gcloud vector-search collections data-objects search

NAME: gcloud vector-search collections data-objects search - search data objects from a Vector Search collection
SYNOPSIS: gcloud vector-search collections data-objects search --collection = COLLECTION --location = LOCATION ( --semantic-search-field = SEMANTIC_SEARCH_FIELD --semantic-search-text = SEMANTIC_SEARCH_TEXT --semantic-task-type = SEMANTIC_TASK_TYPE | --text-search-data-fields =[ DATA_FIELD_NAME , …] --text-search-text = TEXT_SEARCH_TEXT | [ --vector-from-file = VECTOR_FROM_FILE --vector-search-field = VECTOR_SEARCH_FIELD : --distance-metric = DISTANCE_METRIC ]) [ --json-filter = JSON_FILTER ] [ --top-k = TOP_K ] [ --output-data-fields =[ DATA_OUTPUT_FIELD , …] --output-metadata-fields =[ METADATA_OUTPUT_FIELD , …] --output-vector-fields =[ VECTOR_OUTPUT_FIELD , …]] [ --use-index = INDEX | --use-knn ] [ GCLOUD_WIDE_FLAG … ]
DESCRIPTION: Search data objects from a Vector Search collection.
EXAMPLES: To search data objects from collection my-collection in location us-central1 using text search and return 10 results, run:
gcloud vector-search collections data-objects search --collection = my-collection --location = us-central1 --text-search-text = "test" --text-search-data-fields = "text_field" --top-k = 10

To search data objects from collection my-collection in location us-central1 using semantic search and return 5 results, run:

gcloud vector-search collections data-objects search --collection = my-collection --location = us-central1 --semantic-search-text = "sci-fi" --semantic-search-field = "plot_embedding" --semantic-task-type = "retrieval-query" --top-k = 5

To search data objects from collection my-collection in location us-central1 using vector search with an index hint and return 7 results, run:

gcloud vector-search collections data-objects search --collection = my-collection --location = us-central1 --vector-search-field = "genre_embedding" --vector-from-file = "vector.json" --use-index = "my-index" --top-k = 7

To search data objects from collection my-collection in location us-central1 using vector search with KNN for exact results, run:

gcloud vector-search collections data-objects search --collection = my-collection --location = us-central1 --vector-search-field = "genre_embedding" --vector-from-file = "vector.json" --use-knn --top-k = 7
REQUIRED FLAGS: --collection = COLLECTION

The collection to search data objects from.

--location = LOCATION

Location of the collection.

Search type
Exactly one of these must be specified:

Semantic Search

--semantic-search-field = SEMANTIC_SEARCH_FIELD

The vector field to search.
This flag argument must be specified if any of the other arguments in this group are specified.

--semantic-search-text = SEMANTIC_SEARCH_TEXT

The query text for semantic search.
This flag argument must be specified if any of the other arguments in this group are specified.

--semantic-task-type = SEMANTIC_TASK_TYPE

The task type of the query embedding for semantic search. SEMANTIC_TASK_TYPE must be one of:

classification

Specifies that the given text will be classified.

clustering

Specifies that the embeddings will be used for clustering.

code-retrieval-query

Specifies that the embeddings will be used for code retrieval.

fact-verification

Specifies that the embeddings will be used for fact verification.

question-answering

Specifies that the embeddings will be used for question answering.

retrieval-document

Specifies the given text is a document from the corpus being searched.

retrieval-query

Specifies the given text is a query in a search/retrieval setting.

semantic-similarity

Specifies the given text will be used for STS.

This flag argument must be specified if any of the other arguments in this group are specified.

Text Search

--text-search-data-fields =[ DATA_FIELD_NAME ,…]

The data field names to search.
This flag argument must be specified if any of the other arguments in this group are specified.

--text-search-text = TEXT_SEARCH_TEXT

The query text for text search.
This flag argument must be specified if any of the other arguments in this group are specified.

Vector Search

--vector-from-file = VECTOR_FROM_FILE

Path to a JSON file containing dense or sparse vector to search with.

Example file content for dense vector:

{ "dense" : { "values" : [ 0 .7, 0 .6, 0 .5, 0 .4 ] } }

Example file content for sparse vector:

{ "sparse" : { "indices" : [ 1 , 5 , 10 ] , "values" : [ 0 .1, 0 .5, 0 .21 ] } }

This flag argument must be specified if any of the other arguments in this group are specified.

--vector-search-field = VECTOR_SEARCH_FIELD

The vector field to search.
This flag argument must be specified if any of the other arguments in this group are specified.

--distance-metric = DISTANCE_METRIC

The distance metric to use for the KNN search. If not specified, dot-product will be used as the default. DISTANCE_METRIC must be one of:

cosine-distance

Cosine distance metric.

dot-product

Dot product distance metric.
OPTIONAL FLAGS: --json-filter = JSON_FILTER

A filter expression in JSON format to apply to the search, e.g. '{"genre": {"$eq": "sci-fi"}}' .

--top-k = TOP_K

The number of nearest neighbors to return. Default is 10.

Output fields

--output-data-fields =[ DATA_OUTPUT_FIELD ,…]

List of data fields to include in the output. Use * to include all data fields.

--output-metadata-fields =[ METADATA_OUTPUT_FIELD ,…]

List of metadata fields to include in the output. Use * to include all metadata fields.

--output-vector-fields =[ VECTOR_OUTPUT_FIELD ,…]

List of vector fields to include in the output. Use * to include all vector fields.

Search Hint
At most one of these can be specified:

Use Index Options

--use-index = INDEX

Full resource name or ID of the index to use for the search. This flag is compatible only with Semantic Search and Vector Search.

--use-knn

If set to true, the search will use the system's default K-Nearest Neighbor (KNN) index engine. This flag is compatible only with Semantic Search and Vector Search.
GCLOUD WIDE FLAGS: These flags are available to all commands: --access-token-file , --account , --billing-project , --configuration , --flags-file , --flatten , --format , --help , --impersonate-service-account , --log-http , --project , --quiet , --trace-token , --user-output-enabled , --verbosity .
Run $ gcloud help for details.
NOTES: This variant is also available:
gcloud beta vector-search collections data-objects search

gcloud vector-search collections data-objects search Stay organized with collections Save and categorize content based on your preferences.

gcloud vector-search collections data-objects search