Practical insights on search architecture, relevance, and performance from real-world implementations.
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.
We were recently engaged to come up with a simple way to generate Pentaho reports on an Amazon Web Services (AWS) platform. Our client had already created the reports with Pentaho, but preferred not to deploy a full Pentaho BI Server on an EC2 instance. As it happens, this was an excellent opportunity to employ a Lambda function.
Solr has multiple ways of dealing with parent-child relationships, and each have their own features and drawbacks. Here are some of the tips and tricks to consider if you are looking to make a move from Block-Join to a full denormalized solution.
FindTuner’s extensive machine learning and human-touch features enable online retailers to easily meet business objectives while working seamlessly with SAP Commerce and leveraging the search service based on Solr.
