GraphRAG Q&A and Visualization System
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To explore this project in more detail, check out the blog post on Medium: link
Project Overview
Engineered a sophisticated Q&A application utilizing Retrieval-Augmented Generation (RAG) to provide precise answers from uploaded PDF documents. This system combines advanced natural language processing with efficient information retrieval techniques.
Key Features and Technologies
• AI-Powered Responses: Integrated the llama-3.1-8B-Instant model for generating contextually relevant answers
• Efficient Information Retrieval: Employed FAISS for rapid similarity search and data extraction
• PDF Processing: Developed capabilities to automatically segment documents into manageable portions
• User-Centric Interface: Created an intuitive Gradio-based UI with real-time response streaming and dark mode
• NLP Integration: Utilized LangChain to seamlessly combine various natural language processing components
Technical Innovations
• Hybrid Architecture: Engineered a system that blends document comprehension with natural language generation
• Real-Time Interaction: Implemented streaming responses for immediate user feedback
• Flexible Configuration: Designed a modular structure allowing easy adjustment of system prompts, temperature, and output length
• Vector Database Integration: Leveraged advanced indexing for quick and accurate information retrieval
Project Outcomes
• Versatile Q&A Tool: Successfully developed a robust system for document-based query answering
• Enhanced Information Access: Significantly improved the efficiency of extracting insights from complex PDFs
• Optimized User Experience: Delivered a sleek, responsive interface that simplifies interaction with sophisticated AI technology
Skills Showcased
This project demonstrates expertise in Natural Language Processing, Machine Learning, Python Development, API Integration, UI/UX Design, PDF Handling, and Vector Database Management. It represents a synthesis of cutting-edge AI technologies and practical software engineering principles, showcasing the ability to create powerful, user-friendly AI applications.
Explore this project on GitHub: link