README.md

metadata

title: Rag Agent
emoji: 🤖
colorFrom: blue
colorTo: green
sdk: docker
pinned: false

RAG Insight Assistant

A professional, high-end Retrieval-Augmented Generation (RAG) application built with FastAPI, Streamlit, Cohere, and Qdrant.

🚀 Features

• Modern AI UI: Unified landing page and chat interface with a premium dark theme.
• Neural Search: Efficient document retrieval using Qdrant vector database.
• SaaS Styling: Glassmorphism effects, neon borders, and optimized for laptop screens.
• Dynamic Ingestion: Process PDFs into vector embeddings on the fly.
• Scalable Backend: FastAPI-powered engine for query synthesis and generation.

🛠️ Tech Stack

• Frontend: Streamlit
• Backend: FastAPI
• LLM & Embeddings: Cohere
• Vector DB: Qdrant
• Dependency Management: uv

📦 Setup & Running

1. Prerequisites

• Python 3.11+
• uv installed
• Qdrant running locally on port 6333

2. Environment Variables

Create a .env file in the root directory:

COHERE_API_KEY=your_cohere_api_key_here

3. Run the Backend

uv run uvicorn app.main:app --reload --port 8000

4. Run the Frontend

uv run streamlit run ui.py

🐳 Docker Deployment

The project includes a Dockerfile for containerized deployment.

docker build -t rag-assistant .
docker run -p 8000:8000 -p 7860:7860 rag-assistant

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Powered by Cohere and Qdrant • Developed for RAG Insights