EmbedIQ

A cloud-based RAG (Retrieval Augmented Generation) system that leverages high-quality embedding search and LLM context-based answers.

Overview

EmbedIQ provides a powerful solution for both developers and end users:

• For Developers: API endpoints to integrate RAG capabilities into applications
• For End Users: A web interface to query documents and get AI-enhanced responses

Built on a modern tech stack including Python FastAPI, React, PostgreSQL, Docker, and the LightRAG framework.

Features

• Document ingestion and embedding generation
• High-performance vector search for relevant context retrieval
• LLM-powered context-aware responses
• Developer-friendly API with comprehensive documentation
• Intuitive web interface for end users
• Containerized microservices architecture for scalability

Getting Started

Prerequisites

• Python 3.10+
• Node.js 18+
• Docker and Docker Compose
• Git

Development Setup

1. Clone the repository

   git clone <repository-url>
   cd embediq
   

2. Start the services with Docker Compose

   docker-compose up
   

3. Access the applications

   • API: http://localhost:8000
   • API Documentation: http://localhost:8000/docs
   • Frontend: http://localhost:3000

Project Structure

/embediq
├── /api              # FastAPI backend
│   ├── /app
│   │   ├── /core     # Core functionality
│   │   ├── /models   # Data models
│   │   ├── /routers  # API endpoints
│   │   └── /services # Business logic
│   ├── /tests        # Backend tests
│   └── Dockerfile    # API container definition
│
├── /frontend         # React frontend
│   ├── /public       # Static assets
│   ├── /src          # Source code
│   │   ├── /components
│   │   ├── /pages
│   │   ├── /services
│   │   └── /utils
│   ├── /tests        # Frontend tests
│   └── Dockerfile    # Frontend container definition
│
├── /docs             # Documentation
├── /scripts          # Utility scripts
├── docker-compose.yml # Development configuration
└── README.md         # Project overview

API Endpoints

• POST /ingest: Upload documents for embedding generation
• GET/POST /search: Perform embedding search based on query text
• POST /query: Submit a natural language query and get context-based answer
• GET /health: Health check for the API service

Technologies Used

• Backend: Python, FastAPI, SQLAlchemy, Pydantic
• Frontend: React, Vite, Material-UI/Tailwind CSS
• Database: PostgreSQL with vector extensions
• AI/LLM: LightRAG, OpenAI/Anthropic integration
• DevOps: Docker, Docker Compose, GitHub Actions

License

[License information]