📄 AI Multi-Document Summarizer

An AI-powered full-stack application that enables users to upload and process multiple documents, then generates concise summaries or context-aware answers to queries using OpenAI’s LLMs.

The system integrates FastAPI on the backend, React on the frontend for a seamless user experience, and FAISS for efficient vector search—ensuring accurate retrieval and scalable performance even with large document collections.

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🚀 Features

• 📂 Multi-format Uploads – Supports PDF, DOCX, and TXT files
• 🧩 Chunking & Embeddings – Splits documents into chunks and embeds them using OpenAI models
• ⚡ FAISS Vector Search – Fast similarity search across document chunks
• 🤖 AI Summarization & Q&A – Generate summaries and answer user questions with context
• 🌐 Frontend + Backend – Modern React-based frontend powered by FastAPI
• 💾 Persistent Storage – Save FAISS indices and re-use across sessions
• 📑 Export Results – Summaries and Q&A can be saved as JSON or PDF

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🏗️ Project Structure

ai_doc_summarizer/
│
├─ app/                     # Backend (FastAPI)
│   ├─ main.py               # FastAPI entry point
│   ├─ models/
│   │   ├─ embeddings.py     # FAISS embedding and indexing
│   │   └─ llm.py            # LLM summarization/Q&A
│   ├─ core/
│   │   └─ utils.py          # Text extraction, chunking
│   └─ static/uploads/       # Uploaded documents
│
├─ frontend/                 # Frontend (React app)
│   ├─ src/
│   │   ├─ components/       # Upload UI, Results display
│   │   ├─ pages/            # Summarizer, Q&A pages
│   │   └─ App.js            # Main React app
│   └─ package.json
│
├─ faiss_index/              # Saved FAISS indices
├─ outputs/                  # JSON / PDF results
├─ requirements.txt          # Backend dependencies
├─ package.json              # Frontend dependencies
└─ README.md

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⚙️ Tech Stack / Tools

• Backend: FastAPI (Python)

• Frontend: React 18 + Vite, Custom CSS

• LLM Provider: OpenAI GPT models (for summarization & Q&A)

• Vector Database: FAISS

• Utilities:

  • PyMuPDF / PyPDF2 → PDF extraction
  • docx2txt → Word file parsing
  • dotenv → API key management
  • pickle → Metadata persistence

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🔄 Workflow

1. Upload Documents – User uploads PDFs/DOCX/TXT through the frontend.
2. Text Extraction – Backend extracts text and converts to chunks.
3. Embeddings & Indexing – Each chunk is embedded using OpenAI embeddings and stored in a FAISS index.
4. Vector Search – On summarization or Q&A request, FAISS retrieves top relevant chunks.
5. LLM Summarization / Q&A – OpenAI GPT generates summary or answers based on retrieved context.
6. Output Delivery – Results are shown on frontend and stored in /outputs as JSON/PDF.

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⚡ Getting Started

1. Clone the Repository

git clone https://github.com/divyeshmutha12/AI_Document_Summarizer.git
cd ai-doc-summarizer

2. Create Virtual Environment

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

3. Install Dependencies

pip install -r requirements.txt

4. Set API Key

Create a .env file in the root directory:

OPENAI_API_KEY=your_openai_api_key

5. Run the Application

uvicorn app.main:app --reload

Navigate to: http://127.0.0.1:8000

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📝 Example Use Cases

• Generate executive summaries for multiple research papers
• Answer questions based on uploaded compliance/legal docs
• Summarize meeting notes from multiple files
• Knowledge extraction from large document collections

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🔮 Future Scope

• ✅ Support for audio/video transcription (via Whisper)
• ✅ Multi-language summarization
• ✅ Fine-tuned LLM integration for domain-specific tasks

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📜 License

MIT License – Free to use and modify.

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