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 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.

Generally speaking, information retrieval supporting RAG capabilities in agentic generative AI implementations revolves around real-time querying of the 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. There are certain information retrieval approaches that 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. When a user submits a query, the system converts it into a vector and searches for content vectors positioned closest to it in the high-dimensional space. This means that even if exact keywords don’t match, the search can find relevant results based on conceptual semantic similarity. Moreover, in situations where search terms are lexically but not semantically close to entries in the dataset, semantic similarity search will “prefer” semantically similar entries.

For example, given the vectorized dataset: [“building materials”, “plumbing supplies”, “2×2 multiplication result”], the search string “2×4 lumber board” will most likely produce “building materials” as the top matching candidate. Combining semantic search with LLM-driven agents supports natural language alignment across the user-facing and backend data retrieval components of the solution. LLMs process natural language Input provided by the user while semantic search capabilities allow for data retrieval based on the natural language Input formulated by LLMs depending on the end user – agent communication cadence.