InsightDocs AI: Chat with Your PDF

A powerful Streamlit application that allows you to upload PDF documents and chat with them using Retrieval-Augmented Generation (RAG) powered by Google's Gemini AI.

Features

• 📄 PDF Upload & Processing: Upload PDF documents and process them for analysis
• 🤖 AI-Powered Chat: Chat with your documents using Google's Gemini AI models
• 🔍 Intelligent Retrieval: Uses FAISS vector search for efficient document retrieval
• 🎨 Modern UI: Clean and intuitive Streamlit interface
• 🔒 Secure API Key Management: Secure input for your Gemini API key

Prerequisites

• Python 3.8 or higher
• A valid Google Gemini API key (get one from Google AI Studio)

Installation

1. Clone the repository:

git clone <your-repo-url>
cd chat-pdf

2. Install the required dependencies:

pip install -r requirements.txt

Usage

1. Run the Streamlit app:

streamlit run app.py

2. Open your browser and navigate to http://localhost:8501

3. In the sidebar:

   • Enter your Gemini API key
   • Upload a PDF document
   • Click "Process Document"

4. Start chatting with your PDF in the chat interface!

Dependencies

• streamlit: Web app framework
• langchain: Framework for building LLM applications
• langchain-community: Community integrations for LangChain
• langchain-huggingface: HuggingFace integrations
• langchain-google-genai: Google Generative AI integration
• pypdf: PDF processing library
• sentence-transformers: Text embeddings
• faiss-cpu: Vector similarity search

How It Works

1. Document Processing: PDFs are loaded and split into manageable chunks
2. Embeddings: Text chunks are converted to vector embeddings using HuggingFace models
3. Vector Storage: Embeddings are stored in a FAISS vector database
4. Retrieval: When you ask a question, relevant document chunks are retrieved
5. Generation: Gemini AI generates answers based on the retrieved context

Configuration

• Chunk Size: 1000 characters with 200 character overlap
• Embedding Model: all-MiniLM-L6-v2 (fast and efficient)
• Vector Store: FAISS for local similarity search
• LLM: Google Gemini (configurable in the code)

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Test thoroughly
5. Submit a pull request

License

This project is licensed under the MIT License - see the LICENSE file for details.

Disclaimer

This application requires a valid Google Gemini API key. Usage costs may apply based on your API usage. Please refer to Google's pricing for more information.