Building Intelligent Search with Amazon Bedrock and Amazon OpenSearch for hybrid RAG solutions
Building Intelligent Search with Amazon Bedrock and Amazon OpenSearch for hybrid RAG solutions
Publish Date: 2026-04-06 13:49:00
Source Domain: aws.amazon.com
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Agentic Generative AI Assistants: These represent an advanced advancement in AI with dynamic systems powered by large language models (LLMs) that engage in open-ended dialogue and complex tasks through Retrieval-Augmented Generation (RAG).
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RAG and Information Retrieval: RAG combines LLM capabilities with dynamic real-time data retrieval—semantic search enables conceptually relevant data retrieval using vector embeddings, while hybrid search balances semantic understanding with precise text-based filtering.
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Search Techniques: The article illustrates the application of hybrid search techniques combining vector similarity search and text-based filtering, optimizing AI assistants to perform accurate information retrieval by adapting to user needs.
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Architectural Overview: The proposed architecture integrates Amazon Bedrock, Amazon Bedrock AgentCore, and Amazon OpenSearch Serverless to create a scalable, secure, and cost-effective AI assistant capable of real-time conversational interactions.
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Implementation with Strands: Strands, an open-source AI agent framework, is utilized to define and integrate hybrid search functions, enabling more flexible and intelligent agent-based search capabilities.
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Use Cases: The hybrid search implementation is valuable in a variety of domains such as real estate, legal services, healthcare, media, and e-commerce, where it bridges natural language understanding with precise data filtering.
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Significance of Hybrid Approach: The hybrid search strategy enhances the AI assistant’s flexibility and accuracy by adapting retrieval approaches based on context and precise data needs, providing more relevant and comprehensive responses.
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AWS Integration: The combination of AWS services presents a scalable foundation for AI applications with secure management, monitoring, and state-of-the-art retrieval techniques that leverage the full potential of cloud infrastructure.
