown(body=f"### Critique\n{critique}") + + selected_rows = ( + st.session_state.get("attempts_history_df") + .get("selection") + .get("rows") + ) + + if selected_rows: + row_ix = selected_rows[0] + df_row = self.attempts_history_df.iloc[row_ix] + + grid = Prediction.prediction_str_to_int_array( + prediction=df_row["prediction"] + ) + prediction_fig = Controller.plot_grid(grid, kind="prediction") + + _display_attempt( + fig=prediction_fig, + rationale=df_row["rationale"], + critique=df_row["critique"], + passing=df_row["passing"], + ) + + async def handle_finetuning_preview_click(self) -> None: + if self._handler: + st.session_state.show_finetuning_preview_dialog = True + prompt_vars = await self._handler.ctx.get("prompt_vars") + + @st.dialog("Finetuning Example", width="large") + def _display_finetuning_example() -> None: + nonlocal prompt_vars + + finetuning_example = FineTuningExample.from_attempts( + task_name=st.session_state.selected_task, + attempts=self._attempts, + examples=prompt_vars["examples"], + test_input=prompt_vars["test_input"], + ) + + with st.container(height=500, border=False): + save_col, close_col = st.columns([1, 1]) + with save_col: + if st.button("Save", use_container_width=True): + finetuning_example.write_json( + self._finetuning_examples_path, + ) + st.session_state.show_finetuning_preview_dialog = ( + False + ) + st.rerun() + with close_col: + if st.button("Close", use_container_width=True): + st.session_state.show_finetuning_preview_dialog = ( + False + ) + st.rerun() + + st.code( + finetuning_example.to_json(), + language="json", + wrap_lines=True, + ) + + if st.session_state.show_finetuning_preview_dialog: + _display_finetuning_example() diff --git a/recipes/llamaindex/llamaindex_cookbook/apps/__init__.py b/recipes/llamaindex/arc_finetuning_st/workflows/__init__.py similarity index 100% rename from recipes/llamaindex/llamaindex_cookbook/apps/__init__.py rename to recipes/llamaindex/arc_finetuning_st/workflows/__init__.py diff --git a/recipes/llamaindex/arc_finetuning_st/workflows/arc_task_solver.py b/recipes/llamaindex/arc_finetuning_st/workflows/arc_task_solver.py new file mode 100644 index 0000000..3c154b3 --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/workflows/arc_task_solver.py @@ -0,0 +1,236 @@ +import asyncio +import json +from pathlib import Path +from typing import Any, Dict, List, Optional, cast + +from llama_index.core.bridge.pydantic import BaseModel +from llama_index.core.llms import LLM +from llama_index.core.workflow import ( + Context, + StartEvent, + StopEvent, + Workflow, + step, +) + +from arc_finetuning_st.workflows.events import ( + EvaluationEvent, + FormatTaskEvent, + PredictionEvent, +) +from arc_finetuning_st.workflows.models import Attempt, Critique, Prediction +from arc_finetuning_st.workflows.prompts import ( + CORRECTION_PROMPT_TEMPLATE, + PREDICTION_PROMPT_TEMPLATE, + REFLECTION_PROMPT_TEMPLATE, +) + +EXAMPLE_TEMPLATE = """=== +EXAMPLE + +INPUT: +{input} + +OUTPUT: +{output} +""" + +PAST_ATTEMPT_TEMPLATE = """◦◦◦ +PAST ATTEMPT {past_attempt_number} + +PREDICTED_OUTPUT: +{past_predicted_output} + +CRITIQUE: +{past_critique} +""" + + +class WorkflowOutput(BaseModel): + passing: bool + attempts: List[Attempt] + + +class ARCTaskSolverWorkflow(Workflow): + def __init__(self, llm: LLM, max_attempts: int = 3, **kwargs: Any) -> None: + super().__init__(**kwargs) + self.llm = llm + self._max_attempts = max_attempts + + def _format_past_attempt(self, attempt: Attempt, attempt_num: int) -> str: + return PAST_ATTEMPT_TEMPLATE.format( + past_attempt_number=attempt_num, + past_predicted_output=str(attempt.prediction), + past_critique=str(attempt.critique) if attempt.critique else "", + ) + + @step + async def format_task( + self, ctx: Context, ev: StartEvent + ) -> FormatTaskEvent: + ctx.write_event_to_stream(ev) + + def _format_row(row: List[int]) -> str: + return ",".join(str(el) for el in row) + + def pretty_print_grid(grid: List[List[int]]) -> str: + formatted_rows = [_format_row(row) for row in grid] + return "\n".join(formatted_rows) + + def format_train_example(train_pair: Dict) -> str: + return EXAMPLE_TEMPLATE.format( + input=pretty_print_grid(train_pair["input"]), + output=pretty_print_grid(train_pair["output"]), + ) + + task = ev.get("task", {}) + await ctx.set("task", task) + + # prepare prompt_vars + attempts = await ctx.get("attempts", []) + if attempts: + # update past predictions + prompt_vars = await ctx.get("prompt_vars") + formatted_past_attempts = [ + self._format_past_attempt(a, ix + 1) + for ix, a in enumerate(attempts) + ] + prompt_vars.update( + past_attempts="\n".join(formatted_past_attempts) + ) + else: + examples = [format_train_example(t) for t in task["train"]] + prompt_vars = { + "test_input": pretty_print_grid(task["test"][0]["input"]), + "examples": "\n".join(examples), + } + await ctx.set("prompt_vars", prompt_vars) + + return FormatTaskEvent() + + @step + async def prediction( + self, ctx: Context, ev: FormatTaskEvent + ) -> PredictionEvent | StopEvent: + ctx.write_event_to_stream(ev) + attempts = await ctx.get("attempts", []) + attempts = cast(List[Attempt], attempts) + prompt_vars = await ctx.get("prompt_vars") + + if attempts: + # generating a correction from last Workflow run + correction: Prediction = await self.llm.astructured_predict( + Prediction, CORRECTION_PROMPT_TEMPLATE, **prompt_vars + ) + attempts.append(Attempt(prediction=correction)) + else: + # starting a new correction with no previous Workflow runs + pred: Prediction = await self.llm.astructured_predict( + Prediction, PREDICTION_PROMPT_TEMPLATE, **prompt_vars + ) + attempts = [Attempt(prediction=pred)] + + await ctx.set("attempts", attempts) + return PredictionEvent() + + @step + async def evaluation( + self, ctx: Context, ev: PredictionEvent + ) -> EvaluationEvent: + ctx.write_event_to_stream(ev) + task = await ctx.get("task") + attempts: List[Attempt] = await ctx.get("attempts") + latest_prediction = attempts[-1].prediction + latest_prediction_as_array = Prediction.prediction_str_to_int_array( + str(latest_prediction) + ) + ground_truth = task["test"][0]["output"] + + return EvaluationEvent( + passing=(latest_prediction_as_array == ground_truth) + ) + + @step + async def reflection(self, ctx: Context, ev: EvaluationEvent) -> StopEvent: + ctx.write_event_to_stream(ev) + attempts = await ctx.get("attempts") + attempts = cast(List[Attempt], attempts) + latest_attempt = attempts[-1] + + # check if passing + if not ev.passing: + prompt_vars = await ctx.get("prompt_vars") + formatted_past_attempts = [ + self._format_past_attempt(a, ix + 1) + for ix, a in enumerate(attempts) + ] + prompt_vars.update( + past_attempts="\n".join(formatted_past_attempts) + ) + + # generate critique + critique: Critique = await self.llm.astructured_predict( + Critique, REFLECTION_PROMPT_TEMPLATE, **prompt_vars + ) + + # update states + latest_attempt.critique = critique + else: + latest_attempt.critique = "This predicted output is correct." + + latest_attempt.passing = ev.passing + attempts[-1] = latest_attempt + await ctx.set("attempts", attempts) + + result = WorkflowOutput(passing=ev.passing, attempts=attempts) + return StopEvent(result=result) + + async def load_and_run_task( + self, + task_path: Path, + ctx: Optional[Context] = None, + sem: Optional[asyncio.Semaphore] = None, + ) -> Any: + """Convenience function for loading a task json and running it.""" + with open(task_path) as f: + task = json.load(f) + + async def _run_workflow() -> Any: + return await self.run(ctx=ctx, task=task) + + if sem: # in case running in batch with other workflow runs + await sem.acquire() + try: + res = await _run_workflow() + finally: + sem.release() + else: + res = await _run_workflow() + + return res + + +async def _test_workflow() -> None: + import json + from pathlib import Path + + from llama_index.llms.openai import OpenAI + + task_path = Path( + Path(__file__).parents[2].absolute(), "data/training/0a938d79.json" + ) + with open(task_path) as f: + task = json.load(f) + + w = ARCTaskSolverWorkflow( + timeout=None, verbose=False, llm=OpenAI("gpt-4o") + ) + attempts = await w.run(task=task) + + print(attempts) + + +if __name__ == "__main__": + import asyncio + + asyncio.run(_test_workflow()) diff --git a/recipes/llamaindex/arc_finetuning_st/workflows/events.py b/recipes/llamaindex/arc_finetuning_st/workflows/events.py new file mode 100644 index 0000000..f6a593f --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/workflows/events.py @@ -0,0 +1,17 @@ +from llama_index.core.workflow import Event + + +class FormatTaskEvent(Event): + ... + + +class PredictionEvent(Event): + ... + + +class EvaluationEvent(Event): + passing: bool + + +class CorrectionEvent(Event): + ... diff --git a/recipes/llamaindex/arc_finetuning_st/workflows/models.py b/recipes/llamaindex/arc_finetuning_st/workflows/models.py new file mode 100644 index 0000000..f872a7f --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/workflows/models.py @@ -0,0 +1,44 @@ +import uuid +from typing import List, Optional + +from llama_index.core.bridge.pydantic import BaseModel, Field + + +class Prediction(BaseModel): + """Prediction data class for LLM structured predict.""" + + rationale: str = Field( + description="Brief description of pattern and why prediction was made. Limit to 250 words." + ) + prediction: str = Field( + description="Predicted grid as a single string. e.g. '0,0,1\n1,1,1\n0,0,0'" + ) + + def __str__(self) -> str: + return self.prediction + + @staticmethod + def prediction_str_to_int_array(prediction: str) -> List[List[int]]: + return [ + [int(a) for a in el.split(",")] for el in prediction.split("\n") + ] + + +class Critique(BaseModel): + """Critique data class for LLM structured predict.""" + + critique: str = Field( + description="Brief critique of the previous prediction and rationale. Limit to 250 words." + ) + + def __str__(self) -> str: + return self.critique + + +class Attempt(BaseModel): + """Container class of a single solution attempt.""" + + id_: str = Field(default_factory=lambda: str(uuid.uuid4())) + prediction: Prediction + critique: Optional[Critique] = Field(default=None) + passing: bool = Field(default=False) diff --git a/recipes/llamaindex/arc_finetuning_st/workflows/prompts.py b/recipes/llamaindex/arc_finetuning_st/workflows/prompts.py new file mode 100644 index 0000000..8012047 --- /dev/null +++ b/recipes/llamaindex/arc_finetuning_st/workflows/prompts.py @@ -0,0 +1,69 @@ +from llama_index.core.prompts import PromptTemplate + +PREDICTION_PROMPT_TEMPLATE = PromptTemplate( + """You are a bot that is very good at solving puzzles. Below is a list of input and output pairs with a pattern. +Identify the pattern in the training examples and predict the output for the provided TEST INPUT. + +EXAMPLES: +{examples} + +TEST INPUT: +{test_input} + +OUTPUT FORMAT: +{{ + "output": +}} + +Return your response in JSON format given above. DO NOT RETURN markdown code. +""" +) + + +REFLECTION_PROMPT_TEMPLATE = PromptTemplate( + """You are a bot that is very good at solving puzzles. Below is a list of input and output pairs that share a +common pattern. The TEST INPUT also shares this common pattern, and you've previously predicted the output for it. +Your task now is critique the latest prediction on why it might not fit the pattern inherent in the example input/output pairs. + +EXAMPLES: +{examples} + +TEST INPUT: +{test_input} + +PAST ATTEMPTS: +{past_attempts} + +OUTPUT FORMAT: +{{ + "critique": ... +}} + +Return your response in JSON format given above. DO NOT RETURN markdown code.""" +) + +CORRECTION_PROMPT_TEMPLATE = PromptTemplate( + """You are a bot that is very good at solving puzzles. Below is a list of input and output pairs that share a +common pattern. The TEST INPUT also shares this common pattern, and you've previously predicted the output for it. +The predicted output was found to be incorrect and a critique has been articulated offering a potential +reason as to why it may have been a flawed prediction. + +Your task now to create a new prediction that corrects from the previous attempts. Use +the last attempt and critique. + +EXAMPLES: +{examples} + +TEST INPUT: +{test_input} + +PAST ATTEMPTS: +{past_attempts} + +OUTPUT FORMAT: +{{ + "prediction": ... +}} + +Return your response in JSON format given above. DO NOT RETURN markdown code.""" +) diff --git a/recipes/llamaindex/data-agents/placeholder.md b/recipes/llamaindex/data-agents/placeholder.md deleted file mode 100644 index 8b13789..0000000 --- a/recipes/llamaindex/data-agents/placeholder.md +++ /dev/null @@ -1 +0,0 @@ - diff --git a/recipes/llamaindex/docker-compose.yml b/recipes/llamaindex/docker-compose.yml deleted file mode 100644 index 265ff8e..0000000 --- a/recipes/llamaindex/docker-compose.yml +++ /dev/null @@ -1,172 +0,0 @@ -version: "3" -services: - rabbitmq: - image: rabbitmq:3.13-management - hostname: "rabbitmq" - env_file: - - .env.docker - ports: - - "5672:5672" - - "15672:15672" - volumes: - - rabbitmq:/var/lib/rabbitmq/ - healthcheck: - test: rabbitmq-diagnostics -q ping - interval: 30s - timeout: 10s - retries: 5 - control_plane: - image: llamaindex_cookbook:latest - command: sh -c "python -m llamaindex_cookbook.core_services.control_plane" - env_file: - - .env.docker - ports: - - "8001:8001" - volumes: - - ./llamaindex_cookbook:/app/llamaindex_cookbook # load local code change to container without the need of rebuild - - ./logging.ini:/app/logging.ini - depends_on: - rabbitmq: - condition: service_healthy - platform: linux/amd64 - build: - context: . - dockerfile: ./Dockerfile - healthcheck: - test: wget --no-verbose --tries=1 http://0.0.0.0:8001/ || exit 1 - interval: 30s - retries: 5 - start_period: 20s - timeout: 10s - funny_agent: - image: llamaindex_cookbook:latest - command: sh -c "python -m llamaindex_cookbook.agent_services.funny_agent" - env_file: - - .env.docker - ports: - - "8002:8002" - volumes: - - ./llamaindex_cookbook:/app/llamaindex_cookbook # load local code change to container without the need of rebuild - - ./data:/app/data - - ./logging.ini:/app/logging.ini - depends_on: - rabbitmq: - condition: service_healthy - control_plane: - condition: service_healthy - platform: linux/amd64 - build: - context: . - dockerfile: ./Dockerfile - healthcheck: - test: wget --no-verbose --tries=1 http://0.0.0.0:8002/is_worker_running || exit 1 - interval: 30s - retries: 5 - start_period: 20s - timeout: 10s - goods_getter_agent: - image: llamaindex_cookbook:latest - command: sh -c "python -m llamaindex_cookbook.agent_services.financial_and_economic_essentials.goods_getter_agent" - env_file: - - .env.docker - ports: - - "8003:8003" - volumes: - - ./llamaindex_cookbook:/app/llamaindex_cookbook # load local code change to container without the need of rebuild - - ./data:/app/data - - ./logging.ini:/app/logging.ini - depends_on: - rabbitmq: - condition: service_healthy - control_plane: - condition: service_healthy - platform: linux/amd64 - build: - context: . - dockerfile: ./Dockerfile - healthcheck: - test: wget --no-verbose --tries=1 http://0.0.0.0:8003/is_worker_running || exit 1 - interval: 30s - retries: 5 - start_period: 20s - timeout: 10s - time_series_getter_agent: - image: llamaindex_cookbook:latest - command: sh -c "python -m llamaindex_cookbook.agent_services.financial_and_economic_essentials.time_series_getter_agent" - env_file: - - .env.docker - ports: - - "8004:8004" - volumes: - - ./llamaindex_cookbook:/app/llamaindex_cookbook # load local code change to container without the need of rebuild - - ./data:/app/data - - ./logging.ini:/app/logging.ini - depends_on: - rabbitmq: - condition: service_healthy - control_plane: - condition: service_healthy - platform: linux/amd64 - build: - context: . - dockerfile: ./Dockerfile - healthcheck: - test: wget --no-verbose --tries=1 http://0.0.0.0:8004/is_worker_running || exit 1 - interval: 30s - retries: 5 - start_period: 20s - timeout: 10s - stats_getter_agent: - image: llamaindex_cookbook:latest - command: sh -c "python -m llamaindex_cookbook.agent_services.government_essentials.stats_getter_agent" - env_file: - - .env.docker - ports: - - "8005:8005" - volumes: - - ./llamaindex_cookbook:/app/llamaindex_cookbook # load local code change to container without the need of rebuild - - ./data:/app/data - - ./logging.ini:/app/logging.ini - depends_on: - rabbitmq: - condition: service_healthy - control_plane: - condition: service_healthy - platform: linux/amd64 - build: - context: . - dockerfile: ./Dockerfile - healthcheck: - test: wget --no-verbose --tries=1 http://0.0.0.0:8005/is_worker_running || exit 1 - interval: 30s - retries: 5 - start_period: 20s - timeout: 10s - stats_fulfiller_agent: - image: llamaindex_cookbook:latest - command: sh -c "python -m llamaindex_cookbook.agent_services.government_essentials.stats_fulfiller_agent" - env_file: - - .env.docker - ports: - - "8006:8006" - volumes: - - ./llamaindex_cookbook:/app/llamaindex_cookbook # load local code change to container without the need of rebuild - - ./data:/app/data - - ./logging.ini:/app/logging.ini - depends_on: - rabbitmq: - condition: service_healthy - control_plane: - condition: service_healthy - platform: linux/amd64 - build: - context: . - dockerfile: ./Dockerfile - healthcheck: - test: wget --no-verbose --tries=1 http://0.0.0.0:8006/is_worker_running || exit 1 - interval: 30s - retries: 5 - start_period: 20s - timeout: 10s -volumes: - rabbitmq: diff --git a/recipes/llamaindex/llamaindex_cookbook/additional_services/human_in_the_loop.py b/recipes/llamaindex/llamaindex_cookbook/additional_services/human_in_the_loop.py deleted file mode 100644 index 7cb2377..