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Videogame Assist RAG
A Retrieval-Augmented Generation (RAG) system built with Azure OpenAI and Azure Cognitive Search, featuring both console and web interfaces for document-based question answering about video game manuals and guides.
- #RAG
- #Azure AI
- #OpenAI
- #LLMs
- #Streamlit
- #Python
A Retrieval-Augmented Generation (RAG) system built with Azure OpenAI and Azure Cognitive Search, featuring both console and web interfaces for document-based question answering about video game manuals and guides.
🚀 Features
- 📄 PDF Document Processing: Automatically extracts and processes PDF documents using Azure Document Intelligence
- 🔍 Vector Search: Uses Azure Cognitive Search with semantic search capabilities and text embeddings
- 💬 AI-Powered Chat: Leverages Azure OpenAI GPT-4o-mini for intelligent responses
- 🌐 Web Interface: Interactive Streamlit chat interface with conversation history
- 🖥️ Console Interface: Command-line interface for quick interactions
- 📊 Analytics: Chat statistics and export functionality
- 🔒 Secure: Environment-based configuration with no hardcoded secrets
- ⚡ Smart Processing: MD5 hash checking to avoid reprocessing unchanged documents
🏗️ Architecture
This RAG system combines several Azure services:
- Azure OpenAI: For text generation and embeddings
- Azure Cognitive Search: For vector search and document indexing
- Azure Document Intelligence: For PDF text extraction
- Azure Blob Storage: For document storage
