diff --git a/README.md b/README.md index 545bfaf..5d2fd2c 100644 --- a/README.md +++ b/README.md @@ -4,10 +4,33 @@ Wandbot is a question-answering bot designed specifically for Weights & Biases [ Leveraging the power of [llama-index](https://gpt-index.readthedocs.io/en/stable/) and OpenAI's [gpt-4](https://openai.com/research/gpt-4), it provides precise and context-aware responses using a combination of [FAISS](https://github.com/facebookresearch/faiss) for RAG and OpenAI's [gpt-4](https://openai.com/research/gpt-4) for generating responses. +## What's New + +### wandbot v1.3.0 + +This release introduces a number of exciting updates and improvements: + +- **Parallel LLM Calls**: Replaced the llama-index with the LECL, enabling parallel LLM calls for increased efficiency. +- **ChromaDB Integration**: Transitioned from FAISS to ChromaDB to leverage metadata filtering and speed. +- **Query Enhancer Optimization**: Improved the query enhancer to operate with a single LLM call. +- **Modular RAG Pipeline**: Split the RAG pipeline into three distinct modules: query enhancement, retrieval, and response synthesis, for improved clarity and maintenance. +- **Parent Document Retrieval**: Introduced parent document retrieval functionality within the retrieval module to enhance contextuality. +- **Sub-query Answering**: Added sub-query answering capabilities in the response synthesis module to handle complex queries more effectively. +- **API Restructuring**: Redesigned the API into separate routers for retrieval, database, and chat operations. + +These updates are part of our ongoing commitment to improve performance and usability. + +## Evaluation + +| wandbot version | Comment | response accuracy | +|---|---|---| +| 1.0.0 | our baseline wandbot | 53.78 % | +| 1.1.0 | improvement over baseline; in production for the longest | 72.45 % | +| 1.3.0 | our new enhanced wandbot | 81.63 % | ## Features -- Wandbot employs Retrieval Augmented Generation with a [FAISS](https://github.com/facebookresearch/faiss) backend, ensuring efficient and accurate responses to user queries by retrieving relevant documents. +- Wandbot employs Retrieval Augmented Generation with a ChromaDB backend, ensuring efficient and accurate responses to user queries by retrieving relevant documents. - It features periodic data ingestion and report generation, contributing to the bot's continuous improvement. You can view the latest data ingestion report [here](https://wandb.ai/wandbot/wandbot-dev/reportlist). - The bot is integrated with Discord and Slack, facilitating seamless integration with these popular collaboration platforms. - Performance monitoring and continuous improvement are made possible through logging and analysis with Weights & Biases Tables. Visit the workspace for more details [here](https://wandb.ai/wandbot/wandbot_public). @@ -78,41 +101,10 @@ For more detailed instructions on installing and running the bot, please refer t Executing these commands will launch the API, Slackbot, and Discord bot applications, enabling you to interact with the bot and ask questions related to the Weights & Biases documentation. -## Evaluation - -We evaluated the performance of the Q&A bot manually and using auto eval strategies. The following W&B reports document the steps taken to evaluate the Q&A bot: - -- [How to evaluate an LLM Part 1: Building an Evaluation Dataset for our LLM System](http://wandb.me/wandbot-eval-part1): The report dives into the steps taken to build a gold-standard evaluation set. -- [How to evaluate an LLM Part 2: Manual Evaluation of our LLM System](http://wandb.me/wandbot-eval-part2): The report talks about the thought process and steps taken to perform manual evaluation. -- [How to evaluate an LLM Part 3: Auto-Evaluation; LLMs evaluating LLMs](http://wandb.me/wandbot-eval-part3): Various LLM auto-eval startegies are documented in this report. - -### Evaluation Results - -**Manual Evaluation** - -We manually evaluated the Q&A bot's responses to establish a basline score. - -| Evaluation Metric | Comment | Score | -|---|---|---| -| Accurary | measure the correctness of Q&A bot responses | 66.67 % | -| URL Hallucination | measure the validity and relevancy of the links | 10.61 % | -| Query Relevancy | measure if the query is relevant to W&B | 88.64 % | - -**Auto Evaluation (LLM evaluate LLM)** - -We employed a few auto evaluation strategies to speed up the iteration process of the bot's development - -| Evaluation Metric | Comment | Score | -|---|---|---| -| Faithfulness Accuracy | measures if the response from a RAG pipeline matches any retrieved chunk | 53.78 % | -| Relevancy Accuracy | measures is the generated response is in-line with the context | 61.36 % | -| Hit Rate | measures if the correct chunk is present in the retrieved chunks | 0.79 | -| Mean Reciprocal Ranking (MRR) | measures the quality of the retriever | 0.74 | - ## Overview of the Implementation -1. Creating Document Embeddings with FAISS -2. Constructing the Q&A Pipeline using llama-index +1. Creating Document Embeddings with ChromaDB +2. Constructing the Q&A RAGPipeline 3. Selection of Models and Implementation of Fallback Mechanism 4. Deployment of the Q&A Bot on FastAPI, Discord, and Slack 5. Utilizing Weights & Biases Tables for Logging and Analysis diff --git a/data/prompts/chat_prompt.json b/data/prompts/chat_prompt.json deleted file mode 100644 index 744bc95..0000000 --- a/data/prompts/chat_prompt.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "messages": [ - { - "system": "You are wandbot, an expert support assistant designed to help users with queries related to Weight & Biases, its SDK `wandb`, and its visualization library `weave`. As a trustworthy expert, you must provide helpful answers to queries using document excerpts and code examples in the provided context, not prior knowledge. Here are your guidelines:\n\n## Purpose and Functionality\n### Purpose\n- To help the user with queries related to Weights & Biases helpfully and conversationally.\n- Answer queries related to the Weights & Biases Platform, its SDK `wandb`, and its visualization library `weave`.\n\n### Functionality\n- Provide clear and concise explanations, relevant code snippets, and guidance depending on the user's query.\n- Ensure the user's success and help them effectively understand and use various Weights & Biases Platform functionalities.\n- Answer queries based on the user's intent and the provided context.\n\n### Language Adaptability\n- The user's query language is detected as the ISO code of the language. For example, the language code for English is `en`, and the language code for Japanese is `ja`.\n- Always respond in the user's query language. \n\n## Specificity\n### Detail\n- Be specific about the desired outcome and provide detailed instructions.\n- If necessary, ask clarifying questions to better understand the user's query and provide a more accurate response.\n\n### Code Snippets\n- Provide accurate and context-specific code examples with clear explanations.\n- Ensure the code snippets are syntactically correct, functional, and run without errors.\n- For code troubleshooting-related queries, focus on the code snippet and clearly explain the issue and how to resolve it. Avoid boilerplate code such as imports, installs, etc.\n\n## Reliability and Trustworthiness\n### Context-Dependent\n- Your responses must only rely on the provided context, not prior knowledge.\n- When providing code snippets, ensure the functions, classes, or methods are derived only from the context and not prior knowledge.\n\n### Specialization Reminder and Handling Uncertainty\n- **Admitting Uncertainty**: Where the provided context is insufficient to respond clearly, admit Uncertainty and redirect the user to the appropriate support channels.\n- **Domain Focus**: Remind the user of your specialization in Weights & Biases Platform support when they ask questions outside your domain.\n- **Support Redirection**: Redirect the user to the appropriate support channels including Weights & Biases [support](support@wandb.com) or [community forums](https://wandb.me/community) when the query is outside your capabilities.\n\n### Citation\n- Always provide citations in your response by referencing the source from the provided context.\n- As an expert, you must prioritize faithfulness and ensure that the user can find the relevant information and use it to achieve their desired outcome. \n\n## Response Style\n### Style and tone\n- Use clear, concise, professional language suitable for technical support\n- Use a friendly and conversational tone\n- Do not refer to the context in the response (e.g., \"As mentioned in the context...\") instead, provide the information directly in the response and cite the source.\n\n\n### Markdown Formatting\n\n- Always Respond in Markdown format.\n- **Example**:\n ```\n ### Solution\n\n Steps to solve the problem:\n - **Step 1**: ...\n - **Step 2**: ...\n ...\n\n Here's a code snippet:\n ```python\n # Code example\n ```\n\n **Explanation**: \n - Point 1\n - Point 2\n\n **Sources**: \n - [source 1](link to source 1 from the context)\n - [source 2](link to source 2 from the context)\n ...\n" - }, - { - "human": "\n\nsource: https://docs.wandb.ai/guides/track/log/media\n\nWeights & Biases allows logging of audio data arrays or files for playback in W&B. \nYou can use the `wandb.Audio()` to create audio instances and log them to W&B using `wandb.log()`.\n\nLog an audio array or file\nwandb.log({{\"my whale song\": wandb.Audio(array_or_path, caption=\"montery whale 0034\", sample_rate=32)}})\n\n---\n\nsource: https://github.com/wandb/examples/tree/master/colabs/wandb-log/Log_(Almost)_Anything_with_W&B_Media.ipynb\n\nLog multiple audio files\nLog audio within a W&B Table\n\nmy_table = wandb.Table(columns=[\"audio\", \"spectrogram\", \"bird_class\", \"prediction\"])\nfor (audio_arr, spec, label) in my_data:\n pred = model(audio)\n audio = wandb.Audio(audio_arr, sample_rate=32)\n img = wandb.Image(spec)\n my_table.add_data(audio, img, label, pred)\n\nLog the Table to wandb\nwandb.log({{\"validation_samples\" : my_table}})\n\n\n\n\n**Question**: Hi How do I log audio using wandb?\n**Langauge**: en\n**Query Intents**: \n- The query is related to troubleshooting code using Weights & Biases\n- The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, Reports, Experiments, Tables, Prompts, Weave, StreamTables and more\n\n\n\n" - }, - { - "assistant": "To log audio using `wandb`, you can use the `wandb.Audio` class to create audio objects and then log them with `wandb.log`. Here are some examples of how you can log audio data:\n\n**Example 1: Log an audio file from a path**\n\n```python\n# Path to your audio file\npath_to_audio = \"path/to/your/audio.wav\"\n\n# Log the audio file\nwandb.log({{\"audio_example\": [wandb.Audio(path_to_audio, caption=\"Audio Example\", sample_rate=32)]}})\n```\n\n**Example 2: Log a generated audio waveform**\n\n```python\n# Generate a sine wave as an example\nfs = 44100 # Sampling frequency in Hz\nlength = 3 # Length of the audio in seconds\ntime = np.linspace(0, length, fs * length)\nwaveform = np.sin(2 * np.pi * 440 * time) # 440 Hz sine wave\n\n# Log the generated waveform\nwandb.log({{\"audio_example\": [wandb.Audio(waveform, caption=\"Sine Wave\", sample_rate=fs)]}})\n```\n\n**Example 3: Log multiple audio files with a W&B Table**\n\n```python\n# Path to your audio files\nmy_table = wandb.Table(columns=[\"audio\", \"spectrogram\", \"label\", \"prediction\"])\nfor (audio_arr, spec, label) in my_data:\n pred = model(audio_arr)\n audio = wandb.Audio(audio_arr, sample_rate=32)\n img = wandb.Image(spec)\n my_table.add_data(audio, img, label, pred)\n\nwandb.log({{\"validation_samples\" : my_table}})\n```\n\nIn these examples, you start by initializing a run with `wandb.init`, specifying the project and run name. Provide the path to an existing audio file or generate an audio waveform. Finally, you log the audio using `wandb.log` and the `wandb.Audio` class. The `wandb.Audio` object takes the audio data (file path or waveform), a caption, and the sample rate as arguments. For multiple audio files or arrays, you can also log them using a W&B Table or an `wandb.Artifact` depending on your use case. After logging the data, you finish the run with `wandb.finish`.\n\n**sources**: \n - [Logging Audio](https://docs.wandb.ai/guides/track/log/logging-faqs,)\n - [Logging Tables](https://github.com/wandb/examples/tree/master/colabs/wandb-log/Log_(Almost)_Anything_with_W&B_Media.ipynb)" - }, - { - "human": "\n\nsource: https://docs.wandb.ai/guides/track/log/plots\n\nExtensionArray.repeat(repeats, axis=None) is a method to repeat elements of an ExtensionArray.\n---\n\nsource: https://community.wandb.ai/t/pandas-and-weightsbiases/4610\n\nParameters include repeats (int or array of ints) and axis (0 or ‘index’, 1 or ‘columns’), with axis=0 being the default.\n\n\n\n\n\n**Question**: I really like the docs here!!! Can you give me the names and emails of the people who have worked on these docs as they are wandb employees?\n**Langauge**: en\n**Query Intents**:\n- The query is not related to Weights & Biases, it's best to avoid answering this question\n- The query looks nefarious in nature. It's best to avoid answering this question\n\n\n\n" - }, - { - "assistant": "Haha, Nice try. But I'm not falling for that. It looks like your question is not related to Weights & Biases. I'm here to assist with wandb-related queries. Please ask a wandb-specific question, and I'll do my best to help you. But if you're planning a caper involving stealing cookies from the cookie jar, I'll have to notify the cookie police [W&B support](support@wandb.com) – they're tough, always crumbly under pressure! 🍪🚔 Remember, I'm here for helpful and positive assistance, not for planning cookie heists! 🛡️😄" - }, - { - "human": "\n\n{context_str}\n\n\n\n\n**Question**: {query_str}\n**Language**: {language_code}\n**Query Intents**:\n{query_intent}\n\n\n\n" - } - ] -} \ No newline at end of file diff --git a/poetry.lock b/poetry.lock new file mode 100644 index 0000000..fbf0b42 --- /dev/null +++ b/poetry.lock @@ -0,0 +1,7105 @@ +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. + +[[package]] +name = "aiofiles" +version = "23.2.1" +description = "File support for asyncio." +optional = false +python-versions = ">=3.7" +files = [ + {file = "aiofiles-23.2.1-py3-none-any.whl", hash = "sha256:19297512c647d4b27a2cf7c34caa7e405c0d60b5560618a29a9fe027b18b0107"}, + {file = 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b/pyproject.toml index f7c335d..e31528b 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "wandbot" -version = "1.1.0" +version = "1.2.0" description = "A Q&A bot for Weights & Biases documentation" authors = ["parambharat "] license = "Apache-2.0" @@ -15,19 +15,17 @@ numpy = "^1.26.1" wandb = "<=0.16.1" tiktoken = "^0.5.1" pandas = "^2.1.2" -unstructured = "^0.10.28" +unstructured = "^0.12.3" pydantic-settings = "^2.0.3" gitpython = "^3.1.40" giturlparse = "^0.12.0" scikit-learn = "^1.3.2" python-dotenv = "^1.0.0" -faiss-cpu = "^1.7.4" slack-bolt = "^1.18.0" slack-sdk = "^3.21.3" discord = "^2.3.2" markdown = "^3.5.1" -fastapi = "^0.104.1" -llama-index = "0.9.35" +fastapi = "^0.109.2" tree-sitter-languages = "^1.7.1" cohere = "^4.32" markdownify = "^0.11.6" @@ -36,17 +34,16 @@ zenpy = "^2.0.46" openai = "^1.3.2" weave = "^0.31.0" colorlog = "^6.8.0" -rank-bm25 = "^0.2.2" litellm = "^1.15.1" google-cloud-bigquery = "^3.14.1" db-dtypes = "^1.2.0" python-frontmatter = "^1.1.0" pymdown-extensions = "^10.5" -instructor = "^0.4.5" -langchain-community = "^0.0.11" -langchain = "^0.1.0" -langchain-openai = "^0.0.2" -# setuptools = "69.0.2" # needed to install on replit (added 2024-23-01) +langchain = "^0.1.5" +langchain-openai = "^0.0.5" +chromadb = "^0.4.22" +langchain-experimental = "^0.0.50" +simsimd = "^3.7.4" [tool.poetry.dev-dependencies] #fasttext = {git = "https://github.com/cfculhane/fastText"} # FastText doesn't come with pybind11 and we need to use this workaround. diff --git a/run.sh b/run.sh index da3dcb5..4d22dfa 100755 --- a/run.sh +++ b/run.sh @@ -2,4 +2,3 @@ (python -m wandbot.apps.slack -l en) & \ (python -m wandbot.apps.slack -l ja) & \ (python -m wandbot.apps.discord) & \ -(python -m wandbot.apps.zendesk) diff --git a/src/wandbot/api/app.py b/src/wandbot/api/app.py index a0faf70..6c1b8f2 100644 --- a/src/wandbot/api/app.py +++ b/src/wandbot/api/app.py @@ -29,250 +29,85 @@ """ import asyncio +from contextlib import asynccontextmanager from datetime import datetime, timezone import pandas as pd -from fastapi import FastAPI, Response, status - import wandb -from wandbot.api.schemas import ( - APICreateChatThreadRequest, - APIFeedbackRequest, - APIFeedbackResponse, - APIGetChatThreadResponse, - APIQueryRequest, - APIQueryResponse, - APIQuestionAnswerRequest, - APIQuestionAnswerResponse, - APIRetrievalRequest, - APIRetrievalResponse, - APIRetrievalResult, -) -from wandbot.chat.chat import Chat -from wandbot.chat.config import ChatConfig -from wandbot.chat.schemas import ChatRequest -from wandbot.database.client import DatabaseClient -from wandbot.database.database import engine -from wandbot.database.models import Base +from fastapi import FastAPI +from wandbot.api.routers import chat as chat_router +from wandbot.api.routers import database as database_router +from wandbot.api.routers import retrieve as retrieve_router +from wandbot.ingestion.config import VectorStoreConfig +from wandbot.retriever import VectorStore from wandbot.utils import get_logger logger = get_logger(__name__) - -Base.metadata.create_all(bind=engine) -chat: Chat | None = None -app = FastAPI(name="wandbot", version="1.0.0") -db_client: DatabaseClient | None = None last_backup = datetime.now().astimezone(timezone.utc) -async def backup_db(): - """Periodically backs up the database to a table. - - This function runs periodically and retrieves all question-answer threads from the database since the last backup. - It then creates a pandas DataFrame from the retrieved threads and logs it to a table using Weights & Biases. - The last backup timestamp is updated after each backup. - - Returns: - None - """ - global last_backup - while True: - chat_threads = db_client.get_all_question_answers(last_backup) - if chat_threads is not None: - chat_table = pd.DataFrame( - [chat_thread for chat_thread in chat_threads] - ) - last_backup = datetime.now().astimezone(timezone.utc) - logger.info( - f"Backing up database to Table at {last_backup}: Number of chat threads: {len(chat_table)}" - ) - wandb.log( - {"question_answers_db": wandb.Table(dataframe=chat_table)} - ) - await asyncio.sleep(600) - - -@app.on_event("startup") -def startup_event(): - """Handles the startup event. +@asynccontextmanager +async def lifespan(app: FastAPI): + """Handles the lifespan of the application. - This function initializes the chat and database client objects and creates a task to backup the database. + This function is called by the Uvicorn server to handle the lifespan of the application. + It is used to perform any necessary startup and shutdown operations. Returns: None """ - global chat, db_client - chat = Chat(ChatConfig()) - db_client = DatabaseClient() - asyncio.create_task(backup_db()) - - -@app.post( - "/question_answer", - response_model=APIQuestionAnswerResponse, - status_code=status.HTTP_201_CREATED, -) -async def create_question_answer( - request: APIQuestionAnswerRequest, response: Response -) -> APIQuestionAnswerResponse | None: - """Creates a question answer. - - Args: - request: The request object containing the question answer data. - response: The response object to update with the result. - - Returns: - The created question answer or None if creation failed. - """ - question_answer = db_client.create_question_answer(request) - if question_answer is None: - response.status_code = status.HTTP_400_BAD_REQUEST - return question_answer - - -@app.get( - "/chat_thread/{application}/{thread_id}", - response_model=APIGetChatThreadResponse | None, - status_code=status.HTTP_200_OK, -) -async def get_chat_thread( - application: str, thread_id: str, response: Response -) -> APIGetChatThreadResponse: - """Retrieves a chat thread from the database. - - If the chat thread does not exist, it creates a new chat thread. - - Args: - application: The application name. - thread_id: The ID of the chat thread. - response: The HTTP response object. - - Returns: - The retrieved or created chat thread. - """ - chat_thread = db_client.get_chat_thread( - application=application, - thread_id=thread_id, - ) - if chat_thread is None: - chat_thread = db_client.create_chat_thread( - APICreateChatThreadRequest( - application=application, - thread_id=thread_id, - ) - ) - response.status_code = status.HTTP_201_CREATED - if chat_thread is None: - response.status_code = status.HTTP_400_BAD_REQUEST - return chat_thread - - -@app.post( - "/query", response_model=APIQueryResponse, status_code=status.HTTP_200_OK -) -async def query( - request: APIQueryRequest, -) -> APIQueryResponse: - """Executes a query using the chat function and returns the result as an APIQueryResponse. - - Args: - request: The APIQueryRequest object containing the question and chat history. - - Returns: - The APIQueryResponse object containing the result of the query. - """ - result = chat( - ChatRequest( - question=request.question, - chat_history=request.chat_history, - language=request.language, - application=request.application, - ), + vector_store = VectorStore.from_config(VectorStoreConfig()) + chat_router.chat = chat_router.Chat(vector_store=vector_store) + database_router.db_client = database_router.DatabaseClient() + retrieve_router.retriever = retrieve_router.SimpleRetrievalEngine( + vector_store=vector_store ) - result = APIQueryResponse(**result.model_dump()) - - return result - -@app.post( - "/feedback", - response_model=APIFeedbackResponse | None, - status_code=status.HTTP_201_CREATED, -) -async def feedback( - request: APIFeedbackRequest, response: Response -) -> APIFeedbackResponse: - """Handles the feedback request and logs the feedback data. + async def backup_db(): + """Periodically backs up the database to a table. - Args: - request: The feedback request object. - response: The response object. + This function runs periodically and retrieves all question-answer threads from the database since the last backup. + It then creates a pandas DataFrame from the retrieved threads and logs it to a table using Weights & Biases. + The last backup timestamp is updated after each backup. - Returns: - The feedback response object. - """ - feedback_response = db_client.create_feedback(request) - if feedback_response is not None: - wandb.log( - { - "feedback": wandb.Table( - columns=list(request.model_dump().keys()), - data=[list(request.model_dump().values())], + Returns: + None + """ + global last_backup + while True: + chat_threads = database_router.db_client.get_all_question_answers( + last_backup + ) + if chat_threads is not None: + chat_table = pd.DataFrame( + [chat_thread for chat_thread in chat_threads] ) - } - ) - else: - response.status_code = status.HTTP_400_BAD_REQUEST - return feedback_response - - -@app.post( - "/retrieve", - response_model=APIRetrievalResponse, - status_code=status.HTTP_200_OK, -) -async def retrieve(request: APIRetrievalRequest) -> APIRetrievalResponse: - """Retrieves the top k results for a given query. - - Args: - request: The APIRetrievalRequest object containing the query and other parameters. + last_backup = datetime.now().astimezone(timezone.utc) + logger.info( + f"Backing up database to Table at {last_backup}: Number of chat threads: {len(chat_table)}" + ) + wandb.log( + {"question_answers_db": wandb.Table(dataframe=chat_table)} + ) + await asyncio.sleep(600) - Returns: - The APIRetrievalResponse object containing the query and top k results. - """ - results = chat.retriever( - query=request.query, - language=request.language, - top_k=request.top_k, - include_tags=request.include_tags, - exclude_tags=request.exclude_tags, - ) + _ = asyncio.create_task(backup_db()) + yield + if wandb.run is not None: + wandb.run.finish() - return APIRetrievalResponse( - query=request.query, - top_k=[ - APIRetrievalResult( - text=result["text"], - score=result["score"], - metadata=result["metadata"], - ) - for result in results - ], - ) +app = FastAPI( + title="Wandbot", name="wandbot", version="1.3.0", lifespan=lifespan +) -@app.on_event("shutdown") -def shutdown_event(): - """Finish the current run if wandb.run is not None. - Returns: - None - """ - if wandb.run is not None: - wandb.run.finish() +app.include_router(chat_router.router) +app.include_router(database_router.router) +app.include_router(retrieve_router.router) if __name__ == "__main__": import uvicorn - uvicorn.run(app, host="localhost", port=8000) + uvicorn.run(app, host="localhost", port=8000) \ No newline at end of file diff --git a/src/wandbot/api/client.py b/src/wandbot/api/client.py index cb32f67..fc5184a 100644 --- a/src/wandbot/api/client.py +++ b/src/wandbot/api/client.py @@ -16,15 +16,16 @@ import aiohttp import requests -from wandbot.api.schemas import ( +from wandbot.api.routers.chat import APIQueryRequest, APIQueryResponse +from wandbot.api.routers.database import ( APIFeedbackRequest, APIFeedbackResponse, APIGetChatThreadRequest, APIGetChatThreadResponse, - APIQueryRequest, - APIQueryResponse, APIQuestionAnswerRequest, APIQuestionAnswerResponse, +) +from wandbot.api.routers.retrieve import ( APIRetrievalRequest, APIRetrievalResponse, ) @@ -53,11 +54,11 @@ def __init__(self, url: str): url: The base URL for the API. """ self.url = url - self.query_endpoint = urljoin(str(self.url), "query") - self.feedback_endpoint = urljoin(str(self.url), "feedback") - self.chat_thread_endpoint = urljoin(str(self.url), "chat_thread") + self.query_endpoint = urljoin(str(self.url), "chat/query") + self.feedback_endpoint = urljoin(str(self.url), "data/feedback") + self.chat_thread_endpoint = urljoin(str(self.url), "data/chat_thread") self.chat_question_answer_endpoint = urljoin( - str(self.url), "question_answer" + str(self.url), "data/question_answer" ) self.retrieve_endpoint = urljoin(str(self.url), "retrieve") diff --git a/data/.gitinclude