Embeddings

In the rapidly evolving landscape of AI, managing the “plumbing” between your embedding models and your search engine is often a challenge. Developers frequently struggle with switching providers, managing API keys, and maintaining consistent API specifications. LiteLLM solves the model management problem by acting as a universal proxy, while Elasticsearch delivers high-performance Vector Search. By combining them, you can build a search architecture that is both flexible and powerful. In this guide, we will walk through hosting an OpenAI-compatible embedding model using LiteLLM on Docker and consuming it directly from Elasticsearch to perform seamless vector search. ...

Introduction Search is not just traditional TF/IDF any more but the current trend of machine learning and models has opened another dimension for search. This talk gives an overview of: Classic search and its limitations. What is a model and how can you use it. How to use vector search or hybrid search in Elasticsearch. Where OpenAI’s ChatGPT or similar LLMs come into play to with Elastic. Check how to leverage Leverage ChatGPT with Elasticsearch. ...