Practical insights on search architecture, relevance, and performance from real-world implementations.
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.
The functionality of the modern blend of AI has some immensely powerful capabilities that offer a great deal of opportunity. LLMs also have some important flaws. Awareness of what AI is, what it’s capable of (and not capable of) is crucial to weighing opportunity cost of IT time. It’s simply too expensive to not be aware of what AI is.
This article explores the challenges and solutions in integrating Java with large language models (LLMs) for search. It details building a proof-of-concept for leveraging popular LLMs like SBERT and OpenAI, using Python-based tools to bridge gaps in Java compatibility. Learn how vector databases, FastAPI, and Chroma enable powerful search functionalities.
This article details a client success story with using RabbitMQ and MongoDB to Maintain Solr Indexes for high speed, cross data center updates.
This post breaks down how Mongo addresses the fundamental disparity in how to combine vector search results with lexical search results to maximize the benefits of each.
Setting up a search index isn’t just about indexing data and tying a search box to it, it’s about delivering the right results based on what a person searches for and prioritizing the results they are most likely to want.
Thinking about Spring Batch as your framework for loading a Solr index? With some minor customizations, it won’t take much effort. Picking the correct library and understanding some Solr fundamentals will simplify the endeavor.
How many times have we heard that yelled in our general direction? Search engines, and their teams, are only as good as the data they are given. This is true for all search engines, for all time, regardless of any machine learning algorithms or other bells and whistles they may contain.
