📚 Chat with PDFs (RAG)

This application enables users to upload PDF files, process their content, and interact with them through a user-friendly interface. By leveraging advanced NLP models and a vector database, users can ask questions and receive accurate, context-aware answers from the uploaded documents.

• Upload the PDF

• Ask the Quetions

✨ Features

• PDF Upload: Upload multiple PDFs for content analysis.
• Text Processing: Automatically extracts and splits PDF text into manageable chunks.
• Vector Storage: Stores and retrieves document embeddings for fast, similarity-based search.
• Query System: Allows users to ask questions and get relevant answers directly from the PDFs.
• Interactive UI: Built with Streamlit for ease of use.

---

📋 Requirements

The following technologies and libraries are used in this project:

• Programming Language: Python
• Framework: Streamlit
• Libraries:
  • langchain for document processing
  • chromadb for vector storage
  • sentence-transformers for generating embeddings
  • PyPDF2 for PDF text extraction
  • faiss-cpu for fast vector similarity search (optional alternative to Chroma)
  • transformers for advanced NLP tasks
  • torch for deep learning support

---