/RAG-Chatbot-with-Confluence

RAG Chatbot with Confluence

Primary LanguageJupyter NotebookMIT LicenseMIT

LLMs

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

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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