PDF Document Query System

A powerful application that allows you to chat with your PDF documents using advanced AI technologies. Upload any PDF document and ask questions about its content in natural language.

Features

• PDF Processing: Upload and process PDF documents of any size
• Natural Language Queries: Ask questions about your document in plain English
• Accurate Answers: Get precise answers with relevant context from the document
• Source Transparency: View the exact sections of the document used to generate answers
• Efficient Processing: PDFs are processed only once after upload, not with each query

Technology Stack

This application leverages state-of-the-art technologies:

• Streamlit: For the interactive web interface
• LangChain: For orchestrating the RAG workflow
• LangGraph: For creating a structured retrieval and generation pipeline
• HuggingFace Embeddings: Using the "thenlper/gte-large" model for semantic understanding
• Groq LLM: Using Llama 4 models for high-quality answer generation
• PyPDF Loader: For extracting text from PDF documents
• Vector Storage: For efficient semantic search capabilities

How It Works

The application uses a Retrieval Augmented Generation (RAG) approach:

1. Document Processing:

   • When you upload a PDF, the document is processed and converted to vector embeddings
   • The embeddings capture the semantic meaning of the document's content
   • The processed document is stored in-memory for quick retrieval

2. Query Processing:

   • When you ask a question, the system finds the most relevant sections from your document
   • These sections are used as context for the AI to generate accurate answers
   • The system ranks and sorts results by page number for better context coherence

3. Answer Generation:

   • A Llama 4 model from Groq evaluates your question against the retrieved context
   • The model generates a precise answer based solely on the document's content
   • You can view both the answer and the source context used to generate it

Getting Started

Prerequisites

• Python 3.9 or higher
• Groq API key

Installation

1. Clone the repository

   git clone <repository-url>
   cd chat-with-document
   

2. Install the required packages

   pip install -r requirements.txt
   

3. Set up your Groq API key

   • Create a .streamlit/secrets.toml file with your API key:

     GROQ_API_KEY = "your-api-key-here"
     

Running the Application

streamlit run app.py

Usage

1. Upload a PDF: Use the file uploader to select your PDF document
2. Ask a Question: Type your question in the text input field
3. View the Answer: The system will display the answer based on the document content
4. Explore Context: Expand the "Show source context" section to see which parts of the document were used

Limitations

• The application works best with text-based PDFs; scanned documents may not process correctly
• Very large PDFs may take longer to process initially
• The quality of answers depends on the clarity and structure of the source document

Future Improvements

• Support for additional document formats (DOCX, TXT, etc.)
• Multi-document querying
• Persistent storage for processed documents
• Chat history to maintain conversation context

Acknowledgements

• LangChain for the RAG implementation
• Groq for the powerful LLM API
• HuggingFace for the embedding models
• Streamlit for the web interface framework