ikarius6/baymax-rag-system

This RAG system fetches information from your private Confluence as a CSV file, vectorizes and stores the embeddings in ChromaDB, and then uses it via Streamlit or as a Slack bot, interpreting the result with Llama 3.

Baymax - RAG System

This RAG system fetches information from your private Confluence as a CSV file, vectorizes and stores the embeddings in ChromaDB, and then uses it via Streamlit or as a Slack bot, interpreting the result with Llama 3.

Project Structure

RAG Flow Chart: Please refer to the rag_flowchart.png in the repository for a visual representation of the system workflow.

Requirements

• Python 3.8+
• Python packages listed in requirements.txt
• .env file with the following variables:
  • CONFLUENCE_DOMAIN
  • CONFLUENCE_TOKEN
  • CONFLUENCE_SPACE_KEY
  • CONFLUENCE_TEAM_KEY
  • SLACK_BOT_TOKEN
  • SLACK_APP_TOKEN
  • SLACK_SIGNING_SECRET
  • GROQ_API_KEY (optional, for using Groq)
• If you are using a fully local installation, install Llama3 (it should require a good GPU in your system)

Demo

Setup

Clone the repository.

git clone git@github.com:ikarius6/baymax-rag-system.git

Create your local enviroment

python -m venv venv
source venv/bin/activate

Install the necessary packages:

pip install -r requirements.txt

Create a .env file in the root directory with the required environment variables.

# Confluence
CONFLUENCE_DOMAIN="https://yourconfluence.com"
CONFLUENCE_TOKEN=""
CONFLUENCE_SPACE_KEY="SPACE_KEY"
CONFLUENCE_TEAM_KEY="TEAM"

# Groq
GROQ_API_KEY=""

# Slack
SLACK_BOT_TOKEN=''
SLACK_APP_TOKEN=''
SLACK_SIGNING_SECRET=''

Llama3 with Ollama

Install Ollama in your system (https://github.com/ollama/ollama)

curl -fsSL https://ollama.com/install.sh | sh

Download llama3 and start the ollama server

ollama pull llama3
ollama serve

Llama3 with Groq

To use a remote version of Llama3, enable the Grop API by getting your own GROQ_API_KEY

• Go to https://console.groq.com/keys
• Generate a new token
• Ad it to GROQ_API_KEY in your .env

Confluence Token

To get your own CONFLUENCE_TOKEN

• Go to https://yourconfluence.com/plugins/personalaccesstokens/usertokens.action
• Generate a new token
• Add it to CONFLUENCE_TOKEN in your .env

Slack Setup

Import the slack_manifest.yml to your Slack App, then get your access tokens for your .env file.

For SLACK_SIGNING_SECRET go to Basic Information > App Credentials > Signing Secret For SLACK_APP_TOKEN go to Basic Information > App-Level Tokens > Generate Token For SLACK_BOT_TOKEN go to OAuth & Permissions > OAuth Tokens > Bot User OAuth Token

Usage

Fetch Data from Confluence

Make sure you have the cookie.txt file with the session to avoid SSO issues. The cookie can be extracted for any request in the confluence page.

Run app_confluence.py to fetch data from Confluence and save it as a CSV file, the process could take a few minutes:

python app_confluence.py

This process going to create data/kb.csv file with all the necessary data for the next step.

Generate Embeddings

Run index_generator.py to generate embeddings and save them in ChromaDB, this process need to download an embedding model from HuggingFace so the process could take several minutes:

python index_generator.py

Once your vector database is populated you can use your chatbot with Streamlit or as a Slack app.

Use in Streamlit

Run streamlit.py to start the Streamlit application:

streamlit run streamlit.py

Use in Slack

• Set up your application in Slack and get the necessary tokens by using the Slack Setup section.

Run slack.py to start the Slack bot:

python slack.py

Project Files

app_confluence.py

Code to fetch data from Confluence and save it as a CSV file.

index_generator.py

Code to generate embeddings and save them in ChromaDB.

chat.py

Code for the query logic using the stored embeddings.

streamlit.py

Code for the Streamlit user interface.

slack.py

Code for the Slack integration.

utils.py

Helper methods to simplify the operation