Skip to content

PierrunoYT/PDF-Chat-AI

Repository files navigation

PDFChatAI

PDFChatAI is an advanced tool for interacting with PDF documents using AI technology. It processes PDF files, extracts text, generates embeddings, and enables context-aware querying through a user-friendly web interface with asynchronous task processing.

GitHub Repository: https://github.com/PierrunoYT/PDF-Chat-AI

Features

  • Extract text from single or multiple PDF files
  • Clean and preprocess extracted text
  • Generate embeddings for text chunks using OpenAI's models or local models
  • Store extracted text, metadata, and embeddings in a SQLite database
  • Use FAISS for efficient similarity search
  • Perform context-aware querying with conversation history
  • Web interface for uploading PDFs, indexing, and querying
  • Asynchronous task processing using Python's threading module

Requirements

  • Python 3.7+
  • Flask
  • PyPDF2
  • NLTK
  • sentence-transformers
  • FAISS
  • OpenAI API (optional)

Installation

  1. Clone this repository:

    git clone https://github.com/PierrunoYT/PDF-Chat-AI.git
    cd PDF-Chat-AI
    
  2. Create and activate a virtual environment:

    python -m venv venv
    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
    
  3. Install the required packages:

    pip install -r requirements.txt
    
  4. Set up environment variables: Create a .env file in the project root and add the necessary variables.

Usage

  1. Run the Flask application:

    python app.py
    
  2. Open a web browser and navigate to http://localhost:5000 to access the web interface.

  3. Use the web interface to:

    • Upload PDF files
    • Index PDF files
    • Perform context-aware queries
    • View conversation history

File Descriptions

  • app.py: Flask application for the web interface
  • indexing_pipeline.py: Main pipeline for processing and indexing PDFs
  • pdf_processor.py: Functions for extracting text from PDFs
  • database_manager.py: Manages the SQLite database
  • embedding_model.py: Handles embedding generation
  • faiss_manager.py: Manages the FAISS index for similarity search
  • query_processor.py: Processes and expands queries
  • prompt_engineer.py: Generates prompts for context-aware responses
  • openrouter_client.py: Client for interacting with the OpenRouter API

License

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

About

No description, website, or topics provided.

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published