Building Intelligent Search with Amazon Bedrock and Amazon OpenSearch for hybrid RAG solutions

This article explains how to build intelligent search assistants combining semantic and text-based search using Amazon Bedrock, AgentCore, and OpenSearch for hybrid RAG solutions.

• Agentic AI assistants use LLMs with real-time data retrieval (RAG) for dynamic, context-aware responses
• Semantic search finds conceptually similar content; text search provides precise attribute matching
• Hybrid search combines both approaches for accurate results (e.g., "luxury hotel in Miami")
• Agent-based systems dynamically select optimal search strategies based on query characteristics
• Architecture uses serverless components: API Gateway, Bedrock AgentCore, OpenSearch Serverless
• Strands framework defines hybrid search as a tool the agent intelligently invokes
• OpenSearch stores both vector embeddings and structured text fields for flexible querying
• Applicable to real estate, legal research, healthcare, media, and e-commerce use cases

The hybrid approach enables AI assistants to understand natural language intent while applying precise filters, delivering more relevant and comprehensive search results than traditional RAG implementations.