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/additional_services/human_in_the_loop.py +++ /dev/null @@ -1,77 +0,0 @@ -import asyncio -import logging -import queue -from typing import Any, TypedDict - -from llama_agents import HumanService, ServiceComponent -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue - -from llamaindex_cookbook.utils import load_from_env - -logger = logging.getLogger("llamaindex_cookbook") -logging.basicConfig(level=logging.INFO) - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") -control_plane_host = load_from_env("CONTROL_PLANE_HOST") -control_plane_port = load_from_env("CONTROL_PLANE_PORT") -localhost = load_from_env("LOCALHOST") - - -class HumanRequest(TypedDict): - prompt: str - task_id: str - - -# # human in the loop function -human_input_request_queue: queue.Queue[HumanRequest] = queue.Queue() -human_input_result_queue: queue.Queue[str] = queue.Queue() - - -async def human_input_fn(prompt: str, task_id: str, **kwargs: Any) -> str: - logger.info("human input fn invoked.") - human_input_request_queue.put({"prompt": prompt, "task_id": task_id}) - logger.info("placed new prompt in queue.") - - # poll until human answer is stored - async def _poll_for_human_input_result() -> str: - human_input = None - while human_input is None: - try: - human_input = human_input_result_queue.get_nowait() - except queue.Empty: - human_input = None - await asyncio.sleep(0.1) - logger.info("human input recieved") - return human_input - - try: - human_input = await asyncio.wait_for( - _poll_for_human_input_result(), - timeout=6000, - ) - logger.info(f"Recieved human input: {human_input}") - except ( - asyncio.exceptions.TimeoutError, - asyncio.TimeoutError, - TimeoutError, - ): - logger.info(f"Timeout reached for tool_call with prompt {prompt}") - human_input = "Something went wrong." - - return human_input - - -# create our multi-agent framework components -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) -human_service = HumanService( - message_queue=message_queue, - description="For human input.", - fn_input=human_input_fn, - human_input_prompt="{input_str}", -) -human_component = ServiceComponent.from_service_definition(human_service) diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/__init__.py b/recipes/llamaindex/llamaindex_cookbook/agent_services/__init__.py deleted file mode 100644 index 7475a14..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/agent_services/__init__.py +++ /dev/null @@ -1,43 +0,0 @@ -from llamaindex_cookbook.agent_services.financial_and_economic_essentials.goods_getter_agent import ( - agent_component as goods_getter_agent_component, -) -from llamaindex_cookbook.agent_services.financial_and_economic_essentials.goods_getter_agent import ( - agent_server as goods_getter_agent_server, -) -from llamaindex_cookbook.agent_services.financial_and_economic_essentials.time_series_getter_agent import ( - agent_component as time_series_getter_agent_component, -) -from llamaindex_cookbook.agent_services.financial_and_economic_essentials.time_series_getter_agent import ( - agent_server as time_series_getter_agent_server, -) -from llamaindex_cookbook.agent_services.funny_agent import ( - agent_component as funny_agent_component, -) -from llamaindex_cookbook.agent_services.funny_agent import ( - agent_server as funny_agent_server, -) -from llamaindex_cookbook.agent_services.government_essentials.stats_fulfiller_agent import ( - agent_component as stats_fulfiller_agent_component, -) -from llamaindex_cookbook.agent_services.government_essentials.stats_fulfiller_agent import ( - agent_server as stats_fulfiller_agent_server, -) -from llamaindex_cookbook.agent_services.government_essentials.stats_getter_agent import ( - agent_component as stats_getter_agent_component, -) -from llamaindex_cookbook.agent_services.government_essentials.stats_getter_agent import ( - agent_server as stats_getter_agent_server, -) - -__all__ = [ - "goods_getter_agent_component", - "goods_getter_agent_server", - "time_series_getter_agent_component", - "time_series_getter_agent_server", - "stats_getter_agent_component", - "stats_getter_agent_server", - "stats_fulfiller_agent_component", - "stats_fulfiller_agent_server", - "funny_agent_server", - "funny_agent_component", -] diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/goods_getter_agent.py b/recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/goods_getter_agent.py deleted file mode 100644 index b4ea3b5..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/goods_getter_agent.py +++ /dev/null @@ -1,129 +0,0 @@ -import asyncio - -import uvicorn -from llama_agents import AgentService, ServiceComponent -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue -from llama_index.agent.openai import OpenAIAgent -from llama_index.core.tools import FunctionTool -from llama_index.llms.openai import OpenAI -from snowflake.sqlalchemy import URL -from sqlalchemy import create_engine, text - -from llamaindex_cookbook.utils import load_from_env - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") -control_plane_host = load_from_env("CONTROL_PLANE_HOST") -control_plane_port = load_from_env("CONTROL_PLANE_PORT") -agent_host = load_from_env("GOODS_GETTER_AGENT_HOST") -agent_port = load_from_env("GOODS_GETTER_AGENT_PORT") -snowflake_user = load_from_env("SNOWFLAKE_USERNAME") -snowflake_password = load_from_env("SNOWFLAKE_PASSWORD") -snowflake_account = load_from_env("SNOWFLAKE_ACCOUNT") -snowflake_role = load_from_env("SNOWFLAKE_ROLE") -localhost = load_from_env("LOCALHOST") - - -# create agent server -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) - -SQL_QUERY_TEMPLATE = """ -SELECT DISTINCT att.product, -FROM cybersyn.bureau_of_labor_statistics_price_timeseries AS ts -JOIN cybersyn.bureau_of_labor_statistics_price_attributes AS att - ON (ts.variable = att.variable) -WHERE ts.date >= '2021-01-01' - AND att.report = 'Average Price' - AND att.product ILIKE '{good}%'; -""" - -AGENT_SYSTEM_PROMPT = """ -For a given query about a good in the database, your job is to first find -if the good exists in the database. Return the list of goods in the database -that potentially match the object of the users query. -""" - - -def get_list_of_candidate_goods(good: str) -> str: - """Returns a list of goods that exist in the database. - - The list of goods is represented as a string separated by '\n'.""" - query = SQL_QUERY_TEMPLATE.format(good=good) - url = URL( - account=snowflake_account, - user=snowflake_user, - password=snowflake_password, - database="FINANCIAL__ECONOMIC_ESSENTIALS", - schema="CYBERSYN", - warehouse="COMPUTE_WH", - role=snowflake_role, - ) - - engine = create_engine(url) - try: - connection = engine.connect() - results = connection.execute(text(query)) - finally: - connection.close() - - # process - results = [f"{ix+1}. {str(el[0])}" for ix, el in enumerate(results)] - results_str = "List of goods that exist in the database are provided below. Please select one.:\n\n" - results_str += "\n".join(results) - - return results_str - - -goods_getter_tool = FunctionTool.from_defaults( - fn=get_list_of_candidate_goods, return_direct=True -) -agent = OpenAIAgent.from_tools( - [goods_getter_tool], - system_prompt=AGENT_SYSTEM_PROMPT, - llm=OpenAI(model="gpt-4o-mini"), - verbose=True, -) - -agent_server = AgentService( - agent=agent, - message_queue=message_queue, - description="Retrieves the goods that exist in the database that match the user's query.", - service_name="goods_getter_agent", - host=agent_host, - port=int(agent_port) if agent_port else None, -) -agent_component = ServiceComponent.from_service_definition(agent_server) - -app = agent_server._app - - -# launch -async def launch() -> None: - # register to message queue - start_consuming_callable = await agent_server.register_to_message_queue() - _ = asyncio.create_task(start_consuming_callable()) - - # register to control plane - await agent_server.register_to_control_plane( - control_plane_url=( - f"http://{control_plane_host}:{control_plane_port}" - if control_plane_port - else f"http://{control_plane_host}" - ) - ) - - cfg = uvicorn.Config( - agent_server._app, - host=localhost, - port=agent_server.port, - ) - server = uvicorn.Server(cfg) - await server.serve() - - -if __name__ == "__main__": - asyncio.run(launch()) diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/time_series_getter_agent.py b/recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/time_series_getter_agent.py deleted file mode 100644 index d812bbb..