Agentic Engineering & ToolingTools-in-Action30min
Expanding Vector Search Beyond Similarity
This session shows how to build an e-commerce vector search app with Micronaut, LangChain4j, and GraalVM. It covers embedding generation, blending similarity with business and location data, key Micronaut features, and Native Image deployment to create smarter, practical search.
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Alina YurenkoOracle
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Monday, October 5, 16:50-17:20
TBA 2
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Vector search is not hard to prototype: turn data into embeddings, store them, and return the closest matches. But real-world applications need to consider much more: application data, business logic, and practical constraints.
In this session, we’ll go through building a travel application with Micronaut, LangChain4j, and GraalVM.
Some of the topics we’ll cover:
– The strategy for generating vector embeddings
– Combining similarity results with application data and business constraints
– Combining semantic and location-aware search
– Most helpful features of Micronaut Data and Micronaut LangChain4j
– Building and running the application as a Native Image
We’ll see how these pieces work together to turn basic similarity search into a more intelligent and useful application.
In this session, we’ll go through building a travel application with Micronaut, LangChain4j, and GraalVM.
Some of the topics we’ll cover:
– The strategy for generating vector embeddings
– Combining similarity results with application data and business constraints
– Combining semantic and location-aware search
– Most helpful features of Micronaut Data and Micronaut LangChain4j
– Building and running the application as a Native Image
We’ll see how these pieces work together to turn basic similarity search into a more intelligent and useful application.