RAG System

Overview

The RAG (Retrieval-Augmented Generation) system is a core component of the HereFamni project. It integrates retrieval and generation capabilities to address the detection of healthcare-related fake news. By leveraging a database of vector embeddings, the RAG model retrieves relevant content and generates responses augmented by this information.

Features

• Retrieves context-relevant information from a knowledge base.
• Generates responses enriched with retrieved information.
• Exposes API endpoints for integration with external systems.

Setup Instructions

Prerequisites

• Docker and Docker Compose installed.
• A running PostgreSQL database container (configured separately).

Steps

1. Clone the repository:

   git clone https://github.com/HeReFanMi/Rag.git
   cd Rag

2. Build and Run the Docker Container: Build the Docker image and start the containers:

   docker build -t ragystem-flask-app .
   docker-compose up

3. Access the Application: Once the containers are running, access the API at:

   http://localhost:5000

API Endpoints

POST /find_similar_chunks

Description: Retrieve similar content chunks from the knowledge base. Request Body:

{
  "query": "Your query string here"
}

Response:

{
  "results": [
    {"chunk_id": 1, "content": "Sample content", "score": 0.9},
    ...
  ]
}

Environment Variables

The following environment variables are used to configure the system:

• DB_HOST: Hostname or IP address of the database.
• DB_PORT: Port number for the database (default: 5432).
• DB_USER: Username for database access.
• DB_PASSWORD: Password for database access.
• DB_NAME: Database name.

Network Configuration

Ensure that the ragystem_shared-network Docker network is used for communication between containers. Verify it using:

docker network inspect ragystem_shared-network