[8.x] Add new experimental rank_vectors mapping for late-interaction second order ranking (#118804) #119601
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… order ranking (elastic#118804) Late-interaction models are powerful rerankers. While their size and overall cost doesn't lend itself for HNSW indexing, utilizing them as second order "brute-force" reranking can provide excellent boosts in relevance. At generally lower inference times than large cross-encoders. This commit exposes a new experimental `rank_vectors` field that allows for maxSim operations. This unlocks the initial, and most common use of late-interaction dense-models. For example, this is how you would use it via the API: ``` PUT index { "mappings": { "properties": { "late_interaction_vectors": { "type": "rank_vectors" } } } } ``` Then to index: ``` POST index/_doc { "late_interaction_vectors": [[0.1, ...],...] } ``` For querying, scoring can be exposed with scripting: ``` POST index/_search { "query": { "script_score": { "query": { "match_all": {} }, "script": { "source": "maxSimDotProduct(params.query_vector, 'my_vector')", "params": { "query_vector": [[0.42, ...], ...] } } } } } ``` Of course, the initial ranking should be done before re-scoring or combining via the `rescore` parameter, or simply passing whatever first phase retrieval you want as the inner query in `script_score`.
benwtrent
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:Search Relevance/Vectors
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Hi @benwtrent, I've created a changelog YAML for you. Note that since this PR is labelled |
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Backports the following commits to 8.x: