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237 lines
8.4 KiB
Text
237 lines
8.4 KiB
Text
NAME
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gcloud beta vector-search collections data-objects search - search data
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objects from a Vector Search collection
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SYNOPSIS
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gcloud beta vector-search collections data-objects search
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--collection=COLLECTION --location=LOCATION
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(--semantic-search-field=SEMANTIC_SEARCH_FIELD
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--semantic-search-text=SEMANTIC_SEARCH_TEXT
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--semantic-task-type=SEMANTIC_TASK_TYPE
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| --text-search-data-fields=[DATA_FIELD_NAME,...]
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--text-search-text=TEXT_SEARCH_TEXT
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| [--vector-from-file=VECTOR_FROM_FILE
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--vector-search-field=VECTOR_SEARCH_FIELD
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: --distance-metric=DISTANCE_METRIC]) [--json-filter=JSON_FILTER]
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[--top-k=TOP_K]
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[--output-data-fields=[DATA_OUTPUT_FIELD,...]
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--output-metadata-fields=[METADATA_OUTPUT_FIELD,...]
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--output-vector-fields=[VECTOR_OUTPUT_FIELD,...]]
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[--use-knn | [--use-index=INDEX
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: --dense-scann-initial-candidate-count=CANDIDATE_COUNT
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--dense-scann-search-leaves-pct=PERCENTAGE]] [GCLOUD_WIDE_FLAG ...]
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DESCRIPTION
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(BETA) Search data objects from a Vector Search collection.
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EXAMPLES
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To search data objects from collection my-collection in location
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us-central1 using text search and return 10 results, run:
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$ gcloud beta vector-search collections data-objects search \
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--collection=my-collection --location=us-central1 \
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--text-search-text="test" \
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--text-search-data-fields="text_field" --top-k=10
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To search data objects from collection my-collection in location
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us-central1 using semantic search and return 5 results, run:
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$ gcloud beta vector-search collections data-objects search \
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--collection=my-collection --location=us-central1 \
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--semantic-search-text="sci-fi" \
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--semantic-search-field="plot_embedding" \
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--semantic-task-type="retrieval-query" --top-k=5
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To search data objects from collection my-collection in location
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us-central1 using vector search with an index hint and return 7 results,
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run:
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$ gcloud beta vector-search collections data-objects search \
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--collection=my-collection --location=us-central1 \
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--vector-search-field="genre_embedding" \
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--vector-from-file="vector.json" --use-index="my-index" \
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--top-k=7
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To search data objects from collection my-collection in location
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us-central1 using vector search with KNN for exact results, run:
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$ gcloud beta vector-search collections data-objects search \
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--collection=my-collection --location=us-central1 \
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--vector-search-field="genre_embedding" \
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--vector-from-file="vector.json" --use-knn --top-k=7
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REQUIRED FLAGS
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--collection=COLLECTION
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The collection to search data objects from.
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--location=LOCATION
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Location of the collection.
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Search type
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Exactly one of these must be specified:
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Semantic Search
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--semantic-search-field=SEMANTIC_SEARCH_FIELD
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The vector field to search.
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This flag argument must be specified if any of the other arguments in
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this group are specified.
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--semantic-search-text=SEMANTIC_SEARCH_TEXT
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The query text for semantic search.
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This flag argument must be specified if any of the other arguments in
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this group are specified.
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--semantic-task-type=SEMANTIC_TASK_TYPE
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The task type of the query embedding for semantic search.
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SEMANTIC_TASK_TYPE must be one of:
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classification
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Specifies that the given text will be classified.
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clustering
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Specifies that the embeddings will be used for clustering.
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code-retrieval-query
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Specifies that the embeddings will be used for code retrieval.
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fact-verification
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Specifies that the embeddings will be used for fact verification.
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question-answering
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Specifies that the embeddings will be used for question
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answering.
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retrieval-document
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Specifies the given text is a document from the corpus being
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searched.
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retrieval-query
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Specifies the given text is a query in a search/retrieval
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setting.
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semantic-similarity
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Specifies the given text will be used for STS.
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This flag argument must be specified if any of the other arguments in
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this group are specified.
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Text Search
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--text-search-data-fields=[DATA_FIELD_NAME,...]
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The data field names to search.
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This flag argument must be specified if any of the other arguments in
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this group are specified.
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--text-search-text=TEXT_SEARCH_TEXT
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The query text for text search.
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This flag argument must be specified if any of the other arguments in
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this group are specified.
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Vector Search
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--vector-from-file=VECTOR_FROM_FILE
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Path to a JSON file containing dense or sparse vector to search with.
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▸ Example file content for dense vector:
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{
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"dense": {
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"values": [
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0.7,
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0.6,
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0.5,
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0.4
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]
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}
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}
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▸ Example file content for sparse vector:
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{
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"sparse": {
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"indices": [1, 5, 10],
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"values": [0.1, 0.5, 0.21]
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}
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}
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This flag argument must be specified if any of the other arguments in
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this group are specified.
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--vector-search-field=VECTOR_SEARCH_FIELD
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The vector field to search.
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This flag argument must be specified if any of the other arguments in
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this group are specified.
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--distance-metric=DISTANCE_METRIC
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The distance metric to use for the KNN search. If not specified,
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dot-product will be used as the default. DISTANCE_METRIC must be one
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of:
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cosine-distance
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Cosine distance metric.
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dot-product
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Dot product distance metric.
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OPTIONAL FLAGS
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--json-filter=JSON_FILTER
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A filter expression in JSON format to apply to the search, e.g.
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'{"genre": {"$eq": "sci-fi"}}'.
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--top-k=TOP_K
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The number of nearest neighbors to return. Default is 10.
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Output fields
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--output-data-fields=[DATA_OUTPUT_FIELD,...]
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List of data fields to include in the output. Use * to include all data
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fields.
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--output-metadata-fields=[METADATA_OUTPUT_FIELD,...]
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List of metadata fields to include in the output. Use * to include all
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metadata fields.
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--output-vector-fields=[VECTOR_OUTPUT_FIELD,...]
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List of vector fields to include in the output. Use * to include all
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vector fields.
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Search Hint
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At most one of these can be specified:
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--use-knn
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If set to true, the search will use the system's default K-Nearest
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Neighbor (KNN) index engine. This flag is compatible only with
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Semantic Search and Vector Search.
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Or at least one of these can be specified:
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Use Index Options
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--use-index=INDEX
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Full resource name or ID of the index to use for the search. This
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flag is compatible only with Semantic Search and Vector Search.
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This flag argument must be specified if any of the other arguments
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in this group are specified.
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--dense-scann-initial-candidate-count=CANDIDATE_COUNT
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The number of initial candidates for dense ScaNN.
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--dense-scann-search-leaves-pct=PERCENTAGE
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The percentage of leaves to search for dense ScaNN, in the range
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[0, 100].
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GCLOUD WIDE FLAGS
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These flags are available to all commands: --access-token-file, --account,
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--billing-project, --configuration, --flags-file, --flatten, --format,
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--help, --impersonate-service-account, --log-http, --project, --quiet,
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--trace-token, --user-output-enabled, --verbosity.
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Run $ gcloud help for details.
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NOTES
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This command is currently in beta and might change without notice. This
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variant is also available:
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$ gcloud vector-search collections data-objects search
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