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Chat with PDFs - Retrieval-Augmented Generation (RAG) Project

Overview

This project enables users to interact with PDF documents using a Retrieval-Augmented Generation (RAG) approach. Users can upload PDFs, which are converted into vectorized data stored in a database. The system answers user queries by leveraging the vector database and a large language model (LLMs).

Features

  • Upload PDF Documents: Upload one or more PDF files for analysis.
  • Vector Database Creation: Automatically converts uploaded PDFs into embeddings for efficient search and retrieval.
  • Chat with Documents: Ask questions about the content of the PDFs, and the system provides accurate, context-aware answers.
  • Fast and Scalable: Designed for quick query responses, even with large documents.

Getting Started

  • Python: Ensure Python 3.8 or later is installed.

1. Clone repository

git clone https://github.com/nehaejaz/RAG_chatbot.git

2. Install Dependencies

pip install -r requirements.txt

3. Run Application

streamlit run app.py
  1. Go to http://localhost:8000 to access the user interface.