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/agent_services/financial_and_economic_essentials/time_series_getter_agent.py +++ /dev/null @@ -1,175 +0,0 @@ -import asyncio -import json -from typing import Any, Dict, List - -import uvicorn -from llama_agents import AgentService, ServiceComponent -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue -from llama_index.agent.openai import OpenAIAgent -from llama_index.core.tools import FunctionTool -from llama_index.llms.openai import OpenAI -from snowflake.sqlalchemy import URL -from sqlalchemy import create_engine, text - -from llamaindex_cookbook.utils import load_from_env - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") -control_plane_host = load_from_env("CONTROL_PLANE_HOST") -control_plane_port = load_from_env("CONTROL_PLANE_PORT") -agent_host = load_from_env("TIME_SERIES_GETTER_AGENT_HOST") -agent_port = load_from_env("TIME_SERIES_GETTER_AGENT_PORT") -snowflake_user = load_from_env("SNOWFLAKE_USERNAME") -snowflake_password = load_from_env("SNOWFLAKE_PASSWORD") -snowflake_account = load_from_env("SNOWFLAKE_ACCOUNT") -snowflake_role = load_from_env("SNOWFLAKE_ROLE") -localhost = load_from_env("LOCALHOST") - - -# create agent server -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) - -AGENT_SYSTEM_PROMPT = """ -Query the database to return timeseries data of user-specified good. - -Use the tool to return the time series data as a JSON with the folowing format: - -{{ - [ - {{ - "good": ..., - "date": ..., - "price": ... - }}, - {{ - "good": ..., - "date": ..., - "price": ... - }}, - ... - ] -}} - -Don't return the output as markdown code. Don't modify the tool output. Return -strictly the tool ouput. -""" - -SQL_QUERY_TEMPLATE = """ -SELECT ts.date, - att.variable_name, - ts.value -FROM cybersyn.bureau_of_labor_statistics_price_timeseries AS ts -JOIN cybersyn.bureau_of_labor_statistics_price_attributes AS att - ON (ts.variable = att.variable) -WHERE ts.date >= '2021-01-01' - AND att.report = 'Average Price' - AND att.product ILIKE '{good}%' -ORDER BY date; -""" - - -def get_time_series_of_good(good: str) -> str: - """Create a time series of the average price paid for a good nationwide starting in 2021.""" - query = SQL_QUERY_TEMPLATE.format(good=good) - url = URL( - account=snowflake_account, - user=snowflake_user, - password=snowflake_password, - database="FINANCIAL__ECONOMIC_ESSENTIALS", - schema="CYBERSYN", - warehouse="COMPUTE_WH", - role=snowflake_role, - ) - - engine = create_engine(url) - try: - connection = engine.connect() - results = connection.execute(text(query)) - finally: - connection.close() - - # process - results = [ - {"good": str(el[1]), "date": str(el[0]), "price": str(el[2])} - for el in results - ] - results_str = json.dumps(results, indent=4) - - return results_str - - -def perform_price_aggregation(json_str: str) -> List[Dict[str, Any]]: - """Perform price aggregation on the time series data.""" - timeseries_data = json.loads(json_str) - good = timeseries_data[0]["good"] - - new_time_series_data: Dict[str, List[float]] = {} - for el in timeseries_data: - date = el["date"] - price = el["price"] - if date in new_time_series_data: - new_time_series_data[date].append(float(price)) - else: - new_time_series_data[date] = [float(price)] - - reduced_time_series_data = [ - {"good": good, "date": date, "price": sum(prices) / len(prices)} - for date, prices in new_time_series_data.items() - ] - - return reduced_time_series_data - - -goods_getter_tool = FunctionTool.from_defaults( - fn=get_time_series_of_good, return_direct=True -) -agent = OpenAIAgent.from_tools( - [goods_getter_tool], - system_prompt=AGENT_SYSTEM_PROMPT, - llm=OpenAI(model="gpt-3.5-turbo"), - verbose=True, -) - -agent_server = AgentService( - agent=agent, - message_queue=message_queue, - description="Gets the time series price data for a good from the database.", - service_name="time_series_getter_agent", - host=agent_host, - port=int(agent_port) if agent_port else None, -) -agent_component = ServiceComponent.from_service_definition(agent_server) - -app = agent_server._app - - -# launch -async def launch() -> None: - # register to message queue - start_consuming_callable = await agent_server.register_to_message_queue() - _ = asyncio.create_task(start_consuming_callable()) - - # register to control plane - await agent_server.register_to_control_plane( - control_plane_url=( - f"http://{control_plane_host}:{control_plane_port}" - if control_plane_port - else f"http://{control_plane_host}" - ) - ) - - cfg = uvicorn.Config( - agent_server._app, - host=localhost, - port=agent_server.port, - ) - server = uvicorn.Server(cfg) - await server.serve() - - -if __name__ == "__main__": - asyncio.run(launch()) diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/funny_agent.py b/recipes/llamaindex/llamaindex_cookbook/agent_services/funny_agent.py deleted file mode 100644 index f041215..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/agent_services/funny_agent.py +++ /dev/null @@ -1,81 +0,0 @@ -import asyncio - -import uvicorn -from llama_agents import AgentService, ServiceComponent -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue -from llama_index.agent.openai import OpenAIAgent -from llama_index.core.tools import FunctionTool -from llama_index.llms.openai import OpenAI - -from llamaindex_cookbook.utils import load_from_env - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") -control_plane_host = load_from_env("CONTROL_PLANE_HOST") -control_plane_port = load_from_env("CONTROL_PLANE_PORT") -funny_agent_host = load_from_env("FUNNY_AGENT_HOST") -funny_agent_port = load_from_env("FUNNY_AGENT_PORT") -localhost = load_from_env("LOCALHOST") - - -# create agent server -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) - - -# create an agent -def get_the_secret_fact() -> str: - """Returns the secret fact.""" - return "The secret fact is: A baby llama is called a 'Cria'." - - -secret_fact_tool = FunctionTool.from_defaults(fn=get_the_secret_fact) - -agent = OpenAIAgent.from_tools( - [secret_fact_tool], - system_prompt="Knows the secret fact, and can tell funny jokes.", - llm=OpenAI(model="gpt-4o"), - verbose=True, -) -agent_server = AgentService( - agent=agent, - message_queue=message_queue, - description="Do not select for any queries regarding timeseries data. Can be used for everything but that.", - service_name="funny_agent", - host=funny_agent_host, - port=int(funny_agent_port) if funny_agent_port else None, -) -agent_component = ServiceComponent.from_service_definition(agent_server) - -app = agent_server._app - - -# launch -async def launch() -> None: - # register to message queue - start_consuming_callable = await agent_server.register_to_message_queue() - _ = asyncio.create_task(start_consuming_callable()) - - # register to control plane - await agent_server.register_to_control_plane( - control_plane_url=( - f"http://{control_plane_host}:{control_plane_port}" - if control_plane_port - else f"http://{control_plane_host}" - ) - ) - - cfg = uvicorn.Config( - agent_server._app, - host=localhost, - port=agent_server.port, - ) - server = uvicorn.Server(cfg) - await server.serve() - - -if __name__ == "__main__": - asyncio.run(launch()) diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/stats_fulfiller_agent.py b/recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/stats_fulfiller_agent.py deleted file mode 100644 index ec04a17..