[DOCS] Update ES|QL search tutorial for 9.2 #138234
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- Add FORK/FUSE hybrid search with RRF - Add RERANK for semantic reranking - Add COMPLETION for LLM text generation - Add KNN/TEXT_EMBEDDING for vector search - Reorganize steps for better logical flow (FTS → filtering → combining → AI) - Fix QSTR, RERANK, COMPLETION syntax - Use approach-based tabs for hybrid search (OR vs FORK/FUSE) - Add callout annotations with numbered list explanations - Update title to 'ES|QL for search tutorial'
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ioanatia
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looks great! left a few comments
| | WHERE description:"vegetarian recipes" <1> | ||
| | SORT _score DESC | ||
| | LIMIT 100 <2> | ||
| | RERANK "healthy quick meals" ON description WITH { "inference_id": "my_reranker_model" } <3> |
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I would remove WITH { "inference_id": "my_reranker_model" } to simplify the example - and we can say in the explanation that we use the default rerank model that we have available (e.g. Elastic rerank).
we can also mention that you can configure RERANK to work with another model, by pointing the user to the RERANK docs.
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perfect, makes sense
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Not the main point of the update, but maybe worthwhile to take this opportunity to update the errant code highlighting of the colon on line 154? |
Nevermind, I see now that it's not a formatting error, it just wasn't clear when I read it. |
yeah it's such a compact operator that it's hard to inline it nicely, I might wrap it in quotes just so it stands out better :) |
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Pinging @elastic/core-docs (Team:Docs) |
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@ioanatia thanks for the review, I've addressed all your suggestions |
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Closes elastic/docs-content#3250
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It's probably easier to review this by running through the URL preview, rather than inspecting the diff, because the flow has changed significantly. :)
The goal of this tutorial is mainly to pull together the capabilities mentioned on the ESQL for search overview page into concrete examples. It's not a realistic use case tutorial, it's more about demonstrating functionalities. But in the future we could consider chunking this into more targeted tutorials and using less toylike data sets. That's a discussion for a follow-up.
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Summary