From 16d87fcd77f9c7c9db4add4b74c675e9e6257f1d Mon Sep 17 00:00:00 2001 From: Liam Thompson Date: Mon, 12 Aug 2024 13:27:11 +0200 Subject: [PATCH] [DOCS] Add retriever examples, semantic reranking step-by-step instructions --- docs/reference/search/retriever.asciidoc | 294 +++++++++++++++--- .../semantic-reranking.asciidoc | 2 +- 2 files changed, 249 insertions(+), 47 deletions(-) diff --git a/docs/reference/search/retriever.asciidoc b/docs/reference/search/retriever.asciidoc index 1b7376c21daab..e07f90564caaf 100644 --- a/docs/reference/search/retriever.asciidoc +++ b/docs/reference/search/retriever.asciidoc @@ -77,23 +77,48 @@ Collapses the top documents by a specified key into a single top document per ke When a retriever tree contains a compound retriever (a retriever with two or more child retrievers) *only* the query element is allowed. -===== Example +[discrete] +[[standard-retriever-example]] +==== Example [source,js] ---- -GET /index/_search +GET /restaurants/_search { - "retriever": { - "standard": { - "query" { ... }, - "filter" { ... }, - "min_score": ... + "retriever": { <1> + "standard": { <2> + "query": { <3> + "bool": { <4> + "should": [ <5> + { + "match": { <6> + "region": "Austria" + } + } + ], + "filter": [ <7> + { + "term": { <8> + "year": "2019" <9> + } + } + ] } - }, - "size": ... + } + } + } } ---- // NOTCONSOLE +<1> Opens the `retriever` object. +<2> The `standard` retriever is used for definining traditional {es} queries. +<3> The entry point for defining the search query. +<4> The `bool` object allows for combining multiple query clauses logically. +<5> The `should` array indicates conditions under which a document will match. Documents matching these conditions will increase their relevancy score. +<6> The `match` object finds documents where the `region` field contains the word "Austria." +<7> The `filter` array provides filtering conditions that must be met but do not contribute to the relevancy score. +<8> The `term` object is used for exact matches, in this case, filtering documents by the `year` field. +<9> The exact value to match in the `year` field. [[knn-retriever]] ==== kNN Retriever @@ -142,29 +167,39 @@ include::{es-ref-dir}/rest-api/common-parms.asciidoc[tag=knn-similarity] The parameters `query_vector` and `query_vector_builder` cannot be used together. -===== Example: +[discrete] +[[knn-retriever-example]] +==== Example [source,js] ---- -GET /index/_search +GET my-embeddings/_search { - "retriever": { - "knn": { - "field": ..., - "query_vector": ..., - "k": ..., - "num_candidates": ... - } + "retriever": { + "knn": { <1> + "field": "vector", <2> + "query_vector": [10, 22, 77], <3> + "k": 10, <4> + "num_candidates": 10 <5> } + } } ---- // NOTCONSOLE +<1> Configuration for k-nearest neighbor (knn) search, which is based on vector similarity. +<2> Specifies the field name that contains the vectors. +<3> The query vector against which document vectors are compared in the `knn` search. +<4> The number of nearest neighbors to return as top hits. This value must be fewer than or equal to `num_candidates`. +<5> The size of the initial candidate set from which the final `k` nearest neighbors are selected. + [[rrf-retriever]] ==== RRF Retriever -An <> retriever returns top documents based on the RRF formula +An <> retriever returns top documents based on the RRF formula, equally weighting two or more child retrievers. +Reciprocal rank fusion (RRF) is a method for combining multiple result +sets with different relevance indicators into a single result set. ===== Parameters @@ -180,34 +215,111 @@ An RRF retriever is a compound retriever. Child retrievers may not use elements that are restricted by having a compound retriever as part of the retriever tree. -===== Example +[discrete] +[[rrf-retriever-example-hybrid]] +==== Example: