Juribot

Question Answering Bot for legal documents, leveraging from the RAG technology.

Directory Structure

.
├── .gitignore
├── README.md
├── docker-compose.yml
├── backend/
│   ├── .cache/
│   ├── Dockerfile
│   ├── app/
│   ├── requirements.txt
│   ├── scrapers/
│   ├── test/
│   └── .env
├── chroma/
│   └── ...
└── frontend/
    ├── Dockerfile
    ├── streamlitPrototype.py
    ├── requirements.txt
    └── .env

Installation

This project uses Docker Compose to manage its services. To get started, you'll need to have Docker installed on your machine.

1. Clone the repository:

git clone https://github.com/nraffa/juribot.git

2. Navigate to the project directory:

cd juribot

3. Create environment files for the backend and frontend services:

For the backend, create a .env file in the backend/ directory with the following variables:

# backend/.env
OPENAI_API_KEY={API_SECRET}

For the frontend, create a .env file in the frontend/ directory with the following variables:

# frontend/.env
API_URL='http://backend:5000/chain'

4. Build and start the Docker services

docker-compose up --build

Usage

Once the Docker services are up and running, you can access the application at http://localhost:8501 .

Testing the Application

Setup: Loading documents into the Chroma Vectordatabase

Make sure to add the following change to the docker compose file in the chroma db repo within the environment variables:

- ALLOW_RESET=TRUE

Before loading the documents to the DB, make sure the Chroma DB container is running:

• start your docker deamon and then run the following commands:

cd chroma
docker-compose up -d --build

Preparation

Process the document you want to add to the rag pipeline

To load and split documents into chunks, run the vectorDbLoader.py script:

cd backend
python app/services/vectorDbLoader.py

This script loads a document from the specified file path, splits it into chunks, and stores the chunks in the Chroma DB.

Making requests directly to the API

You can test the application by sending POST requests to the /chain endpoint. Here's an example Python script to test the application:

import requests

data = {
    "message": "what is your name?",
    "chat_history": ""
}

response = requests.post("[hosting url]/chain", json=data)
print(response.text)

To continue the conversation, update the message and chat_history in the data dictionary and send another request.

Another alternative is accessing the Swagger Ui endpoint in the hosting url for example: http://127.0.0.1:8000/docs and use the UI for playing around with the API.

Unit Testing

Unit tests for the application are located in the backend/test directory. The main test file is testCollection.py.

To run the tests, navigate to the backend directory and execute the following command:

python -m unittest test/testCollection.py

Contributing

Contributions are welcome. Please open an issue or submit a pull request.