RAG Application

This is a Retrieval-Augmented Generation (RAG) application built using FastAPI and LangChain. The application is designed to provide efficient retrieval and generation of information based on user queries.

Project Structure

rag-app
├── app
│   ├── __init__.py
│   ├── main.py
│   ├── api
│   │   ├── __init__.py
│   │   ├── endpoints.py
│   └── services
│       ├── __init__.py
│       ├── langchain_service.py
│       └── retrieval_service.py
├── models
│   ├── __init__.py
│   └── document_model.py
├── utils
│   ├── __init__.py
│   └── helpers.py
├── tests
│   ├── __init__.py
│   ├── test_endpoints.py
│   └── test_services.py
├── .gitignore
├── requirements.txt
├── README.md
└── Dockerfile

Setup Instructions

1. Clone the repository:

   git clone <repository-url>
   cd rag-app

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate  # On Windows use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Run the application:

   uvicorn app.main:app --reload

Usage

• The application exposes various API endpoints for interacting with the RAG functionality.
• You can access the API documentation at http://localhost:8000/docs after running the application.

Testing

• To run the tests, use the following command:

  pytest

Docker

To build and run the application using Docker, execute the following commands:

1. Build the Docker image:

   docker build -t rag-app .

2. Run the Docker container:

   docker run -p 8000:8000 rag-app

Contributing

Contributions are welcome! Please open an issue or submit a pull request for any enhancements or bug fixes.

License

This project is licensed under the MIT License. See the LICENSE file for details.