| ---
|
| 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](https://github.com/astral-sh/uv) installed
|
| - [Qdrant](https://qdrant.tech/) running locally on port 6333
|
|
|
| ### 2. Environment Variables
|
| Create a `.env` file in the root directory:
|
| ```env
|
| COHERE_API_KEY=your_cohere_api_key_here
|
| ```
|
|
|
| ### 3. Run the Backend
|
| ```bash
|
| uv run uvicorn app.main:app --reload --port 8000
|
| ```
|
|
|
| ### 4. Run the Frontend
|
| ```bash
|
| uv run streamlit run ui.py
|
| ```
|
|
|
| ## 🐳 Docker Deployment
|
| The project includes a `Dockerfile` for containerized deployment.
|
| ```bash
|
| docker build -t rag-assistant .
|
| docker run -p 8000:8000 -p 7860:7860 rag-assistant
|
| ```
|
|
|
| ---
|
| *Powered by Cohere and Qdrant • Developed for RAG Insights*
|
|
|
