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Learn to build a production-ready RAG (Retrieval-Augmented Generation) AI agent in Python through this comprehensive step-by-step tutorial. Master the essential components needed to deploy AI applications in real-world environments, including observability, logging, retries, throttling, and rate limiting that distinguish production-grade applications from basic tutorials. Explore the complete architecture using modern tools like Inngest for workflow orchestration, Qdrant for vector database management, and LlamaIndex for document processing. Set up your development environment with proper API configurations and Inngest dev server integration. Implement vector database functionality for efficient document storage and retrieval. Process and chunk PDF documents for optimal AI consumption. Build querying capabilities for your vector database to enable intelligent document retrieval. Integrate a user-friendly frontend interface to interact with your RAG system. Apply advanced production concepts including rate limiting, throttling, and concurrency management to ensure your application can handle real-world traffic and usage patterns.