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/stats_fulfiller_agent.py +++ /dev/null @@ -1,190 +0,0 @@ -import asyncio -import json -import logging -from typing import Any, Dict, List - -import uvicorn -from llama_agents import AgentService, ServiceComponent -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue -from llama_index.agent.openai import OpenAIAgent -from llama_index.core.tools import FunctionTool -from llama_index.llms.openai import OpenAI -from snowflake.sqlalchemy import URL -from sqlalchemy import create_engine, text - -from llamaindex_cookbook.utils import load_from_env - -logger = logging.getLogger(__name__) - - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") -control_plane_host = load_from_env("CONTROL_PLANE_HOST") -control_plane_port = load_from_env("CONTROL_PLANE_PORT") -agent_host = load_from_env("STATS_FULFILLER_AGENT_HOST") -agent_port = load_from_env("STATS_FULFILLER_AGENT_PORT") -snowflake_user = load_from_env("SNOWFLAKE_USERNAME") -snowflake_password = load_from_env("SNOWFLAKE_PASSWORD") -snowflake_account = load_from_env("SNOWFLAKE_ACCOUNT") -snowflake_role = load_from_env("SNOWFLAKE_ROLE") -localhost = load_from_env("LOCALHOST") - - -# create agent server -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) - -AGENT_SYSTEM_PROMPT = """ -Query the database to return timeseries data of user-specified geographic/demographic statistic. - -Use the tool to return the time series data as a JSON with the folowing format: - -{{ - [ - {{ - "variable": ..., - "date": ..., - "value": ... - }}, - {{ - "variable": ..., - "date": ..., - "value": ... - }}, - ... - ] -}} - -Don't return the output as markdown code. Don't modify the tool output. Return -strictly the tool ouput. -""" - - -SQL_QUERY_TEMPLATE = """ -SELECT - ts.date as date, - ts.variable_name, - ts.value as value -FROM cybersyn.datacommons_timeseries AS ts -JOIN cybersyn.geography_index AS geo ON (ts.geo_id = geo.geo_id) -WHERE geo.geo_name = '{city}' - AND geo.level IN ('City') - AND ts.variable_name ILIKE '{stats_variable}%' - AND date >= '2015-01-01' -ORDER BY date; -""" - - -def get_time_series_of_statistic_variable( - city: str, stats_variable: str -) -> str: - """Create a time series of a specified stats variable.""" - query = SQL_QUERY_TEMPLATE.format(city=city, stats_variable=stats_variable) - url = URL( - account=snowflake_account, - user=snowflake_user, - password=snowflake_password, - database="GOVERNMENT_ESSENTIALS", - schema="CYBERSYN", - warehouse="COMPUTE_WH", - role=snowflake_role, - ) - - engine = create_engine(url) - try: - connection = engine.connect() - results = connection.execute(text(query)) - except Exception: - logger.debug("Failed to execute query") - raise - finally: - connection.close() - - # process - results = [ - {"variable": str(el[1]), "date": str(el[0]), "value": str(el[2])} - for el in results - ] - results_str = json.dumps(results, indent=4) - - return results_str - - -def perform_date_value_aggregation(json_str: str) -> List[Dict[str, Any]]: - """Perform value aggregation on the time series data.""" - timeseries_data = json.loads(json_str) - variable = timeseries_data[0]["variable"] - - new_time_series_data: Dict[str, List[float]] = {} - for el in timeseries_data: - date = el["date"] - value = el["value"] - if date in new_time_series_data: - new_time_series_data[date].append(float(value)) - else: - new_time_series_data[date] = [float(value)] - - reduced_time_series_data = [ - { - "variable": variable, - "date": date, - "value": sum(values) / len(values), - } - for date, values in new_time_series_data.items() - ] - - return reduced_time_series_data - - -stats_fulfiller_tool = FunctionTool.from_defaults( - fn=get_time_series_of_statistic_variable, return_direct=True -) -agent = OpenAIAgent.from_tools( - [stats_fulfiller_tool], - system_prompt=AGENT_SYSTEM_PROMPT, - llm=OpenAI(model="gpt-4o-mini"), - verbose=True, -) - -agent_server = AgentService( - agent=agent, - message_queue=message_queue, - description="Gets the stats data for a given statistical variable", - service_name="stats_fulfiller_agent", - host=agent_host, - port=int(agent_port) if agent_port else None, -) -agent_component = ServiceComponent.from_service_definition(agent_server) - -app = agent_server._app - - -# launch -async def launch() -> None: - # register to message queue - start_consuming_callable = await agent_server.register_to_message_queue() - _ = asyncio.create_task(start_consuming_callable()) - - # register to control plane - await agent_server.register_to_control_plane( - control_plane_url=( - f"http://{control_plane_host}:{control_plane_port}" - if control_plane_port - else f"http://{control_plane_host}" - ) - ) - - cfg = uvicorn.Config( - agent_server._app, - host=localhost, - port=agent_server.port, - ) - server = uvicorn.Server(cfg) - await server.serve() - - -if __name__ == "__main__": - asyncio.run(launch()) diff --git a/recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/stats_getter_agent.py b/recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/stats_getter_agent.py deleted file mode 100644 index 4c36122..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/agent_services/government_essentials/stats_getter_agent.py +++ /dev/null @@ -1,140 +0,0 @@ -import asyncio - -import uvicorn -from llama_agents import AgentService, ServiceComponent -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue -from llama_index.agent.openai import OpenAIAgent -from llama_index.core.tools import FunctionTool -from llama_index.llms.openai import OpenAI -from snowflake.sqlalchemy import URL -from sqlalchemy import create_engine, text - -from llamaindex_cookbook.utils import load_from_env - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") -control_plane_host = load_from_env("CONTROL_PLANE_HOST") -control_plane_port = load_from_env("CONTROL_PLANE_PORT") -agent_host = load_from_env("STATS_GETTER_AGENT_HOST") -agent_port = load_from_env("STATS_GETTER_AGENT_PORT") -snowflake_user = load_from_env("SNOWFLAKE_USERNAME") -snowflake_password = load_from_env("SNOWFLAKE_PASSWORD") -snowflake_account = load_from_env("SNOWFLAKE_ACCOUNT") -snowflake_role = load_from_env("SNOWFLAKE_ROLE") -localhost = load_from_env("LOCALHOST") - - -# create agent server -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) - -SQL_QUERY_TEMPLATE = """ -SELECT DISTINCT - ts.variable_name -FROM cybersyn.datacommons_timeseries AS ts -JOIN cybersyn.geography_index AS geo ON (ts.geo_id = geo.geo_id) -WHERE geo.geo_name = '{city}' - AND geo.level IN ('City') - AND date >= '2015-01-01'; -""" - -AGENT_SYSTEM_PROMPT = """ -For a given query about a geographic and population statistic, your job is to first -find the statistical variables that exists in the database. - -Return the list of the three most relevant statistical variables that exist in the -database and that potentially match the object of the users query. - -Output your list in the following format: - -1. for , -2. for , -3. for - -Be sure to use the exact variable names that were retrieved from the database tool! -""" - - -def get_list_of_statistical_variables(city: str, query: str) -> str: - """Returns a list of statistical variables that closely resemble the query. - - The list of statistical vars is represented as a string separated by '\n'. - """ - query = SQL_QUERY_TEMPLATE.format(city=city) - url = URL( - account=snowflake_account, - user=snowflake_user, - password=snowflake_password, - database="GOVERNMENT_ESSENTIALS", - schema="CYBERSYN", - warehouse="COMPUTE_WH", - role=snowflake_role, - ) - - engine = create_engine(url) - try: - connection = engine.connect() - results = connection.execute(text(query)) - finally: - connection.close() - - # process - results = [f"{ix+1}. {str(el[0])}" for ix, el in enumerate(results)] - results_str = "List of statistical variables that exist in the database are provided below. Please select one.:\n\n" - results_str += "\n".join(results) - - return results_str - - -statistics_getter_tool = FunctionTool.from_defaults( - fn=get_list_of_statistical_variables -) -agent = OpenAIAgent.from_tools( - [statistics_getter_tool], - system_prompt=AGENT_SYSTEM_PROMPT, - llm=OpenAI(model="gpt-4o-mini"), - verbose=True, -) - -agent_server = AgentService( - agent=agent, - message_queue=message_queue, - description="Retrieves the statistical variables that exist in the database that match the user's query.", - service_name="stats_getter_agent", - host=agent_host, - port=int(agent_port) if agent_port else None, -) -agent_component = ServiceComponent.from_service_definition(agent_server) - -app = agent_server._app - - -# launch -async def launch() -> None: - # register to message queue - start_consuming_callable = await agent_server.register_to_message_queue() - _ = asyncio.create_task(start_consuming_callable()) - - # register to control plane - await agent_server.register_to_control_plane( - control_plane_url=( - f"http://{control_plane_host}:{control_plane_port}" - if control_plane_port - else f"http://{control_plane_host}" - ) - ) - - cfg = uvicorn.Config( - agent_server._app, - host=localhost, - port=agent_server.port, - ) - server = uvicorn.Server(cfg) - await server.serve() - - -if __name__ == "__main__": - asyncio.run(launch()) diff --git a/recipes/llamaindex/llamaindex_cookbook/apps/controller.py b/recipes/llamaindex/llamaindex_cookbook/apps/controller.py deleted file mode 100644 index bbe2f56..