Agentic retrieval with LangChain and Amazon Bedrock Knowledge Bases
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This article demonstrates how to implement agentic retrieval using LangChain and Amazon Bedrock Knowledge Bases to handle complex, multi-part user queries more effectively than standard single-shot retrieval. While standard retrieval uses a single vector search that often misses specific intents in compound questions, agentic retrieval breaks the query into sub-queries, evaluates evidence sufficiency, and iterates as needed. The authors provide a technical walkthrough for setting up the knowledge base, configuring IAM permissions, and integrating both retrieval methods into a LangChain application. They highlight that agentic retrieval incurs higher costs and latency but offers significantly better recall on multi-hop or comparative questions. The post also explains how to access trace events via direct API calls to inspect the planning process and notes current integration friction, such as the need to wrap agentic functions in RunnableLambda objects. Ultimately, the authors recommend routing queries based on their complexity, using standard retrieval for simple questions and agentic retrieval for complex ones to balance cost and accuracy.
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