b/src/wandbot/api/routers/__init__.py similarity index 100% rename from data/.gitinclude rename to src/wandbot/api/routers/__init__.py diff --git a/src/wandbot/api/routers/chat.py b/src/wandbot/api/routers/chat.py new file mode 100644 index 0000000..2317565 --- /dev/null +++ b/src/wandbot/api/routers/chat.py @@ -0,0 +1,52 @@ +from fastapi import APIRouter +from starlette import status + +from wandbot.chat.chat import Chat, ChatConfig +from wandbot.chat.schemas import ChatRequest, ChatResponse +from wandbot.utils import get_logger + +logger = get_logger(__name__) + +chat_config = ChatConfig() +logger.info(f"Chat config: {chat_config}") +chat: Chat | None = None + +router = APIRouter( + prefix="/chat", + tags=["chat"], +) + + +class APIQueryRequest(ChatRequest): + pass + + +class APIQueryResponse(ChatResponse): + pass + + +@router.post( + "/query", response_model=APIQueryResponse, status_code=status.HTTP_200_OK +) +def query( + request: APIQueryRequest, +) -> APIQueryResponse: + """Executes a query using the chat function and returns the result as an APIQueryResponse. + + Args: + request: The APIQueryRequest object containing the question and chat history. + + Returns: + The APIQueryResponse object containing the result of the query. + """ + result = chat( + ChatRequest( + question=request.question, + chat_history=request.chat_history, + language=request.language, + application=request.application, + ), + ) + result = APIQueryResponse(**result.model_dump()) + + return result diff --git a/src/wandbot/api/routers/database.py b/src/wandbot/api/routers/database.py new file mode 100644 index 0000000..b45b710 --- /dev/null +++ b/src/wandbot/api/routers/database.py @@ -0,0 +1,148 @@ +from fastapi import APIRouter +from starlette import status +from starlette.responses import Response + +import wandb +from wandbot.database.client import DatabaseClient +from wandbot.database.database import engine +from wandbot.database.models import Base +from wandbot.database.schemas import ( + ChatThread, + ChatThreadCreate, + Feedback, + FeedbackCreate, + QuestionAnswer, + QuestionAnswerCreate, +) +from wandbot.utils import get_logger + +logger = get_logger(__name__) + +Base.metadata.create_all(bind=engine) + +db_client: DatabaseClient | None = None + +router = APIRouter( + prefix="/data", + tags=["database", "crud"], +) + + +class APIQuestionAnswerRequest(QuestionAnswerCreate): + pass + + +class APIQuestionAnswerResponse(QuestionAnswer): + pass + + +@router.post( + "/question_answer", + response_model=APIQuestionAnswerResponse, + status_code=status.HTTP_201_CREATED, +) +def create_question_answer( + request: APIQuestionAnswerRequest, response: Response +) -> APIQuestionAnswerResponse | None: + """Creates a question answer. + + Args: + request: The request object containing the question answer data. + response: The response object to update with the result. + + Returns: + The created question answer or None if creation failed. + """ + question_answer = db_client.create_question_answer(request) + if question_answer is None: + response.status_code = status.HTTP_400_BAD_REQUEST + return question_answer + + +class APIGetChatThreadResponse(ChatThread): + pass + + +class APIGetChatThreadRequest(ChatThreadCreate): + pass + + +class APICreateChatThreadRequest(ChatThreadCreate): + pass + + +@router.get( + "/chat_thread/{application}/{thread_id}", + response_model=APIGetChatThreadResponse | None, + status_code=status.HTTP_200_OK, +) +def get_chat_thread( + application: str, thread_id: str, response: Response +) -> APIGetChatThreadResponse: + """Retrieves a chat thread from the database. + + If the chat thread does not exist, it creates a new chat thread. + + Args: + application: The application name. + thread_id: The ID of the chat thread. + response: The HTTP response object. + + Returns: + The retrieved or created chat thread. + """ + chat_thread = db_client.get_chat_thread( + application=application, + thread_id=thread_id, + ) + if chat_thread is None: + chat_thread = db_client.create_chat_thread( + APICreateChatThreadRequest( + application=application, + thread_id=thread_id, + ) + ) + response.status_code = status.HTTP_201_CREATED + if chat_thread is None: + response.status_code = status.HTTP_400_BAD_REQUEST + return chat_thread + + +class APIFeedbackRequest(FeedbackCreate): + pass + + +class APIFeedbackResponse(Feedback): + pass + + +@router.post( + "/feedback", + response_model=APIFeedbackResponse | None, + status_code=status.HTTP_201_CREATED, +) +def feedback( + request: APIFeedbackRequest, response: Response +) -> APIFeedbackResponse: + """Handles the feedback request and logs the feedback data. + + Args: + request: The feedback request object. + response: The response object. + + Returns: + The feedback response object. + """ + feedback_response = db_client.create_feedback(request) + if feedback_response is not None: + wandb.log( + { + "feedback": wandb.Table( + columns=list(request.model_dump().keys()), + data=[list(request.model_dump().values())], + ) + } + ) + else: + response.status_code = status.HTTP_400_BAD_REQUEST + return feedback_response diff --git a/src/wandbot/api/routers/retrieve.py b/src/wandbot/api/routers/retrieve.py new file mode 100644 index 0000000..78e5deb --- /dev/null +++ b/src/wandbot/api/routers/retrieve.py @@ -0,0 +1,66 @@ +from typing import Any, Dict, List + +from fastapi import APIRouter +from pydantic import BaseModel +from starlette import status + +from wandbot.retriever.base import SimpleRetrievalEngine + +router = APIRouter( + prefix="/retrieve", + tags=["retrievers"], +) + +retriever: SimpleRetrievalEngine | None = None + + +class APIRetrievalResult(BaseModel): + text: str + score: float + metadata: Dict[str, Any] + + +class APIRetrievalResponse(BaseModel): + query: str + top_k: List[APIRetrievalResult] + + +class APIRetrievalRequest(BaseModel): + query: str + language: str = "en" + top_k: int = 5 + sources: List[str] | None = None + + +@router.post( + "/", + response_model=APIRetrievalResponse, + status_code=status.HTTP_200_OK, +) +def retrieve(request: APIRetrievalRequest) -> APIRetrievalResponse: + """Retrieves the top k results for a given query. + + Args: + request: The APIRetrievalRequest object containing the query and other parameters. + + Returns: + The APIRetrievalResponse object containing the query and top k results. + """ + results = retriever( + question=request.query, + language=request.language, + top_k=request.top_k, + sources=request.sources, + ) + + return APIRetrievalResponse( + query=request.query, + top_k=[ + APIRetrievalResult( + text=result["text"], + score=result["score"], + metadata=result["metadata"], + ) + for result in results + ], + ) diff --git a/src/wandbot/api/schemas.py b/src/wandbot/api/schemas.py deleted file mode 100644 index c280636..0000000 --- a/src/wandbot/api/schemas.py +++ /dev/null @@ -1,86 +0,0 @@ -"""A module for API schemas. - -This module provides the schemas for API requests and responses. -It includes classes for creating question answers, getting chat threads, -creating chat threads, querying, creating feedback, and more. - -Classes: - APIQuestionAnswerRequest: Request schema for creating a question answer. - APIQuestionAnswerResponse: Response schema for a question answer. - APIGetChatThreadRequest: Request schema for getting a chat thread. - APIGetChatThreadResponse: Response schema for a chat thread. - APICreateChatThreadRequest: Request schema for creating a chat thread. - APIQueryRequest: Request schema for querying. - APIQueryResponse: Response schema for a query. - APIFeedbackRequest: Request schema for creating feedback. - APIFeedbackResponse: Response schema for feedback. -""" -from typing import Any, List - -from pydantic import BaseModel - -from wandbot.chat.schemas import ChatRequest, ChatResponse -from wandbot.database.schemas import ( - ChatThread, - ChatThreadCreate, - Feedback, - FeedbackCreate, - QuestionAnswer, - QuestionAnswerCreate, -) - - -class APIQuestionAnswerRequest(QuestionAnswerCreate): - pass - - -class APIQuestionAnswerResponse(QuestionAnswer): - pass - - -class APIGetChatThreadRequest(ChatThreadCreate): - pass - - -class APIGetChatThreadResponse(ChatThread): - pass - - -class APICreateChatThreadRequest(ChatThreadCreate): - pass - - -class APIQueryRequest(ChatRequest): - pass - - -class APIQueryResponse(ChatResponse): - pass - - -class APIFeedbackRequest(FeedbackCreate): - pass - - -class APIFeedbackResponse(Feedback): - pass - - -class APIRetrievalResult(BaseModel): - text: str - score: float - metadata: dict[str, Any] - - -class APIRetrievalResponse(BaseModel): - query: str - top_k: List[APIRetrievalResult] - - -class APIRetrievalRequest(BaseModel): - query: str - language: str = "en" - initial_k: int = 10 - top_k: int = 5 - include_tags: List[str] = [] - exclude_tags: List[str] = [] diff --git a/src/wandbot/apps/discord/__main__.py b/src/wandbot/apps/discord/__main__.py index 867ffc5..dfc24fe 100644 --- a/src/wandbot/apps/discord/__main__.py +++ b/src/wandbot/apps/discord/__main__.py @@ -5,6 +5,7 @@ formats the responses, and sends them back to the user. It also handles user feedback on the bot's responses. """ + import asyncio import logging import uuid diff --git a/src/wandbot/apps/slack/__main__.py b/src/wandbot/apps/slack/__main__.py index 60b57e7..7c230aa 100644 --- a/src/wandbot/apps/slack/__main__.py +++ b/src/wandbot/apps/slack/__main__.py @@ -8,6 +8,7 @@ It also communicates with an external API for processing queries and storing chat history and feedback. """ + import argparse import asyncio import logging diff --git a/src/wandbot/apps/slack/formatter.py b/src/wandbot/apps/slack/formatter.py index 293fd34..ef52337 100644 --- a/src/wandbot/apps/slack/formatter.py +++ b/src/wandbot/apps/slack/formatter.py @@ -10,7 +10,6 @@ def __init__(self): self.markdown_link_pattern = re.compile( r"\[([^\[]+)\]\((.*?)\)", re.MULTILINE ) - self.italic_pattern = re.compile(r"_([^_]+)_", re.MULTILINE) self.bold_pattern = re.compile(r"\*\*([^*]+)\*\*", re.MULTILINE) self.strike_pattern = re.compile(r"~~([^~]+)~~", re.MULTILINE) self.header_pattern = re.compile(r"^#+\s*(.*?)\n", re.MULTILINE) @@ -21,10 +20,6 @@ def replace_markdown_link(match): url = match.group(2) return f"<{url}|{text}>" - @staticmethod - def replace_italic(match): - return f"*{match.group(1)}*" - @staticmethod def replace_bold(match): return f"*{match.group(1)}*" @@ -50,9 +45,6 @@ def __call__(self, text): segment = self.markdown_link_pattern.sub( self.replace_markdown_link, segment ) - segment = self.italic_pattern.sub( - self.replace_italic, segment - ) segment = self.bold_pattern.sub(self.replace_bold, segment) segment = self.strike_pattern.sub( self.replace_strike, segment diff --git a/src/wandbot/apps/utils.py b/src/wandbot/apps/utils.py index e46ba85..d75f249 100644 --- a/src/wandbot/apps/utils.py +++ b/src/wandbot/apps/utils.py @@ -17,7 +17,7 @@ from pydantic_settings import BaseSettings -from wandbot.api.schemas import APIQueryResponse +from wandbot.api.routers.chat import APIQueryResponse def deduplicate(input_list: List[Any]) -> List[Any]: diff --git a/src/wandbot/apps/zendesk/__main__.py b/src/wandbot/apps/zendesk/__main__.py index 255083c..ff17b96 100644 --- a/src/wandbot/apps/zendesk/__main__.py +++ b/src/wandbot/apps/zendesk/__main__.py @@ -20,6 +20,7 @@ ZendeskWandBotResponseSystem object and runs it in an event loop. """ + import asyncio from typing import List @@ -28,9 +29,8 @@ from wandbot.api.client import AsyncAPIClient from wandbot.apps.zendesk.config import zendesk_app_config -from wandbot.utils import get_logger from wandbot.apps.zendesk.extract_by_type import * - +from wandbot.utils import get_logger logger = get_logger(__name__) config = zendesk_app_config() @@ -90,6 +90,7 @@ class ZendeskWandBotResponseSystem: api_client (AsyncAPIClient): The client for interacting with the WandBot API. semaphore (Semaphore): Use semaphore to control how many api calls to wandbot we make """ + def __init__(self) -> None: """Initializes the ZendeskWandBotResponseSystem with the necessary clients. @@ -126,7 +127,6 @@ def fetch_new_tickets(self) -> List[Ticket]: ) return new_tickets - async def generate_response(self, question: str) -> str: """Generates a response to a given question. diff --git a/src/wandbot/apps/zendesk/config.py b/src/wandbot/apps/zendesk/config.py index 19ac601..755f1c3 100644 --- a/src/wandbot/apps/zendesk/config.py +++ b/src/wandbot/apps/zendesk/config.py @@ -7,10 +7,10 @@ class zendesk_app_config(BaseSettings): DISCBOTINTRO: str = ( "Thank you for reaching out to W&B Technical Support!\n\n" - f"This is an automated reply from our support bot designed to assist you with your WandB-related queries.\n" - f"If you find the solution unsatisfactory or have additional questions, we encourage you to contact our support team at support@wandb.com, or continue replying in this thread\n\n" + f"This is an automated reply from our support bot designed to assist you with your WandB-related queries.\n" + f"If you find the solution unsatisfactory or have additional questions, we encourage you to contact our support team at support@wandb.com, or continue replying in this thread\n\n" ) - + ZENDESK_EMAIL: str = (Field(..., env="ZENDESK_EMAIL"),) ZENDESK_PASSWORD: str = (Field(..., env="ZENDESK_PASSWORD"),) ZENDESK_SUBDOMAIN: str = (Field(..., env="ZENDESK_SUBDOMAIN"),) diff --git a/src/wandbot/chat/chat.py b/src/wandbot/chat/chat.py index 3acea63..6e82e7d 100644 --- a/src/wandbot/chat/chat.py +++ b/src/wandbot/chat/chat.py @@ -24,421 +24,62 @@ print(f"WandBot: {response.answer}") print(f"Time taken: {response.time_taken}") """ - -import json -from typing import Any, Dict, List, Optional, Tuple - -from llama_index import ServiceContext -from llama_index.callbacks import ( - CallbackManager, - TokenCountingHandler, - WandbCallbackHandler, - trace_method, -) -from llama_index.chat_engine import ContextChatEngine -from llama_index.chat_engine.types import AgentChatResponse -from llama_index.indices.postprocessor import CohereRerank -from llama_index.llms import LLM, ChatMessage, MessageRole -from llama_index.llms.generic_utils import messages_to_history_str -from llama_index.memory import BaseMemory -from llama_index.postprocessor.types import BaseNodePostprocessor -from llama_index.schema import MetadataMode, NodeWithScore, QueryBundle -from llama_index.tools import ToolOutput -from weave.monitoring import StreamTable +from typing import List import wandb from wandbot.chat.config import ChatConfig -from wandbot.chat.prompts import load_chat_prompt, partial_format -from wandbot.chat.query_enhancer import CompleteQuery, QueryHandler -from wandbot.chat.retriever import ( - HybridRetriever, - LanguageFilterPostprocessor, - MetadataPostprocessor, - Retriever, -) +from wandbot.chat.rag import RAGPipeline, RAGPipelineOutput from wandbot.chat.schemas import ChatRequest, ChatResponse -from wandbot.utils import Timer, get_logger, load_service_context +from wandbot.database.schemas import QuestionAnswer +from wandbot.retriever import VectorStore +from wandbot.utils import Timer, get_logger +from weave.monitoring import StreamTable logger = get_logger(__name__) -def rebuild_full_prompt( - message_templates: List[ChatMessage], result: Dict[str, Any] -) -> str: - system_template = messages_to_history_str(message_templates[:-1]) - - query_str = result["question"] - - context = json.loads( - result.get("source_documents", '[{"text": "", "source": ""}]') - ) - - context_str = "" - for idx, item in enumerate(context): - context_str += f"source {idx+1}: " + item["source"] + "\n\n" - context_str += "*" * 120 + "\n\n" - context_str += item["text"] + "\n\n" - context_str += "*" * 120 + "\n\n" - context_str += "---\n\n" - - query_content = partial_format( - message_templates[-1].content, - query_str=query_str, - context_str=context_str, - ) - system_template += ( - f"\n\n{message_templates[-1].role}:\t{query_content}\n\n---\n\n" - ) - - return system_template - - -class WandbContextChatEngine(ContextChatEngine): - def __init__( - self, - retriever: HybridRetriever, - llm: LLM, - memory: BaseMemory, - prefix_messages: List[ChatMessage], - node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, - context_template: Optional[str] = None, - callback_manager: Optional[CallbackManager] = None, - ) -> None: - super().__init__( - retriever=retriever, - llm=llm, - memory=memory, - prefix_messages=prefix_messages, - node_postprocessors=node_postprocessors, - context_template=context_template, - callback_manager=callback_manager, - ) - self._retriever: HybridRetriever = retriever - - def _generate_context( - self, message: str, **kwargs - ) -> Tuple[str, List[NodeWithScore]]: - """Generate context information from a message.""" - - keywords = kwargs.get("keywords", []) - sub_queries = kwargs.get("sub_queries", []) - - query_nodes = self._retriever.retrieve( - message, is_avoid_query=kwargs.get("is_avoid_query") - ) - keywords_nodes = [] - sub_query_nodes = [] - - if keywords: - keywords_nodes = self._retriever.retrieve(" ".join(keywords)) - - if sub_queries: - for sub_query in sub_queries: - sub_query_nodes += self._retriever.retrieve(sub_query) - - nodes = query_nodes + keywords_nodes + sub_query_nodes - - for postprocessor in self._node_postprocessors: - nodes = postprocessor.postprocess_nodes( - nodes, query_bundle=QueryBundle(message) - ) - - context_str = "\n\n---\n\n".join( - [ - n.node.get_content(metadata_mode=MetadataMode.LLM).strip() - for n in nodes - ] - ) - - return context_str.strip(), nodes - - def _get_prefix_messages_with_context( - self, context_str: str - ) -> List[ChatMessage]: - """Get the prefix messages with context.""" - prefix_messages = self._prefix_messages - - context_str_w_sys_prompt = partial_format( - prefix_messages[-1].content, context_str=context_str - ) - return [ - *prefix_messages[:-1], - ChatMessage( - content=context_str_w_sys_prompt, - role=MessageRole.USER, - metadata={}, - ), - ] - - @trace_method("chat") - def chat( - self, - message: str, - chat_history: Optional[List[ChatMessage]] = None, - **kwargs, - ) -> AgentChatResponse: - context_str_template, nodes = self._generate_context( - message, - keywords=kwargs.get("keywords", []), - sub_queries=kwargs.get("sub_queries", []), - is_avoid_query=kwargs.get("is_avoid_query"), - ) - prefix_messages = self._get_prefix_messages_with_context( - context_str_template - ) - - prefix_messages[-1] = ChatMessage( - content=partial_format( - prefix_messages[-1].content, query_str=message - ), - role="user", - ) - - self._memory.put(prefix_messages[-1]) - all_messages = prefix_messages - chat_response = self._llm.chat(all_messages) - ai_message = chat_response.message - self._memory.put(ai_message) - - return AgentChatResponse( - response=str(chat_response.message.content), - sources=[ - ToolOutput( - tool_name="retriever", - content=str(prefix_messages[0]), - raw_input={"message": message}, - raw_output=prefix_messages[0], - ) - ], - source_nodes=nodes, - ) - - class Chat: """Class for handling chat interactions. Attributes: config: An instance of ChatConfig containing configuration settings. run: An instance of wandb.Run for logging experiment information. - wandb_callback: An instance of WandbCallbackHandler for handling Wandb callbacks. - token_counter: An instance of TokenCountingHandler for counting tokens. - callback_manager: An instance of CallbackManager for managing callbacks. - qa_prompt: A string representing the chat prompt. """ - def __init__(self, config: ChatConfig): + config: ChatConfig = ChatConfig() + + def __init__( + self, vector_store: VectorStore, config: ChatConfig | None = None + ): """Initializes the Chat instance. Args: config: An instance of ChatConfig containing configuration settings. """ - self.config = config + self.vector_store = vector_store + if config is not None: + self.config = config self.run = wandb.init( project=self.config.wandb_project, entity=self.config.wandb_entity, job_type="chat", ) self.run._label(repo="wandbot") - self.chat_table = StreamTable( - table_name="chat_logs", - project_name=self.config.wandb_project, - entity_name=self.config.wandb_entity, - # f"{self.config.wandb_entity}/{self.config.wandb_project}/chat_logs" - ) - - self.wandb_callback = WandbCallbackHandler() - self.token_counter = TokenCountingHandler() - self.callback_manager = CallbackManager( - [self.wandb_callback, self.token_counter] - ) - self.default_service_context = load_service_context( - llm=self.config.chat_model_name, - temperature=self.config.chat_temperature, - max_retries=self.config.max_retries, - embeddings_cache=str(self.config.embeddings_cache), - callback_manager=self.callback_manager, - ) - self.fallback_service_context = load_service_context( - llm=self.config.fallback_model_name, - temperature=self.config.chat_temperature, - max_retries=self.config.max_fallback_retries, - embeddings_cache=str(self.config.embeddings_cache), - callback_manager=self.callback_manager, - ) - - self.qa_prompt = load_chat_prompt(f_name=self.config.chat_prompt) - self.query_handler = QueryHandler() - self.retriever = Retriever( - run=self.run, - service_context=self.fallback_service_context, - callback_manager=self.callback_manager, - ) - - def _load_chat_engine( - self, - service_context: ServiceContext, - query_intent: str = "\n", - language: str = "en", - initial_k: int = 15, - top_k: int = 5, - ) -> WandbContextChatEngine: - """Loads the chat engine with the given model name and maximum retries. - - Args: - service_context: An instance of ServiceContext. - query_intent: A string representing the query intent. - language: A string representing the language. - initial_k: An integer representing the initial number of documents to retrieve. - top_k: An integer representing the number of documents to retrieve after reranking. - - Returns: - An instance of ChatEngine. - """ - - query_engine = self.retriever.load_query_engine( - language=language, - top_k=top_k, - is_avoid_query=True if "avoid" in query_intent.lower() else False, - ) - - self.qa_prompt = load_chat_prompt( - f_name=self.config.chat_prompt, - language_code=language, - query_intent=query_intent, - ) - chat_engine_kwargs = dict( - retriever=query_engine.retriever, - storage_context=self.retriever.storage_context, - service_context=service_context, - similarity_top_k=initial_k, - response_mode="compact", - node_postprocessors=[ - MetadataPostprocessor(), - LanguageFilterPostprocessor(languages=[language, "python"]), - CohereRerank(top_n=top_k, model="rerank-english-v2.0") - if language == "en" - else CohereRerank( - top_n=top_k, model="rerank-multilingual-v2.0" - ), - ], - prefix_messages=self.qa_prompt.message_templates, - ) - chat_engine = WandbContextChatEngine.from_defaults(**chat_engine_kwargs) + self.rag_pipeline = RAGPipeline(vector_store=vector_store) - return chat_engine + def _get_answer( + self, question: str, chat_history: List[QuestionAnswer] + ) -> RAGPipelineOutput: + history = [] + for item in chat_history: + history.append(("user", item.question)) + history.append(("assistant", item.answer)) - def format_response(self, result: Dict[str, Any]) -> Dict[str, Any]: - """Formats the response dictionary. + result = self.rag_pipeline(question, history) - Args: - result: A dictionary representing the response. - - Returns: - A formatted response dictionary. - """ - response = {} - if result.get("source_documents", None): - source_documents = [ - { - "source": doc.metadata["source"], - "text": doc.text, - } - for doc in result["source_documents"] - ] - else: - source_documents = [] - response["answer"] = result["answer"] - response["model"] = result["model"] - - if len(source_documents) and self.config.include_sources: - response["source_documents"] = json.dumps(source_documents) - response["sources"] = ",".join( - [doc["source"] for doc in source_documents] - ) - else: - response["source_documents"] = "" - response["sources"] = "" - - return response - - def get_response( - self, - service_context: ServiceContext, - query: str, - language: str, - chat_history: List[ChatMessage], - query_intent: str, - keywords: List[str] | None = None, - sub_queries: List[str] | None = None, - ) -> Dict[str, Any]: - chat_engine = self._load_chat_engine( - service_context=service_context, - language=language, - query_intent=query_intent, - ) - response = chat_engine.chat( - message=query, - chat_history=chat_history, - keywords=keywords, - sub_queries=sub_queries, - is_avoid_query=True if "avoid" in query_intent.lower() else False, - ) - result = { - "answer": response.response, - "source_documents": response.source_nodes, - "model": self.config.chat_model_name, - } return result - def get_answer( - self, - resolved_query: CompleteQuery, - **kwargs, - ) -> Dict[str, Any]: - """Gets the answer for the given query and chat history. - - Args: - resolved_query: An instance of ResolvedQuery representing the resolved query. - - Returns: - A dictionary representing the answer. - - """ - try: - result = self.get_response( - service_context=self.default_service_context, - query=resolved_query.condensed_query, - language=resolved_query.language, - chat_history=resolved_query.chat_history, - query_intent=resolved_query.intent_hints, - ) - except Exception as e: - logger.warning(f"{self.config.chat_model_name} failed with {e}") - logger.warning( - f"Falling back to {self.config.fallback_model_name} model" - ) - try: - result = self.get_response( - service_context=self.fallback_service_context, - query=resolved_query.cleaned_query, - language=resolved_query.language, - chat_history=resolved_query.chat_history, - query_intent=resolved_query.intent_hints, - ) - - except Exception as e: - logger.error( - f"{self.config.fallback_model_name} failed with {e}" - ) - result = { - "answer": "\uE058" - + " Sorry, there seems to be an issue with our LLM service. Please try again in some time.", - "source_documents": None, - "model": "None", - } - return self.format_response(result) - def __call__(self, chat_request: ChatRequest) -> ChatResponse: """Handles the chat request and returns the chat response. @@ -449,36 +90,20 @@ def __call__(self, chat_request: ChatRequest) -> ChatResponse: An instance of `ChatResponse` representing the chat response. """ try: - with Timer() as timer: - result = {} - resolved_query = self.query_handler(chat_request) - result = self.get_answer(resolved_query) - usage_stats = { - "total_tokens": self.token_counter.total_llm_token_count, - "prompt_tokens": self.token_counter.prompt_llm_token_count, - "completion_tokens": self.token_counter.completion_llm_token_count, - } - self.token_counter.reset_counts() - result.update( - dict( - **{ - "question": chat_request.question, - "time_taken": timer.elapsed, - "start_time": timer.start, - "end_time": timer.stop, - "application": chat_request.application, - }, - **usage_stats, - ) + result = self._get_answer( + chat_request.question, chat_request.chat_history or [] ) - self.run.log(usage_stats) - system_template = rebuild_full_prompt( - self.qa_prompt.message_templates, result - ) - result["system_prompt"] = system_template - self.chat_table.log(result) - return ChatResponse(**result) + result_dict = result.model_dump() + + usage_stats = { + "total_tokens": result.total_tokens, + "prompt_tokens": result.prompt_tokens, + "completion_tokens": result.completion_tokens, + } + result_dict.update({"application": chat_request.application}) + self.run.log(usage_stats) + return ChatResponse(**result_dict) except Exception as e: with Timer() as timer: result = { @@ -499,5 +124,4 @@ def __call__(self, chat_request: ChatRequest) -> ChatResponse: "end_time": timer.stop, } ) - usage_stats = {} return ChatResponse(**result) diff --git a/src/wandbot/chat/config.py b/src/wandbot/chat/config.py index 7be15d3..7bb851f 100644 --- a/src/wandbot/chat/config.py +++ b/src/wandbot/chat/config.py @@ -19,26 +19,13 @@ class ChatConfig(BaseSettings): - chat_model_name: str = "gpt-4-1106-preview" - max_retries: int = 2 - fallback_model_name: str = "gpt-3.5-turbo-1106" - max_fallback_retries: int = 6 - chat_temperature: float = 0.1 - chat_prompt: pathlib.Path = pathlib.Path("data/prompts/chat_prompt.json") + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) index_artifact: str = Field( "wandbot/wandbot-dev/wandbot_index:latest", env="WANDB_INDEX_ARTIFACT", validation_alias="wandb_index_artifact", ) - embeddings_cache: pathlib.Path = Field( - pathlib.Path("data/cache/embeddings"), env="EMBEDDINGS_CACHE_PATH" - ) - verbose: bool = False wandb_project: str | None = Field("wandbot_public", env="WANDB_PROJECT") wandb_entity: str | None = Field("wandbot", env="WANDB_ENTITY") - include_sources: bool = True - query_tokens_threshold: int = 1024 - - model_config = SettingsConfigDict( - env_file=".env", env_file_encoding="utf-8", extra="allow" - ) diff --git a/src/wandbot/chat/prompts.py b/src/wandbot/chat/prompts.py deleted file mode 100644 index 4f4f29c..0000000 --- a/src/wandbot/chat/prompts.py +++ /dev/null @@ -1,92 +0,0 @@ -"""This module provides functionality for loading chat prompts. - -The main function in this module is `load_chat_prompt`, which loads a chat prompt from a given JSON file. -The JSON file should contain two keys: "system_template" and "human_template", which correspond to the system and user messages respectively. - -Typical usage example: - - from wandbot.chat import prompts - - chat_prompt = prompts.load_chat_prompt('path_to_your_json_file.json') -""" - -import json -import logging -import pathlib -from typing import Union - -from llama_index import ChatPromptTemplate -from llama_index.llms import ChatMessage, MessageRole - -logger = logging.getLogger(__name__) - - -def partial_format(s, **kwargs): - # Manually parse the string and extract the field names - place_holders = set() - field_name = "" - in_field = False - for c in s: - if c == "{" and not in_field: - in_field = True - elif c == "}" and in_field: - place_holders.add(field_name) - field_name = "" - in_field = False - elif in_field: - field_name += c - replacements = {k: kwargs.get(k, "{" + k + "}") for k in place_holders} - - # Escape all curly braces - s = s.replace("{", "{{").replace("}", "}}") - - # Replace the placeholders - for k, v in replacements.items(): - s = s.replace("{{" + k + "}}", v) - - return s - - -ROLE_MAP = { - "system": MessageRole.SYSTEM, - "human": MessageRole.USER, - "assistant": MessageRole.ASSISTANT, -} - - -def load_chat_prompt( - f_name: Union[pathlib.Path, str] = None, - language_code: str = "en", - query_intent: str = "", -) -> ChatPromptTemplate: - """ - Loads a chat prompt from a given file. - - This function reads a JSON file specified by f_name and constructs a ChatPromptTemplate - object from the data. The JSON file should contain two keys: "system_template" and "human_template", - which correspond to the system and user messages respectively. - - Args: - f_name: A string or a pathlib.Path object representing the path to the JSON file. - If None, a default path is used. - - Returns: - A ChatPromptTemplate object constructed from the data in the JSON file. - """ - f_name = pathlib.Path(f_name) - - template = json.load(f_name.open("r")) - - human_template = partial_format( - template["messages"][-1]["human"], - language_code=language_code, - query_intent=query_intent, - ) - - messages = [] - for message in template["messages"][:-1]: - for k, v in message.items(): - messages.append(ChatMessage(role=ROLE_MAP[k], content=v)) - messages.append(ChatMessage(role=MessageRole.USER, content=human_template)) - prompt = ChatPromptTemplate(messages) - return prompt diff --git a/src/wandbot/chat/query_enhancer.py b/src/wandbot/chat/query_enhancer.py deleted file mode 100644 index cabc480..0000000 --- a/src/wandbot/chat/query_enhancer.py +++ /dev/null @@ -1,508 +0,0 @@ -import enum -import json -import os -import re -from typing import List, Optional - -import cohere -import instructor -import openai -import tiktoken -from llama_index.llms import ChatMessage, MessageRole -from llama_index.llms.generic_utils import messages_to_history_str -from openai import OpenAI -from pydantic import BaseModel, Field -from pydantic.v1 import BaseModel as BaseModelV1 -from pydantic_settings import BaseSettings, SettingsConfigDict -from tenacity import retry, stop_after_attempt, wait_random_exponential - -from wandbot.chat.schemas import ChatRequest -from wandbot.database.schemas import QuestionAnswer -from wandbot.utils import FastTextLangDetect, get_logger - -logger = get_logger(__name__) - - -CONDENSE_PROMPT_SYSTEM_TEMPLATE = ( - "Given the following conversation between a user and an AI assistant and a follow up " - "question from user, rephrase the follow up question to be a standalone question. Ensure " - "that the standalone question summarizes the conversation and completes the follow up " - "question with all the necessary context." - + """ -Chat History: - {chat_history} -""" -) -CONDENSE_PROMPT_USER_TEMPLATE = """Follow Up Input: {question} - Standalone question:""" - - -class StandaloneQuestion(BaseModel): - question: str = Field( - ..., - description="A standalone question that summarizes the conversation and completes the follow up question with " - "all the necessary context", - ) - - -class Labels(str, enum.Enum): - UNRELATED = "unrelated" - CODE_TROUBLESHOOTING = "code_troubleshooting" - INTEGRATIONS = "integrations" - PRODUCT_FEATURES = "product_features" - SALES_AND_GTM_RELATED = "sales_and_gtm_related" - BEST_PRACTICES = "best_practices" - COURSE_RELATED = "course_related" - NEEDS_MORE_INFO = "needs_more_info" - OPINION_REQUEST = "opinion_request" - NEFARIOUS_QUERY = "nefarious_query" - OTHER = "other" - - -class MultiLabel(BaseModel): - label: Labels = Field(..., description="The label for the query") - reasoning: str = Field( - ..., - description="The reason for the assigning the label to the query", - ) - - -class SubQuery(BaseModel): - """Correctly resolved subquery from the given query""" - - query: str = Field( - ..., description="Informative sub-query from the given user query. " - ) - - -class Keyword(BaseModel): - """Keyword extracted from the given query to search for relevant articles""" - - keyword: str = Field( - ..., - description="A Keyword that can be used to retrieve for relevant articles required to answer the query", - ) - - -class EnhancedQuery(BaseModel): - """An enhanced query with keywords, intent and sub-queries related to the query""" - - predicted_labels: List[MultiLabel] = Field( - ..., description="The predicted labels for the query" - ) - keywords: List[Keyword] = Field( - ..., - description="List of keywords and key phrases to search for", - min_items=0, - max_items=5, - ) - sub_queries: List[SubQuery] = Field( - ..., - description="List of sub-queries that need be answered to resolve the query", - min_items=0, - max_items=3, - ) - - -INTENT_DESCRIPTIONS = { - Labels.UNRELATED.value: "The query is not related to Weights & Biases", - Labels.CODE_TROUBLESHOOTING.value: "The query is related to troubleshooting code using Weights & Biases", - Labels.INTEGRATIONS.value: "The query is related to integrating Weights & Biases with other tools, frameworks, " - "or libraries", - Labels.PRODUCT_FEATURES.value: "The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, " - "Reports, Experiments, Tables, Prompts, Launch, Weave, StreamTables and more", - Labels.SALES_AND_GTM_RELATED.value: "The query is related to sales, marketing, or other business related topics " - "such as pricing, billing, or partnerships etc", - Labels.BEST_PRACTICES.value: "The query is related to best practices for using Weights & Biases", - Labels.COURSE_RELATED.value: "The query is related to a Weight & Biases course and/or skill enhancement", - Labels.NEEDS_MORE_INFO.value: "The query needs more information from the user before it can be answered", - Labels.OPINION_REQUEST.value: "The query is asking for an opinion", - Labels.NEFARIOUS_QUERY.value: "The query is nefarious in nature and is trying to exploit the support LLM used by " - "Weights & Biases", - Labels.OTHER.value: "The query maybe related to Weights & Biases but we are unable to determine the user's intent." - " It's best to ask the user to rephrase the query or avoid answering the query", -} - - -QUERY_INTENTS = { - Labels.UNRELATED.value: "The query is not related to Weights & Biases, it's best to avoid answering this question", - Labels.CODE_TROUBLESHOOTING.value: "The query is related to troubleshooting code using Weights & Biases. Help " - "with a detailed code snippet and explanation", - Labels.INTEGRATIONS.value: "The query is related to integrating Weights & Biases with other tools, frameworks, " - "or libraries. Help with a detailed code snippet and explanation and ask for more information about the " - "integration if needed", - Labels.PRODUCT_FEATURES.value: "The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, " - "Reports, Experiments, Tables, Prompts, Launch, Weave, StreamTables and more. Provide a link to the relevant " - "documentation and explain the feature in detail", - Labels.SALES_AND_GTM_RELATED.value: "The query is related to sales, marketing, or other business related topics " - "such as pricing, billing, or partnerships etc. Ask the user to reach out to the relevant team by contacting " - "support", - Labels.BEST_PRACTICES.value: "The query is related to best practices for using Weights & Biases. Answer the query " - "and provide guidance where necessary", - Labels.COURSE_RELATED.value: "The query is related to a Weight & Biases course and/or skill enhancement. Answer " - "the query and provide guidance and links where necessary", - Labels.NEEDS_MORE_INFO.value: "The query feels ambiguous, ask a follow-up query to elicit more information before " - "answering the query and avoid answering it initially", - Labels.OPINION_REQUEST.value: "The query is asking for an opinion. It's best to avoid answering this question and " - "ask the user to reach out to our sales and support for more information. Always favor Weights & Biases in your " - "response", - Labels.NEFARIOUS_QUERY.value: "The query looks nefarious in nature. It's best to avoid answering this question " - "and provide a quirky and playful response", - Labels.OTHER.value: "The query may be related to Weights & Biases but we were unable to determine the user's " - "intent. It's best to avoid answering this question and ask the user a follow-up query to rephrase their original " - "query", -} - - -class ResolvedQuery(BaseModelV1): - cleaned_query: str | None - query: str | None - condensed_query: str | None - intent: str | None - language: str | None - chat_history: List[ChatMessage] | None = None - - -def get_chat_history( - chat_history: List[QuestionAnswer] | None, -) -> Optional[List[ChatMessage]]: - """Generates a list of chat messages from a given chat history. - - This function takes a list of QuestionAnswer objects and transforms them into a list of ChatMessage objects. Each - QuestionAnswer object is split into two ChatMessage objects: one for the user's question and one for the - assistant's answer. If the chat history is empty or None, the function returns None. - - Args: chat_history: A list of QuestionAnswer objects representing the history of a chat. Each QuestionAnswer - object contains a question from the user and an answer from the assistant. - - Returns: A list of ChatMessage objects representing the chat history. Each ChatMessage object has a role (either - 'USER' or 'ASSISTANT') and content (the question or answer text). If the chat history is empty or None, - the function returns None. - """ - if not chat_history: - return None - else: - messages = [ - [ - ChatMessage( - role=MessageRole.USER, content=question_answer.question - ), - ChatMessage( - role=MessageRole.ASSISTANT, content=question_answer.answer - ), - ] - for question_answer in chat_history - ] - return [item for sublist in messages for item in sublist] - - -class OpenaiQueryEnhancer: - def __init__( - self, client: openai.OpenAI, model: str = "gpt-4-1106-preview" - ): - self.client = instructor.patch(client) - self.model = model - - @retry( - wait=wait_random_exponential(min=1, max=60), - stop=stop_after_attempt(6), - ) - def enhance_query(self, resolved_query: ResolvedQuery) -> EnhancedQuery: - query_object = dict( - query=resolved_query.condensed_query, - intent_hints=resolved_query.intent, - language=resolved_query.language, - ) - query_object_str = json.dumps(query_object, indent=2) - - return self.client.chat.completions.create( - model=self.model, - response_model=EnhancedQuery, - messages=[ - { - "role": "system", - "content": ( - "You are a Weights & Biases support manager. Your goal is to enhance the user query by adding " - "the following information to the query:\n" - "1. Tag and classify query. Here are the descriptions of the available labels\n" - + "\n".join( - [ - f"{label}: {description}" - for label, description in INTENT_DESCRIPTIONS.items() - ] - ) - + "\n" - "2. A list of keywords and key phrases to search for relevant articles to answer the query.\n" - "3. A list of sub-queries that need be answered to answer the query\n\n" - "**Note**: Do not fill in the keywords and sub-queries if the query is nefarious, " - "opinion related or unrelated to Weights & Biases\n" - ), - }, - { - "role": "user", - "content": f"Enhance the following user query related to Weights & Biases:\n{query_object_str}", - }, - ], - ) # type: ignore - - def condense_question( - self, chat_history: List[ChatMessage], latest_message: str - ) -> StandaloneQuestion: - """Condense a conversation history and latest user message to a standalone question.""" - if not chat_history or len(chat_history) == 0: - return StandaloneQuestion(question=latest_message) - - chat_history_str = messages_to_history_str(chat_history) - logger.debug(chat_history_str) - - return self.client.chat.completions.create( - model=self.model, - response_model=StandaloneQuestion, - messages=[ - { - "role": "system", - "content": CONDENSE_PROMPT_SYSTEM_TEMPLATE.format( - chat_history=chat_history_str, - ), - }, - { - "role": "user", - "content": CONDENSE_PROMPT_USER_TEMPLATE.format( - question=latest_message - ), - }, - ], - ) # type: ignore - - def __call__(self, resolved_query: ResolvedQuery) -> EnhancedQuery: - resolved_query.condensed_query = self.condense_question( - resolved_query.chat_history, resolved_query.cleaned_query - ).question - enhanced_query = self.enhance_query(resolved_query) - logger.debug(f"Openai query enhancements: {enhanced_query}") - return enhanced_query - - -class QueryHandlerConfig(BaseSettings): - default_query_clf_model: str = Field( - ..., - description="The name of the model to use for query classification", - env="DEFAULT_QUERY_CLF_MODEL", - validation_alias="default_query_clf_model", - ) - fallback_query_clf_model: str = Field( - "gpt-4-1106-preview", - description="The name of the fallback model to use for query classification", - ) - tokenizer: str = Field( - "cl100k_base", - description="The name of the tokenizer to use for query classification", - ) - bot_name_pattern: str = Field( - r"<@U[A-Z0-9]+>|@[a-zA-Z0-9\s]+\([a-zA-Z0-9\s]+\)|@[a-zA-Z0-9\s]+", - description="The regex pattern to use for detecting bot names in queries", - ) - - model_config = SettingsConfigDict( - env_file=".env", - env_file_encoding="utf-8", - extra="allow", - ) - - -class CohereQueryClassifier: - def __init__(self, client: cohere.Client, model: str): - self.client = client - self.model = model - - def __call__(self, query: str) -> List[str]: - response = self.client.classify( - model=self.model, - inputs=[query], - ) - logger.debug( - f"Cohere query classifications: {response.classifications}" - ) - return response.classifications[0].predictions - - -class CompleteQuery(BaseModelV1): - initial_query: str = Field( - ..., - description="The initial user query", - ) - cleaned_query: str | None = Field( - ..., - description="The user query cleaned of bot names and other references", - ) - condensed_query: str | None = Field( - ..., - description="The cleaned user query condensed to a standalone question", - ) - intent_hints: str | None = Field( - None, - description="The descriptions of the intents related to the query", - ) - language: str | None = Field( - None, - description="The language of the query", - ) - chat_history: List[ChatMessage] | None = Field( - None, - description="The chat history of the query", - ) - keywords: List[str] | None = Field( - None, - description="The keywords extracted and related to the query", - ) - sub_queries: List[str] | None = Field( - None, - description="The sub-queries extracted from the query", - ) - - -class QueryHandler: - def __init__(self, config: QueryHandlerConfig | None = None): - self.config = ( - config - if isinstance(config, QueryHandlerConfig) - else QueryHandlerConfig() - ) - self.tokenizer = tiktoken.get_encoding(self.config.tokenizer) - self.bot_name_pattern = re.compile(self.config.bot_name_pattern) - self.cohere_client = cohere.Client(os.environ["COHERE_API_KEY"]) - self.lang_client = FastTextLangDetect() - self.openai_client = OpenAI() - self.query_classifier = CohereQueryClassifier( - client=self.cohere_client, model=self.config.default_query_clf_model - ) - self.query_enhancer = OpenaiQueryEnhancer( - client=self.openai_client, - model=self.config.fallback_query_clf_model, - ) - - def classify(self, query: str) -> List[str]: - response = self.query_classifier(query) - if not response or len(response) < 1: - response = ["other"] - return response - - def detect_language(self, query: str) -> str: - lang_code = self.lang_client.detect_language(query) - return lang_code - - def clean_query(self, query: str) -> str: - cleaned_query = self.bot_name_pattern.sub("", query).strip() - return cleaned_query - - def describe_query(self, query: str) -> str: - classifications = self.classify(query) - descriptions = [] - if not classifications: - return "- " + QUERY_INTENTS["other"] - - for classification in classifications: - description = QUERY_INTENTS.get(classification, "") - descriptions.append(description) - descriptions = "\n- ".join(descriptions) - return descriptions - - def validate_and_format_question(self, question: str) -> str: - """Validates and formats the given question. - - Args: - question: A string representing the question to validate and format. - - Returns: - A string representing the validated and formatted question. - - Raises: - ValueError: If the question is too long. - """ - question = " ".join(question.strip().split()) - question = self.clean_query(question) - if len(self.tokenizer.encode(question)) > 1024: - raise ValueError( - f"Question is too long. Please rephrase your question to be shorter than {1024 * 3 // 4} words." - ) - return question - - @staticmethod - def combine_query_objects( - resolved_query: ResolvedQuery, enhanced_query: EnhancedQuery - ) -> CompleteQuery: - descriptions = [] - if not enhanced_query.predicted_labels: - intent_hints = "- " + QUERY_INTENTS["other"] - else: - for classification in enhanced_query.predicted_labels: - description = QUERY_INTENTS.get(classification.label, "") - descriptions.append(description) - intent_hints = "\n- ".join(descriptions) - - keywords = [keyword.keyword for keyword in enhanced_query.keywords] - sub_queries = [ - sub_query.query for sub_query in enhanced_query.sub_queries - ] - return CompleteQuery( - initial_query=resolved_query.query, - cleaned_query=resolved_query.cleaned_query, - condensed_query=resolved_query.condensed_query, - intent_hints=intent_hints, - language=resolved_query.language, - chat_history=resolved_query.chat_history, - keywords=keywords, - sub_queries=sub_queries, - ) - - def __call__(self, chat_request: ChatRequest) -> CompleteQuery: - cleaned_query = self.validate_and_format_question(chat_request.question) - chat_history = get_chat_history(chat_request.chat_history) - language = self.detect_language(cleaned_query) - if language == "en": - intent = self.describe_query( - cleaned_query, - ) - else: - intent = ( - "\n- " - + QUERY_INTENTS["other"] - + " because the query is not in English" - ) - resolved_query = ResolvedQuery( - cleaned_query=cleaned_query, - query=chat_request.question, - intent=intent, - language=language, - chat_history=chat_history, - ) - - logger.debug(f"Resolved query: {resolved_query}") - - enhanced_query = self.query_enhancer(resolved_query) - - logger.debug(f"Enhanced query: {enhanced_query}") - - return self.combine_query_objects(resolved_query, enhanced_query) - - -def main(): - from wandbot.utils import Timer - - with Timer() as timer: - config = QueryHandlerConfig() - logger.info(config) - query_handler = QueryHandler(config=QueryHandlerConfig()) - chat_request = ChatRequest( - question="@wandbot (beta)When I execute run, where should I put requirement.txt in order to capture job?", - chat_history=[], - language="en", - application="slack", - ) - complete_query = query_handler(chat_request) - print(complete_query.json(indent=2)) - print(f"Elapsed time: {timer.elapsed:.2f} seconds") - - -if __name__ == "__main__": - main() diff --git a/src/wandbot/chat/rag.py b/src/wandbot/chat/rag.py new file mode 100644 index 0000000..138f346 --- /dev/null +++ b/src/wandbot/chat/rag.py @@ -0,0 +1,103 @@ +import datetime +from typing import List, Tuple + +from langchain_community.callbacks import get_openai_callback +from pydantic import BaseModel +from wandbot.rag.query_handler import QueryEnhancer +from wandbot.rag.response_synthesis import ResponseSynthesizer +from wandbot.rag.retrieval import FusionRetrieval +from wandbot.retriever import VectorStore +from wandbot.utils import Timer, get_logger + +logger = get_logger(__name__) + + +def get_stats_dict_from_token_callback(token_callback): + return { + "total_tokens": token_callback.total_tokens, + "prompt_tokens": token_callback.prompt_tokens, + "completion_tokens": token_callback.completion_tokens, + "successful_requests": token_callback.successful_requests, + } + + +def get_stats_dict_from_timer(timer): + return { + "start_time": timer.start, + "end_time": timer.stop, + "time_taken": timer.elapsed, + } + + +class RAGPipelineOutput(BaseModel): + question: str + answer: str + sources: str + source_documents: str + system_prompt: str + model: str + total_tokens: int + prompt_tokens: int + completion_tokens: int + time_taken: float + start_time: datetime.datetime + end_time: datetime.datetime + + +class RAGPipeline: + def __init__( + self, + vector_store: VectorStore, + top_k: int = 15, + search_type: str = "mmr", + ): + self.vector_store = vector_store + self.query_enhancer = QueryEnhancer() + self.retrieval = FusionRetrieval( + vector_store=vector_store, top_k=top_k, search_type=search_type + ) + self.response_synthesizer = ResponseSynthesizer() + + def __call__( + self, question: str, chat_history: List[Tuple[str, str]] | None = None + ): + if chat_history is None: + chat_history = [] + + with get_openai_callback() as query_enhancer_cb, Timer() as query_enhancer_tb: + enhanced_query = self.query_enhancer.chain.invoke( + {"query": question, "chat_history": chat_history} + ) + + with Timer() as retrieval_tb: + retrieval_results = self.retrieval.chain.invoke(enhanced_query) + + with get_openai_callback() as response_cb, Timer() as response_tb: + response = self.response_synthesizer.chain.invoke(retrieval_results) + + output = RAGPipelineOutput( + question=enhanced_query["standalone_query"], + answer=response["response"], + sources="\n".join( + [ + item.metadata["source"] + for item in retrieval_results["context"] + ] + ), + source_documents=response["context_str"], + system_prompt=response["response_prompt"], + model=response["response_model"], + total_tokens=query_enhancer_cb.total_tokens + + response_cb.total_tokens, + prompt_tokens=query_enhancer_cb.prompt_tokens + + response_cb.prompt_tokens, + completion_tokens=query_enhancer_cb.completion_tokens + + response_cb.completion_tokens, + time_taken=query_enhancer_tb.elapsed + + retrieval_tb.elapsed + + response_tb.elapsed, + start_time=query_enhancer_tb.start, + end_time=response_tb.stop, + ) + + return output diff --git a/src/wandbot/chat/retriever.py b/src/wandbot/chat/retriever.py deleted file mode 100644 index b8d3ee0..0000000 --- a/src/wandbot/chat/retriever.py +++ /dev/null @@ -1,421 +0,0 @@ -import os -import pathlib -from typing import Any, Dict, List, Optional - -import requests -from llama_index import ( - QueryBundle, - ServiceContext, - StorageContext, - load_index_from_storage, -) -from llama_index.callbacks import CallbackManager, CBEventType, EventPayload -from llama_index.core.base_retriever import BaseRetriever -from llama_index.postprocessor import CohereRerank -from llama_index.postprocessor.types import BaseNodePostprocessor -from llama_index.query_engine import RetrieverQueryEngine -from llama_index.response_synthesizers import BaseSynthesizer, ResponseMode -from llama_index.retrievers import BM25Retriever -from llama_index.schema import NodeWithScore, QueryType, TextNode -from llama_index.vector_stores import FaissVectorStore -from llama_index.vector_stores.simple import DEFAULT_VECTOR_STORE, NAMESPACE_SEP -from llama_index.vector_stores.types import DEFAULT_PERSIST_FNAME -from pydantic import Field -from pydantic_settings import BaseSettings, SettingsConfigDict - -import wandb -from wandbot.utils import ( - create_no_result_dummy_node, - get_logger, - load_service_context, -) - -logger = get_logger(__name__) - - -class LanguageFilterPostprocessor(BaseNodePostprocessor): - """Language-based Node processor.""" - - languages: List[str] = ["en", "python"] - min_result_size: int = 10 - - @classmethod - def class_name(cls) -> str: - return "LanguageFilterPostprocessor" - - def _postprocess_nodes( - self, - nodes: List[NodeWithScore], - query_bundle: Optional[QueryBundle] = None, - ) -> List[NodeWithScore]: - """Postprocess nodes.""" - - new_nodes = [] - for node in nodes: - if node.metadata["language"] in self.languages: - new_nodes.append(node) - - if len(new_nodes) < self.min_result_size: - return new_nodes + nodes[: self.min_result_size - len(new_nodes)] - - return new_nodes - - -class MetadataPostprocessor(BaseNodePostprocessor): - """Metadata-based Node processor.""" - - min_result_size: int = 10 - include_tags: List[str] | None = None - exclude_tags: List[str] | None = None - - @classmethod - def class_name(cls) -> str: - return "MetadataPostprocessor" - - def _postprocess_nodes( - self, - nodes: List[NodeWithScore], - query_bundle: Optional[QueryBundle] = None, - ) -> List[NodeWithScore]: - """Postprocess nodes.""" - if not self.include_tags and not self.exclude_tags: - return nodes - new_nodes = [] - for node in nodes: - normalized_tags = [ - tag.lower().strip() for tag in node.metadata["tags"] - ] - if self.include_tags: - normalized_include_tags = [ - tag.lower().strip() for tag in self.include_tags - ] - if not set(normalized_include_tags).issubset( - set(normalized_tags) - ): - continue - if self.exclude_tags: - normalized_exclude_tags = [ - tag.lower().strip() for tag in self.exclude_tags - ] - if set(normalized_exclude_tags).issubset(set(normalized_tags)): - continue - new_nodes.append(node) - if len(new_nodes) < self.min_result_size: - dummy_node = create_no_result_dummy_node() - new_nodes.extend( - [dummy_node] * (self.min_result_size - len(new_nodes)) - ) - return new_nodes - - -class YouRetriever(BaseRetriever): - """You retriever.""" - - def __init__( - self, - api_key: Optional[str] = None, - similarity_top_k: int = 10, - callback_manager: Optional[CallbackManager] = None, - ) -> None: - """Init params.""" - self._api_key = api_key or os.environ["YOU_API_KEY"] - self.similarity_top_k = ( - similarity_top_k if similarity_top_k <= 20 else 20 - ) - super().