Elasticsearch Labs AI Search Notebooks



Prototype semantic search, RAG, hybrid retrieval, and AI-powered relevance workflows in Elasticsearch without starting from an empty notebook. This package gives search engineers and AI builders a curated copy of Elastic’s official notebook and example-app material so they can move from “how would this work?” to a running experiment faster.

When to use this package

Use this download when you need a practical starting point for an AI search proof of concept, customer demo, internal enablement session, or relevance experiment. It is especially useful when a team wants to compare ELSER, dense embeddings, hybrid retrieval, reranking, LangChain, Bedrock, Cohere, Hugging Face, or chatbot-style RAG patterns with Elasticsearch as the retrieval layer.

Why it is useful

The value of this package is that it collects working examples from Elastic’s own Search Labs repository in one place. Instead of hunting through articles or assembling a notebook from scattered snippets, users get runnable patterns they can inspect, adapt, and turn into their own prototypes. The notebooks show the shape of the full workflow: prepare sample data, create mappings, configure inference or model integrations, run retrieval queries, evaluate relevance behavior, and connect the results to application patterns.

For teams early in an AI search project, this helps answer which approach is worth testing first. For teams already using Elasticsearch, it provides reference implementations for adding semantic search, RAG, reranking, or hybrid search to an existing search experience. For solution architects and consultants, it is a compact enablement pack for showing what modern Elasticsearch search applications can look like.

What is included

• Executable Jupyter notebooks for keyword, hybrid, semantic, multilingual, ELSER, query rules, inference, learning-to-rank, semantic text, and reranking workflows.
• Generative AI notebooks for question answering, chatbots, OpenAI, Bedrock, Hugging Face, Cohere, and LangChain integrations.
• Example apps for chatbot RAG, internal knowledge search, relevance workbench, and search tutorials.

Who it helps

Search engineers can use the notebooks to test retrieval strategies and relevance improvements. AI application builders can use them to connect Elasticsearch with LLM and embedding providers. Solution architects can use the package to demonstrate concrete search experiences, from semantic search quick starts to chatbot-style RAG and relevance workbench examples.

Compatibility

The repository README does not declare a single package-wide Elastic Stack version. Compatibility was checked and intentionally left unset.

Source and license

Source: elastic/elasticsearch-labs. The upstream repository is licensed under Apache 2.0.

The attached ZIP contains the reusable upstream files plus the available license or notice files.

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Tested versions
ECS compliant