Relevance workbench
In this workbench, you can compare our Elastic Learned Sparse Encoder model (with or without RRF) and traditional textual search using BM25.
Start comparing different hybrid search techniques using TMDB's movies dataset as sample data. Or fork the code and ingest your own data to try it on your own!
Try these queries to get started:
- "The matrix"
- "Movies in Space"
- "Superhero animated movies"
Notice how some queries work great for both search techniques. For example, 'The Matrix' performs well with both models. However, for queries like "Superhero animated movies", the Elastic Learned Sparse Encoder model outperforms BM25. This can be attributed to the semantic search capabilities of the model.
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Observability
Root cause analysis with the AI Assistant for Observability
Follow the step-by-step process of using the AI Assistant for Observability to perform root cause analysis of a Service Level Objective (SLO) alert.
Search
AI Playground - Roll up your sleeves
Test the latest AI search capabilities with AI Playground, now in Elasticsearch. Ingest your own data or use our sample data to explore how to build RAG systems, test different LLMs from various providers like OpenAI, Amazon Bedrock, Anthropic and more.
Platform
Elastic Serverless
