📚 AI-Powered PDF Knowledge Base API

Ask questions directly to your PDFs.
This project provides a Flask backend that allows you to:

• Upload PDFs
• Extract and embed text into a vector database (Pinecone/Weaviate/Faiss)
• Query documents via a REST or GraphQL API

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🚀 Features

• 📄 Upload PDFs – research papers, reports, manuals, or any document
• 🧩 Text Extraction – automatically parses PDF text
• 🧠 AI Embeddings – generates vector representations of document chunks
• 📦 Vector Database – stores embeddings in Pinecone / Weaviate / Faiss
• 🔎 Intelligent Search – query documents in natural language (“Ask your PDF”)
• 🌐 API Ready – REST + optional GraphQL endpoints for easy integration
• 💻 Frontend Friendly – works with Next.js, React, or any frontend

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🛠 Tech Stack

• Backend: Flask (Python)
• Database: Pinecone / Weaviate / Faiss
• AI Embeddings: OpenAI / Hugging Face
• API: REST + GraphQL (optional)
• Frontend (Demo): Next.js

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📂 Project Structure

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├── app.py              # Flask app entry point
├── routes/             # API routes (upload, query, etc.)
├── services/           # PDF parsing, embedding, DB handling
└── README.md           # Documentation