Wandbot is a question-answering bot designed specifically for Weights & Biases documentation.

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.

English

Japanese

• 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.
• 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.
• Wandbot has a fallback mechanism for model selection, which is used when GPT-4 fails to generate a response.
• The bot's performance is evaluated using a mix of metrics, including retrieval accuracy, string similarity, and the correctness of model-generated responses.
• Curious about the custom system prompt used by the bot? You can view the full prompt here.

The project is built with Python version >=3.10.0,<3.11 and utilizes poetry for managing dependencies. Follow the steps below to install the necessary dependencies:

git clone [email protected]:wandb/wandbot.git
pip install poetry
cd wandbot
poetry install --all-extras
# Depending on which platform you want to run on run the following command:
# poetry install --extras discord # for discord
# poetry install --extras slack # for slack
# poetry install --extras api # for api

The data ingestion module pulls code and markdown from Weights & Biases repositories docodile and examples ingests them into vectorstores for the retrieval augmented generation pipeline. To ingest the data run the following command from the root of the repository

poetry run python -m src.wandbot.ingestion

You will notice that the data is ingested into the data/cache directory and stored in three different directories raw_data, vectorstore with individual files for each step of the ingestion process. These datasets are also stored as wandb artifacts in the project defined in the environment variable WANDB_PROJECT and can be accessed from the wandb dashboard.

Before running the Q&A bot, ensure the following environment variables are set:

OPENAI_API_KEY
COHERE_API_KEY
SLACK_EN_APP_TOKEN
SLACK_EN_BOT_TOKEN
SLACK_EN_SIGNING_SECRET
SLACK_JA_APP_TOKEN
SLACK_JA_BOT_TOKEN
SLACK_JA_SIGNING_SECRET
WANDB_API_KEY
DISCORD_BOT_TOKEN
COHERE_API_KEY
WANDBOT_API_URL="http://localhost:8000"
WANDB_TRACING_ENABLED="true"
WANDB_PROJECT="wandbot-dev"
WANDB_ENTITY="wandbot"

Once these environment variables are set, you can start the Q&A bot application using the following commands:

(poetry run uvicorn wandbot.api.app:app --host="0.0.0.0" --port=8000 > api.log 2>&1) & \
(poetry run python -m wandbot.apps.slack -l en > slack_en_app.log 2>&1) & \
(poetry run python -m wandbot.apps.slack -l ja > slack_ja_app.log 2>&1) & \
(poetry run python -m wandbot.apps.discord > discord_app.log 2>&1)

You might need to then call the endpoint to trigger the final wandbot app initialisation:

curl http://localhost:8000/

For more detailed instructions on installing and running the bot, please refer to the run.sh file located in the root of the repository.

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.

Make sure to set the environments in your terminal.

set -o allexport; source .env; set +o allexport

Launch the wandbot with 8 workers. This speeds up evaluation

WANDBOT_EVALUATION=1 gunicorn wandbot.api.app:app --bind 0.0.0.0:8000 --timeout=200 --workers=8 --worker-class uvicorn.workers.UvicornWorker

Set up for evaluation

wandbot/src/wandbot/evaluation/config.py

• evaluation_strategy_name : attribute name in Weave Evaluation dashboard
• eval_dataset :
  • Latest English evaluation dataset: "weave:///wandbot/wandbot-eval/object/wandbot_eval_data:eCQQ0GjM077wi4ykTWYhLPRpuGIaXbMwUGEB7IyHlFU"
  • Latest Japanese evaluation dataset: "weave:///wandbot/wandbot-eval-jp/object/wandbot_eval_data_jp:oCWifIAtEVCkSjushP0bOEc5GnhsMUYXURwQznBeKLA"
• eval_judge_model : model used for judge
• wandb_entity : wandb entity name for record
• wandb_project : wandb project name for record

Launch W&B Weave evaluation

python src/wandbot/evaluation/weave_eval/main.py

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
6. Evaluating the Performance of the Q&A Bot

You can monitor the usage of the bot in the following project: https://wandb.ai/wandbot/wandbot_public