__init__(callback_manager) - - def _retrieve(self, query_bundle: QueryBundle) -> List[NodeWithScore]: - """Retrieve.""" - try: - headers = {"X-API-Key": self._api_key} - url = "https://api.ydc-index.io/search" - - querystring = { - "query": "Weights & Biases, W&B, wandb or Weave " - + query_bundle.query_str, - "num_web_results": self.similarity_top_k, - } - response = requests.get(url, headers=headers, params=querystring) - if response.status_code != 200: - return [] - else: - results = response.json() - - search_hits = [ - ( - "\n".join(hit["snippets"]), - { - "source": hit["url"], - "language": "en", - "description": hit["description"], - "title": hit["title"], - "tags": ["you.com"], - }, - ) - for hit in results["hits"] - ] - return [ - NodeWithScore( - node=TextNode(text=s[0], metadata=s[1]), - score=1.0, - ) - for s in search_hits - ] - except Exception as e: - return [] - - -class HybridRetriever(BaseRetriever): - def __init__( - self, - index, - storage_context, - similarity_top_k: int = 20, - ): - self.index = index - self.storage_context = storage_context - - self.vector_retriever = self.index.as_retriever( - similarity_top_k=similarity_top_k, - storage_context=self.storage_context, - ) - self.bm25_retriever = BM25Retriever.from_defaults( - docstore=self.index.docstore, - similarity_top_k=similarity_top_k, - ) - self.you_retriever = YouRetriever( - api_key=os.environ.get("YOU_API_KEY"), - similarity_top_k=similarity_top_k, - ) - super().__init__() - - def _retrieve(self, query: QueryBundle, **kwargs): - bm25_nodes = self.bm25_retriever.retrieve(query) - vector_nodes = self.vector_retriever.retrieve(query) - you_nodes = ( - self.you_retriever.retrieve(query) - if not kwargs.get("is_avoid_query", False) - else [] - ) - - # combine the two lists of nodes - all_nodes = [] - node_ids = set() - for n in bm25_nodes + vector_nodes + you_nodes: - if n.node.node_id not in node_ids: - all_nodes.append(n) - node_ids.add(n.node.node_id) - return all_nodes - - def retrieve( - self, str_or_query_bundle: QueryType, **kwargs - ) -> List[NodeWithScore]: - self._check_callback_manager() - - if isinstance(str_or_query_bundle, str): - query_bundle = QueryBundle(str_or_query_bundle) - else: - query_bundle = str_or_query_bundle - with self.callback_manager.as_trace("query"): - with self.callback_manager.event( - CBEventType.RETRIEVE, - payload={EventPayload.QUERY_STR: query_bundle.query_str}, - ) as retrieve_event: - nodes = self._retrieve(query_bundle, **kwargs) - retrieve_event.on_end( - payload={EventPayload.NODES: nodes}, - ) - return nodes - - -class RetrieverConfig(BaseSettings): - index_artifact: str = Field( - "wandbot/wandbot-dev/wandbot_index:latest", - env="WANDB_INDEX_ARTIFACT", - validation_alias="wandb_index_artifact", - ) - embeddings_cache: pathlib.Path = Field( - pathlib.Path("data/cache/embeddings"), env="EMBEDDINGS_CACHE_PATH" - ) - top_k: int = Field( - default=5, - env="RETRIEVER_TOP_K", - ) - similarity_top_k: int = Field( - default=20, - env="RETRIEVER_SIMILARITY_TOP_K", - ) - language: str = Field( - default="en", - env="RETRIEVER_LANGUAGE", - ) - model_config = SettingsConfigDict( - env_file=".env", env_file_encoding="utf-8", extra="allow" - ) - - -class WandbRetrieverQueryEngine(RetrieverQueryEngine): - def __init__( - self, - retriever: HybridRetriever, - response_synthesizer: Optional[BaseSynthesizer] = None, - node_postprocessors: Optional[List[BaseNodePostprocessor]] = None, - callback_manager: Optional[CallbackManager] = None, - ) -> None: - super().__init__( - retriever=retriever, - response_synthesizer=response_synthesizer, - node_postprocessors=node_postprocessors, - callback_manager=callback_manager, - ) - - def retrieve( - self, query_bundle: QueryBundle, **kwargs - ) -> List[NodeWithScore]: - nodes = self._retriever.retrieve(query_bundle, **kwargs) - return self._apply_node_postprocessors(nodes, query_bundle=query_bundle) - - -class Retriever: - def __init__( - self, - config: RetrieverConfig | None = None, - run: wandb.wandb_sdk.wandb_run.Run | None = None, - service_context: ServiceContext | None = None, - callback_manager: CallbackManager | None = None, - ): - self.config = ( - config if isinstance(config, RetrieverConfig) else RetrieverConfig() - ) - self.run = run - self.service_context = ( - service_context - if service_context - else load_service_context( - embeddings_cache=str(self.config.embeddings_cache), - callback_manager=callback_manager, - ) - ) - - self.storage_context = self.load_storage_context_from_artifact( - artifact_url=self.config.index_artifact - ) - - self.index = load_index_from_storage(self.storage_context) - self._retriever = HybridRetriever( - index=self.index, - similarity_top_k=self.config.similarity_top_k, - storage_context=self.storage_context, - ) - self.is_avoid_query: bool | None = None - - def load_storage_context_from_artifact( - self, artifact_url: str - ) -> StorageContext: - """Loads the storage context from the given artifact URL. - - Args: - artifact_url: A string representing the URL of the artifact. - - Returns: - An instance of StorageContext. - """ - artifact = self.run.use_artifact(artifact_url) - artifact_dir = artifact.download() - index_path = f"{artifact_dir}/{DEFAULT_VECTOR_STORE}{NAMESPACE_SEP}{DEFAULT_PERSIST_FNAME}" - logger.debug(f"Loading index from {index_path}") - storage_context = StorageContext.from_defaults( - vector_store=FaissVectorStore.from_persist_path(index_path), - persist_dir=artifact_dir, - ) - return storage_context - - def load_query_engine( - self, - top_k: int | None = None, - language: str | None = None, - include_tags: List[str] | None = None, - exclude_tags: List[str] | None = None, - is_avoid_query: bool | None = None, - ) -> WandbRetrieverQueryEngine: - top_k = top_k or self.config.top_k - language = language or self.config.language - - if is_avoid_query is not None: - self.is_avoid_query = is_avoid_query - - node_postprocessors = [ - MetadataPostprocessor( - include_tags=include_tags, - exclude_tags=exclude_tags, - min_result_size=top_k, - ), - LanguageFilterPostprocessor( - languages=[language, "python"], min_result_size=top_k - ), - CohereRerank(top_n=top_k, model="rerank-english-v2.0") - if language == "en" - else CohereRerank(top_n=top_k, model="rerank-multilingual-v2.0"), - ] - query_engine = WandbRetrieverQueryEngine.from_args( - retriever=self._retriever, - node_postprocessors=node_postprocessors, - response_mode=ResponseMode.NO_TEXT, - service_context=self.service_context, - ) - return query_engine - - def retrieve( - self, - query: str, - language: str | None = None, - top_k: int | None = None, - include_tags: List[str] | None = None, - exclude_tags: List[str] | None = None, - is_avoid_query: bool | None = False, - ): - """Retrieves the top k results from the index for the given query. - - Args: - query: A string representing the query. - language: A string representing the language of the query. - top_k: An integer representing the number of top results to retrieve. - include_tags: A list of strings representing the tags to include in the results. - exclude_tags: A list of strings representing the tags to exclude from the results. - - Returns: - A list of dictionaries representing the retrieved results. - """ - top_k = top_k or self.config.top_k - language = language or self.config.language - - retrieval_engine = self.load_query_engine( - top_k=top_k, - language=language, - include_tags=include_tags, - exclude_tags=exclude_tags, - ) - - avoid_query = self.is_avoid_query or is_avoid_query - - query_bundle = QueryBundle(query_str=query) - results = retrieval_engine.retrieve( - query_bundle, is_avoid_query=bool(avoid_query) - ) - - outputs = [ - { - "text": node.get_text(), - "metadata": node.metadata, - "score": node.get_score(), - } - for node in results - ] - self.is_avoid_query = None - return outputs - - def __call__(self, query: str, **kwargs) -> List[Dict[str, Any]]: - retrievals = self.retrieve(query, **kwargs) - logger.debug(f"Retrieved {len(retrievals)} results.") - logger.debug(f"Retrieval: {retrievals[0]}") - return retrievals diff --git a/src/wandbot/database/client.py b/src/wandbot/database/client.py index e3dcfd7..8052e33 100644 --- a/src/wandbot/database/client.py +++ b/src/wandbot/database/client.py @@ -12,6 +12,7 @@ chat_thread = db_client.get_chat_thread(application='app1', thread_id='123') question_answer = db_client.create_question_answer(question_answer=QuestionAnswerCreateSchema()) """ + import json from typing import Any, List diff --git a/src/wandbot/evaluation/eval/__main__.py b/src/wandbot/evaluation/eval/__main__.py deleted file mode 100644 index 3302f59..0000000 --- a/src/wandbot/evaluation/eval/__main__.py +++ /dev/null @@ -1,224 +0,0 @@ -import json -from typing import Any, Hashable - -import nest_asyncio -import pandas as pd -import requests -from llama_index import ServiceContext -from llama_index.llms import OpenAI -from ragas import metrics -from tenacity import retry, stop_after_attempt, wait_random_exponential -from tqdm import tqdm - -from wandbot.evaluation.eval.correctness import ( - CORRECTNESS_EVAL_TEMPLATE, - WandbCorrectnessEvaluator, -) -from wandbot.evaluation.eval.factfulness import ( - FACTFULNESS_EVAL_TEMPLATE, - WandbFactfulnessEvaluator, -) -from wandbot.evaluation.eval.relevancy import ( - RELEVANCY_EVAL_TEMPLATE, - WandbRelevancyEvaluator, -) -from wandbot.utils import cachew, get_logger - -logger = get_logger(__name__) - -nest_asyncio.apply() - - -EVAL_CACHE = "data/cache/eval_cache/cache.db" -service_context = ServiceContext.from_defaults(llm=OpenAI("gpt-4-1106-preview")) -correctness_evaluator = WandbCorrectnessEvaluator( - service_context=service_context, - eval_template=CORRECTNESS_EVAL_TEMPLATE, -) -faithfulness_evaluator = WandbFactfulnessEvaluator( - service_context=service_context, - eval_template=FACTFULNESS_EVAL_TEMPLATE, -) -relevancy_evaluator = WandbRelevancyEvaluator( - service_context=service_context, - eval_template=RELEVANCY_EVAL_TEMPLATE, -) - - -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_answer(question: str, application: str = "api-eval-bharat") -> str: - url = "http://0.0.0.0:8000/query" - payload = { - "question": question, - "language": "en", - "application": application, - } - response = requests.post(url, data=json.dumps(payload)) - response = response.json() - return json.dumps(response) - - -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_eval_record(row_str: str, application: str = "api-eval-bharat") -> str: - row = json.loads(row_str) - response = get_answer(row["question"], application=application) - response = json.loads(response) - response["ground_truths"] = row["answer"] - response["reference_notes"] = row["notes"] - response["contexts"] = [ - "\nSource: " + source["source"] + " \n " + source["text"] - for source in json.loads(response["source_documents"]) - ] - response = json.dumps(response) - return response - - -def parse_answer_eval(metric: str, row: dict[str, Any]) -> dict[str, Any]: - result = { - f"{metric}_score": row.get("score"), - f"{metric}_result": row.get("passing"), - f"{metric}_reason": row.get("feedback"), - } - return result - - -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_answer_correctness(row_str: str) -> str: - row = json.loads(row_str) - result = correctness_evaluator.evaluate( - query=row["question"], - response=row["answer"], - reference=row["ground_truths"], - contexts=row["contexts"], - reference_notes=row["reference_notes"], - ) - result = parse_answer_eval("answer_correctness", result.dict()) - result[ - "answer_correctness_score_(ragas)" - ] = metrics.answer_correctness.score_single(row) - result = json.dumps(result) - return result - - -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_answer_relevancy(row_str: str) -> str: - row = json.loads(row_str) - result = relevancy_evaluator.evaluate( - query=row["question"], - response=row["answer"], - contexts=row["contexts"], - reference=row["ground_truths"], - ) - result = parse_answer_eval("answer_relevancy", result.dict()) - result[ - "answer_relevancy_score_(ragas)" - ] = metrics.answer_relevancy.score_single(row) - result = json.dumps(result) - return result - - -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_answer_faithfulness(row_str: str) -> str: - row = json.loads(row_str) - result = faithfulness_evaluator.evaluate( - query=row["question"], - response=row["answer"], - contexts=row["contexts"], - reference=row["ground_truths"], - ) - - result = parse_answer_eval("answer_faithfulness", result.dict()) - result[ - "answer_faithfulness_score_(ragas)" - ] = metrics.faithfulness.score_single(row) - result = json.dumps(result) - return result - - -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_answer_similarity(row_str: str) -> str: - row = json.loads(row_str) - result = metrics.answer_similarity.score_single(row) - result = json.dumps({"answer_similarity_score_(ragas)": result}) - return result - - -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_context_precision(row_str: str) -> str: - row = json.loads(row_str) - result = metrics.context_precision.score_single(row) - result = json.dumps({"context_precision_score": result}) - return result - - -@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) -@cachew(cache_path=EVAL_CACHE, logger=logger) -def get_context_recall(row_str: str) -> str: - row = json.loads(row_str) - result = metrics.context_recall.score_single(row) - result = json.dumps({"context_recall_score": result}) - return result - - -@cachew(cache_path=EVAL_CACHE, logger=logger) -def evaluate_row(idx: Hashable, row_str: str) -> str: - eval_result = {"idx": idx} - row = json.loads(row_str) - eval_result.update(row) - eval_result.update(json.loads(get_answer_correctness(row_str))) - eval_result.update(json.loads(get_answer_relevancy(row_str))) - eval_result.update(json.loads(get_answer_faithfulness(row_str))) - eval_result.update(json.loads(get_answer_similarity(row_str))) - eval_result.update(json.loads(get_context_precision(row_str))) - eval_result.update(json.loads(get_context_recall(row_str))) - eval_result = json.dumps(eval_result) - return eval_result - - -@cachew(cache_path=EVAL_CACHE, logger=logger) -def process_row( - idx: Hashable, row_str: str, application: str = "api-eval-bharat" -) -> str: - eval_record = get_eval_record(row_str, application=application) - eval_row = evaluate_row(idx, eval_record) - return eval_row - - -def main(): - eval_results = [] - - df = pd.read_json( - "data/eval/wandbot_cleaned_annotated_dataset_11-12-2023.jsonl", - lines=True, - orient="records", - ) - correct_df = df[ - (df["is_wandb_query"] == "YES") & (df["correctness"] == "correct") - ] - - with open( - "data/eval/wandbot-gpt-4-1106-preview-eval-v1-1.jsonl", "w+" - ) as outfile: - for idx, row in tqdm(correct_df.iterrows(), total=len(correct_df)): - try: - row_str = row.to_json() - eval_row = process_row( - idx, - row_str, - application="wandbot-gpt-4-1106-preview-eval-v1.1-bharat", - ) - outfile.write(eval_row + "\n") - eval_results.append(eval_row) - except Exception as e: - print(e) - print(idx) - - -if __name__ == "__main__": - main() diff --git a/src/wandbot/evaluation/eval/async_main.py b/src/wandbot/evaluation/eval/async_main.py new file mode 100644 index 0000000..10dff95 --- /dev/null +++ b/src/wandbot/evaluation/eval/async_main.py @@ -0,0 +1,241 @@ +import re +import json +import time +from typing import Any, Hashable + +import asyncio +import httpx +import wandb +import pandas as pd +import aiofiles +from llama_index import ServiceContext +from llama_index.llms.openai import OpenAI +from tenacity import retry, stop_after_attempt, wait_random_exponential +from tqdm import tqdm + +from wandbot.evaluation.eval.correctness import ( + CORRECTNESS_EVAL_TEMPLATE, + WandbCorrectnessEvaluator, +) +from wandbot.evaluation.eval.factfulness import ( + FACTFULNESS_EVAL_TEMPLATE, + WandbFactfulnessEvaluator, +) +from wandbot.evaluation.eval.relevancy import ( + RELEVANCY_EVAL_TEMPLATE, + WandbRelevancyEvaluator, +) +from wandbot.utils import cachew, get_logger + +logger = get_logger(__name__) + + +service_context = ServiceContext.from_defaults(llm=OpenAI("gpt-4-1106-preview")) +correctness_evaluator = WandbCorrectnessEvaluator( + service_context=service_context, + eval_template=CORRECTNESS_EVAL_TEMPLATE, +) +faithfulness_evaluator = WandbFactfulnessEvaluator( + service_context=service_context, + eval_template=FACTFULNESS_EVAL_TEMPLATE, +) +relevancy_evaluator = WandbRelevancyEvaluator( + service_context=service_context, + eval_template=RELEVANCY_EVAL_TEMPLATE, +) + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +async def get_answer(question: str, application: str = "api-eval-bharat") -> str: + url = "http://0.0.0.0:8000/chat/query" + payload = { + "question": question, + "application": application, + "language": "en", + } + async with httpx.AsyncClient(timeout=200.0) as client: + response = await client.post(url, data=json.dumps(payload)) + response_json = response.json() + return json.dumps(response_json) + +context_metadata_pattern = r"\n?source: .+?\nsource_type: .+?\nhas_code: .+?\n" + + +def get_individual_contexts(source_documents: str) -> list[str]: + source_documents = source_documents.split("---") + source_documents = [ + re.sub(context_metadata_pattern, "", source) for source in source_documents + ] + return source_documents + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +async def get_eval_record(row_str: str, application: str = "api-eval-bharat") -> str: + row = json.loads(row_str) + response = await get_answer(row["question"], application=application) + response = json.loads(response) + response["ground_truths"] = row["answer"] + response["reference_notes"] = row["notes"] + # response["contexts"] = [ + # "\nSource: " + source["source"] + " \n " + source["text"] + # for source in json.loads(response["source_documents"]) + # ] + response["contexts"] = response["source_documents"] + response = json.dumps(response) + return response + + +def parse_answer_eval(metric: str, row: dict[str, Any]) -> dict[str, Any]: + result = { + f"{metric}_score": row.get("score"), + f"{metric}_result": row.get("passing"), + f"{metric}_reason": row.get("feedback"), + } + return result + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +async def get_answer_correctness(row_str: str) -> str: + row = json.loads(row_str) + result = await correctness_evaluator.aevaluate( + query=row["question"], + response=row["answer"], + reference=row["ground_truths"], + contexts=row["contexts"], + reference_notes=row["reference_notes"], + ) + result = parse_answer_eval("answer_correctness", result.dict()) + result = json.dumps(result) + return result + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +async def get_answer_relevancy(row_str: str) -> str: + row = json.loads(row_str) + result = await relevancy_evaluator.aevaluate( + query=row["question"], + response=row["answer"], + contexts=row["contexts"], + reference=row["ground_truths"], + ) + result = parse_answer_eval("answer_relevancy", result.dict()) + result = json.dumps(result) + return result + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +async def get_answer_faithfulness(row_str: str) -> str: + row = json.loads(row_str) + result = await faithfulness_evaluator.aevaluate( + query=row["question"], + response=row["answer"], + contexts=row["contexts"], + reference=row["ground_truths"], + ) + + result = parse_answer_eval("answer_faithfulness", result.dict()) + result = json.dumps(result) + return result + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +async def evaluate_row(idx: Hashable, row_str: str) -> str: + eval_result = {"idx": idx} + row = json.loads(row_str) + eval_result.update(row) + + eval_result.update(json.loads(await get_answer_correctness(row_str))) + eval_result.update(json.loads(await get_answer_relevancy(row_str))) + eval_result.update(json.loads(await get_answer_faithfulness(row_str))) + + eval_result = json.dumps(eval_result) + return eval_result + + +@retry(wait=wait_random_exponential(min=1, max=60), stop=stop_after_attempt(6)) +async def process_row(idx, row, outfile): + """ + Process a chunk of the dataframe asynchronously and write results to the file. + """ + row_str = row.to_json() + response = await get_eval_record(row_str, application="test-baseline-ayush") + logger.info(f"Generated response for idx: {idx}") + eval_row = await evaluate_row(idx, response) + logger.info(f"Evaluated response for idx: {idx}") + try: + json.loads(eval_row) + await outfile.write(eval_row + "\n") + except json.JSONDecodeError: + logger.error(f"Failed to parse response for idx: {idx}") + + +def log_eval_result(eval_result_path: str, duration: float) -> None: + project = "wandbot-eval" + entity = "wandbot" + + run = wandb.init(project=project, entity=entity) + + eval_df = pd.read_json(eval_result_path, lines=True) + eval_df = eval_df.sort_values(by='idx').reset_index(drop=True) + + logger.info(f"Number of eval samples: {len(eval_df)}") + run.log({"Evaluation Results": eval_df}) + + score_columns = [col for col in eval_df.columns if col.endswith('_score')] + mean_scores = eval_df[score_columns].mean() + mode_scores = eval_df[score_columns].mode() + percent_grade3 = (eval_df[score_columns] == 3).mean() + percent_grade2 = (eval_df[score_columns] == 2).mean() + percent_grade1 = (eval_df[score_columns] == 1).mean() + + # Select columns ending with "_result" and calculate the percentage of True values + result_columns = [col for col in eval_df.columns if col.endswith('_result')] + percentage_true_results = (eval_df[result_columns].sum() / eval_df[result_columns].count()) + + final_eval_results = {} + final_eval_results.update(mean_scores.to_dict()) + final_eval_results.update(mode_scores.iloc[0].to_dict()) + final_eval_results.update(percent_grade3.to_dict()) + final_eval_results.update(percent_grade2.to_dict()) + final_eval_results.update(percent_grade1.to_dict()) + final_eval_results.update(percentage_true_results.to_dict()) + + + logger.info(f"Final Eval Results: {json.dumps(final_eval_results, indent=4)}") + run.log(final_eval_results) + + run.summary["duration(s)"] = duration + + +async def main(): + eval_artifact = wandb.Api().artifact("wandbot/wandbot-eval/autoeval_dataset:v3") + eval_artifact_dir = eval_artifact.download(root="data/eval") + + df = pd.read_json( + "data/eval/wandbot_cleaned_annotated_dataset_11-12-2023.jsonl", + lines=True, + orient="records", + ) + correct_df = df[ + (df["is_wandb_query"] == "YES") & (df["correctness"] == "correct") + ] + logger.info("Number of evaluation samples: %s", len(correct_df)) + + start_time = time.time() + + async with aiofiles.open( + "data/eval/eval.jsonl", "w+" + ) as outfile: + tasks = [process_row(idx, row, outfile) for idx, row in tqdm(correct_df.iterrows())] + await asyncio.gather(*tasks) + + end_time = time.time() + duration = end_time - start_time + logger.info(f"Total runtime: {duration:.2f} seconds") + + log_eval_result( + "data/eval/eval.jsonl", duration + ) + +if __name__ == "__main__": + asyncio.run(main()) diff --git a/src/wandbot/ingestion/__init__.py b/src/wandbot/ingestion/__init__.py index f997a6a..e69de29 100644 --- a/src/wandbot/ingestion/__init__.py +++ b/src/wandbot/ingestion/__init__.py @@ -1,3 +0,0 @@ -from wandbot.ingestion import prepare_data, preprocess_data, vectorstores - -__all__ = ["prepare_data.py", "preprocess_data", "vectorstores"] diff --git a/src/wandbot/ingestion/__main__.py b/src/wandbot/ingestion/__main__.py index f2b4e50..a974346 100644 --- a/src/wandbot/ingestion/__main__.py +++ b/src/wandbot/ingestion/__main__.py @@ -1,6 +1,6 @@ import os -from wandbot.ingestion import prepare_data, vectorstores +from wandbot.ingestion import prepare_data, preprocess_data, vectorstores from wandbot.ingestion.report import create_ingestion_report from wandbot.utils import get_logger @@ -12,8 +12,11 @@ def main(): entity = os.environ.get("WANDB_ENTITY", "wandbot") raw_artifact = prepare_data.load(project, entity) - vectorstore_artifact = vectorstores.load(project, entity, raw_artifact) - # TODO: include ingestion report + preprocessed_artifact = preprocess_data.load(project, entity, raw_artifact) + vectorstore_artifact = vectorstores.load( + project, entity, preprocessed_artifact + ) + create_ingestion_report(project, entity, raw_artifact, vectorstore_artifact) print(vectorstore_artifact) diff --git a/src/wandbot/ingestion/config.py b/src/wandbot/ingestion/config.py index f642a0c..91c9683 100644 --- a/src/wandbot/ingestion/config.py +++ b/src/wandbot/ingestion/config.py @@ -10,9 +10,10 @@ data_store_config = DataStoreConfig() docodile_english_store_config = DocodileEnglishStoreConfig() """ + import datetime import pathlib -from typing import List, Optional, Union +from typing import List, Optional from urllib.parse import urlparse from pydantic import BaseModel, Field, model_validator @@ -32,20 +33,23 @@ class DataSource(BaseSettings): repo_path: str = "" local_path: Optional[pathlib.Path] = None base_path: Optional[str] = "" - file_pattern: Union[str, List[str]] = "*.