MUSE: Multi-PDF Information Retrieval Chat App 🚀

MUSE (Multi-PDF Information Retrieval Chat App) is an advanced application designed to efficiently extract and retrieve information from multiple PDF documents through an intuitive chat interface.

Key Features

• Multi-PDF Support: Seamlessly process and analyze multiple PDF documents simultaneously.
• Chat Interface: User-friendly conversational interface for querying document content.
• Advanced NLP Techniques: Leveraging cutting-edge Natural Language Processing for enhanced information retrieval.

Technologies Used

• Named Entity Recognition (NER): Identifies and extracts key entities such as people, locations, and organizations from the text.
• OLLAMA: Employed for targeted information retrieval, focusing on the most relevant passages within the PDFs.
• LLAMA3: Utilizes advanced language understanding for accurate question answering.

Performance

• Achieved an 8% accuracy improvement through the integration of Named Entity Recognition.

Potential Applications

• Beyond simple question answering, MUSE allows users to target specific entities within PDFs, opening up a wide range of potential use cases in research, academia, and business intelligence.

Future Development

We're constantly working to improve MUSE and expand its capabilities. Future updates may include:

• Support for additional file formats
• Enhanced entity relationship mapping
• Integration with external knowledge bases

Getting Started

Follow these steps to set up and run the MUSE app:

1. Create a virtual environment:

   conda create -p venv python==3.10

2. Activate the virtual environment:

   conda activate venv/

3. Install the required libraries:

   pip install -r requirements.txt

4. Run the model:

   ollama pull nomic-embed-text

5. Start the application:

   chainlit run app.py

Note: Make sure you have Ollama installed. Enter your GROQ API Key in .env file.

Contributing

Welcome contributions to the MUSE project.