The RAG (Retrieval-Augmented Generation) application is designed to manage and search complaints using a FastAPI backend. It utilizes OpenAI's embedding capabilities to find similar complaints based on user input and geographical location.

rag-app
├── src
│   ├── api
│   │   └── app.py          # FastAPI application and API endpoints
│   ├── models
│   │   └── complaint.py     # Pydantic model for complaints
│   ├── services
│   │   ├── database.py      # Database initialization and interaction
│   │   ├── embeddings.py     # Function to generate text embeddings
│   │   └── filtering.py      # Function to filter complaints by location
│   └── utils
│       └── config.py        # Configuration settings and environment variable loading
├── complaints.json           # Initial complaints data in JSON format
├── .env                      # Environment variables (e.g., API keys)
├── requirements.txt          # Project dependencies
└── README.md                 # Project documentation

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 the required dependencies:

   pip install -r requirements.txt
   

4. Set up environment variables:

   • Create a .env file in the root directory and add your OpenAI API key:

     OPENAI_API_KEY=your_api_key_here
     

5. Load vector embeddings

   python -m src.services.data
   

6. Run the application:

   python app.py
   

• The API provides an endpoint to search for similar complaints based on a description, victim, suspect, and geographical location.
• Use tools like Postman or curl to interact with the API.