Vector embeddings power a lot of modern search and retrieval systems. In practice, though, choosing an embedding model is less about leaderboards and more about engineering tradeoffs:
In this post I will walk through a small benchmark setup for four popular self hosted embedding models.
If you are like most ecommerce teams, your on-site search is treated like plumbing. As long as it returns something and does not crash, it quietly stays off the roadmap.
Yet that small box at the top of your site is one of the highest intent, highest margin levers you have.
There are multiple ways to import a CSV into Elasticsearch. For this how-to, we will explain two different ways to do it. The first one is with Logstash, and the second one is with Python.
In today’s data-driven world, efficient search is critical. Traditional keyword search falls short when understanding context and user intent. Semantic search solves this problem by understanding meanings rather than just matching words.
In this tutorial, I’ll guide you through setting up Elasticsearch with the Elastic Learned Sparse EncodeR (ELSER) model for powerful semantic search capabilities. ELSER is Elastic’s specialized ML model that creates sparse vector representations to efficiently capture semantic meaning while maintaining computational performance.
This is part two of the three-part RAG series. In this part, we will set up a Python script to load a dataset and pass the embedded text to OpenSearch.
In this guide, we explore how to get started with OpenSearch and OpenSearch Dashboards using Docker. This setup forms the foundation for creating a local RAG environment.