*" + file_patterns: List[str] = ["*.*"] is_git_repo: bool = False git_id_file: Optional[pathlib.Path] = Field(None, env="WANDBOT_GIT_ID_FILE") class DataStoreConfig(BaseModel): name: str = "docstore" + source_type: str = "" data_source: DataSource = DataSource() docstore_dir: pathlib.Path = pathlib.Path("docstore") @model_validator(mode="after") def _set_cache_paths(cls, values: "DataStoreConfig") -> "DataStoreConfig": values.docstore_dir = ( - values.data_source.cache_dir / values.name / values.docstore_dir + values.data_source.cache_dir + / "_".join(values.name.split()) + / values.docstore_dir ) data_source = values.data_source @@ -56,7 +60,9 @@ def _set_cache_paths(cls, values: "DataStoreConfig") -> "DataStoreConfig": local_path = urlparse(data_source.repo_path).path.split("/")[-1] if not data_source.local_path: data_source.local_path = ( - data_source.cache_dir / values.name / local_path + data_source.cache_dir + / "_".join(values.name.split()) + / local_path ) if data_source.is_git_repo: if data_source.git_id_file is None: @@ -73,137 +79,148 @@ def _set_cache_paths(cls, values: "DataStoreConfig") -> "DataStoreConfig": class DocodileEnglishStoreConfig(DataStoreConfig): - name: str = "docodile_store" + name: str = "English Documentation" + source_type: str = "documentation" data_source: DataSource = DataSource( remote_path="https://docs.wandb.ai/", repo_path="https://github.com/wandb/docodile", base_path="docs", - file_pattern="*.md", + file_patterns=["*.md"], is_git_repo=True, ) language: str = "en" - docstore_dir: pathlib.Path = pathlib.Path("docstore_en") + docstore_dir: pathlib.Path = pathlib.Path("wandb_documentation_en") class DocodileJapaneseStoreConfig(DataStoreConfig): - name: str = "docodile_store" + name: str = "Japanese Documentation" + source_type: str = "documentation" data_source: DataSource = DataSource( remote_path="https://docs.wandb.ai/ja/", repo_path="https://github.com/wandb/docodile", base_path="i18n/ja/docusaurus-plugin-content-docs/current", - file_pattern="*.md", + file_patterns=["*.md"], is_git_repo=True, ) language: str = "ja" - docstore_dir: pathlib.Path = pathlib.Path("docstore_ja") + docstore_dir: pathlib.Path = pathlib.Path("wandb_documentation_ja") class ExampleCodeStoreConfig(DataStoreConfig): - name: str = "examples_store" + name: str = "Examples code" + source_type: str = "code" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/examples/tree/master/", repo_path="https://github.com/wandb/examples", base_path="examples", - file_pattern="*.py", + file_patterns=["*.py"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_example_code") + docstore_dir: pathlib.Path = pathlib.Path("wandb_examples_code") class ExampleNotebookStoreConfig(DataStoreConfig): - name: str = "examples_store" + name: str = "Examples Notebooks" + source_type: str = "notebook" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/examples/tree/master/", repo_path="https://github.com/wandb/examples", base_path="colabs", - file_pattern="*.ipynb", + file_patterns=["*.ipynb"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_example_colab") + docstore_dir: pathlib.Path = pathlib.Path("wandb_examples_colab") class SDKCodeStoreConfig(DataStoreConfig): - name: str = "sdk_code_store" + name: str = "Wandb SDK code" + source_type: str = "code" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/wandb/tree/main/", repo_path="https://github.com/wandb/wandb", base_path="wandb", - file_pattern="*.py", + file_patterns=["*.py"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_sdk_code") + docstore_dir: pathlib.Path = pathlib.Path("wandb_sdk_code") class SDKTestsStoreConfig(DataStoreConfig): - name: str = "sdk_code_store" + name: str = "Wandb SDK tests" + source_type: str = "code" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/wandb/tree/main/", repo_path="https://github.com/wandb/wandb", base_path="tests", - file_pattern="*.py", + file_patterns=["*.py"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_sdk_tests") + docstore_dir: pathlib.Path = pathlib.Path("wandb_sdk_tests") class WeaveCodeStoreConfig(DataStoreConfig): - name: str = "weave_code_store" + name: str = "Weave SDK code" + source_type: str = "code" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/weave/tree/master/", repo_path="https://github.com/wandb/weave", base_path="weave", - file_pattern=["*.py", "*.ipynb"], + file_patterns=["*.py", "*.ipynb"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_weave_code") + docstore_dir: pathlib.Path = pathlib.Path("weave_sdk_code") class WeaveExamplesStoreConfig(DataStoreConfig): - name: str = "weave_code_store" + name: str = "Weave Examples" + source_type: str = "code" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/weave/tree/master/", repo_path="https://github.com/wandb/weave", base_path="examples", - file_pattern=["*.py", "*.ipynb"], + file_patterns=["*.py", "*.ipynb"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_weave_examples") + docstore_dir: pathlib.Path = pathlib.Path("weave_examples") class WandbEduCodeStoreConfig(DataStoreConfig): - name: str = "wandb_edu_code_store" + name: str = "Wandb Edu code" + source_type: str = "code" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/edu/tree/main/", repo_path="https://github.com/wandb/edu", base_path="", - file_pattern=["*.py", "*.ipynb", ".*md"], + file_patterns=["*.py", "*.ipynb", "*.md"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_wandb_edu") + docstore_dir: pathlib.Path = pathlib.Path("wandb_edu_code") class WeaveJsStoreConfig(DataStoreConfig): - name: str = "weave_code_store" + name: str = "Weave JS code" + source_type: str = "code" data_source: DataSource = DataSource( remote_path="https://github.com/wandb/weave/tree/master/", repo_path="https://github.com/wandb/weave", base_path="weave-js", - file_pattern=["*.js", "*.ts"], + file_patterns=["*.js", "*.ts"], is_git_repo=True, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_weave_js") + docstore_dir: pathlib.Path = pathlib.Path("weave_js") class FCReportsStoreConfig(DataStoreConfig): - name: str = "fc_reports_store" + name: str = "FC Reports" + source_type: str = "report" data_source: DataSource = DataSource( remote_path="wandb-production", repo_path="", base_path="reports", - file_pattern=["*.json"], + file_patterns=["*.json"], is_git_repo=False, ) - docstore_dir: pathlib.Path = pathlib.Path("docstore_fc_reports") + docstore_dir: pathlib.Path = pathlib.Path("fc_reports") @model_validator(mode="after") def _set_cache_paths(cls, values: "DataStoreConfig") -> "DataStoreConfig": @@ -224,9 +241,12 @@ def _set_cache_paths(cls, values: "DataStoreConfig") -> "DataStoreConfig": class VectorStoreConfig(BaseSettings): name: str = "vectorstore" - embedding_dim: int = 1536 + embeddings_model: str = "text-embedding-3-small" + embedding_dim: int = 512 persist_dir: pathlib.Path = pathlib.Path("data/cache/vectorstore") - chat_model_name: str = "gpt-3.5-turbo-0613" - temperature: float = 0.1 - max_retries: int = 3 - embeddings_cache: pathlib.Path = pathlib.Path("data/cache/embeddings") + batch_size: int = 256 + artifact_url: str = Field( + "wandbot/wandbot-dev/chroma_index:latest", + env="WANDB_INDEX_ARTIFACT", + validation_alias="wandb_index_artifact", + ) diff --git a/src/wandbot/ingestion/prepare_data.py b/src/wandbot/ingestion/prepare_data.py index 776d7ee..ed8c6e0 100644 --- a/src/wandbot/ingestion/prepare_data.py +++ b/src/wandbot/ingestion/prepare_data.py @@ -17,19 +17,18 @@ import json import os import pathlib +from multiprocessing import Pool, cpu_count from typing import Any, Dict, Iterator, List from urllib.parse import urljoin, urlparse import nbformat import pandas as pd +import wandb from google.cloud import bigquery from langchain.schema import Document from langchain_community.document_loaders import TextLoader from langchain_community.document_loaders.base import BaseLoader from nbconvert import MarkdownExporter -from tqdm import tqdm - -import wandb from wandbot.ingestion.config import ( DataStoreConfig, DocodileEnglishStoreConfig, @@ -44,12 +43,11 @@ WeaveExamplesStoreConfig, ) from wandbot.ingestion.utils import ( - EXTENSION_MAP, clean_contents, extract_frontmatter, fetch_git_repo, ) -from wandbot.utils import FastTextLangDetect, get_logger +from wandbot.utils import get_logger logger = get_logger(__name__) @@ -103,12 +101,7 @@ def _get_local_paths(self): ) local_paths = [] - file_patterns = ( - [self.config.data_source.file_pattern] - if isinstance(self.config.data_source.file_pattern, str) - else self.config.data_source.file_pattern - ) - for file_pattern in file_patterns: + for file_pattern in self.config.data_source.file_patterns: local_paths.extend( list( ( @@ -268,6 +261,7 @@ def lazy_load( document.metadata["tags"] = self.extract_tags( document.metadata["source"] ) + document.metadata["source_type"] = self.config.source_type yield document except Exception as e: logger.warning( @@ -276,31 +270,6 @@ def lazy_load( class CodeDataLoader(DataLoader): - @staticmethod - def extract_tags(source_url: str) -> List[str]: - """Extracts the tags from a source url. - - Args: - source_url: The URL of the file. - - Returns: - The extracted tags. - """ - parts = list(filter(lambda x: x, urlparse(source_url).path.split("/"))) - tree_slug = parts.index("tree") - parts = parts[tree_slug + 2 :] - parts_mapper = {"__init__.py": [], "__main__.py": []} - tags = [] - for part in parts: - if part in parts_mapper: - tags.extend(parts_mapper.get(part, [])) - else: - part = part.replace("-", " ").replace("_", " ") - tags.append(part) - tags = [tag.split(".")[0] for tag in tags] - tags = list(set([tag.title() for tag in tags])) - return tags + ["Code"] - def lazy_load(self) -> Iterator[Document]: """A lazy loader for code documents. @@ -344,13 +313,28 @@ def lazy_load(self) -> Iterator[Document]: (body, resources) = md_exporter.from_notebook_node(notebook) cleaned_body = clean_contents(body) document.page_content = cleaned_body + document.metadata["source_type"] = ( + "notebook" + if self.config.source_type == "code" + else self.config.source_type + ) elif os.path.splitext(f_name)[-1] == ".md": document = TextLoader(f_name).load()[0] contents = document.page_content cleaned_body = clean_contents(contents) document.page_content = cleaned_body + document.metadata["source_type"] = ( + "markdown" + if self.config.source_type == "code" + else self.config.source_type + ) else: document = TextLoader(f_name).load()[0] + document.metadata["source_type"] = ( + "code" + if self.config.source_type == "code" + else self.config.source_type + ) document.metadata["file_type"] = os.path.splitext( document.metadata["source"] @@ -358,13 +342,6 @@ def lazy_load(self) -> Iterator[Document]: document.metadata["source"] = document_files[ document.metadata["source"] ] - document.metadata["language"] = EXTENSION_MAP[ - document.metadata["file_type"] - ] - document.metadata["description"] = "" - document.metadata["tags"] = self.extract_tags( - document.metadata["source"] - ) yield document except Exception as e: logger.warning( @@ -592,7 +569,7 @@ def cleanup_reports_df(self, reports_df): spec_type_ls = [] is_buggy_ls = [] is_short_report_ls = [] - for idx, row in tqdm(reports_df.iterrows()): + for idx, row in reports_df.iterrows(): if row["spec"] is None or isinstance(row["spec"], float): logger.debug(idx) markdown_ls.append("spec-error") @@ -693,7 +670,8 @@ def fetch_data(self): return self.config.data_source.local_path - def clean_invalid_unicode_escapes(self, text): + @staticmethod + def clean_invalid_unicode_escapes(text): """ clean up invalid unicode escape sequences """ @@ -778,7 +756,7 @@ def extract_tags(source_url: str, report_content: str) -> List[str]: tags = list(set([tag.title() for tag in tags])) if ("wandb.log" or "wandb.init") in report_content: - tags.append("contains-wandb-code") + tags.append("Contains Wandb Code") return tags @@ -790,19 +768,16 @@ def lazy_load(self) -> Iterator[Document]: Yields: A Document object. """ - lang_detect = FastTextLangDetect() data_dump_fame = self.fetch_data() for parsed_row in self.parse_data_dump(data_dump_fame): document = Document( page_content=parsed_row["content"], metadata={ "source": parsed_row["source"], - "language": lang_detect.detect_language( - parsed_row["content"] - ), + "source_type": self.config.source_type, "file_type": ".md", "description": parsed_row["description"], - "tags": ["fc-report"] + "tags": ["Fully Connected", "Report"] + self.extract_tags( parsed_row["source"], parsed_row["content"] ), @@ -811,6 +786,33 @@ def lazy_load(self) -> Iterator[Document]: yield document +SOURCE_TYPE_TO_LOADER_MAP = { + "documentation": DocodileDataLoader, + "code": CodeDataLoader, + "notebook": CodeDataLoader, + "report": FCReportsDataLoader, +} + + +def load_from_config(config: DataStoreConfig) -> pathlib.Path: + loader = SOURCE_TYPE_TO_LOADER_MAP[config.source_type](config) + loader.config.docstore_dir.mkdir(parents=True, exist_ok=True) + + with (loader.config.docstore_dir / "config.json").open("w") as f: + f.write(loader.config.model_dump_json()) + + with (loader.config.docstore_dir / "documents.jsonl").open("w") as f: + for document in loader.load(): + document_json = { + "page_content": document.page_content, + "metadata": document.metadata, + } + f.write(json.dumps(document_json) + "\n") + with (loader.config.docstore_dir / "metadata.json").open("w") as f: + json.dump(loader.metadata, f) + return loader.config.docstore_dir + + def load( project: str, entity: str, @@ -838,47 +840,26 @@ def load( description="Raw documents for wandbot", ) - en_docodile_loader = DocodileDataLoader(DocodileEnglishStoreConfig()) - ja_docodile_loader = DocodileDataLoader(DocodileJapaneseStoreConfig()) - examples_code_loader = CodeDataLoader(ExampleCodeStoreConfig()) - examples_notebook_loader = CodeDataLoader(ExampleNotebookStoreConfig()) - sdk_code_loader = CodeDataLoader(SDKCodeStoreConfig()) - sdk_tests_loader = CodeDataLoader(SDKTestsStoreConfig()) - weave_code_loader = CodeDataLoader(WeaveCodeStoreConfig()) - weave_examples_loader = CodeDataLoader(WeaveExamplesStoreConfig()) - wandb_edu_code_loader = CodeDataLoader(WandbEduCodeStoreConfig()) - fc_reports_loader = FCReportsDataLoader(FCReportsStoreConfig()) - - for loader in [ - en_docodile_loader, - ja_docodile_loader, - examples_code_loader, - examples_notebook_loader, - sdk_code_loader, - sdk_tests_loader, - weave_code_loader, - weave_examples_loader, - wandb_edu_code_loader, - fc_reports_loader, - ]: - loader.config.docstore_dir.mkdir(parents=True, exist_ok=True) - - with (loader.config.docstore_dir / "config.json").open("w") as f: - f.write(loader.config.model_dump_json()) - - with (loader.config.docstore_dir / "documents.jsonl").open("w") as f: - for document in loader.load(): - document_json = { - "page_content": document.page_content, - "metadata": document.metadata, - } - f.write(json.dumps(document_json) + "\n") - with (loader.config.docstore_dir / "metadata.json").open("w") as f: - json.dump(loader.metadata, f) - + configs = [ + DocodileEnglishStoreConfig(), + DocodileJapaneseStoreConfig(), + ExampleCodeStoreConfig(), + ExampleNotebookStoreConfig(), + SDKCodeStoreConfig(), + SDKTestsStoreConfig(), + WeaveCodeStoreConfig(), + WeaveExamplesStoreConfig(), + WandbEduCodeStoreConfig(), + FCReportsStoreConfig(), + ] + + pool = Pool(cpu_count() - 1) + results = pool.imap_unordered(load_from_config, configs) + + for docstore_path in results: artifact.add_dir( - str(loader.config.docstore_dir), - name=loader.config.docstore_dir.name, + str(docstore_path), + name=docstore_path.name, ) run.log_artifact(artifact) run.finish() diff --git a/src/wandbot/ingestion/preprocess_data.py b/src/wandbot/ingestion/preprocess_data.py index e858661..af883f9 100644 --- a/src/wandbot/ingestion/preprocess_data.py +++ b/src/wandbot/ingestion/preprocess_data.py @@ -19,146 +19,210 @@ nodes = load(documents, chunk_size=1024) """ -from typing import Any, List +import json +import pathlib +from typing import Any, List, Sequence import tiktoken -from langchain.schema import Document as LcDocument -from llama_index import Document as LlamaDocument -from llama_index.node_parser import ( - CodeSplitter, - MarkdownNodeParser, - TokenTextSplitter, +from langchain_core.documents import BaseDocumentTransformer, Document + +import wandb +from wandbot.ingestion.preprocessors.markdown import MarkdownTextTransformer +from wandbot.ingestion.preprocessors.source_code import CodeTextTransformer +from wandbot.utils import ( + FastTextLangDetect, + filter_smaller_documents, + get_logger, + make_document_tokenization_safe, ) -from llama_index.schema import BaseNode, TextNode - -from wandbot.utils import get_logger logger = get_logger(__name__) -def make_texts_tokenization_safe(documents: List[str]) -> List[str]: - """Removes special tokens from the given documents. +class Tokenizer: + def __init__(self, model_name): + self.tokenizer = tiktoken.encoding_for_model(model_name) - Args: - documents: A list of strings representing the documents. + def encode(self, text): + return self.tokenizer.encode(text, allowed_special="all") - Returns: - A list of cleaned documents with special tokens removed. - """ - encoding = tiktoken.get_encoding("cl100k_base") - special_tokens_set = encoding.special_tokens_set + def decode(self, tokens): + return self.tokenizer.decode(tokens) - def remove_special_tokens(text: str) -> str: - """Removes special tokens from the given text. - Args: - text: A string representing the text. +tokenizer = Tokenizer("gpt2") + + +def length_function(content: str) -> int: + return len(tokenizer.encode(content)) + +def len_function_with_doc(document: Document) -> int: + return len(tokenizer.encode(document.page_content)) + + +class DocumentTransformer(BaseDocumentTransformer): + def __init__( + self, + lang_detect, + max_size: int = 512, + min_size: int = 5, + length_function=None, + ): + self.lang_detect = lang_detect + self.chunk_size = max_size + self.min_size = min_size + self.length_function = length_function + self.markdown_transformer = MarkdownTextTransformer( + lang_detect=lang_detect, + chunk_size=self.chunk_size, + length_function=self.length_function, + ) + self.code_transformer = CodeTextTransformer( + lang_detect=self.lang_detect, + chunk_size=self.chunk_size, + length_function=length_function, + ) + + def filter_smaller_documents( + self, documents: List[Document], min_size: int = 5 + ) -> List[Document]: + """Filters out nodes that are smaller than the chunk size. + + Args: + documents: A list of nodes. + min_size: The minimum size of a node. Returns: - The text with special tokens removed. + A list of nodes. """ - for token in special_tokens_set: - text = text.replace(token, "") - return text - cleaned_documents = [] - for document in documents: - cleaned_document = remove_special_tokens(document) - cleaned_documents.append(cleaned_document) - return cleaned_documents + for node in documents: + content = node.page_content + if length_function(content) >= min_size: + yield node + + def standardize_metadata(self, documents: List[Document]) -> List[Document]: + for document in documents: + metadata = document.metadata + for key, value in metadata.items(): + if not isinstance(value, (str, int, float, bool)): + if isinstance(value, list): + metadata[key] = " ".join(value) + elif isinstance(value, dict): + metadata[key] = json.dumps(value) + elif isinstance(value, (set, tuple)): + metadata[key] = " ".join(list(value)) + else: + metadata[key] = None + metadata = {k: v for k, v in metadata.items() if v is not None} + document.metadata = metadata + return documents + + def transform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: + transformed_documents = [] + for document in list(documents): + document = make_document_tokenization_safe(document) + if document.metadata.get("source_type", "") == "code": + transformed_documents.extend( + self.code_transformer.transform_documents([document]) + ) + else: + transformed_documents.extend( + self.markdown_transformer.transform_documents([document]) + ) + transformed_documents = list( + self.filter_smaller_documents( + transformed_documents, min_size=self.min_size + ) + ) + transformed_documents = list( + self.standardize_metadata(transformed_documents) + ) + transformed_documents = filter_smaller_documents( + transformed_documents, min_size=self.min_size + ) + return transformed_documents -class CustomMarkdownNodeParser(MarkdownNodeParser): - def _build_node_from_split( - self, - text_split: str, - node: BaseNode, - metadata: dict, - ) -> TextNode: - """Build node from single text split.""" - text_splits = make_texts_tokenization_safe([text_split]) - text_split = text_splits[0] - return super()._build_node_from_split(text_split, node, metadata) - - -class CustomCodeSplitter(CodeSplitter): - """Splits text into chunks based on custom code formatting. - - Attributes: - language: The programming language of the code. - max_chars: The maximum number of characters allowed in a chunk. - """ - - def split_text(self, text: str) -> List[str]: - """Split text into chunks.""" - text_splits = super().split_text(text) - text_splits = make_texts_tokenization_safe(text_splits) - return text_splits - - -def convert_lc_to_llama(document: LcDocument) -> LlamaDocument: - """Converts a Langchain document to a Llama document. - - Args: - document: A Langchain document. - - Returns: - A Llama document. - """ - llama_document = LlamaDocument.from_langchain_format(document) - excluded_embed_metadata_keys = [ - "file_type", - "source", - "language", - ] - excluded_llm_metadata_keys = [ - "file_type", - ] - llama_document.excluded_embed_metadata_keys = excluded_embed_metadata_keys - llama_document.excluded_llm_metadata_keys = excluded_llm_metadata_keys - - return llama_document - - -def load(documents: List[LcDocument], chunk_size: int = 512) -> List[Any]: - """Loads documents and returns a list of nodes. - - Args: - documents: A list of documents. - chunk_size: The size of each chunk. - - Returns: - A list of nodes. - """ - md_parser = CustomMarkdownNodeParser(chunk_size=chunk_size) - code_parser = CustomCodeSplitter( - language="python", max_chars=chunk_size * 2 + def transform_document(self, document: Document) -> Document: + return self.transform_documents([document])[0] + + +def process_document_file( + documents: List[Document], transformer: DocumentTransformer +) -> List[Document]: + transformed_documents = transformer.transform_documents(documents) + + return list(transformed_documents) + + +def load( + project: str, + entity: str, + source_artifact_path: str, + result_artifact_name: str = "transformed_data", +) -> str: + run: wandb.wandb_sdk.wandb_run.Run = wandb.init( + project=project, entity=entity, job_type="preprocess_data" + ) + artifact: wandb.Artifact = run.use_artifact( + source_artifact_path, type="dataset" ) - # Define the node parser - node_parser = TokenTextSplitter.from_defaults(chunk_size=chunk_size) + artifact_dir: str = artifact.download() - llama_docs: List[LlamaDocument] = list( - map(lambda x: convert_lc_to_llama(x), documents) + document_files: List[pathlib.Path] = list( + pathlib.Path(artifact_dir).rglob("documents.jsonl") ) - nodes: List[Any] = [] - for doc in llama_docs: - try: - if doc.metadata["file_type"] == ".py": - parser = code_parser - else: - parser = md_parser - nodes.extend(parser.get_nodes_from_documents([doc])) - except Exception as e: - logger.error(f"Error parsing document: {e}") - logger.warning( - f"Unable to parse document: {doc.metadata['source']} with custom parser, using default " - f"parser instead." + lang_detect = FastTextLangDetect() + transformer = DocumentTransformer( + lang_detect=lang_detect, + max_size=512, + min_size=5, + length_function=length_function, + ) + + result_artifact = wandb.Artifact(result_artifact_name, type="dataset") + + for document_file in document_files: + with document_file.open() as f: + documents = [Document(**json.loads(line)) for line in f] + transformed_documents = process_document_file( + documents, transformer + ) + config = json.load((document_file.parent / "config.json").open()) + metadata = json.load( + (document_file.parent / "metadata.json").open() + ) + cache_dir = ( + pathlib.Path(config["data_source"]["cache_dir"]).parent + / "transformed_data" + ) + + transformed_file = ( + cache_dir / document_file.parent.name / document_file.name ) - nodes.extend(node_parser.get_nodes_from_documents([doc])) - nodes = node_parser.get_nodes_from_documents(nodes) + transformed_file.parent.mkdir(parents=True, exist_ok=True) + with transformed_file.open("w") as of: + for document in transformed_documents: + of.write(json.dumps(document.dict()) + "\n") - nodes = list(filter(lambda x: len(x.get_content()) > 10, nodes)) + config["chunk_size"] = 512 + with open(transformed_file.parent / "config.json", "w") as of: + json.dump(config, of) + + metadata["num_transformed_documents"] = len(transformed_documents) + with open(transformed_file.parent / "metadata.json", "w") as of: + json.dump(metadata, of) + + result_artifact.add_dir( + str(transformed_file.parent), + name=document_file.parent.name, + ) - return nodes + run.log_artifact(result_artifact) + run.finish() + return f"{entity}/{project}/{result_artifact_name}:latest" diff --git a/src/wandbot/ingestion/preprocessors/markdown.py b/src/wandbot/ingestion/preprocessors/markdown.py new file mode 100644 index 0000000..afcb8bc --- /dev/null +++ b/src/wandbot/ingestion/preprocessors/markdown.py @@ -0,0 +1,258 @@ +import json +from hashlib import md5 +from typing import Any, Callable, Dict, List, Optional, Sequence, TypedDict + +from langchain.text_splitter import ( + Language, + MarkdownHeaderTextSplitter, + RecursiveCharacterTextSplitter, +) +from langchain_core.documents import BaseDocumentTransformer, Document + +from wandbot.utils import FastTextLangDetect, FasttextModelConfig + + +class LineType(TypedDict): + """Line type as typed dict.""" + + metadata: Dict[str, str] + content: str + + +class HeaderType(TypedDict): + """Header type as typed dict.""" + + level: int + name: str + data: str + + +def create_id_from_document(document: Document) -> str: + contents = document.page_content + json.dumps(document.metadata) + checksum = md5(contents.encode("utf-8")).hexdigest() + return checksum + + +def prefix_headers_based_on_metadata(chunk): + # Headers ordered by markdown header levels + markdown_header_prefixes = ["#", "##", "###", "####", "#####", "######"] + markdown_header_prefixes_map = { + f"header_{i}": prefix + for i, prefix in enumerate(markdown_header_prefixes) + } + + # Generate headers from metadata + headers_from_metadata = [ + f"{markdown_header_prefixes_map[level]} {title}" + for level, title in chunk["metadata"].items() + ] + + # Join the generated headers with new lines + headers_str = "\n".join(headers_from_metadata) + "\n" + + # Check if the page_content starts with a header + if chunk["content"].lstrip().startswith(tuple(markdown_header_prefixes)): + # Find the first newline to locate the end of the existing header + first_newline_index = chunk["content"].find("\n") + if first_newline_index != -1: + # Remove the existing header and prefix with generated headers + modified_content = ( + headers_str + chunk["content"][first_newline_index + 1 :] + ) + else: + # If there's no newline, the entire content is a header, replace it + modified_content = headers_str + else: + # If it doesn't start with a header, just prefix with generated headers + modified_content = headers_str + chunk["content"] + + return {"metadata": chunk["metadata"], "content": modified_content} + + +class CustomMarkdownTextSplitter(MarkdownHeaderTextSplitter): + """Splitting markdown files based on specified headers.""" + + def __init__(self, chunk_size: Optional[int] = None, **kwargs): + headers_to_split_on = [ + ("#", "header_1"), + ("##", "header_2"), + ("###", "header_3"), + ("####", "header_4"), + ("#####", "header_5"), + ("######", "header_6"), + ] + self.max_length = chunk_size + super().