README.md

metadata

title: RAG Project
emoji: 🧠
colorFrom: blue
colorTo: purple
sdk: docker
app_port: 8000
python_version: 3.1

🚀 RAG System with LangChain and FastAPI 🌐

Welcome to this repository! This project demonstrates how to build a powerful RAG system using LangChain and FastAPI for generating contextually relevant and accurate responses by integrating external data into the generative process.

📋 Project Overview

The RAG system combines retrieval and generation to provide smarter AI-driven responses. Using LangChain for document handling and embeddings, and FastAPI for deploying a fast, scalable API, this project includes:

• 🗂️ Document Loading: Load data from various sources (text, PDFs, etc.).
• ✂️ Text Splitting: Break large documents into manageable chunks.
• 🧠 Embeddings: Generate vector embeddings for efficient search and retrieval.
• 🔍 Vector Stores: Store embeddings in a vector store for fast similarity searches.
• 🔧 Retrieval: Retrieve the most relevant document chunks based on user queries.
• 💬 Generative Response: Use retrieved data with language models (LLMs) to generate accurate, context-aware answers.
• 🌐 FastAPI: Deploy the RAG system as a scalable API for easy interaction.

⚙️ Setup and Installation

Prerequisites

Make sure you have the following installed:

• 🐍 Python 3.10+
• 🐳 Docker (optional, for deployment)
• 🛠️ PostgreSQL or FAISS (for vector storage)

Installation Steps

1. Clone the repository:

   git clone https://github.com/yadavkapil23/RAG_Project.git
   

2. Set up a virtual environment:

   python -m venv venv
   source venv/bin/activate   # For Linux/Mac
   venv\Scripts\activate      # For Windows
   

3. Install dependencies:

   pip install -r requirements.txt
   

4. Run the FastAPI server:

   uvicorn main:app --reload
   

   Now, your FastAPI app will be running at http://127.0.0.1:8000 🎉!

Set up Ollama 🦙

This project uses Ollama to run local large language models.

1. Install Ollama: Follow the instructions on the Ollama website to download and install Ollama.

2. Pull a model: Pull a model to use with the application. This project uses llama3.

   ollama pull llama3
   

🛠️ Features

• Retrieval-Augmented Generation: Combines the best of both worlds—retrieving relevant data and generating insightful responses.
• Scalable API: FastAPI makes it easy to deploy and scale the RAG system.
• Document Handling: Supports multiple document types for loading and processing.
• Vector Embeddings: Efficient search with FAISS or other vector stores.

🛡️ Security

• 🔐 OAuth2 and API Key authentication support for secure API access.
• 🔒 TLS/SSL for encrypting data in transit.
• 🛡️ Data encryption for sensitive document storage.

🚀 Deployment

Hugging Face Spaces (Docker) Deployment

This project is configured for a Hugging Face Space using the Docker runtime.

1. Push this repository to GitHub (or connect local).
2. Create a new Space on Hugging Face → Choose "Docker" SDK.
3. Point it to this repo. Spaces will build using the Dockerfile and run uvicorn binding to the provided PORT.
4. Ensure the file data/sample.pdf exists (or replace it) to allow FAISS index creation on startup.

Notes:

• Models Qwen/Qwen2-0.5B-Instruct and all-MiniLM-L6-v2 will be downloaded on first run; initial cold start may take several minutes.
• Dependencies are CPU-friendly; no GPU is required.
• If you see OOM, consider reducing max_new_tokens in vector_rag.py or swapping to an even smaller instruct model.

Docker Deployment (Local)

If you want to deploy your RAG system using Docker, simply build the Docker image and run the container:

docker build -t rag-system .
docker run -p 8000:8000 rag-system

Cloud Deployment

Deploy your RAG system to the cloud using platforms like AWS, Azure, or Google Cloud with minimal setup.

🧠 Future Enhancements

• 🔄 Real-time Data Integration: Add real-time data sources for dynamic responses.
• 🤖 Advanced Retrieval Techniques: Implement deep learning-based retrievers for better query understanding.
• 📊 Monitoring Tools: Add monitoring with tools like Prometheus or Grafana for performance insights.

🤝 Contributing

Want to contribute? Feel free to fork this repository, submit a pull request, or open an issue. We welcome all contributions! 🛠️

📄 License

This project is licensed under the MIT License.

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🎉 Thank you for checking out the RAG System with LangChain and FastAPI! If you have any questions or suggestions, feel free to reach out or open an issue. Let's build something amazing!