# Building Production RAG Systems with Heterogeneous Data at AWS GenAIIC ## Overview and Context AWS GenAIIC shares their experience building production-grade RAG systems that handle heterogeneous data formats including text, structured data, and images. The case study provides detailed technical insights and best practices derived from real customer implementations across multiple industries. ## Key Technical Components ### Router Implementation - Routers direct queries to appropriate processing pipelines based on data type and query intent - Implementation uses smaller models like Claude Haiku for efficient routing - XML-formatted prompts improve routing accuracy and maintainability - Router includes capability to request clarification for ambiguous queries - Alternative implementation available using Bedrock Converse API's native tool use ### Structured Data Handling - Leverages LLMs' code generation capabilities instead of direct table analysis - Uses prompts to generate Python/SQL code for data analysis - Custom execution pipeline with safety checks and output handling - Results can be returned directly or processed through LLM for natural language responses - Built-in handling for edge cases like NaN values ### Multimodal RAG Architecture Two main approaches detailed: - Multimodal Embeddings Approach: - Caption-Based Approach: ## Implementation Details ### Vector Storage and Retrieval - Uses OpenSearch for vector storage - Implements k-NN search for similarity matching - Stores both raw data and embeddings - Includes metadata for enhanced retrieval ### Data Processing Pipeline - Base64 encoding for image handling - Structured error handling - Modular components for each processing step - Flexible output formatting ## Industry Applications The case study covers implementations across multiple sectors: - Technical Assistance - Oil and Gas - Financial Analysis - Industrial Maintenance - E-commerce ## Best Practices and Recommendations ### Router Design - Use smaller models for routing to minimize latency - Include explanation requirements in prompts - Build in clarification mechanisms - Consider explicit routing options for users ### Code Generation - Implement safety checks for generated code - Consider direct result return for large outputs - Handle edge cases explicitly - Use structured variable naming conventions ### Multimodal Implementation - Choose approach based on specific needs: - Consider latency requirements - Balance cost vs. detail requirements ### General Architecture - Build modular components - Implement robust error handling - Consider scaling requirements - Plan for monitoring and evaluation ## Technical Challenges and Solutions ### Latency Management - Router optimization - Efficient code execution - Smart output handling - Balanced processing pipelines ### Data Quality - Structured error handling - NaN value management - Image processing optimization - Caption quality control ### Scale Considerations - Vector database optimization - Efficient embedding storage - Processing pipeline efficiency - Resource utilization management ## Future Considerations - Expansion to other modalities (audio, video) - Enhanced routing capabilities - Improved code generation - Advanced multimodal processing - Integration with emerging model capabilities The case study demonstrates the practical implementation of advanced RAG systems in production environments, providing valuable insights for organizations looking to build similar systems. The modular approach and detailed technical considerations provide a solid foundation for production deployments.