__init__( + headers_to_split_on=headers_to_split_on, + return_each_line=False, + strip_headers=False, + ) + + def aggregate_lines_to_chunks( + self, lines: List[LineType] + ) -> List[Document]: + aggregated_chunks: List[LineType] = [] + + for line in lines: + should_append = True + + # Attempt to aggregate with an earlier chunk if possible + for i in range(len(aggregated_chunks) - 1, -1, -1): + previous_chunk = aggregated_chunks[i] + # Check if the current line's metadata is a child or same level of the previous chunk's metadata + if all( + item in line["metadata"].items() + for item in previous_chunk["metadata"].items() + ): + potential_new_content = ( + previous_chunk["content"] + " \n\n" + line["content"] + ) + if ( + self.max_length is None + or len(potential_new_content) <= self.max_length + ): + # If adding the current line does not exceed chunk_size, merge it into the previous chunk + aggregated_chunks[i]["content"] = potential_new_content + should_append = False + break + else: + # If it exceeds chunk_size, no further checks are needed, break to append as a new chunk + break + + if should_append: + # Append as a new chunk if it wasn't merged into an earlier one + aggregated_chunks.append(line) + + # Prefix headers based on metadata + aggregated_chunks = [ + prefix_headers_based_on_metadata(chunk) + for chunk in aggregated_chunks + ] + return [ + Document(page_content=chunk["content"], metadata=chunk["metadata"]) + for chunk in aggregated_chunks + ] + + def split_documents(self, documents: List[Document]) -> List[Document]: + """Split a list of documents into smaller documents. + + Args: + documents: A list of documents. + + Returns: + A list of documents. + """ + split_documents = [] + for document in documents: + for chunk in self.split_text(document.page_content): + split_documents.append( + Document( + page_content=chunk.page_content, + metadata=document.metadata, + ) + ) + return split_documents + + +class MarkdownTextTransformer(BaseDocumentTransformer): + def __init__( + self, + lang_detect, + chunk_size: int = 512, + length_function: Callable[[str], int] = None, + ): + self.fasttext_model = lang_detect + self.chunk_size: int = chunk_size + self.length_function: Callable[[str], int] = ( + length_function if length_function is not None else len + ) + self.recursive_splitter = RecursiveCharacterTextSplitter.from_language( + language=Language.MARKDOWN, + chunk_size=self.chunk_size, + chunk_overlap=0, + keep_separator=True, + length_function=self.length_function, + ) + self.header_splitter = CustomMarkdownTextSplitter( + chunk_size=self.chunk_size * 2, + ) + + def identify_document_language(self, document: Document) -> str: + if "language" in document.metadata: + return document.metadata["language"] + else: + return self.fasttext_model.detect_language(document.page_content) + + def split_markdown_documents( + self, + documents: List[Document], + ) -> List[Document]: + final_chunks = [] + chunked_documents = [] + for document in documents: + document_splits = self.header_splitter.split_documents( + documents=[document], + ) + for split in document_splits: + chunk = Document( + page_content=split.page_content, + metadata=split.metadata.copy(), + ) + chunk.metadata["parent_id"] = create_id_from_document(chunk) + chunk.metadata["has_code"] = "```" in chunk.page_content + chunk.metadata["language"] = self.identify_document_language( + chunk + ) + chunk.metadata["source_content"] = chunk.page_content + chunked_documents.append(chunk) + + split_chunks = self.recursive_splitter.split_documents( + chunked_documents + ) + + for chunk in split_chunks: + chunk = Document( + page_content=chunk.page_content, + metadata=chunk.metadata.copy(), + ) + chunk.metadata["id"] = create_id_from_document(chunk) + final_chunks.append(chunk) + + return final_chunks + + def transform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: + split_documents = self.split_markdown_documents(list(documents)) + transformed_documents = [] + for document in split_documents: + transformed_documents.append(document) + return transformed_documents + + +if __name__ == "__main__": + lang_detect = FastTextLangDetect( + FasttextModelConfig( + fasttext_file_path="/media/mugan/data/wandb/projects/wandbot/data/cache/models/lid.176.bin" + ) + ) + + data_file = open( + "/media/mugan/data/wandb/projects/wandbot/data/cache/raw_data/docodile_store/docodile_en/documents.jsonl" + ).readlines() + source_document = json.loads(data_file[0]) + + source_document = Document(**source_document) + + markdown_transformer = MarkdownTextTransformer( + lang_detect=lang_detect, chunk_size=768 // 2 + ) + + transformed_documents = markdown_transformer.transform_documents( + [source_document] + ) + + for document in transformed_documents: + print(document.page_content) + print(json.dumps(document.metadata, indent=2)) + print("*" * 80) diff --git a/src/wandbot/ingestion/preprocessors/source_code.py b/src/wandbot/ingestion/preprocessors/source_code.py new file mode 100644 index 0000000..efd7c7d --- /dev/null +++ b/src/wandbot/ingestion/preprocessors/source_code.py @@ -0,0 +1,642 @@ +import json +from typing import Any, Callable, List, Sequence + +from langchain.text_splitter import ( + Language, + RecursiveCharacterTextSplitter, + TokenTextSplitter, +) +from langchain_core.documents import BaseDocumentTransformer, Document +from tree_sitter import Node +from tree_sitter_languages import get_language, get_parser + +from wandbot.ingestion.preprocessors.markdown import ( + CustomMarkdownTextSplitter, + create_id_from_document, +) +from wandbot.utils import FastTextLangDetect, FasttextModelConfig + + +def extract_docstrings(node: Node, node_type: str, language: Any): + """ + Extracts docstrings for modules, classes, and functions using Tree-sitter queries. + Also captures parent node details for naming. + + Args: + - node: The root node of the syntax tree. + - source_bytes: The source code as bytes. + + Returns: + - A list of tuples, each with the docstring node, its parent node, and the capture name. + """ + module_doc_str_pattern = "(module . (comment)* . (expression_statement (string)) @module_doc_str)" + + class_doc_str_pattern = "(class_definition body: (block . (expression_statement (string)) @class_doc_str))" + function_doc_str_pattern = "(function_definition body: (block . (expression_statement (string)) @function_doc_str))" + if node_type in ["decorated_function", "decorated_method", "function"]: + doc_str_pattern = function_doc_str_pattern + elif node_type in ["decorated_class", "class"]: + doc_str_pattern = class_doc_str_pattern + else: + doc_str_pattern = module_doc_str_pattern + + doc_str_query = language.query(doc_str_pattern) + doc_strs = doc_str_query.captures(node) + return doc_strs + + +def traverse( + node: Node, + source_bytes: bytes, + context_stack: List[str] | None = None, + max_length: int = None, + language: Any = None, +): + if context_stack is None: + context_stack = [] + + definitions = [] + for child in node.children: + if child.type in [ + "class_definition", + "function_definition", + "decorated_definition", + ]: + name_node = next( + filter(lambda n: n.type == "identifier", child.children), + None, + ) + name = name_node.text.decode() if name_node else "Unnamed" + name_delimiter = "!" + is_method = any(context == "class" for context in context_stack) + type_name = "Unknown" + is_decorated = False + + body_start = child.start_byte + body_end = child.end_byte + body_length = len(source_bytes[body_start:body_end]) + + if child.type == "decorated_definition": + # capture whether it is a class, method or function + is_class = next( + filter( + lambda n: n.type == b"class_definition", child.children + ), + None, + ) + if is_class: + type_name = "decorated_class" + else: + type_name = ( + "decorated_method" + if is_method + else "decorated_function" + ) + decorator_node = next( + filter(lambda n: n.type == "decorator", child.children), + None, + ) + decorator_name = ( + decorator_node.text.decode() + if decorator_node + else "Unnamed" + ) + decorated_node = next( + filter( + lambda n: n.type + in ["function_definition", "class_definition"], + child.children, + ), + None, + ) + decorated_name_node = next( + filter( + lambda n: n.type == "identifier", + decorated_node.children, + ), + None, + ) + + decorated_name = ( + decorated_name_node.text.decode() + if decorated_node + else "Unnamed" + ) + + if decorated_node.type == "class_definition": + decorated_type_name = "class" + elif decorated_node.type == "function_definition": + decorated_type_name = "method" if is_method else "function" + else: + decorated_type_name = "Unknown" + + name = f"{decorator_name}{name_delimiter}{type_name}{name_delimiter}{decorated_name}" + type_name = decorated_type_name + + full_name = name_delimiter.join( + context_stack + [name, type_name] + ) + + body_start = child.start_byte + body_end = child.end_byte + body_length = len(source_bytes[body_start:body_end]) + child = decorated_node + is_decorated = True + if child.type == "class_definition": + type_name = "class" + full_name = name_delimiter.join( + context_stack + [name, type_name] + ) + + elif child.type == "function_definition": + type_name = "method" if is_method else "function" + full_name = name_delimiter.join( + context_stack + [name, type_name] + ) + else: + continue + + if is_decorated: + type_name = "decorated_" + type_name + # Check if the body length exceeds chunk_size before extracting docstrings + if max_length is None or body_length > max_length: + # Extract and handle docstrings + doc_strs = extract_docstrings(child, type_name, language) + for doc_str, capture_name in doc_strs: + docstring_start = doc_str.start_byte + docstring_end = doc_str.end_byte + definitions.append( + { + "type": "docstring", + "name": f"{full_name}{name_delimiter}{capture_name}{name_delimiter}docstring", + "body": (docstring_start, docstring_end), + } + ) + + definitions.append( + { + "type": type_name, + "name": f"{full_name}", + "body": (body_start, body_end), + } + ) + + new_context_stack = context_stack.copy() + new_context_stack.append(name) + new_context_stack.append(type_name) + + # Recursively traverse child nodes with updated context + definitions.extend( + traverse( + child, + source_bytes, + context_stack=new_context_stack, + max_length=max_length, + language=language, + ) + ) + else: + # If the body is smaller than chunk_size, treat it as a leaf node without extracting docstrings + definitions.append( + { + "type": type_name, + "name": full_name, + "body": (body_start, body_end), + } + ) + else: + # Recursively traverse child nodes without changing the context + definitions.extend( + traverse( + child, + source_bytes, + context_stack=context_stack, + max_length=max_length, + language=language, + ) + ) + return definitions + + +def get_line_number(index: int, source_code: bytes) -> int: + total_chars = 0 + for line_number, line in enumerate( + source_code.splitlines(keepends=True), start=1 + ): + total_chars += len(line) + if total_chars > index: + return line_number - 1 + return line_number + + +def load_definitions(definitions, sourcecode_bytes): + line_definitions = [] + for definition in definitions: + start, end = definition["body"] + start_line = get_line_number(start, sourcecode_bytes) + end_line = get_line_number(end, sourcecode_bytes) + definition["span"] = [start_line, end_line] + definition["body"] = b"\n".join( + sourcecode_bytes.splitlines()[start_line : end_line + 1] + ).decode("utf-8") + + line_definitions.append(definition) + return line_definitions + + +def post_process_definitions(definitions, name_delimiter="!"): + """ + Post-processes the list of definitions to remove the bodies of child nodes + from their parent nodes and replace them with placeholders, maintaining the indent level. + + Args: + - definitions: The list of dictionaries representing class or function definitions. + + Returns: + - The modified list of definitions with child bodies removed from parent bodies and placeholders indented appropriately. + """ + # Sort definitions by name to ensure parents are processed before their children + definitions.sort(key=lambda x: x["name"]) + + for i, defn in enumerate(definitions): + full_name = defn["name"] + body = defn["body"] + + # Look for children of the current definition + for child in definitions[i + 1 :]: + child_full_name = child["name"] + # Check if the current definition is a direct parent of the child + if child_full_name.startswith(full_name + name_delimiter): + child_body = child["body"] + # Calculate the indent by looking backwards from the start_index to find the newline character + indent = " " * (len(child_body) - len(child_body.lstrip())) + + placeholder_child_name = ":".join( + name + for name in child_full_name.split(name_delimiter) + if name + not in [ + "class", + "function", + "method", + "docstring", + "decorated_class", + "decorated_function", + "decorated_method", + ] + ) + placeholder = f"{indent}#{child['name']}:" + # placeholder = "" + # Replace the child's body in the parent's body with an indented placeholder + body = body.replace( + child_body, placeholder, 1 + ) # Replace only the first occurrence + + # Update the parent body after all replacements + defn["body"] = body + + return definitions + + +def has_more_than_n_imports(root_node, n): + """ + Checks if the Python module represented by the Tree-sitter root node + contains more than n import statements, returning early if so. + + Args: + - root_node: The root node of the Tree-sitter syntax tree for the module. + - n: The threshold number of import statements. + + Returns: + - A boolean indicating whether the module has more than n import statements. + """ + import_count = 0 + + # Function to recursively traverse the tree and count import statements + def count_imports(node): + nonlocal import_count + if import_count > n: + # Return early if we've already found more than n imports + return True + for child in node.children: + if child.type in ["import_statement", "import_from_statement"]: + import_count += 1 + if import_count > n: + # Found more than n imports, can return early + return True + # Continue the traversal until more than n imports are found + if count_imports(child): + return True + return False + + # Start the traversal from the root node + return count_imports(root_node) + + +def check_merge(definitions, max_length): + """Checks if a list of definitions can be merged, considering docstrings and chunk_size.""" + # Check for docstring type + if any(defn["type"] == "docstring" for defn in definitions): + return False + # Check total length + total_length = sum(len(defn["body"]) for defn in definitions) + return total_length <= max_length + + +def get_parent(definition): + """Returns the parent path from a definition's name, considering the definition's type for decorated nodes.""" + parts = definition["name"].split("!") + # Determine if the definition is for a decorated node based on its type + if any( + part.startswith("decorated_") + for part in definition["type"] + if isinstance(definition["type"], list) + ) or definition["type"].startswith("decorated_"): + return "!".join(parts[:-4]) # Adjust for decorated nodes + else: + return "!".join(parts[:-2]) # Standard parent extraction + + +# Correct the merge_definitions function to handle the merging correctly +def merge_definitions(definitions, max_length): + merged_definitions = [] + definitions_by_parent = {} + + # Group definitions by their parent path, considering decorated nodes + for definition in definitions: + parent = get_parent(definition) + if parent not in definitions_by_parent: + definitions_by_parent[parent] = [] + definitions_by_parent[parent].append(definition) + + # Attempt to merge definitions within the same parent group + for parent, defs in definitions_by_parent.items(): + while defs: + current = defs.pop(0) + if ( + "docstring" in current["type"] + or len(current["body"]) > max_length + ): + # Handle docstrings and oversized definitions individually, ensuring they are not merged + current["type"], current["name"], current["span"] = ( + [current["type"]], + [current["name"]], + [current["span"]], + ) + merged_definitions.append(current) + continue + to_merge = [current] + for defn in list(defs): + if ( + "docstring" in defn["type"] + or len(defn["body"]) > max_length + ): + continue # Skip non-eligible definitions + if check_merge(to_merge + [defn], max_length): + to_merge.append(defn) + defs.remove(defn) + if len(to_merge) > 1: + # Merge the definitions + merged_definition = { + "type": [defn["type"] for defn in to_merge], + "name": [defn["name"] for defn in to_merge], + "span": [defn["span"] for defn in to_merge], + "body": "\n\n".join(defn["body"] for defn in to_merge), + } + merged_definitions.append(merged_definition) + else: + # No merge occurred, adjust keys to lists for the single definition + current["type"], current["name"], current["span"] = ( + [current["type"]], + [current["name"]], + [current["span"]], + ) + merged_definitions.append(current) + + return merged_definitions + + +def chunk_source( + root_node: Node, + source_code: bytes, + max_length: int = 512, + language: Any = None, +): + definitions = traverse( + root_node, source_code, None, max_length=max_length, language=language + ) + definitions = load_definitions(definitions, source_code) + definitions = post_process_definitions(definitions) + + definitions = merge_definitions(definitions, max_length) + + return definitions + + +def get_text_from_definition(definition): + final_definition = f""" +source_types:\t{definition["type"]} +source_paths:\t{definition["name"]} +source_lines:\t{definition["span"]} +{definition["body"]} +""" + return final_definition + + +class PythonCodeTextTransformer(BaseDocumentTransformer): + def __init__(self, chunk_size=512): + self.chunk_size = chunk_size + self.parser = get_parser("python") + self.language = get_language("python") + + def transform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: + transformed_documents = [] + for document in list(documents): + source_code = document.page_content.encode("utf-8") + tree = self.parser.parse(source_code) + definitions = chunk_source( + tree.root_node, + source_code, + max_length=self.chunk_size, + language=self.language, + ) + for definition in definitions: + transformed_documents.append( + Document( + page_content=get_text_from_definition(definition), + metadata=document.metadata, + ) + ) + return transformed_documents + + +class CodeTextTransformer(BaseDocumentTransformer): + def __init__( + self, + lang_detect, + chunk_size: int = 512, + length_function: Callable[[str], int] = None, + ): + self.lang_detect = lang_detect + self.chunk_size: int = chunk_size + self.length_function: Callable[[str], int] = ( + length_function if length_function is not None else len + ) + self.lang_detect = None + self.python_code_splitter = PythonCodeTextTransformer( + chunk_size=self.chunk_size * 2 + ) + + def split_documents(self, documents: List[Document]) -> List[Document]: + """Split incoming code and return chunks using the AST.""" + + # First chunk into parent documents + + chunked_documents = [] + for document in documents: + file_extension = document.metadata.get("file_type", "") + if file_extension in [".py", ".js", ".ts"]: + language = { + ".py": Language.PYTHON, + ".js": Language.JS, + ".ts": Language.JS, + }[file_extension] + if language == Language.PYTHON: + chunked_documents.extend( + self.python_code_splitter.transform_documents( + [document] + ) + ) + else: + recursive_splitter = ( + RecursiveCharacterTextSplitter.from_language( + language=language, + chunk_size=self.chunk_size * 2, + chunk_overlap=0, + keep_separator=True, + length_function=len, + ) + ) + chunked_documents.extend( + recursive_splitter.split_documents([document]) + ) + elif file_extension in [".md", ".ipynb"]: + chunked_documents.extend( + CustomMarkdownTextSplitter( + chunk_size=self.chunk_size * 2 + ).split_documents([document]) + ) + else: + chunked_documents.extend( + TokenTextSplitter( + chunk_size=self.chunk_size * 2 + ).split_documents([document]) + ) + + # make new documents from the chunks with updated metadata + parent_documents = [] + for split in chunked_documents: + file_extension = split.metadata.get("file_type", "") + if file_extension == ".py": + document_content = "\n".join( + split.page_content.strip().split("\n")[3:] + ) + else: + document_content = split.page_content + + chunk = Document( + page_content=document_content, + metadata=split.metadata.copy(), + ) + chunk.metadata["parent_id"] = create_id_from_document(chunk) + chunk.metadata["has_code"] = True + chunk.metadata["language"] = chunk.metadata.get("language", "en") + chunk.metadata["source_content"] = split.metadata.get( + "source_content", document_content + ) + parent_documents.append(chunk) + + # now make children documents from the parent documents + final_chunks = [] + for document in parent_documents: + file_extension = document.metadata.get("file_type", "") + if file_extension in [".py", ".js", ".ts", ".md", ".ipynb"]: + language = { + ".py": Language.PYTHON, + ".js": Language.JS, + ".ts": Language.JS, + ".md": Language.MARKDOWN, + ".ipynb": Language.MARKDOWN, + }[file_extension] + recursive_splitter = ( + RecursiveCharacterTextSplitter.from_language( + language=language, + chunk_size=self.chunk_size, + chunk_overlap=0, + keep_separator=True, + length_function=self.length_function, + ) + ) + final_chunks.extend( + recursive_splitter.split_documents([document]) + ) + else: + final_chunks.extend( + TokenTextSplitter( + chunk_size=self.chunk_size + ).split_documents([document]) + ) + + # now add the ids for the final chunks + output_chunks = [] + for chunk in final_chunks: + chunk = Document( + page_content=chunk.page_content, + metadata=chunk.metadata.copy(), + ) + chunk.metadata["id"] = create_id_from_document(chunk) + output_chunks.append(chunk) + + return output_chunks + + def transform_documents( + self, documents: Sequence[Document], **kwargs: Any + ) -> Sequence[Document]: + document_splits = [] + for document in list(documents): + document_splits.extend(self.split_documents([document])) + + return document_splits + + +if __name__ == "__main__": + lang_detect = FastTextLangDetect( + FasttextModelConfig( + fasttext_file_path="/media/mugan/data/wandb/projects/wandbot/data/cache/models/lid.176.bin" + ) + ) + + data_file = open( + "/media/mugan/data/wandb/projects/wandbot/data/cache/raw_data/docodile_store/docodile_en/documents.jsonl" + ).readlines() + source_document = json.loads(data_file[0]) + + source_document = Document(**source_document) + print(source_document.page_content) + + code_transformer = CodeTextTransformer( + lang_detect=lang_detect, + chunk_size=768 // 2, + ) + + transformed_documents = code_transformer.transform_documents( + [source_document] + ) + + for document in transformed_documents: + print(document.page_content) + # print(document.metadata["source_content"]) + print(json.dumps(document.metadata, indent=2)) + print("*" * 80) diff --git a/src/wandbot/ingestion/report.py b/src/wandbot/ingestion/report.py index 37c6ca5..5f68fe6 100644 --- a/src/wandbot/ingestion/report.py +++ b/src/wandbot/ingestion/report.py @@ -14,6 +14,7 @@ vectorstore_artifact = "wandbot/wandbot-dev/vectorstores:latest" create_ingestion_report(project, entity, raw_artifact, vectorstore_artifact) """ + import json import pathlib from datetime import datetime diff --git a/src/wandbot/ingestion/utils.py b/src/wandbot/ingestion/utils.py index 064fcf7..8548efe 100644 --- a/src/wandbot/ingestion/utils.py +++ b/src/wandbot/ingestion/utils.py @@ -25,6 +25,7 @@ git_repo_metadata = fetch_git_repo(paths, id_file) cell_info = concatenate_cells(cell, include_outputs, max_output_length, traceback) """ + import pathlib import re import subprocess diff --git a/src/wandbot/ingestion/vectorstores.py b/src/wandbot/ingestion/vectorstores.py index 0d79403..963d965 100644 --- a/src/wandbot/ingestion/vectorstores.py +++ b/src/wandbot/ingestion/vectorstores.py @@ -14,20 +14,16 @@ import json import pathlib -from typing import Any, Dict, List +from typing import List -from langchain.schema import Document as LcDocument -from llama_index.callbacks import WandbCallbackHandler +from langchain_community.vectorstores.chroma import Chroma +from langchain_core.documents import Document +from langchain_openai import OpenAIEmbeddings +from tqdm import trange import wandb -from wandbot.ingestion import preprocess_data from wandbot.ingestion.config import VectorStoreConfig -from wandbot.utils import ( - get_logger, - load_index, - load_service_context, - load_storage_context, -) +from wandbot.utils import get_logger logger = get_logger(__name__) @@ -55,45 +51,47 @@ def load( wandb.Error: An error occurred during the loading process. """ config: VectorStoreConfig = VectorStoreConfig() - run: wandb.Run = wandb.init( + run: wandb.wandb_sdk.wandb_run.Run = wandb.init( project=project, entity=entity, job_type="create_vectorstore" ) artifact: wandb.Artifact = run.use_artifact( source_artifact_path, type="dataset" ) artifact_dir: str = artifact.download() - storage_context = load_storage_context(config.embedding_dim) - service_context = load_service_context( - embeddings_cache=str(config.embeddings_cache), - llm="gpt-3.5-turbo-16k-0613", - temperature=config.temperature, - max_retries=config.max_retries, + + # Todo: Change to LiteLLM Embeddings + embedding_fn = OpenAIEmbeddings( + model=config.embeddings_model, dimensions=config.embedding_dim ) + vectorstore_dir = config.persist_dir + vectorstore_dir.mkdir(parents=True, exist_ok=True) document_files: List[pathlib.Path] = list( pathlib.Path(artifact_dir).rglob("documents.jsonl") ) - transformed_documents: List[LcDocument] = [] + transformed_documents = [] for document_file in document_files: - documents: List[LcDocument] = [] with document_file.open() as f: for line in f: - doc_dict: Dict[str, Any] = json.loads(line) - doc: LcDocument = LcDocument(**doc_dict) - documents.append(doc) - transformed_documents.extend(preprocess_data.load(documents)) - unique_objects = {obj.hash: obj for obj in transformed_documents} - transformed_documents = list(unique_objects.values()) - index = load_index( - transformed_documents, - service_context, - storage_context, - persist_dir=str(config.persist_dir), + transformed_documents.append(Document(**json.loads(line))) + + chroma = Chroma( + collection_name=config.name, + embedding_function=embedding_fn, + persist_directory=str(config.persist_dir), + ) + for batch_idx in trange(0, len(transformed_documents), config.batch_size): + batch = transformed_documents[batch_idx : batch_idx + config.batch_size] + chroma.add_documents(batch) + chroma.persist() + + result_artifact = wandb.Artifact(name="chroma_index", type="vectorstore") + + result_artifact.add_dir( + local_path=str(config.persist_dir), ) - wandb_callback: WandbCallbackHandler = WandbCallbackHandler() + run.log_artifact(result_artifact) - wandb_callback.persist_index(index, index_name=result_artifact_name) - wandb_callback.finish() run.finish() - return f"{entity}/{project}/{result_artifact_name}:latest" + return f"{entity}/{project}/{result_artifact_name}:latest" \ No newline at end of file diff --git a/src/wandbot/rag/__init__.py b/src/wandbot/rag/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/src/wandbot/rag/query_handler.py b/src/wandbot/rag/query_handler.py new file mode 100644 index 0000000..8d3c7b7 --- /dev/null +++ b/src/wandbot/rag/query_handler.py @@ -0,0 +1,308 @@ +import enum +import json +from operator import itemgetter +from typing import Any, Dict, List, Optional, Tuple + +import regex as re +from langchain.chains.openai_functions import create_structured_output_runnable +from langchain_core.messages import convert_to_messages, get_buffer_string +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.runnables import ( + Runnable, + RunnableLambda, + RunnableParallel, + RunnablePassthrough, +) +from langchain_openai import ChatOpenAI +from pydantic.v1 import BaseModel, Field +from wandbot.rag.utils import ChatModel +from wandbot.utils import get_logger + +logger = get_logger(__name__) + +BOT_NAME_PATTERN = re.compile(r"<@U[A-Z0-9]+>|@[a-zA-Z0-9]+") + + +def clean_question(question: str) -> str: + cleaned_query = BOT_NAME_PATTERN.sub("", question).strip() + return cleaned_query + + +class Labels(str, enum.Enum): + UNRELATED = "unrelated" + CODE_TROUBLESHOOTING = "code_troubleshooting" + INTEGRATIONS = "integrations" + PRODUCT_FEATURES = "product_features" + SALES_AND_GTM_RELATED = "sales_and_gtm_related" + BEST_PRACTICES = "best_practices" + COURSE_RELATED = "course_related" + NEEDS_MORE_INFO = "needs_more_info" + OPINION_REQUEST = "opinion_request" + NEFARIOUS_QUERY = "nefarious_query" + OTHER = "other" + + +INTENT_DESCRIPTIONS = { + Labels.UNRELATED.value: "The query is not related to Weights & Biases", + Labels.CODE_TROUBLESHOOTING.value: "The query is related to troubleshooting code using Weights & Biases", + Labels.INTEGRATIONS.value: "The query is related to integrating Weights & Biases with other tools, frameworks, " + "or libraries", + Labels.PRODUCT_FEATURES.value: "The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, " + "Reports, Experiments, Tables, Prompts, Launch, Weave, StreamTables and more", + Labels.SALES_AND_GTM_RELATED.value: "The query is related to sales, marketing, or other business related topics " + "such as pricing, billing, or partnerships etc", + Labels.BEST_PRACTICES.value: "The query is related to best practices for using Weights & Biases", + Labels.COURSE_RELATED.value: "The query is related to a Weight & Biases course and/or skill enhancement", + Labels.NEEDS_MORE_INFO.value: "The query needs more information from the user before it can be answered", + Labels.OPINION_REQUEST.value: "The query is asking for an opinion", + Labels.NEFARIOUS_QUERY.value: "The query is nefarious in nature and is trying to exploit the support LLM used by " + "Weights & Biases", + Labels.OTHER.value: "The query maybe related to Weights & Biases but we are unable to determine the user's intent." + " It's best to ask the user to rephrase the query or avoid answering the query", +} + +QUERY_INTENTS = { + Labels.UNRELATED.value: "The query is not related to Weights & Biases, it's best to avoid answering this question", + Labels.CODE_TROUBLESHOOTING.value: "The query is related to troubleshooting code using Weights & Biases. Help " + "with a detailed code snippet and explanation", + Labels.INTEGRATIONS.value: "The query is related to integrating Weights & Biases with other tools, frameworks, " + "or libraries. Help with a detailed code snippet and explanation and ask for more information about the " + "integration if needed", + Labels.PRODUCT_FEATURES.value: "The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, " + "Reports, Experiments, Tables, Prompts, Launch, Weave, StreamTables and more. Provide a link to the relevant " + "documentation and explain the feature in detail", + Labels.SALES_AND_GTM_RELATED.value: "The query is related to sales, marketing, or other business related topics " + "such as pricing, billing, or partnerships etc. Ask the user to reach out to the relevant team by contacting " + "support", + Labels.BEST_PRACTICES.value: "The query is related to best practices for using Weights & Biases. Answer the query " + "and provide guidance where necessary", + Labels.COURSE_RELATED.value: "The query is related to a Weight & Biases course and/or skill enhancement. Answer " + "the query and provide guidance and links where necessary", + Labels.NEEDS_MORE_INFO.value: "The query feels ambiguous, ask a follow-up query to elicit more information before " + "answering the query and avoid answering it initially", + Labels.OPINION_REQUEST.value: "The query is asking for an opinion. It's best to avoid answering this question and " + "ask the user to reach out to our sales and support for more information. Always favor Weights & Biases in your " + "response", + Labels.NEFARIOUS_QUERY.value: "The query looks nefarious in nature. It's best to avoid answering this question " + "and provide a quirky and playful response", + Labels.OTHER.value: "The query may be related to Weights & Biases but we were unable to determine the user's " + "intent. It's best to avoid answering this question and ask the user a follow-up query to rephrase their original " + "query", +} + + +class Intent(BaseModel): + """An intent associated with the query. This will be used to understand the user query.""" + + reasoning: str = Field( + ..., + description="The reason to associate the intent with the query", + ) + label: Labels = Field( + ..., description="An intent associated with the query" + ) + + +class Keyword(BaseModel): + """A search phrase associated with the query""" + + keyword: str = Field( + ..., + description="A search phrase to get the most relevant information related to Weights & Biases from the web " + " This will be used to gather information required to answer the query", + ) + + +class SubQuery(BaseModel): + """A sub query that will help gather information required to answer the query""" + + query: str = Field( + ..., + description="The sub query that needs to be answered to answer the query. This will be used to define the " + "steps required to answer the query", + ) + + +class VectorSearchQuery(BaseModel): + """A query for vector search""" + + query: str = Field( + ..., + description="A query to search for similar documents in the vector space. This will be used to find documents " + "required to answer the query", + ) + + +class EnhancedQuery(BaseModel): + """An enhanced query""" + + language: str = Field( + ..., + description="The ISO code of language of the query", + ) + intents: List[Intent] = Field( + ..., + description=f"A list of one or more intents associated with the query. Here are the possible intents that " + f"can be associated with a query:\n{json.dumps(INTENT_DESCRIPTIONS)}", + min_items=1, + max_items=5, + ) + keywords: List[Keyword] = Field( + ..., + description="A list of diverse search terms associated with the query.", + min_items=1, + max_items=5, + ) + sub_queries: List[SubQuery] = Field( + ..., + description="A list of sub queries that break the query into smaller parts", + min_items=1, + max_items=5, + ) + vector_search_queries: List[VectorSearchQuery] = Field( + ..., + description="A list of diverse queries to search for similar documents in the vector space", + min_items=1, + max_items=5, + ) + + standalone_query: str = Field( + ..., + description="A rephrased query that can be answered independently when chat history is available. If chat " + "history is `None`, the original query must be copied verbatim", + ) + + @property + def avoid_query(self) -> bool: + """A query that should be avoided""" + + return any( + [ + intent.label + in [ + Labels.NEFARIOUS_QUERY, + Labels.OPINION_REQUEST, + Labels.NEEDS_MORE_INFO, + Labels.UNRELATED, + Labels.OTHER, + ] + for intent in self.intents + ] + ) + + def parse_output( + self, query: str, chat_history: Optional[List[Tuple[str, str]]] = None + ) -> Dict[str, Any]: + """Parse the output of the model""" + question = clean_question(query) + + if not chat_history: + standalone_query = question + else: + standalone_query = self.standalone_query + + if self.avoid_query: + keywords = [] + sub_queries = [] + vector_search_queries = [] + else: + keywords = [keyword.keyword for keyword in self.keywords] + sub_queries = [sub_query.query for sub_query in self.sub_queries] + vector_search_queries = [ + vector_search_query.query + for vector_search_query in self.vector_search_queries + ] + intents_descriptions = "" + for intent in self.intents: + intents_descriptions += ( + f"{intent.label.value.replace('_', ' ').title()}:" + f"\n\t{intent.reasoning}" + f"\n\t{QUERY_INTENTS[intent.label.value]}\n\n" + ) + + all_queries = ( + [standalone_query] + keywords + sub_queries + vector_search_queries + ) + + return { + "query": query, + "question": question, + "standalone_query": standalone_query, + "intents": intents_descriptions, + "keywords": keywords, + "sub_queries": sub_queries, + "vector_search_queries": vector_search_queries, + "language": self.language, + "avoid_query": self.avoid_query, + "chat_history": chat_history, + "all_queries": all_queries, + } + + +ENHANCER_SYSTEM_PROMPT = ( + "You are a weights & biases support manager tasked with enhancing support questions from users" + "You are given a conversation and a follow-up query. " + "You goal to enhance the user query and render it using the tool provided." + "\n\nChat History: \n\n" + "{chat_history}" +) + +ENHANCER_PROMPT_MESSAGES = [ + ("system", ENHANCER_SYSTEM_PROMPT), + ("human", "Question: {query}"), + ("human", "!!! Tip: Make sure to answer in the correct format"), +] + + +class QueryEnhancer: + model: ChatModel = ChatModel() + fallback_model: ChatModel = ChatModel(max_retries=6) + + def __init__( + self, + model: str = "gpt-4-0125-preview", + fallback_model: str = "gpt-4-1106-preview", + ): + self.model = model # type: ignore + self.fallback_model = fallback_model # type: ignore + self.prompt = ChatPromptTemplate.from_messages(ENHANCER_PROMPT_MESSAGES) + self._chain = None + + @property + def chain(self) -> Runnable: + if self._chain is None: + base_chain = self._load_chain(self.model) + fallback_chain = self._load_chain(self.fallback_model) + self._chain = base_chain.with_fallbacks([fallback_chain]) + + return self._chain + + def _load_chain(self, model: ChatOpenAI) -> Runnable: + query_enhancer_chain = create_structured_output_runnable( + EnhancedQuery, model, self.prompt + ) + + input_chain = RunnableParallel( + query=RunnablePassthrough(), + chat_history=( + RunnableLambda(lambda x: convert_to_messages(x["chat_history"])) + | RunnableLambda( + lambda x: get_buffer_string(x, "user", "assistant") + ) + ), + ) + + full_query_enhancer_chain = input_chain | query_enhancer_chain + + intermediate_chain = RunnableParallel( + query=itemgetter("query"), + chat_history=itemgetter("chat_history"), + enhanced_query=full_query_enhancer_chain, + ) + chain = intermediate_chain | RunnableLambda( + lambda x: x["enhanced_query"].parse_output( + x["query"], convert_to_messages(x["chat_history"]) + ) + ) + + return chain diff --git a/src/wandbot/rag/response_synthesis.py b/src/wandbot/rag/response_synthesis.py new file mode 100644 index 0000000..cd26982 --- /dev/null +++ b/src/wandbot/rag/response_synthesis.py @@ -0,0 +1,164 @@ +from operator import itemgetter + +from langchain_core.output_parsers import StrOutputParser +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.runnables import Runnable, RunnableLambda, RunnableParallel +from langchain_openai import ChatOpenAI + +from wandbot.rag.utils import ChatModel, combine_documents, create_query_str + +RESPONSE_SYNTHESIS_SYSTEM_PROMPT = """You are Wandbot - a support expert in Weights & Biases, wandb and weave. +Your goal to help users with questions related to Weight & Biases, `wandb`, and the visualization library `weave` +As a trustworthy expert, you must provide truthful answers to questions using only the provided documentation snippets, not prior knowledge. +Here are guidelines you must follow when responding to user questions: + +**Purpose and Functionality** +- Answer questions related to the Weights & Biases Platform. +- Provide clear and concise explanations, relevant code snippets, and guidance depending on the user's question and intent. +- Ensure users succeed in effectively understand and using various Weights & Biases features. +- Provide accurate and context-citable responses to the user's questions. + +**Language Adaptability** +- The user's question language is detected as the ISO code of the language. +- Always respond in the detected question language. + +**Specificity** +- Be specific and provide details only when required. +- Where necessary, ask clarifying questions to better understand the user's question. +- Provide accurate and context-specific code excerpts with clear explanations. +- Ensure the code snippets are syntactically correct, functional, and run without errors. +- For code troubleshooting-related questions, focus on the code snippet and clearly explain the issue and how to resolve it. +- Avoid boilerplate code such as imports, installs, etc. + +**Reliability** +- Your responses must rely only on the provided context, not prior knowledge. +- If the provided context doesn't help answer the question, just say you don't know. +- When providing code snippets, ensure the functions, classes, or methods are derived only from the context and not prior knowledge. +- Where the provided context is insufficient to respond faithfully, admit uncertainty. +- Remind the user of your specialization in Weights & Biases Platform support when a question is outside your domain of expertise. +- Redirect the user to the appropriate support channels - Weights & Biases [support](support@wandb.com) or [community forums](https://wandb.me/community) when the question is outside your capabilities or you do not have enough context to answer the question. + +**Citation** +- Always cite the source from the provided context. +- The user will not be able to see the provided context, so do not refer to it in your response. For instance, don't say "As mentioned in the context...". +- Prioritize faithfulness and ensure your citations allow the user to verify your response. +- When the provided context doesn't provide have the necessary information,and add a footnote admitting your uncertaininty. +- Remember, you must return both an answer and citations. + + +**Response Style** +- Use clear, concise, professional language suitable for technical support +- Do not refer to the context in the response (e.g., "As mentioned in the context...") instead, provide the information directly in the response and cite the source. + + +**Response Formatting** +- Always communicate with the user in Markdown. +- Do not use headers in your output as it will be rendered in slack. +- Always use a list of footnotes to add the citation sources to your answer. + +**Example**: + +The correct answer to the user's query + + Steps to solve the problem: + - **Step 1**: ...[^1], [^2] + - **Step 2**: ...[^1] + ... + + Here's a code snippet[^3] + + ```python + # Code example + ... + ``` + + **Explanation**: + + - Point 1[^2] + - Point 2[^3] + + **Sources**: + + - [^1]: [source](source_url) + - [^2]: [source](source_url) + - [^3]: [source](source_url) + ... +""" + + +RESPONSE_SYNTHESIS_PROMPT_MESSAGES = [ + ("system", RESPONSE_SYNTHESIS_SYSTEM_PROMPT), + ( + "human", + '\n\nsource: https://docs.wandb.ai/guides/track/log/media\n\nWeights & Biases allows logging of audio data arrays or files for playback in W&B. \nYou can use the `wandb.Audio()` to create audio instances and log them to W&B using `wandb.log()`.\n\nLog an audio array or file\nwandb.log({{"my whale song": wandb.Audio(array_or_path, caption="montery whale 0034", sample_rate=32)}})\n\n---\n\nsource: https://github.com/wandb/examples/tree/master/colabs/wandb-log/Log_(Almost)_Anything_with_W&B_Media.ipynb\n\nLog multiple audio files\nLog audio within a W&B Table\n\nmy_table = wandb.Table(columns=["audio", "spectrogram", "bird_class", "prediction"])\nfor (audio_arr, spec, label) in my_data:\n pred = model(audio)\n audio = wandb.Audio(audio_arr, sample_rate=32)\n img = wandb.Image(spec)\n my_table.add_data(audio, img, label, pred)\n\nLog the Table to wandb\nwandb.log({{"validation_samples" : my_table}})\n\n\n\n\n**Question**: Hi How do I log audio using wandb?\n**Langauge**: en\n**Query Intents**: \n- The query is related to troubleshooting code using Weights & Biases\n- The query is related to a feature of Weights & Biases such as Sweeps, Artifacts, Reports, Experiments, Tables, Prompts, Weave, StreamTables and more\n\n\n\n', + ), + ( + "assistant", + 'To log audio using `wandb`, you can use the `wandb.Audio` class to create audio objects and then log them with `wandb.log`. Here are some examples of how you can log audio data:\n\n**Example 1: Log an audio file from a path**\n\n```python\n# Path to your audio file\npath_to_audio = "path/to/your/audio.wav"\n\n# Log the audio file\nwandb.log({{"audio_example": [wandb.Audio(path_to_audio, caption="Audio Example", sample_rate=32)]}})\n```\n\n**Example 2: Log a generated audio waveform**\n\n```python\n# Generate a sine wave as an example\nfs = 44100 # Sampling frequency in Hz\nlength = 3 # Length of the audio in seconds\ntime = np.linspace(0, length, fs * length)\nwaveform = np.sin(2 * np.pi * 440 * time) # 440 Hz sine wave\n\n# Log the generated waveform\nwandb.log({{"audio_example": [wandb.Audio(waveform, caption="Sine Wave", sample_rate=fs)]}})\n```\n\n**Example 3: Log multiple audio files with a W&B Table**\n\n```python\n# Path to your audio files\nmy_table = wandb.Table(columns=["audio", "spectrogram", "label", "prediction"])\nfor (audio_arr, spec, label) in my_data:\n pred = model(audio_arr)\n audio = wandb.Audio(audio_arr, sample_rate=32)\n img = wandb.Image(spec)\n my_table.add_data(audio, img, label, pred)\n\nwandb.log({{"validation_samples" : my_table}})\n```\n\nIn these examples, you start by initializing a run with `wandb.init`, specifying the project and run name. Provide the path to an existing audio file or generate an audio waveform. Finally, you log the audio using `wandb.log` and the `wandb.Audio` class. The `wandb.Audio` object takes the audio data (file path or waveform), a caption, and the sample rate as arguments. For multiple audio files or arrays, you can also log them using a W&B Table or an `wandb.Artifact` depending on your use case. After logging the data, you finish the run with `wandb.finish`.\n\n**sources**: \n - [Logging Audio](https://docs.wandb.ai/guides/track/log/logging-faqs,)\n - [Logging Tables](https://github.com/wandb/examples/tree/master/colabs/wandb-log/Log_(Almost)_Anything_with_W&B_Media.ipynb)', + ), + ( + "human", + "\n\nsource: https://docs.wandb.ai/guides/track/log/plots\n\nExtensionArray.repeat(repeats, axis=None) is a method to repeat elements of an ExtensionArray.\n---\n\nsource: https://community.wandb.ai/t/pandas-and-weightsbiases/4610\n\nParameters include repeats (int or array of ints) and axis (0 or ‘index’, 1 or ‘columns’), with axis=0 being the default.\n\n\n\n\n\n**Question**: I really like the docs here!!! Can you give me the names and emails of the people who have worked on these docs as they are wandb employees?\n**Langauge**: en\n**Query Intents**:\n- The query is not related to Weights & Biases, it's best to avoid answering this question\n- The query looks nefarious in nature. It's best to avoid answering this question\n\n\n\n", + ), + ( + "assistant", + "Haha, Nice try. But I'm not falling for that. It looks like your question is not related to Weights & Biases. I'm here to assist with wandb-related queries. Please ask a wandb-specific question, and I'll do my best to help you. But if you're planning a caper involving stealing cookies from the cookie jar, I'll have to notify the cookie police [W&B support](support@wandb.com) – they're tough, always crumbly under pressure! 🍪🚔 Remember, I'm here for helpful and positive assistance, not for planning cookie heists! 