Hybrid search + +A simple hybrid search example (lexical search + dense vector search) combining a `standard` retriever with a `knn` retriever using RRF: [source,js] ---- -GET /index/_search +GET /restaurants/_search { - "retriever": { - "rrf": { - "retrievers": [ - { - "standard" { ... } - }, - { - "knn": { ... } - } - ], - "rank_constant": ... - "rank_window_size": ... + "retriever": { + "rrf": { <1> + "retrievers": [ <2> + { + "standard": { <3> + "query": { + "multi_match": { + "query": "San Francisco", + "fields": [ + "city", + "region" + ] + } + } + } + }, + { + "knn": { <4> + "field": "vector", + "query_vector": [10, 22, 77], + "k": 10, + "num_candidates": 10 + } } + ] + , + "rank_constant": 0.3, <5> + "rank_window_size": 50 <6> } + } } ---- // NOTCONSOLE +<1> Defines a retriever tree with an RRF retriever. +<2> The sub-retriever array. +<3> The first sub-retriever is a `standard` retriever. +<4> The second sub-retriever is a `knn` retriever. +<5> The rank constant for the RRF retriever. +<6> The rank window size for the RRF retriever. + +[discrete] +[[rrf-retriever-example-hybrid-sparse]] +==== Example: Hybrid search with sparse vectors + +A more complex hybrid search example (lexical search + ELSER sparse vector search + dense vector search) using RRF: + +[source,js] +---- +GET movies/_search +{ + "retriever": { + "rrf": { + "retrievers": [ + { + "standard": { + "query": { + "sparse_vector": { + "field": "plot_embedding", + "inference_id": ".elser_model_2", + "query": "films that explore psychological depths" + } + } + } + }, + { + "standard": { + "query": { + "multi_match": { + "query": "crime", + "fields": [ + "plot", + "title" + ] + } + } + } + }, + { + "knn": { + "field": "vector", + "query_vector": [10, 22, 77], + "k": 10, + "num_candidates": 10 + } + } + ] + } + } +} +---- [[text-similarity-reranker-retriever]] ==== Text Similarity Re-ranker Retriever -The `text_similarity_reranker` is a type of retriever that enhances search results by re-ranking documents based on semantic similarity to a specified inference text, using a machine learning model. +The `text_similarity_reranker` retriever uses a machine learning model to improve search results by reordering the top-k documents based on their semantic similarity to the query. [TIP] ==== @@ -223,8 +335,9 @@ Currently you can: * Integrate directly with the <> using the `rerank` task type * Integrate directly with the <> using the `rerank` task type -* Upload a model to {es} with {eland-docs}/machine-learning.html#ml-nlp-pytorch[Eland] +* Upload a model to {es} with {eland-docs}/machine-learning.html#ml-nlp-pytorch[Eland] using the `text_similarity` NLP task type. ** Then set up an <> with the `rerank` task type +** Refer to the <> on this page for a step-by-step guide. ===== Parameters @@ -257,27 +370,116 @@ Sets a minimum threshold score for including documents in the re-ranked results. A text similarity re-ranker retriever is a compound retriever. Child retrievers may not use elements that are restricted by having a compound retriever as part of the retriever tree. -===== Example +[discrete] +[[text-similarity-reranker-retriever-example-cohere]] +==== Example: Cohere Rerank + +This example enables out-of-the-box semantic search by reranking top documents using the Cohere Rerank inference endpoint. This approach eliminate the need to generate and store embeddings for all indexed documents. +This requires a <> using the `rerank` task type. [source,js] ---- GET /index/_search { - "retriever": { - "text_similarity_reranker": { - "retriever": { - "standard": { ... } - }, - "field": "text", - "inference_id": "my-cohere-rerank-model", - "inference_text": "Most famous landmark in Paris", - "rank_window_size": 100, - "min_score": 0.5 + "retriever":{ + "text_similarity_reranker":{ + "retriever":{ + "standard":{ + "query":{ + "match_phrase":{ + "text":"landmark in Paris" + } + } + } + }, + "field":"text", + "inference_id":"my-cohere-rerank-model", + "inference_text":"Most famous landmark in Paris", + "rank_window_size":100, + "min_score":0.5 + } + } +} +---- +// NOTCONSOLE + +[discrete] +[[text-similarity-reranker-retriever-example-eland]] +==== Example: Semantic reranking with a Hugging Face model + +The following example uses the `cross-encoder/ms-marco-MiniLM-L-6-v2` model from Hugging Face to rerank search results based on semantic similarity. +The model must be uploaded to {es} using https://www.elastic.co/guide/en/elasticsearch/client/eland/current/machine-learning.html#ml-nlp-pytorch[Eland]. + +[TIP] +==== +Refer to {ml-docs}/ml-nlp-model-ref.html#ml-nlp-model-ref-text-similarity[the Elastic NLP model reference] for a list of third party text similarity models supported by {es}. +==== + +Follow these steps to load the model and create a semantic reranker. + +. Install Eland using `pip` ++ +[source,sh] +---- +python -m pip install eland +---- ++ +. Upload the model to {es} using Eland. This example assumes you have an Elastic Cloud deployment and an API key. Refer to the https://www.elastic.co/guide/en/elasticsearch/client/eland/current/machine-learning.html#ml-nlp-pytorch-auth[Eland documentation] for more authentication options. ++ +[source,sh] +---- +eland_import_hub_model \ + --cloud-id $CLOUD_ID \ + --es-api-key $ES_API_KEY \ + --hub-model-id cross-encoder/ms-marco-MiniLM-L-6-v2 \ + --task-type c \ + --clear-previous \ + --start +---- ++ +. Create an inference endpoint for the `rerank` task ++ +[source,js] +---- +PUT _inference/rerank/my-msmarco-minilm-model +{ + "service": "elasticsearch", + "service_settings": { + "num_allocations": 1, + "num_threads": 1, + "model_id": "cross-encoder__ms-marco-minilm-l-6-v2" + } +} +---- ++ +. Define a `text_similarity_rerank` retriever. ++ +[source,js] +---- +POST movies/_search +{ + "retriever": { + "text_similarity_reranker": { + "retriever": { + "standard": { + "query": { + "match": { + "genre": "drama" + } + } } + }, + "field": "plot", + "inference_id": "my-msmarco-minilm-model", + "inference_text": "films that explore psychological depths" } + } } ---- // NOTCONSOLE ++ +This retriever uses a standard `match` query to search the `movie` index for films tagged with the genre "drama". +It then re-ranks the results based on semantic similarity to the text in the `inference_text` parameter, using the model we uploaded to {es}. ==== Using `from` and `size` with a retriever tree diff --git a/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc b/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc index f25741fca0b8f..add2d7455983e 100644 --- a/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc +++ b/docs/reference/search/search-your-data/retrievers-reranking/semantic-reranking.asciidoc @@ -94,7 +94,7 @@ Currently you can: ** Integrate directly with the <> using the `rerank` task type ** Integrate directly with the <> using the `rerank` task type -** Upload a model to {es} from Hugging Face with {eland-docs}/machine-learning.html#ml-nlp-pytorch[Eland] +** Upload a model to {es} from Hugging Face with {eland-docs}/machine-learning.html#ml-nlp-pytorch[Eland]. You'll need to use the `text_similarity` NLP task type when loading the model using Eland. Refer to {ml-docs}/ml-nlp-model-ref.html#ml-nlp-model-ref-text-similarity[the Elastic NLP model reference] for a list of third party text similarity models supported by {es} for semantic reranking. *** Then set up an <> with the `rerank` task type . *Create a `rerank` task using the <>*. The Inference API creates an inference endpoint and configures your chosen machine learning model to perform the reranking task.