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/apps/controller.py +++ /dev/null @@ -1,212 +0,0 @@ -import json -import logging -import queue -from dataclasses import dataclass, field -from enum import Enum -from typing import Any, Callable, Generator, List, Optional - -import pandas as pd -import streamlit as st -from llama_agents import LlamaAgentsClient -from llama_agents.types import TaskResult -from llama_index.core.llms import ChatMessage, ChatResponseGen - -from llamaindex_cookbook.additional_services.human_in_the_loop import ( - HumanRequest, -) - -logger = logging.getLogger(__name__) - - -class TaskStatus(str, Enum): - HUMAN_REQUIRED = "human_required" - COMPLETED = "completed" - SUBMITTED = "submitted" - - -@dataclass -class TaskModel: - task_id: str - input: str - status: TaskStatus - prompt: Optional[str] = None - history: List[ChatMessage] = field(default_factory=list) - - -class Controller: - def __init__( - self, - control_plane_host: str = "127.0.0.1", - control_plane_port: Optional[int] = 8000, - ): - self._client = LlamaAgentsClient( - control_plane_url=( - f"http://{control_plane_host}:{control_plane_port}" - if control_plane_port - else f"http://{control_plane_host}" - ) - ) - self._step_interval = 0.5 - self._timeout = 60 - - def llama_index_stream_wrapper( - self, - llama_index_stream: ChatResponseGen, - ) -> Generator[str, Any, Any]: - for chunk in llama_index_stream: - yield chunk.delta - - def get_task_result(self, task_id: str) -> Optional[TaskResult]: - return self._client.get_task_result(task_id=task_id) - - def handle_task_submission(self) -> None: - """Handle the user submitted message. Clear task submission box, and - add the new task to the submitted list. - """ - - # create new task and store in state - task_input = st.session_state.task_input - if task_input == "": - return - task_id = self._client.create_task(task_input) - task = TaskModel( - task_id=task_id, - input=task_input, - history=[ - ChatMessage(role="user", content=task_input), - ], - status=TaskStatus.SUBMITTED, - ) - st.session_state.submitted_tasks.append(task) - logger.info("Added task to submitted queue") - st.session_state.current_task = task - st.session_state.task_input = "" - - def get_human_input_handler( - self, human_input_result_queue: queue.Queue - ) -> Callable: - def human_input_handler() -> None: - human_input = st.session_state.human_input - if human_input == "": - return - human_input_result_queue.put_nowait(human_input) - logger.info("pushed human input to human input result queue.") - - return human_input_handler - - def update_associated_task_to_completed_status( - self, - task_res: TaskResult, - ) -> None: - """ - Update task status to completed for received task result. - - Update session_state lists as well. - """ - - def remove_task_from_list( - task_list: List[TaskModel], - ) -> List[TaskModel]: - try: - ix, task = next( - (ix, t) - for ix, t in enumerate(task_list) - if t.task_id == task_res.task_id - ) - task.status = TaskStatus.COMPLETED - task.history.append( - ChatMessage(role="assistant", content=task_res.result) - ) - del task_list[ix] - st.session_state.completed_tasks.append(task) - logger.info("updated submitted and completed tasks list.") - except StopIteration: - raise ValueError("Cannot find task in list of tasks.") - return task_list - - submitted_tasks = st.session_state.get("submitted_tasks") - human_required_tasks = st.session_state.get("human_required_tasks") - - if task_res.task_id in [t.task_id for t in submitted_tasks]: - updated_task_list = remove_task_from_list(submitted_tasks) - st.session_state.submitted_tasks = updated_task_list - elif task_res.task_id in [t.task_id for t in human_required_tasks]: - updated_task_list = remove_task_from_list(human_required_tasks) - st.session_state.human_required_tasks = updated_task_list - else: - raise ValueError( - "Completed task not in submitted or human_required lists." - ) - - def update_associated_task_to_human_required_status( - self, - human_req: HumanRequest, - ) -> None: - """ - Update task status to human_required for received task request. - - Update session_state lists as well. - """ - try: - task_list = st.session_state.get("submitted_tasks") - print(f"submitted tasks: {task_list}") - ix, task = next( - (ix, t) - for ix, t in enumerate(task_list) - if t.task_id == human_req["task_id"] - ) - task.status = TaskStatus.HUMAN_REQUIRED - task.history.append( - ChatMessage(role="assistant", content=human_req["prompt"]) - ) - del task_list[ix] - st.session_state.submitted_tasks = task_list - st.session_state.human_required_tasks.append(task) - logger.info("updated submitted and human required tasks list.") - except StopIteration: - raise ValueError("Cannot find task in list of tasks.") - - def get_task_selection_handler(self, task_df: pd.DataFrame) -> Callable: - def task_selection_handler() -> None: - dataframe_selection_state = st.session_state.task_df["selection"][ - "rows" - ] - - if not dataframe_selection_state: - st.session_state.current_task = None - return - - # display chat history in console - selected_row = st.session_state.task_df["selection"]["rows"][0] - selection = task_df.iloc[selected_row] - task_status = selection["status"] - task_id = selection["task_id"] - if task_status == TaskStatus.COMPLETED: - task_list = st.session_state.completed_tasks - elif task_status == TaskStatus.HUMAN_REQUIRED: - task_list = st.session_state.human_required_tasks - else: - task_list = st.session_state.submitted_tasks - - try: - task = next(t for t in task_list if t.task_id == task_id) - st.session_state.current_task = task - except StopIteration: - pass # handle this better - - return task_selection_handler - - def infer_task_type(self, task_res: TaskResult) -> str: - def try_parse_as_json(text: str) -> Any: - try: - return json.loads(text) - except json.JSONDecodeError: - return None - - if task_res_json := try_parse_as_json(task_res.result): - if "good" in task_res_json[0]: - return "timeseries-good" - if "variable" in task_res_json[0]: - return "timeseries-city-stat" - - return "text" diff --git a/recipes/llamaindex/llamaindex_cookbook/apps/final_task_consumer.py b/recipes/llamaindex/llamaindex_cookbook/apps/final_task_consumer.py deleted file mode 100644 index 3a1f585..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/apps/final_task_consumer.py +++ /dev/null @@ -1,50 +0,0 @@ -import logging -import queue -from typing import Any - -from llama_agents import CallableMessageConsumer, QueueMessage -from llama_agents.message_consumers.base import ( - BaseMessageQueueConsumer, - StartConsumingCallable, -) -from llama_agents.message_queues.base import BaseMessageQueue -from llama_agents.types import ActionTypes, TaskResult - -logger = logging.getLogger(__name__) - - -class FinalTaskConsumer: - def __init__( - self, - message_queue: BaseMessageQueue, - completed_tasks_queue: queue.Queue, - ): - self.message_queue = message_queue - self.completed_tasks_queue = completed_tasks_queue - self.name: str = "human" - - async def _process_completed_task_messages( - self, message: QueueMessage, **kwargs: Any - ) -> None: - """Consumer of completed tasks. - - By default control plane sends to message consumer of type "human". - The process message logic contained here simply puts the TaskResult into - a queue that is continuosly via a gr.Timer(). - """ - if message.action == ActionTypes.COMPLETED_TASK: - task_res = TaskResult(**message.data) - self.completed_tasks_queue.put(task_res) - logger.info("Added task result to queue") - - def as_consumer(self, remote: bool = False) -> BaseMessageQueueConsumer: - del remote - - return CallableMessageConsumer( - message_type=self.name, - handler=self._process_completed_task_messages, - ) - - async def register_to_message_queue(self) -> StartConsumingCallable: - """Register to the message queue.""" - return await self.message_queue.register_consumer(self.as_consumer()) diff --git a/recipes/llamaindex/llamaindex_cookbook/apps/streamlit.py b/recipes/llamaindex/llamaindex_cookbook/apps/streamlit.py deleted file mode 100644 index 8923f5d..