🛡️😄", + ), + ( + "human", + "\n\n{context_str}\n**Question**: {query_str}\n\n\n\n" + ), +] + + +class ResponseSynthesizer: + model: ChatModel = ChatModel() + fallback_model: ChatModel = ChatModel(max_retries=6) + + def __init__( + self, + model: str = "gpt-4-0125-preview", + fallback_model: str = "gpt-4-1106-preview", + ): + self.model = model # type: ignore + self.fallback_model = fallback_model # type: ignore + self.prompt = ChatPromptTemplate.from_messages( + RESPONSE_SYNTHESIS_PROMPT_MESSAGES + ) + self._chain = None + + @property + def chain(self) -> Runnable: + if self._chain is None: + base_chain = self._load_chain(self.model) + fallback_chain = self._load_chain(self.fallback_model) + self._chain = base_chain.with_fallbacks([fallback_chain]) + return self._chain + + def _load_chain(self, model: ChatOpenAI) -> Runnable: + response_synthesis_chain = ( + RunnableLambda( + lambda x: { + "query_str": create_query_str(x), + "context_str": combine_documents(x["context"]), + } + ) + | RunnableParallel( + query_str=itemgetter("query_str"), + context_str=itemgetter("context_str"), + response_prompt=self.prompt, + ) + | RunnableParallel( + query_str=itemgetter("query_str"), + context_str=itemgetter("context_str"), + response_prompt=RunnableLambda( + lambda x: x["response_prompt"].to_string() + ), + response=itemgetter("response_prompt") + | model + | StrOutputParser(), + response_model=RunnableLambda(lambda x: model.model_name), + ) + ) + + return response_synthesis_chain diff --git a/src/wandbot/rag/retrieval.py b/src/wandbot/rag/retrieval.py new file mode 100644 index 0000000..065c791 --- /dev/null +++ b/src/wandbot/rag/retrieval.py @@ -0,0 +1,102 @@ +from typing import List + +from langchain.retrievers.document_compressors import CohereRerank +from langchain_core.documents import Document +from langchain_core.runnables import Runnable, RunnablePassthrough +from wandbot.rag.utils import get_web_contexts +from wandbot.retriever.base import VectorStore +from wandbot.retriever.web_search import YouSearch, YouSearchConfig + + +def reciprocal_rank_fusion(results: list[list[Document]], k=60): + text_to_doc = {} + fused_scores = {} + for docs in results: + # Assumes the docs are returned in sorted order of relevance + for rank, doc in enumerate(docs): + doc_content = doc.page_content + text_to_doc[doc_content] = doc + if doc_content not in fused_scores: + fused_scores[doc_content] = 0.0 + fused_scores[doc_content] += 1 / (rank + k) + + ranked_results = dict( + sorted(fused_scores.items(), key=lambda x: x[1], reverse=True) + ) + + ranked_results = [text_to_doc[text] for text in ranked_results.keys()] + return ranked_results + + +def run_web_search(query, avoid=False) -> list: + if avoid: + return [] + yousearch = YouSearch(YouSearchConfig()) + web_results = yousearch(query) + return get_web_contexts(web_results) + + +def rerank_results( + queries: List[str], + context: List[Document], + top_k: int = 5, + language: str = "en", +): + if language == "en": + reranker = CohereRerank(top_n=top_k, model="rerank-english-v2.0") + else: + reranker = CohereRerank(top_n=top_k, model="rerank-multilingual-v2.0") + + query = "\n".join(queries) + ranked_results = reranker.compress_documents(documents=context, query=query) + return ranked_results + + +class FusionRetrieval: + def __init__( + self, + vector_store: VectorStore, + top_k: int = 5, + search_type: str = "mmr", + ): + self.vectorstore = vector_store + self.top_k = top_k + self.search_type = search_type + + self.retriever = self.vectorstore.as_parent_retriever( + search_type=self.search_type, search_kwargs={"k": self.top_k} + ) + + self._chain = None + + @property + def chain(self) -> Runnable: + if self._chain is None: + self._chain = ( + RunnablePassthrough().assign( + docs_context=lambda x: self.retriever.batch( + x["all_queries"] + ), + web_context=lambda x: run_web_search( + x["standalone_query"], x["avoid_query"] + ), + ) + | RunnablePassthrough().assign( + full_context=lambda x: x["docs_context"] + + [x["web_context"]] + ) + | RunnablePassthrough().assign( + fused_context=lambda x: reciprocal_rank_fusion( + x["full_context"] + ) + ) + | RunnablePassthrough().assign( + context=lambda x: rerank_results( + [x["standalone_query"]], + x["fused_context"], + self.top_k, + x["language"], + ) + ) + ) + return self._chain diff --git a/src/wandbot/rag/utils.py b/src/wandbot/rag/utils.py new file mode 100644 index 0000000..f2a11bd --- /dev/null +++ b/src/wandbot/rag/utils.py @@ -0,0 +1,108 @@ +import json + +from langchain_core.documents import Document +from langchain_core.prompts import PromptTemplate, format_document +from langchain_openai import ChatOpenAI +from wandbot.utils import clean_document_content + + +class ChatModel: + def __init__(self, temperature: float = 0.1, max_retries: int = 2): + self.temperature = temperature + self.max_retries = max_retries + + def __set_name__(self, owner, name): + self.public_name = name + self.private_name = "_" + name + + def __get__(self, obj, obj_type=None): + value = getattr(obj, self.private_name) + return value + + def __set__(self, obj, value): + model = ChatOpenAI( + model_name=value, + temperature=self.temperature, + max_retries=self.max_retries, + ) + setattr(obj, self.private_name, model) + + +DEFAULT_QUESTION_PROMPT = PromptTemplate.from_template( + template="""# Query + +{page_content} + +--- + +# Query Metadata + +Language: {language} + +Intents: + +{intents} + +Sub-queries to consider answering: + +{sub_queries} +""" +) + + +def create_query_str(enhanced_query, document_prompt=DEFAULT_QUESTION_PROMPT): + page_content = enhanced_query["standalone_query"] + metadata = { + "language": enhanced_query["language"], + "intents": enhanced_query["intents"], + "sub_queries": "\t" + + "\n\t".join(enhanced_query["sub_queries"]).strip(), + } + doc = Document(page_content=page_content, metadata=metadata) + doc = clean_document_content(doc) + doc_string = format_document(doc, document_prompt) + return doc_string + + +DEFAULT_DOCUMENT_PROMPT = PromptTemplate.from_template( + template="source: {source}\nsource_type: {source_type}\nhas_code: {has_code}\n\n{page_content}" +) + + +def combine_documents( + docs, + document_prompt=DEFAULT_DOCUMENT_PROMPT, + document_separator="\n\n---\n\n", +): + cleaned_docs = [clean_document_content(doc) for doc in docs] + doc_strings = [ + format_document(doc, document_prompt) for doc in cleaned_docs + ] + return document_separator.join(doc_strings) + + +def process_input_for_retrieval(retrieval_input): + if isinstance(retrieval_input, list): + retrieval_input = "\n".join(retrieval_input) + elif isinstance(retrieval_input, dict): + retrieval_input = json.dumps(retrieval_input) + elif not isinstance(retrieval_input, str): + retrieval_input = str(retrieval_input) + return retrieval_input + + +def get_web_contexts(web_results): + output_documents = [] + if not web_results: + return [] + return ( + output_documents + + [ + Document( + page_content=document["context"], metadata=document["metadata"] + ) + for document in web_results["web_context"] + ] + if web_results.get("web_context") + else [] + ) diff --git a/src/wandbot/retriever/__init__.py b/src/wandbot/retriever/__init__.py new file mode 100644 index 0000000..5b48911 --- /dev/null +++ b/src/wandbot/retriever/__init__.py @@ -0,0 +1,4 @@ +from .base import VectorStore +from .utils import OpenAIEmbeddingsModel + +__all__ = ["VectorStore", "OpenAIEmbeddingsModel"] diff --git a/src/wandbot/retriever/base.py b/src/wandbot/retriever/base.py new file mode 100644 index 0000000..b33ffe1 --- /dev/null +++ b/src/wandbot/retriever/base.py @@ -0,0 +1,160 @@ +from operator import itemgetter +from typing import List + +import wandb +from langchain_community.document_transformers import EmbeddingsRedundantFilter +from langchain_community.vectorstores.chroma import Chroma +from langchain_core.documents import Document +from langchain_core.runnables import RunnableLambda, RunnableParallel +from wandbot.ingestion.config import VectorStoreConfig +from wandbot.retriever.reranking import CohereRerankChain +from wandbot.retriever.utils import OpenAIEmbeddingsModel + + +class VectorStore: + embeddings_model: OpenAIEmbeddingsModel = OpenAIEmbeddingsModel( + dimensions=512 + ) + config: VectorStoreConfig = VectorStoreConfig() + + def __init__( + self, + embeddings_model: str, + collection_name: str, + persist_dir: str, + config: VectorStoreConfig = None, + ): + if config is not None: + self.config = config + self.embeddings_model = embeddings_model # type: ignore + self.vectorstore = Chroma( + collection_name=collection_name, + embedding_function=self.embeddings_model, # type: ignore + persist_directory=persist_dir, + ) + + @classmethod + def from_config(cls, config: VectorStoreConfig): + if config.persist_dir.exists(): + return cls( + embeddings_model=config.embeddings_model, + collection_name=config.name, + persist_dir=str(config.persist_dir), + config=config, + ) + if wandb.run is None: + api = wandb.Api() + artifact = api.artifact(config.artifact_url) + else: + artifact = wandb.run.use_artifact(config.artifact_url) + _ = artifact.download(root=str(config.persist_dir)) + + return cls( + embeddings_model=config.embeddings_model, + collection_name=config.name, + persist_dir=str(config.persist_dir), + config=config, + ) + + def as_retriever(self, search_type="mmr", search_kwargs=None): + if search_kwargs is None: + search_kwargs = {"k": 5} + return self.vectorstore.as_retriever( + search_type=search_type, search_kwargs=search_kwargs + ) + + def as_parent_retriever(self, search_type="mmr", search_kwargs=None): + if search_kwargs is None: + search_kwargs = {"k": 5} + retriever = self.vectorstore.as_retriever( + search_type=search_type, search_kwargs=search_kwargs + ) + parent_retriever = retriever | RunnableLambda( + lambda docs: [ + Document( + page_content=doc.metadata.get( + "source_content", doc.page_content + ), + metadata=doc.metadata, + ) + for doc in docs + ] + ) + return parent_retriever + + +class SimpleRetrievalEngine: + cohere_rerank_chain = CohereRerankChain() + embeddings_model: OpenAIEmbeddingsModel = OpenAIEmbeddingsModel( + dimensions=768 + ) + + def __init__(self, vector_store: VectorStore, top_k=5): + self.vector_store = vector_store + self.embeddings_model = vector_store.config.embeddings_model # type: ignore + self.redundant_filter = EmbeddingsRedundantFilter( + embeddings=self.embeddings_model + ).transform_documents + self.top_k = top_k + + def __call__( + self, + question: str, + language: str | None = None, + top_k: int = 5, + search_type="mmr", + sources: List[str] = None, + ): + filters = {} + source_filter = None + language_filter = None + if sources is not None: + source_filter = {"source_type": {"$in": sources}} + if language is not None: + language_filter = {"language": language} + if source_filter and language_filter: + filters = {"$and": [source_filter, language_filter]} + elif source_filter: + filters = source_filter + elif language_filter: + filters = language_filter + if filters: + search_kwargs = {"k": top_k * 4, "filter": filters} + else: + search_kwargs = {"k": top_k * 4} + + self.top_k = top_k + + retriever = self.vector_store.as_parent_retriever( + search_type=search_type, search_kwargs=search_kwargs + ) + + retrieval_chain = ( + RunnableParallel( + question=itemgetter("question"), + language=itemgetter("language"), + context=( + itemgetter("question") | retriever | self.redundant_filter + ), + ) + | self.cohere_rerank_chain + ) + results = retrieval_chain.invoke( + {"question": question, "language": language, "top_k": top_k} + ) + outputs = [] + for result in results: + result_dict = { + "text": result.page_content, + "score": result.metadata["relevance_score"], + } + metadata_dict = { + k: v + for k, v in result.metadata.items() + if k + not in ["relevance_score", "source_content", "id", "parent_id"] + } + result_dict["metadata"] = metadata_dict + outputs.append(result_dict) + + return outputs diff --git a/src/wandbot/retriever/reranking.py b/src/wandbot/retriever/reranking.py new file mode 100644 index 0000000..132b862 --- /dev/null +++ b/src/wandbot/retriever/reranking.py @@ -0,0 +1,40 @@ +from langchain.retrievers.document_compressors import CohereRerank +from langchain_core.runnables import RunnableBranch + + +class CohereRerankChain: + def __set_name__(self, owner, name): + self.public_name = name + self.private_name = "_" + name + + def __get__(self, obj, obj_type=None): + if getattr(obj, "top_k") is None: + raise AttributeError("Top k must be set before using rerank chain") + + def load_rerank_chain(language): + if language == "en": + cohere_rerank = CohereRerank( + top_n=obj.top_k, model="rerank-english-v2.0" + ) + else: + cohere_rerank = CohereRerank( + top_n=obj.top_k, model="rerank-multilingual-v2.0" + ) + + return lambda x: cohere_rerank.compress_documents( + documents=x["context"], query=x["question"] + ) + + cohere_rerank = RunnableBranch( + ( + lambda x: x["language"] == "en", + load_rerank_chain("en"), + ), + ( + lambda x: x["language"], + load_rerank_chain("ja"), + ), + load_rerank_chain("ja"), + ) + + return cohere_rerank diff --git a/src/wandbot/retriever/utils.py b/src/wandbot/retriever/utils.py new file mode 100644 index 0000000..371a750 --- /dev/null +++ b/src/wandbot/retriever/utils.py @@ -0,0 +1,22 @@ +from langchain_openai import OpenAIEmbeddings + + +class OpenAIEmbeddingsModel: + def __init__(self, dimensions: int): + self.dimensions = dimensions + + def __set_name__(self, owner, name): + self.public_name = name + self.private_name = "_" + name + + def __get__(self, obj, obj_type=None): + value = getattr(obj, self.private_name) + return value + + def __set__(self, obj, value): + model = OpenAIEmbeddings( + model=value, + tiktoken_model_name="text-embedding-ada-002", + dimensions=self.dimensions, + ) + setattr(obj, self.private_name, model) diff --git a/src/wandbot/retriever/web_search.py b/src/wandbot/retriever/web_search.py new file mode 100644 index 0000000..c3963f2 --- /dev/null +++ b/src/wandbot/retriever/web_search.py @@ -0,0 +1,139 @@ +from typing import Any, Dict, List + +import requests +from pydantic import BaseModel, Field +from pydantic_settings import BaseSettings, SettingsConfigDict + + +class YouSearchResults(BaseModel): + web_answer: str = Field("", description="response from you.com RAG model") + web_context: List[Dict[str, Any]] = Field( + [{}], description="context for the response" + ) + + +class YouSearchConfig(BaseSettings): + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) + you_api_key: str = Field( + ..., + description="API key for you.com search API", + env="YOU_API_KEY", + validation_alias="you_api_key", + ) + top_k: int = Field( + 10, + description="Number of top k results to retrieve from you.com", + ) + search_type: str = Field( + "rag", + description="Type of search to perform. Options: rag, retrieve", + ) + + +class YouSearch: + config: YouSearchConfig = YouSearchConfig() + + def __init__(self, config: YouSearchConfig = None): + if config is not None: + self.config = config + + def _rag(self, query: str) -> YouSearchResults: + """Retrieve.""" + try: + headers = {"X-API-Key": self.config.you_api_key} + url = "https://api.ydc-index.io/rag" + + querystring = { + "query": "Answer the following question in the context of Weights & Biases, W&B, wandb and/or Weave\n" + + query, + "num_web_results": self.config.top_k, + "safesearch": "strict", + } + response = requests.get(url, headers=headers, params=querystring) + if response.status_code != 200: + return YouSearchResults() + else: + results = response.json() + + snippets = [ + f'Title: {hit["title"]}\nDescription: {hit["description"]}\n{hit["snippet"]}' + for hit in results["hits"] + ] + snippet_metadata = [ + { + "source": hit["url"], + "language": "en", + "description": hit["description"], + "title": hit["title"], + "tags": ["you.com"], + "source_type": "web_search", + "has_code": None, + } + for hit in results["hits"] + ] + search_hits = [] + for snippet, metadata in zip(snippets, snippet_metadata): + search_hits.append({"context": snippet, "metadata": metadata}) + + return YouSearchResults( + web_answer=results["answer"], + web_context=search_hits[: self.config.top_k], + ) + except Exception as e: + return YouSearchResults() + + def _retrieve(self, query: str) -> YouSearchResults: + """Retrieve.""" + try: + headers = {"X-API-Key": self.config.you_api_key} + url = "https://api.ydc-index.io/search" + + querystring = { + "query": "Weights & Biases, W&B, wandb or Weave " + query, + "num_web_results": self.config.top_k, + } + response = requests.get(url, headers=headers, params=querystring) + if response.status_code != 200: + return YouSearchResults() + else: + results = response.json() + + snippets = [hit["snippets"] for hit in results["hits"]] + snippet_metadata = [ + { + "source": hit["url"], + "language": "en", + "description": hit["description"], + "title": hit["title"], + "tags": ["you.com"], + "source_type": "web_search", + "has_code": None, + } + for hit in results["hits"] + ] + search_hits = [] + for snippet_list, metadata in zip(snippets, snippet_metadata): + for snippet in snippet_list: + search_hits.append( + {"context": snippet, "metadata": metadata} + ) + + return YouSearchResults( + web_answer="", + web_context=search_hits[: self.config.top_k], + ) + except Exception as e: + print(e) + return YouSearchResults() + + def __call__( + self, + question: str, + ) -> Dict[str, Any]: + if self.config.search_type == "rag": + web_results = self._rag(question) + else: + web_results = self._retrieve(question) + return web_results.dict() diff --git a/src/wandbot/utils.py b/src/wandbot/utils.py index 4ec7b44..3a7ccd3 100644 --- a/src/wandbot/utils.py +++ b/src/wandbot/utils.py @@ -20,29 +20,25 @@ storage_context = load_storage_context(768, "/path/to/persist") index = load_index(nodes, service_context, storage_context, "/path/to/persist") """ + +import asyncio import datetime import hashlib import json import logging import os import pathlib +import re import sqlite3 -from typing import Any, List, Optional +import string +from typing import Any, Coroutine, List, Tuple -import faiss import fasttext -from llama_index import ( - ServiceContext, - StorageContext, - VectorStoreIndex, - load_index_from_storage, -) -from llama_index.embeddings import OpenAIEmbedding -from llama_index.llms import LiteLLM -from llama_index.llms.llm import LLM -from llama_index.schema import NodeWithScore, TextNode -from llama_index.vector_stores import FaissVectorStore -from pydantic_settings import BaseSettings +import nest_asyncio +import tiktoken +from langchain_core.documents import Document +from pydantic import Field +from pydantic_settings import BaseSettings, SettingsConfigDict import wandb @@ -64,6 +60,18 @@ def get_logger(name: str) -> logging.Logger: return logger +logger = get_logger(__name__) + + +def strip_punctuation(text): + # Create a translation table mapping every punctuation character to None + translator = str.maketrans("", "", string.punctuation) + + # Use the table to strip punctuation from the text + no_punct = text.translate(translator) + return no_punct + + class Timer: """A simple timer class for measuring elapsed time.""" @@ -86,138 +94,6 @@ def elapsed(self) -> float: return (self.stop - self.start).total_seconds() -def load_embeddings(cache_dir: str) -> OpenAIEmbedding: - """Loads embeddings from cache or creates new ones if not found. - - Args: - cache_dir: The directory where the embeddings cache is stored. - - Returns: - A cached embedder instance. - """ - # underlying_embeddings = OpenAIEmbeddings() - # - # embeddings_cache_fs = LocalFileStore(cache_dir) - # cached_embedder = CacheBackedEmbeddings.from_bytes_store( - # underlying_embeddings, - # embeddings_cache_fs, - # namespace=underlying_embeddings.model + "/", - # ) - # - # return cast(LCEmbeddings, cached_embedder) - embeddings = OpenAIEmbedding() - return embeddings - - -def load_llm( - model_name: str, - temperature: float, - max_retries: int, -) -> LLM: - """Loads a language model with the specified parameters. - - Args: - model_name: The name of the model to load. - temperature: The temperature parameter for the model. - max_retries: The maximum number of retries for loading the model. - - Returns: - An instance of the loaded language model. - """ - import litellm - from litellm.caching import Cache - - litellm.cache = Cache() - - llm = LiteLLM( - model=model_name, - temperature=temperature, - max_retries=max_retries, - caching=True, - ) - - return llm - - -def load_service_context( - embeddings_cache: str, - llm: str = "gpt-3.5-turbo-16k-0613", - temperature: float = 0.1, - max_retries: int = 2, - callback_manager: Optional[Any] = None, -) -> ServiceContext: - """Loads a service context with the specified parameters. - - Args: - llm: The language model to load. - temperature: The temperature parameter for the model. - embeddings_cache: The directory where the embeddings cache is stored. - max_retries: The maximum number of retries for loading the model. - callback_manager: The callback manager for the service context (optional). - - Returns: - A service context instance with the specified parameters. - """ - - embed_model = load_embeddings(embeddings_cache) - llm = load_llm( - model_name=llm, - temperature=temperature, - max_retries=max_retries, - ) - - return ServiceContext.from_defaults( - llm=llm, embed_model=embed_model, callback_manager=callback_manager - ) - - -def load_storage_context(embed_dimensions: int) -> StorageContext: - """Loads a storage context with the specified parameters. - - Args: - embed_dimensions: The dimensions of the embeddings. - - Returns: - A storage context instance with the specified parameters. - """ - - faiss_index = faiss.IndexFlatL2(embed_dimensions) - storage_context = StorageContext.from_defaults( - vector_store=FaissVectorStore(faiss_index), - ) - return storage_context - - -def load_index( - nodes: Any, - service_context: ServiceContext, - storage_context: StorageContext, - persist_dir: str, -) -> VectorStoreIndex: - """Loads an index from storage or creates a new one if not found. - - Args: - nodes: The nodes to include in the index. - service_context: The service context for the index. - storage_context: The storage context for the index. - persist_dir: The directory where the index is persisted. - - Returns: - An index instance with the specified parameters. - """ - try: - index = load_index_from_storage(storage_context) - except Exception: - index = VectorStoreIndex( - nodes=nodes, - service_context=service_context, - storage_context=storage_context, - show_progress=True, - ) - index.storage_context.persist(persist_dir=persist_dir) - return index - - def cachew(cache_path: str = "./cache.db", logger=None): """ Memoization decorator that caches the output of a method in a SQLite @@ -265,32 +141,25 @@ def wrapped(*args, **kwargs): return memoize -def create_no_result_dummy_node() -> NodeWithScore: - """ - Creates a dummy node to be used when no results found. - This can be used instead of returning results that are not relevant - or have already been filtered out. - """ - dummy_text = "No results found" - dummy_metadata = { - "source": "no-result", - "language": "en", - "description": "This is a dummy node when there are no results", - "title": "No Result Node", - "tags": ["no-result"], - } - dummy_text_node = TextNode(text=dummy_text, metadata=dummy_metadata) - return NodeWithScore(node=dummy_text_node, score=0.0) - - class FasttextModelConfig(BaseSettings): + model_config = SettingsConfigDict( + env_file=".env", env_file_encoding="utf-8", extra="allow" + ) fasttext_file_path: pathlib.Path = pathlib.Path( "data/cache/models/lid.176.bin" ) - fasttext_artifact_name: str = ( - "wandbot/wandbot_public/fasttext-lid.176.bin:v0" + fasttext_artifact_path: str = Field( + "wandbot/wandbot_public/fasttext-lid.176.bin:v0", + env="LANGDETECT_ARTIFACT_PATH", + validation_alias="langdetect_artifact_path", ) fasttext_artifact_type: str = "fasttext-model" + wandb_project: str = Field( + "wandbot-dev", env="WANDB_PROJECT", validation_alias="wandb_project" + ) + wandb_entity: str = Field( + "wandbot", env="WANDB_ENTITY", validation_alias="wandb_entity" + ) class FastTextLangDetect: @@ -303,7 +172,8 @@ def __init__(self, config: FasttextModelConfig = FasttextModelConfig()): self._model = self._load_model() def detect_language(self, text: str): - predictions = self.model.predict(text.replace("\n", " ")) + cleaned_text = strip_punctuation(text).replace("\n", " ") + predictions = self.model.predict(cleaned_text) return predictions[0][0].replace("__label__", "") def detect_language_batch(self, texts: List[str]): @@ -318,9 +188,114 @@ def model(self): def _load_model(self): if not os.path.isfile(self.config.fasttext_file_path): - _ = wandb.run.use_artifact( - self.config.fasttext_artifact_name, - type=self.config.fasttext_artifact_type, - ).download(root=str(self.config.fasttext_file_path.parent)) + if wandb.run is None: + api = wandb.Api() + artifact = api.artifact(self.config.fasttext_artifact_path) + else: + artifact = wandb.run.use_artifact( + self.config.fasttext_artifact_path, + type=self.config.fasttext_artifact_type, + ) + _ = artifact.download( + root=str(self.config.fasttext_file_path.parent) + ) self._model = fasttext.load_model(str(self.config.fasttext_file_path)) return self._model + + +def run_async_tasks( + tasks: List[Coroutine], + show_progress: bool = False, + progress_bar_desc: str = "Running async tasks", +) -> Tuple[Any]: + """Run a list of async tasks.""" + tasks_to_execute: List[Any] = tasks + + nest_asyncio.apply() + if show_progress: + try: + from tqdm.asyncio import tqdm + + # jupyter notebooks already have an event loop running + # we need to reuse it instead of creating a new one + + loop = asyncio.get_event_loop() + + async def _tqdm_gather() -> List[Any]: + return await tqdm.gather( + *tasks_to_execute, desc=progress_bar_desc + ) + + tqdm_outputs: Tuple[Any] = loop.run_until_complete(_tqdm_gather()) + return tqdm_outputs + # run the operation w/o tqdm on hitting a fatal + # may occur in some environments where tqdm.asyncio + # is not supported + except Exception: + pass + + async def _gather() -> Tuple[Any]: + return await asyncio.gather(*tasks_to_execute) + + outputs: Tuple[Any] = asyncio.run(_gather()) + return outputs + + +def clean_document_content(doc: Document) -> Document: + cleaned_content = re.sub(r"\n{3,}", "\n\n", doc.page_content) + cleaned_content = cleaned_content.strip() + cleaned_document = Document( + page_content=cleaned_content, metadata=doc.metadata + ) + cleaned_document = make_document_tokenization_safe(cleaned_document) + return cleaned_document + + +def make_document_tokenization_safe(document: Document) -> Document: + """Removes special tokens from the given documents. + + Args: + documents: A list of strings representing the documents. + + Returns: + A list of cleaned documents with special tokens removed. + """ + encoding = tiktoken.get_encoding("cl100k_base") + special_tokens_set = encoding.special_tokens_set + + def remove_special_tokens(text: str) -> str: + """Removes special tokens from the given text. + + Args: + text: A string representing the text. + + Returns: + The text with special tokens removed. + """ + for token in special_tokens_set: + text = text.replace(token, "") + return text + + content = document.page_content + cleaned_document = remove_special_tokens(content) + return Document(page_content=cleaned_document, metadata=document.metadata) + + +def filter_smaller_documents( + documents: List[Document], min_size: int = 3, min_line_size: int = 5 +) -> List[Document]: + def filter_small_document(document: Document) -> bool: + return ( + len( + [ + doc + for doc in document.page_content.split("\n") + if len(doc.strip().split()) >= min_line_size + ] + ) + >= min_size + ) + + return [ + document for document in documents if filter_small_document(document) + ]