Build Intelligent Search with Amazon Bedrock and OpenSearch

Agentic generative AI assistants represent a significant advancement in artificial intelligence, featuring dynamic systems powered by large language models (LLMs) that engage in open-ended dialogue and tackle complex tasks. Unlike basic chatbots, these implementations possess broad intelligence, maintaining multi-step conversations while adapting to user needs and executing necessary backend tasks. These systems retrieve business-specific data in real-time through API calls and database lookups, incorporating this information into LLM-generated responses or providing it alongside them using predefined standards. This combination of LLM capabilities with dynamic data retrieval is known as Retrieval-Augmented Generation (RAG).

For example, an agentic assistant handling hotel booking would first query a database to find properties that match the guest’s specific requirements. The assistant would then make API calls to retrieve real-time information about room availability and current rates. This retrieved data can be handled in two ways: either the LLM can process it to generate a comprehensive response, or it can be displayed alongside an LLM-generated summary. Both approaches allow guests to receive precise, current information that’s integrated into their ongoing conversation with the assistant.

In this post, we show how to implement a generative AI agentic assistant that uses both semantic and text-based search using Amazon Bedrock, Amazon Bedrock AgentCore, Strands Agents, and Amazon OpenSearch. Information retrieval approaches in RAG systems generally revolve around real-time querying of backend data sources or communicating with an API. The responses are then factored into the subsequent steps performed by the implementation.

From a high-level system design and implementation perspective, this step is not specific to generative AI-based solutions: Databases, APIs, and systems relying on integration with them have been around for a long time. Certain information retrieval approaches have emerged alongside agentic AI implementations, most notably, semantic search-based data lookups. They retrieve data based on the meaning of the search phrase as opposed to keyword or pattern lexical similarity.

Vector embeddings are precomputed and stored in vector databases, enabling efficient similarity calculations at query time. The core principle of Vector Similarity Search (VSS) involves finding the closest matches between these numerical representations using mathematical distance metrics such as cosine similarity or Euclidean distance. These mathematical functions are particularly efficient when searching through large corpora of data because the vector representations are precomputed. Bi-encoder models are commonly used in this process. They separately encode the query and documents into vectors, enabling efficient similarity comparisons at scale without requiring the model to process query-document pairs together.