RAG Chatbot with Confluence

LLMs

Help desk allows you to create a Question Answering bot with a streamlit UI using your company Confluence data.

How to use

• Create a virtual environnement:

  • python3.10 -m venv .venv
  • source .venv/bin/activate
  • pip install -r requirements.txt

• Copy the env.template and fill your environment variables

  • cp .env.template .env

• Check the config.py and env.template file.

• To collect data from Confluence you will have to:

  • Create your own Conluence space with page informations
  • Create and feed your API key here
  • Insert in the env file:
    • the space_key: https://yoursite.atlassian.com/wiki/spaces/<space_key>/pages/
    • the space_name: <space_name>/spaces/<space_key>/pages/
    • the email adress you used for your Confluence space
    • the OpenAI API key

• To run the streamlit app run:

cd src
streamlit run streamlit.py

• To evaluate the quality of the RAG model:

# First replace the evaluation dataset file in the data folder with your topic questions
cd src
python evaluate.py

• To use and deep dive with the notebook

ipython kernel install --name RAG --user  # Add the notebook kernel
jupyter lab

How it works ?

.
├── data/
    ├── evaluation_dataset.tsv  # Questions and answers useful for evaluation

├── docs/                       # Documentation files
├── src/                        # The main directory for computer demo
    ├── __init__.py
    ├── load_db.py              # Load data from confluence and creates smart chunks
    ├── help_desk.py            # Instantiates the LLMs, retriever and chain
    ├── main.py                 # Run the Chatbot for a simple question
    ├── streamlit.py            # Run the Chatbot in streamlit where you can ask your own questions
    ├── evaluate.py             # Evaluate the RAG model based on questions-answers samples

├── notebooks/                  # Interactive code, useful for try and learn
├── config.py
├── .env.template               # Environment variables to feed
├── .gitignore
├── LICENSE                     # MIT License
├── README.md                   # Where to start
└── requirements.txt            # The dependencies

The process is the following:

• Loading data from Confluence
  • You can keep the Markdown style using the keep_markdown_format option added in our MR
  • See the help_desk.ipynb for a more deep dive analysis
  • Otherwise you cannot split text in a smart manner using the MarkdownHeaderTextSplitter
• Load data
• Markdown and RecursiveCharacterTextSplitter
• LLM used: Open AI LLM and embedding
• The QARetrievalChain
• Streamlit as a data interface