SnapRetrieve

An AI-powered Retrieval-Augmented Generation (RAG) system that allows users to upload documents and interact with them using intelligent semantic search and context-aware responses.

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Features

• Document upload and processing
• Semantic search using vector embeddings
• Context-aware AI responses
• Fast document retrieval pipeline
• Interactive user-friendly interface

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Tech Stack

Frontend

• React.js
• JavaScript
• CSS

Backend

• Python
• FastAPI

AI / ML

• Retrieval-Augmented Generation (RAG)
• Vector Embeddings
• LLM Integration

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Project Structure

SnapRetrieve/
│── frontend/
│── backend/
│── README.md
│── requirements.txt
│── .env.example

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Installation

Clone Repository

git clone https://github.com/Adk-2/SnapRetrieve.git
cd SnapRetrieve

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Backend Setup

pip install -r requirements.txt

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Frontend Setup

npm install

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Environment Variables

Create a .env file in the root directory:

API_KEY=your_api_key_here

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Run the Project

Start Backend

python app.py

Start Frontend

npm run dev

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How It Works

1. Upload a document
2. System processes and generates embeddings
3. User asks questions
4. Relevant context is retrieved
5. AI generates accurate responses

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Future Improvements

• Multi-document support
• Better vector database integration
• Authentication system
• Deployment on cloud

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Author

Ayush Kottary

Computer Science student passionate about AI, Machine Learning, and intelligent systems.

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Repository

https://github.com/Adk-2/SnapRetrieve