Document Processing API with Semantic Search

A FastAPI-based service for processing PDFs/text documents, generating embeddings, and enabling semantic search using Supabase and pgvector.

✨ Key Features

• Document Processing: Upload and chunk PDFs/text files with semantic splitting
• Vector Search: Find relevant document sections using embedding similarity
• Supabase Integration: Store documents + embeddings with pgvector for efficient search
• REST API: Well-structured endpoints with Swagger UI documentation

🛠️ Tech Stack

• Backend: Python (FastAPI)
• Database: Supabase (PostgreSQL + pgvector)
• Embeddings: [sentence-transformers]

🚀 Quick Start

1. Setup environment:

   git clone https://github.com/nishchalacharya/Document-Processor-.git
   cd Document-Processor
   pip install -r requirements.txt

2.Configure environment variables
SUPABASE_URL=your-supabase-url
SUPABASE_KEY=your-supabase-key
EMBEDDING_MODEL=your-model-name
```bash
3.Run API:
   uvicorn main:app --reload