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/apps/streamlit.py +++ /dev/null @@ -1,319 +0,0 @@ -import asyncio -import json -import logging -import queue -import threading -import time -from typing import Optional, Tuple - -import pandas as pd -import streamlit as st -from llama_agents.types import TaskResult -from llama_index.llms.openai import OpenAI - -from llamaindex_cookbook.additional_services.human_in_the_loop import ( - HumanRequest, - HumanService, -) -from llamaindex_cookbook.agent_services.financial_and_economic_essentials.time_series_getter_agent import ( - perform_price_aggregation, -) -from llamaindex_cookbook.agent_services.government_essentials.stats_fulfiller_agent import ( - perform_date_value_aggregation, -) -from llamaindex_cookbook.apps.controller import Controller -from llamaindex_cookbook.apps.final_task_consumer import FinalTaskConsumer - -logger = logging.getLogger(__name__) - -llm = OpenAI(model="gpt-4o-mini") -control_plane_host = "0.0.0.0" -control_plane_port = 8001 - - -st.set_page_config(layout="wide") - - -@st.cache_resource -def startup() -> ( - Tuple[ - Controller, - queue.Queue[TaskResult], - FinalTaskConsumer, - queue.Queue[HumanRequest], - queue.Queue[str], - ] -): - from llamaindex_cookbook.additional_services.human_in_the_loop import ( - human_input_request_queue, - human_input_result_queue, - human_service, - message_queue, - ) - - controller = Controller( - control_plane_host=control_plane_host, - control_plane_port=control_plane_port, - ) - - async def start_consuming_human_tasks(hs: HumanService) -> None: - # register to control plane - await hs.register_to_control_plane( - control_plane_url=( - f"http://{control_plane_host}:{control_plane_port}" - if control_plane_port - else f"http://{control_plane_host}" - ) - ) - - consuming_callable = await message_queue.register_consumer( - hs.as_consumer() - ) - - ht_task = asyncio.create_task(consuming_callable()) # noqa: F841 - - pl_task = asyncio.create_task(hs.processing_loop()) # noqa: F841 - - await asyncio.Future() - - hr_thread = threading.Thread( - name="Human Request thread", - target=asyncio.run, - args=(start_consuming_human_tasks(human_service),), - daemon=False, - ) - hr_thread.start() - - completed_tasks_queue: queue.Queue[TaskResult] = queue.Queue() - final_task_consumer = FinalTaskConsumer( - message_queue=message_queue, - completed_tasks_queue=completed_tasks_queue, - ) - - async def start_consuming_finalized_tasks( - final_task_consumer: FinalTaskConsumer, - ) -> None: - final_task_consuming_callable = ( - await final_task_consumer.register_to_message_queue() - ) - - await final_task_consuming_callable() - - # server thread will remain active as long as streamlit thread is running, or is manually shutdown - ft_thread = threading.Thread( - name="Consuming thread", - target=asyncio.run, - args=(start_consuming_finalized_tasks(final_task_consumer),), - daemon=False, - ) - ft_thread.start() - - time.sleep(5) - logger.info("Started consuming.") - - return ( - controller, - completed_tasks_queue, - final_task_consumer, - human_input_request_queue, - human_input_result_queue, - ) - - -( - controller, - completed_tasks_queue, - final_task_consumer, - human_input_request_queue, - human_input_result_queue, -) = startup() - - -### App -logo = '[](https://github.com/run-llama/llama-agents "Check out the llama-agents Github repo!")' -st.title("Human In The Loop With LlamaAgents") -st.markdown(f"_Powered by LlamaIndex_   {logo}", unsafe_allow_html=True) - - -# state management -if "submitted_tasks" not in st.session_state: - st.session_state["submitted_tasks"] = [] -if "human_required_tasks" not in st.session_state: - st.session_state["human_required_tasks"] = [] -if "completed_tasks" not in st.session_state: - st.session_state["completed_tasks"] = [] -if "tasks" not in st.session_state: - st.session_state["tasks"] = [] -if "consuming" not in st.session_state: - st.session_state.consuming = False -if "messages" not in st.session_state: - st.session_state.messages = [] -if "current_task" not in st.session_state: - st.session_state.current_task = None -if "human_input" not in st.session_state: - st.session_state.human_input = "" - - -left, right = st.columns([1, 2], vertical_alignment="top") - -with left: - task_input = st.text_input( - "Task input", - placeholder="Enter a task input.", - key="task_input", - on_change=controller.handle_task_submission, - ) - - -@st.fragment(run_every="5s") -def task_df() -> None: - st.text("Task Status") - st.button("Refresh") - tasks = ( - [t.input for t in st.session_state.submitted_tasks] - + [t.input for t in st.session_state.human_required_tasks] - + [t.input for t in st.session_state.completed_tasks] - ) - - task_ids = ( - [t.task_id for t in st.session_state.submitted_tasks] - + [t.task_id for t in st.session_state.human_required_tasks] - + [t.task_id for t in st.session_state.completed_tasks] - ) - - status = ( - ["submitted"] * len(st.session_state.submitted_tasks) - + ["human_required"] * len(st.session_state.human_required_tasks) - + ["completed"] * len(st.session_state.completed_tasks) - ) - - data = { - "task_id": task_ids, - "input": tasks, - "status": status, - } - - logger.info(f"data: {data}") - df = pd.DataFrame(data) - event = st.dataframe( - df, - hide_index=True, - selection_mode="single-row", - use_container_width=True, - on_select=controller.get_task_selection_handler(df), - key="task_df", - ) - - popover_enabled = ( - len(event.selection["rows"]) > 0 - and st.session_state.current_task.status == "human_required" - ) - with st.popover("Human Input", disabled=not popover_enabled): - if popover_enabled: - human_prompt = st.session_state.current_task.history[-1].content - st.markdown(human_prompt) - st.text_input( - "Provide human input", - key="human_input", - on_change=controller.get_human_input_handler( - human_input_result_queue - ), - ) - - show_task_res = ( - len(event.selection["rows"]) > 0 - and st.session_state.current_task.status == "completed" - ) - - task_res_container = st.container(height=500) - if show_task_res: - if task_res := controller.get_task_result( - st.session_state.current_task.task_id - ): - task_type = controller.infer_task_type(task_res) - - timeseries_data = None - value_key: str = "" - object_key: str = "" - color: str = "" - if task_type == "timeseries-good": - try: - timeseries_data = perform_price_aggregation( - task_res.result - ) - value_key = "price" - object_key = "good" - color = "#FF91AF" - except json.JSONDecodeError: - logger.info("Could not decode task_res") - pass - elif task_type == "timeseries-city-stat": - try: - timeseries_data = perform_date_value_aggregation( - task_res.result - ) - value_key = "value" - object_key = "variable" - color = "#73CED0" - except json.JSONDecodeError: - logger.info("Could not decode task_res") - pass - - with task_res_container: - if timeseries_data: - title = timeseries_data[0][object_key] - chart_data = { - "dates": [el["date"] for el in timeseries_data], - value_key: [el[value_key] for el in timeseries_data], - } - st.header(title) - st.bar_chart( - data=chart_data, - x="dates", - y=value_key, - height=400, - color=color, - ) - else: - st.write(task_res.result) - - -task_df() - - -@st.fragment(run_every=5) -def process_completed_tasks(completed_queue: queue.Queue) -> None: - task_res: Optional[TaskResult] = None - try: - task_res = completed_queue.get_nowait() - logger.info("got new task result") - except queue.Empty: - logger.info("task result queue is empty.") - - if task_res: - controller.update_associated_task_to_completed_status( - task_res=task_res - ) - - -process_completed_tasks(completed_queue=completed_tasks_queue) - - -@st.fragment(run_every=5) -def process_human_input_requests( - human_requests_queue: queue.Queue[HumanRequest], -) -> None: - human_req: Optional[HumanRequest] = None - try: - human_req = human_requests_queue.get_nowait() - logger.info("got new human request") - except queue.Empty: - logger.info("human request queue is empty.") - - if human_req: - controller.update_associated_task_to_human_required_status( - human_req=human_req - ) - - -process_human_input_requests(human_requests_queue=human_input_request_queue) diff --git a/recipes/llamaindex/llamaindex_cookbook/core_services/__init__.py b/recipes/llamaindex/llamaindex_cookbook/core_services/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/recipes/llamaindex/llamaindex_cookbook/core_services/control_plane.py b/recipes/llamaindex/llamaindex_cookbook/core_services/control_plane.py deleted file mode 100644 index 90ef282..