DocuQuery AI

Overview

DocuQuery AI is an advanced document question-answering system that leverages Retrieval-Augmented Generation (RAG) to provide intelligent responses from various document sources. Built with state-of-the-art NLP techniques and modern AI technologies, it offers a streamlined interface for extracting and querying information from multiple document formats.

Features

Document Processing

• Multiple Format Support: Process documents in various formats:
  • PDF files
  • Microsoft Word documents (DOCX)
  • Plain text files (TXT)
  • Web URLs
• Smart Text Extraction: Efficient text extraction with automatic content parsing
• Chunk-based Processing: Intelligent document chunking for optimal processing

AI-Powered Analysis

• Vector-based Search: FAISS-powered vector store for efficient similarity search
• Advanced Embeddings: Utilizes HuggingFace's sentence transformers for text embeddings
• Intelligent QA: Leverages Meta's Llama model for generating accurate responses

User Interface

• Interactive Web Interface: Built with Streamlit for a seamless user experience
• Real-time Processing: Dynamic document processing and question answering
• Session Management: Maintain context across multiple queries
• Error Handling: Robust error handling with informative feedback

Installation

Prerequisites

• Python 3.8 or higher
• Git
• Hugging Face API key

Setup

1. Clone the repository:

git clone https://github.com/your-username/docuquery-ai.git
cd docuquery-ai

2. Create and activate a virtual environment (recommended):

python -m venv venv
source venv/bin/activate  # On Windows: venv\Scripts\activate

3. Install dependencies:

pip install -r requirements.txt

4. Create secret_api_keys.py in the project root:

huggingface_api_key = "your-api-key-here"

Project Structure

docuquery-ai/
├── app.py              # Complete code of the application
├── requirements.txt
├── README.md
└── secret_api_keys.py         # API keys (not tracked in git)

Usage

1. Start the application:

streamlit run app.py

2. Select input type:

   • Upload a document (PDF/DOCX/TXT)
   • Paste a URL
   • Enter text directly

3. Process the input and wait for confirmation

4. Ask questions about your document in natural language

Configuration

Key parameters can be adjusted in src/config.py:

• MAX_FILE_SIZE: Maximum allowed file size (default: 10MB)
• CHUNK_SIZE: Text chunk size for processing
• CHUNK_OVERLAP: Overlap between chunks
• EMBEDDING_MODEL: HuggingFace model for embeddings
• LLM_MODEL: Language model for question answering

Dependencies

• streamlit: Web interface
• langchain: Document processing and QA chains
• faiss-cpu: Vector similarity search
• PyPDF2: PDF processing
• python-docx: DOCX processing
• huggingface-hub: AI model access
• sentence-transformers: Text embeddings

Contributing

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

License

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

Acknowledgments

• HuggingFace for providing the model infrastructure
• Streamlit for the web framework
• FAISS for vector similarity search
• LangChain for document processing capabilities

Contact

For questions and support, please open an issue in the GitHub repository.