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.
Explore similar demos
Search
Movie search application
Try out this live demo of a movie search application built on top of Elasticsearch.
The first part is a front end application implemented using Search UI. Feel free to just play with it or clone the code and try yourself!
Search
Grounded Conversational AI with Elastic Playground
Follow this step-by-step demo of Elastic Playground, a chat interface that allows you to combine multiple data sources in real time with the power of large language models (LLMs) for retrieval augmented generation (RAG).
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.
