05 — Design Retrieval Mechanisms for FM Augmentation

05 — Design Retrieval Mechanisms for FM Augmentation

Module Introduction

• Introduction
• Key topics

Document Segmentation Fundamentals

• Introduction to document segmentation
• Chunking fundamentals
• Purpose of segmentation in foundation model pipelines
• Tradeoffs between relevance and context preservation
• Evaluation metrics for segmentation quality
• Hierarchical chunking strategies
• Content-aware parsing
• Recursive chunking implementation
• Parent-child relationship maintenance
• Dynamic chunk sizing based on semantic boundaries

Custom And Managed Chunking Strategies

• Introduction
• Amazon Bedrock native chunking
• Configuration and integration
• Custom Lambda-based chunking
• Lambda implementation patterns
• Selection criteria and decision framework

Introduction To Vector Search

• Introduction
• Vector embeddings and semantic representation
• Foundation model text transformation process
• Semantic similarity and vector space
• Vector search versus keyword search
• When to use each approach
• AWS Vector search services
• Service comparison guide
• Service selection guidelines
• Detailed service specifications

Embedding Strategy And Optimization

• Introduction
• Embedding model evaluation
• Performance metrics and benchmarking
• Model comparison framework
• Batch embedding generation
• AWS batch processing architecture
• Cost optimization strategies
• Embedding strategy selection
• Implementation best practices

Amazon Bedrock Knowledge Bases

• Introduction
• Creating and configuring knowledge bases
• Supported vector stores and configuration
• Data source connectors and integration
• Data ingestion pipeline design and optimization
• Semantic retrieval and RAG implementation
• Query interface introduction
• Hybrid search implementation
• Advanced retrieval strategies
• Update and maintenance strategies
• Document freshness and lifecycle management

Advanced Relevance Engineering With Rerankers

• Introduction
• Reranking fundamentals and architecture
• Evolution beyond basic retrieval systems
• Reranker model types and characteristics
• Amazon Bedrock reranker models integration
• Bedrock reranker capabilities and model options
• API specifications and input/output formats
• Hybrid search architecture with reranking
• Multi-signal retrieval architecture design
• OpenSearch implementation with reranking
• Result merging and fusion strategies
• Performance and quality optimization
• Evaluation frameworks and metrics
• Latency management and optimization

Advanced Query Processing Techniques

• Introduction
• Query expansion with Amazon Bedrock
• Implementation with Bedrock
• Query decomposition with AWS Lambda
• Lambda implementation patterns
• Query transformation workflows
• Workflow orchestration
• Performance optimization
• Integration and deployment
• System integration patterns
• Practical implementation example
• E-commerce search scenario
• Challenges and limitations
• Technical challenges
• Operational limitations

End-to-end Query Processing Systems

• Introduction
• System integration architecture and design
• Connecting expansion, decomposition, and transformation
• Latency, throughput, and cost optimization
• Performance evaluation and quality assessment
• Retrieval quality metrics implementation
• Real-world implementation case study
• Query context management with AgentCore Episodic Memory
• A/B testing and iterative refinement
• Legal technology case study analysis
• Implementation patterns and architecture decisions
• Performance outcomes and optimization results
• Advanced integration techniques and future considerations
• Adaptive processing and machine learning integration

Standardized Function Calling For Vector Search

• Introduction
• Function interface design principles
• Parameter standardization and validation
• Error handling conventions and response formatting
• Vector search function patterns
• Query construction Functions
• Result processing and formatting functions
• Implementation approaches and architecture
• AWS Lambda function design and deployment
• API gateway integration and security
• Cross-service authentication and integration
• Healthcare use case implementation
• Clinical search system development
• Clinical decision support integration

Api Patterns For Retrieval Augmentation

• Introduction
• API Design for Retrieval Services
• Synchronous vs. asynchronous processing patterns
• Request/response standardization
• Query parameter normalization and result management
• Query parameter normalization strategies
• Result pagination and formatting
• Metadata inclusion standards
• Cross-service integration and interoperability
• OpenAPI specification development
• Version compatibility management
• Financial services use case implementation
• Standardized function calling interface design
• Fraud detection model integration

Integration With Foundation Models

• Introduction
• Foundation model integration requirements
• Context window considerations and management
• Input formatting standards and response parsing
• Retrieval-augmented generation workflows
• Pre-processing pipelines and content preparation
• Post-processing techniques and response enhancement
• Caching strategies and performance optimization
• Error handling and fallback mechanisms
• Graceful degradation approaches
• Timeout management and alternative retrieval paths
• System resilience and reliability patterns
• Government agency use case implementation
• Unified retrieval augmentation API development
• Document processing and citizen services integration

Module Summary

• Recap and next steps
• Resources