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

2026-04-06 · Source: Artificial Intelligence · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Data Science & Analytics, Cloud Computing & IT Infrastructure · Depth: Intermediate, long

Summary

Agentic generative AI assistants, powered by large language models (LLMs), represent a significant advancement over basic chatbots by engaging in open-ended dialogue and tackling complex tasks through multi-step conversations. These systems retrieve real-time, business-specific data via API calls and database lookups, integrating this information into LLM-generated responses, a process known as Retrieval-Augmented Generation (RAG). While semantic search, which uses vector embeddings and mathematical distance metrics like cosine similarity, excels at conceptual understanding, it struggles with precise attribute matching. To address this, a hybrid search approach combines semantic search with text-based filtering for structured attributes like location or dates. An architecture leveraging Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents, and Amazon OpenSearch Serverless enables this dynamic hybrid search, allowing the LLM to determine the optimal search strategy based on user queries, ensuring both conceptual relevance and precise matching.

Key takeaway

For AI Engineers building conversational assistants, adopting a hybrid search strategy is crucial for delivering accurate and contextually relevant results. Your systems will benefit from combining semantic understanding with precise attribute filtering, especially in scenarios requiring both conceptual relevance and exact data matching. Consider integrating AWS services like Amazon Bedrock AgentCore and OpenSearch Serverless to orchestrate dynamic search strategies and ensure scalability and security.

Key insights

Hybrid search combining semantic and text-based methods significantly enhances AI assistant accuracy and adaptability.

Principles

• Agentic LLMs dynamically adapt search strategies.
• Semantic search excels at conceptual understanding.
• Text-based search provides precise attribute matching.

Method

Implement hybrid search by categorizing data for semantic and text search, using vector embeddings for semantic data, and a unified OpenSearch index for both, enabling adaptive query types.

In practice

• Use Amazon Bedrock for LLM orchestration.
• Employ Amazon OpenSearch Serverless for hybrid search.
• Define search functions as "tools" for agents.

Topics

• Agentic AI Assistants
• Retrieval-Augmented Generation
• Hybrid Search
• Amazon Bedrock
• Amazon OpenSearch Serverless

Code references

• awslabs/amazon-bedrock-agentcore-samples

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence.