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[DOCS] Add snippet tests to retriever API docs #113289

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merged 4 commits into from
Sep 23, 2024
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[DOCS] Add snippet tests to retriever API docs #113289

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63ce49f
[DOCS] Add snippet tests to retriever API docs
leemthompo Sep 20, 2024
29c3a72
Can test console with annotations, simplifies things, update skips ex…
leemthompo Sep 20, 2024
83fe9f2
Add snippet test teardown
leemthompo Sep 23, 2024
816c491
Consolidate example indices
leemthompo Sep 23, 2024
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70 changes: 52 additions & 18 deletions docs/reference/search/retriever.asciidoc
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Original file line number Diff line number Diff line change
Expand Up @@ -81,7 +81,43 @@ retrievers) *only* the query element is allowed.
[[standard-retriever-example]]
==== Example

[source,js]
////
[source,console]
----
PUT /restaurants
{
"mappings": {
"properties": {
"region": { "type": "keyword" },
"year": { "type": "keyword" },
"vector": {
"type": "dense_vector",
"dims": 3
}
}
}
}

POST /restaurants/_bulk?refresh
{"index":{}}
{"region": "Austria", "year": "2019", "vector": [10, 22, 77]}
{"index":{}}
{"region": "France", "year": "2019", "vector": [10, 22, 78]}
{"index":{}}
{"region": "Austria", "year": "2020", "vector": [10, 22, 79]}
{"index":{}}
{"region": "France", "year": "2020", "vector": [10, 22, 80]}
----
// TESTSETUP

[source,console]
--------------------------------------------------
DELETE /restaurants
--------------------------------------------------
// TEARDOWN
////

[source,console]
----
GET /restaurants/_search
{
Expand Down Expand Up @@ -109,9 +145,8 @@ GET /restaurants/_search
}
}
----
// NOTCONSOLE
<1> Opens the `retriever` object.
<2> The `standard` retriever is used for definining traditional {es} queries.
<2> The `standard` retriever is used for defining 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.
Expand Down Expand Up @@ -171,9 +206,9 @@ The parameters `query_vector` and `query_vector_builder` cannot be used together
[[knn-retriever-example]]
==== Example

[source,js]
[source,console]
----
GET my-embeddings/_search
GET /restaurants/_search
{
"retriever": {
"knn": { <1>
Expand All @@ -185,8 +220,7 @@ GET my-embeddings/_search
}
}
----
// NOTCONSOLE

// TEST[continued]
<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.
Expand Down Expand Up @@ -223,7 +257,7 @@ the retriever tree.

A simple hybrid search example (lexical search + dense vector search) combining a `standard` retriever with a `knn` retriever using RRF:

[source,js]
[source,console]
----
GET /restaurants/_search
{
Expand All @@ -234,7 +268,7 @@ GET /restaurants/_search
"standard": { <3>
"query": {
"multi_match": {
"query": "San Francisco",
"query": "Austria",
"fields": [
"city",
"region"
Expand All @@ -258,7 +292,7 @@ GET /restaurants/_search
}
}
----
// NOTCONSOLE
// TEST[continued]
<1> Defines a retriever tree with an RRF retriever.
<2> The sub-retriever array.
<3> The first sub-retriever is a `standard` retriever.
Expand All @@ -272,7 +306,7 @@ GET /restaurants/_search

A more complex hybrid search example (lexical search + ELSER sparse vector search + dense vector search) using RRF:

[source,js]
[source,console]
----
GET movies/_search
{
Expand Down Expand Up @@ -316,7 +350,7 @@ GET movies/_search
}
}
----
// NOTCONSOLE
// TEST[skip:uses ELSER]
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👍 Nice comment explanations


[[text-similarity-reranker-retriever]]
==== Text Similarity Re-ranker Retriever
Expand Down Expand Up @@ -390,7 +424,7 @@ A text similarity re-ranker retriever is a compound retriever. Child retrievers
This example enables out-of-the-box semantic search by re-ranking top documents using the Cohere Rerank API. This approach eliminate the need to generate and store embeddings for all indexed documents.
This requires a <<infer-service-cohere,Cohere Rerank inference endpoint>> using the `rerank` task type.

[source,js]
[source,console]
----
GET /index/_search
{
Expand All @@ -414,7 +448,7 @@ GET /index/_search
}
}
----
// NOTCONSOLE
// TEST[skip:uses ML]

[discrete]
[[text-similarity-reranker-retriever-example-eland]]
Expand Down Expand Up @@ -452,7 +486,7 @@ eland_import_hub_model \
+
. Create an inference endpoint for the `rerank` task
+
[source,js]
[source,console]
----
PUT _inference/rerank/my-msmarco-minilm-model
{
Expand All @@ -464,11 +498,11 @@ PUT _inference/rerank/my-msmarco-minilm-model
}
}
----
// NOTCONSOLE
// TEST[skip:uses ML]
+
. Define a `text_similarity_rerank` retriever.
+
[source,js]
[source,console]
----
POST movies/_search
{
Expand All @@ -490,7 +524,7 @@ POST movies/_search
}
}
----
// NOTCONSOLE
// TEST[skip:uses ML]
+
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}.
Expand Down