📄 PDFInsight

An AI-powered local-first system for extracting insights from PDF documents using Retrieval-Augmented Generation (RAG). This app loads documents, processes them into chunks, generates embeddings using sentence-transformers, and performs semantic search via a vector store (e.g., FAISS). A FastAPI backend can be used for interactive queries and integration.

---

🧠 Features

• 📂 Load and parse PDF or text-based documents
• 🧼 Preprocess and chunk documents for optimal embedding
• 🔎 Semantic search using vector similarity (e.g., FAISS)
• 🧠 Sentence-transformer-based embedding generation
• 🔄 Retrieval-Augmented Generation engine (RAG)
• 🚀 FastAPI backend for RESTful document insight queries
• 🛠️ Modular, clean, and extensible codebase

---

🗂 Project Structure

pdfinsight/
├── app/
│   ├── main.py                   # Application entry point (e.g., FastAPI setup)
│   │
│   ├── loaders/
│   │   └── document_loader.py    # Load PDF/text files into memory
│   │
│   ├── processors/
│   │   └── document_processor.py # Clean and split text into chunks
│   │
│   ├── embeddings/
│   │   └── embedding_service.py  # Generate vector embeddings for text chunks
│   │
│   ├── vectorstores/
│   │   └── vector_store.py       # Store and query embedding vectors using FAISS or similar
│   │
│   └── engines/
│       └── rag_engine.py         # Perform RAG (retrieve + generate)
│
├── requirements.txt              # Python dependencies
├── README.md                     # Project documentation (this file)
└── .gitignore                    # Git ignored files

---

🔧 Tech Stack

• Python 3.9+
• PdfPlumber (for PDF parsing)
• langchain
• sentence-transformers
• Chroma (for vector search)
• FastAPI + Uvicorn (for API layer)

---

🪪 License

This project is licensed under the MIT License.

---

🤝 Contributing

See CONTRIBUTING.md for guidelines.

---

📬 Contact

For questions, suggestions, or feedback, open an issue or contact @TEJAS-SAI-PRASHAD-K on GitHub.

---