📖🔊 Chat With Confluence

Overview

Use Embedded Vector Store and an LLM to chat with your Confluence docs

✨ Only need a Confluence Account and OpenAI account.

Uses (all via Langchain):

• Confluence API
• Hugging Face
• ChromaDB
• OpenAI

Approach

1. Create a local embedding vector store of the Confluence docs
2. use the embedding vector store to get relevant texts from the store
3. provide the llm with the received texts + question as a prompt to answer the question

Usage

0. Pre-requisites

• Confluence API Key
• OpenAI account and API Key

1. Set up

Create your .env file

cp .env.example .env

Set the values (don't worry, this file is in .gitignore)

(Optional) Get in a virtual environment

python3 -m venv pyenv
source pyenv/bin/activate

Install python requirements

python3 -m pip install -r requirements.txt

2. Create your vector store ("process")

Use the process subcommand Your confluence space key can be found in it's URL like https://example.atlassian.net/wiki/spaces/<space-key>

python3 src/main.py --confluence-space-key ABC process

Note that subsequent runs with the same arguments will replace the previous vector store for that confluence space

3. Chat with your vector store ("chat")

To start the chat application using an existing dataset, use the chat subcommand:

python3 src/main.py --confluence-space-key ABC chat

The Streamlit chat app will run, and you can interact with the chatbot at http://localhost:8501 (or the next available port) to ask questions about the repository.

4. (optional) Exit the virtual environment

deactivate

Help

For complete CLI instructions run python src/main.py --help

Thanks to

The overall structure was pulled from peterw/Chat-With-Github-Repo

License

MIT License