LLMs

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

How to use

• Make sure you have installed uv: https://docs.astral.sh/uv/getting-started/installation/

• Create a virtual environnement:

  • uv lock && uv sync
  • source .venv/bin/activate

• Copy the env.template file and fill in the environment variables

  • cp .env.template .env

OPENAI_API_KEY=<your_openai_api_key>
CONFLUENCE_USERNAME=<your @easypark.net email>
# create CONFLUENCE_API_KEY https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account
CONFLUENCE_API_KEY=<your_confluence_api_key>
CONFLUENCE_BASE_URL=https://easypark.jira.com/wiki/
CONFLUENCE_SPACE_KEY=EP # or any other space 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
├── .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