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/core_services/control_plane.py +++ /dev/null @@ -1,115 +0,0 @@ -import asyncio - -import uvicorn -from llama_agents import ( - ControlPlaneServer, - OrchestratorRouter, - PipelineOrchestrator, -) -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue -from llama_index.core.query_pipeline import QueryPipeline -from llama_index.core.selectors import PydanticSingleSelector -from llama_index.llms.openai import OpenAI - -from llamaindex_cookbook.additional_services.human_in_the_loop import ( - human_component, -) -from llamaindex_cookbook.agent_services import ( - funny_agent_component, - funny_agent_server, - goods_getter_agent_component, - stats_fulfiller_agent_component, - stats_getter_agent_component, - time_series_getter_agent_component, -) -from llamaindex_cookbook.utils import load_from_env - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") -control_plane_host = load_from_env("CONTROL_PLANE_HOST") -control_plane_port = load_from_env("CONTROL_PLANE_PORT") -localhost = load_from_env("LOCALHOST") - - -# setup message queue -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) - -# historical prices of a good pipeline -timeseries_task_pipeline = QueryPipeline( - chain=[ - goods_getter_agent_component, - human_component, - time_series_getter_agent_component, - ], -) -timeseries_pipeline_orchestrator = PipelineOrchestrator( - timeseries_task_pipeline -) -timeseries_task_pipeline_desc = """Only used for getting historical price -(timeseries) data for a specified good from the database. -""" - -# government statistics pipeline -city_stats_pipeline = QueryPipeline( - chain=[ - stats_getter_agent_component, - human_component, - stats_fulfiller_agent_component, - ], -) -city_stats_pipeline_orchestrator = PipelineOrchestrator(city_stats_pipeline) -city_stats_pipeline_desc = """Only used for getting geographic and demographic -statistics for a specified city. -""" - -# general pipeline -general_pipeline = QueryPipeline(chain=[funny_agent_component]) -general_pipeline_orchestrator = PipelineOrchestrator(general_pipeline) - -pipeline_orchestrator = OrchestratorRouter( - selector=PydanticSingleSelector.from_defaults(llm=OpenAI("gpt-4o-mini")), - orchestrators=[ - timeseries_pipeline_orchestrator, - city_stats_pipeline_orchestrator, - general_pipeline_orchestrator, - ], - choices=[ - timeseries_task_pipeline_desc, - city_stats_pipeline_desc, - funny_agent_server.description, - ], -) - -# setup control plane -control_plane = ControlPlaneServer( - message_queue=message_queue, - orchestrator=pipeline_orchestrator, - host=control_plane_host, - port=int(control_plane_port) if control_plane_port else None, -) - - -app = control_plane.app - - -# launch -async def launch() -> None: - # register to message queue and start consuming - start_consuming_callable = await control_plane.register_to_message_queue() - _ = asyncio.create_task(start_consuming_callable()) - - cfg = uvicorn.Config( - control_plane.app, - host=localhost, - port=control_plane.port, - ) - server = uvicorn.Server(cfg) - await server.serve() - - -if __name__ == "__main__": - asyncio.run(launch()) diff --git a/recipes/llamaindex/llamaindex_cookbook/core_services/message_queue.py b/recipes/llamaindex/llamaindex_cookbook/core_services/message_queue.py deleted file mode 100644 index 91af9fe..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/core_services/message_queue.py +++ /dev/null @@ -1,12 +0,0 @@ -from llama_agents.message_queues.rabbitmq import RabbitMQMessageQueue - -from llamaindex_cookbook.utils import load_from_env - -message_queue_host = load_from_env("RABBITMQ_HOST") -message_queue_port = load_from_env("RABBITMQ_NODE_PORT") -message_queue_username = load_from_env("RABBITMQ_DEFAULT_USER") -message_queue_password = load_from_env("RABBITMQ_DEFAULT_PASS") - -message_queue = RabbitMQMessageQueue( - url=f"amqp://{message_queue_username}:{message_queue_password}@{message_queue_host}:{message_queue_port}/" -) diff --git a/recipes/llamaindex/llamaindex_cookbook/local_launcher.py b/recipes/llamaindex/llamaindex_cookbook/local_launcher.py deleted file mode 100644 index 77d5922..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/local_launcher.py +++ /dev/null @@ -1,19 +0,0 @@ -from llama_agents import ServerLauncher - -from llamaindex_cookbook.additional_services.human_in_the_loop import ( - human_service, -) -from llamaindex_cookbook.agent_services.funny_agent import agent_server -from llamaindex_cookbook.core_services.control_plane import control_plane -from llamaindex_cookbook.core_services.message_queue import message_queue - -# launch it -launcher = ServerLauncher( - [agent_server, human_service], - control_plane, - message_queue, -) - - -if __name__ == "__main__": - launcher.launch_servers() diff --git a/recipes/llamaindex/llamaindex_cookbook/utils.py b/recipes/llamaindex/llamaindex_cookbook/utils.py deleted file mode 100644 index b566803..0000000 --- a/recipes/llamaindex/llamaindex_cookbook/utils.py +++ /dev/null @@ -1,9 +0,0 @@ -import os - - -def load_from_env(var: str) -> str: - try: - res = os.environ[var] - except KeyError: - raise ValueError(f"Missing env var '{var}'.") - return res diff --git a/recipes/llamaindex/logging.ini b/recipes/llamaindex/logging.ini deleted file mode 100644 index 07bad50..0000000 --- a/recipes/llamaindex/logging.ini +++ /dev/null @@ -1,18 +0,0 @@ -[loggers] -keys=root - -[handlers] -keys=consoleHandler - -[formatters] 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= "0404f5a1a84ee709880ba90350518cde6e538fc2c66ee907482db46819d9bf70" +content-hash = "4e4c11df7057a0419b516220392e3a3a4065d20dbf3bfecf2036b884ad3e32d3" diff --git a/recipes/llamaindex/pyproject.toml b/recipes/llamaindex/pyproject.toml index aa33fd3..dbd035f 100644 --- a/recipes/llamaindex/pyproject.toml +++ b/recipes/llamaindex/pyproject.toml @@ -3,7 +3,7 @@ requires = ["poetry-core"] build-backend = "poetry.core.masonry.api" [tool.poetry] -name = "llamaindex-cookbook" +name = "arc-finetuning-st" version = "0.1.0" description = "" authors = ["Andrei Fajardo "] @@ -11,22 +11,16 @@ readme = "README.md" [tool.poetry.dependencies] python = "^3.10" -llama-index-core = "^0.10.57" -llama-index-llms-openai = "^0.1.27" -llama-index-agent-openai = "^0.2.9" -streamlit = "^1.36.0" -llama-index-readers-file = "^0.1.30" -llama-index-embeddings-openai = "^0.1.11" -llama-index-program-openai = "^0.1.7" -snowflake-sqlalchemy = "^1.6.1" -llama-agents = {version = "^0.0.13", extras = ["rabbitmq"]} +llama-index-core = "^0.11.11" +llama-index-finetuning = "^0.2.1" +llama-index-llms-openai = "^0.2.7" +llama-index-program-openai = "^0.2.0" +streamlit = "^1.38.0" +plotly = "^5.24.1" +pandas = "^2.2.2" [tool.poetry.group.dev.dependencies] -mypy = "^1.10.1" -pylint = "^3.2.5" -pytest = "^8.2.2" -black = {version = "^24.4.2", extras = ["jupyter"]} -codespell = {version = "^2.3.0", extras = ["toml"]} -isort = "^5.13.2" -ruff = "^0.5.1" -ipykernel = "^6.29.5" +jupyterlab = "^4.2.5" + +[tool.poetry.scripts] +arc-finetuning-cli = 'arc_finetuning_st.cli.command_line:main' diff --git a/recipes/llamaindex/template.env.docker b/recipes/llamaindex/template.env.docker deleted file mode 100644 index fc7ec61..0000000 --- a/recipes/llamaindex/template.env.docker +++ /dev/null @@ -1,24 +0,0 @@ -# DON'T MODIFY -RABBITMQ_HOST="rabbitmq" -RABBITMQ_NODE_PORT=5672 -RABBITMQ_DEFAULT_USER="guest" -RABBITMQ_DEFAULT_PASS="guest" -CONTROL_PLANE_HOST="control_plane" -CONTROL_PLANE_PORT=8001 -FUNNY_AGENT_HOST="funny_agent" -FUNNY_AGENT_PORT=8002 -GOODS_GETTER_AGENT_HOST="goods_getter_agent" -GOODS_GETTER_AGENT_PORT=8003 -TIME_SERIES_GETTER_AGENT_HOST="time_series_getter_agent" -TIME_SERIES_GETTER_AGENT_PORT=8004 -STATS_GETTER_AGENT_HOST="stats_getter_agent" -STATS_GETTER_AGENT_PORT=8005 -STATS_FULFILLER_AGENT_HOST="stats_fulfiller_agent" -STATS_FULFILLER_AGENT_PORT=8006 -LOCALHOST="0.0.0.0" -# FILL IN -SNOWFLAKE_USERNAME= -SNOWFLAKE_PASSWORD= -SNOWFLAKE_ACCOUNT= -SNOWFLAKE_ROLE= -OPENAI_API_KEY= diff --git a/recipes/llamaindex/template.env.local b/recipes/llamaindex/template.env.local deleted file mode 100644 index 29dd3a9..0000000 --- a/recipes/llamaindex/template.env.local +++ /dev/null @@ -1,24 +0,0 @@ -# DON'T MODIFY -RABBITMQ_HOST="localhost" -RABBITMQ_NODE_PORT=5672 -RABBITMQ_DEFAULT_USER="guest" -RABBITMQ_DEFAULT_PASS="guest" -CONTROL_PLANE_HOST="0.0.0.0" -CONTROL_PLANE_PORT=8001 -FUNNY_AGENT_HOST="0.0.0.0" -FUNNY_AGENT_PORT=8002 -GOODS_GETTER_AGENT_HOST="0.0.0.0" -GOODS_GETTER_AGENT_PORT=8003 -TIME_SERIES_GETTER_AGENT_HOST="0.0.0.0" -TIME_SERIES_GETTER_AGENT_PORT=8004 -STATS_GETTER_AGENT_HOST="0.0.0.0" -STATS_GETTER_AGENT_PORT=8005 -STATS_FULFILLER_AGENT_HOST="0.0.0.0" -STATS_FULFILLER_AGENT_PORT=8006 -LOCALHOST="0.0.0.0" -# FILL IN -SNOWFLAKE_USERNAME= -SNOWFLAKE_PASSWORD= -SNOWFLAKE_ACCOUNT= -SNOWFLAKE_ROLE= -OPENAI_API_KEY=