Practical insights on search architecture, relevance, and performance from real-world implementations.
Category Routed Aliases can displace the Gateway Anchor Collection as they create collections and change alias ordering. A CRA-native anchor and ZooKeeper reconciliation preserve distributed query coordination as the alias evolves.
A load balancer distributed requests evenly across a 40-node SolrCloud cluster, yet query coordination concentrated on only a few nodes. We trace the unexpected alias-routing behavior behind the hotspot and explain the anchor collection pattern that restored balance.
Longer queries can challenge lexical search. Learn how important-term boosting can preserve query intent, improve relevance, and better match what users are actually looking for.
Hybrid search doesn’t have to mean merging separate lexical and vector result sets. Explore an alternative architecture that uses semantic search for candidate generation while preserving the relevance, business logic, facets, and control of your existing search platform.
Modern product discovery requires more than traditional search alone. Learn how Large Language Models (LLMs) and BM25-based search engines work together to understand shopper intent, improve search relevance, and deliver more effective ecommerce search experiences.
Building a search proof of concept requires a different mindset than building a production system. This article shares practical OpenSearch tips for rapid prototyping, including vector embeddings, kNN search, hybrid search, indexing strategies, and UI considerations drawn from real-world experimentation.
Traditional search works well when users are looking for specific items. But workflows centered around building cohesive environments, such as set design and scene construction, introduce a different kind of retrieval problem. This article explores how OpenSearch, vector embeddings, and multimodal retrieval techniques were used to experiment with scene-oriented discovery and cohesive item selection.
Search governance adds structure, but it’s only part of the system. As shopper behavior, product catalogs, and business priorities constantly change, search needs to go beyond decisioning and continuously adapt. Governance, control, and optimization work together to keep results aligned with shopper behavior.
This article discusses how Model Context Protocol (MCP) expands AI chatbot capabilities with live data, actionable tools, and smarter workflows to improve customer interactions.
