gcp.vertex.getAiIndex

func LookupAiIndex(ctx *Context, args *LookupAiIndexArgs, opts ...InvokeOption) (*LookupAiIndexResult, error)
func LookupAiIndexOutput(ctx *Context, args *LookupAiIndexOutputArgs, opts ...InvokeOption) LookupAiIndexResultOutput

> Note: This function is named LookupAiIndex in the Go SDK.

Name string

The name of the index.

Region string

The region of the index.

---

Project string

The ID of the project in which the resource belongs.

Name string

The name of the index.

Region string

The region of the index.

---

Project string

The ID of the project in which the resource belongs.

name String

The name of the index.

region String

The region of the index.

---

project String

The ID of the project in which the resource belongs.

name string

The name of the index.

region string

The region of the index.

---

project string

The ID of the project in which the resource belongs.

name str

The name of the index.

region str

The region of the index.

---

project str

The ID of the project in which the resource belongs.

name String

The name of the index.

region String

The region of the index.

---

project String

The ID of the project in which the resource belongs.

CreateTime string

DeployedIndexes List<GetAiIndexDeployedIndex>

Description string

DisplayName string

EffectiveLabels Dictionary<string, string>

Etag string

Id string

The provider-assigned unique ID for this managed resource.

IndexStats List<GetAiIndexIndexStat>

IndexUpdateMethod string

Labels Dictionary<string, string>

MetadataSchemaUri string

Metadatas List<GetAiIndexMetadata>

Name string

PulumiLabels Dictionary<string, string>

Region string

UpdateTime string

Project string

CreateTime string

DeployedIndexes []GetAiIndexDeployedIndex

Description string

DisplayName string

EffectiveLabels map[string]string

Etag string

Id string

The provider-assigned unique ID for this managed resource.

IndexStats []GetAiIndexIndexStat

IndexUpdateMethod string

Labels map[string]string

MetadataSchemaUri string

Metadatas []GetAiIndexMetadata

Name string

PulumiLabels map[string]string

Region string

UpdateTime string

Project string

createTime String

deployedIndexes List<GetAiIndexDeployedIndex>

description String

displayName String

effectiveLabels Map<String,String>

etag String

id String

The provider-assigned unique ID for this managed resource.

indexStats List<GetAiIndexIndexStat>

indexUpdateMethod String

labels Map<String,String>

metadataSchemaUri String

metadatas List<GetAiIndexMetadata>

name String

pulumiLabels Map<String,String>

region String

updateTime String

project String

createTime string

deployedIndexes GetAiIndexDeployedIndex[]

description string

displayName string

effectiveLabels {[key: string]: string}

etag string

id string

The provider-assigned unique ID for this managed resource.

indexStats GetAiIndexIndexStat[]

indexUpdateMethod string

labels {[key: string]: string}

metadataSchemaUri string

metadatas GetAiIndexMetadata[]

name string

pulumiLabels {[key: string]: string}

region string

updateTime string

project string

create_time str

deployed_indexes Sequence[GetAiIndexDeployedIndex]

description str

display_name str

effective_labels Mapping[str, str]

etag str

id str

The provider-assigned unique ID for this managed resource.

index_stats Sequence[GetAiIndexIndexStat]

index_update_method str

labels Mapping[str, str]

metadata_schema_uri str

metadatas Sequence[GetAiIndexMetadata]

name str

pulumi_labels Mapping[str, str]

region str

update_time str

project str

createTime String

deployedIndexes List<Property Map>

description String

displayName String

effectiveLabels Map<String>

etag String

id String

The provider-assigned unique ID for this managed resource.

indexStats List<Property Map>

indexUpdateMethod String

labels Map<String>

metadataSchemaUri String

metadatas List<Property Map>

name String

pulumiLabels Map<String>

region String

updateTime String

project String

DeployedIndexId string

The ID of the DeployedIndex in the above IndexEndpoint.

IndexEndpoint string

A resource name of the IndexEndpoint.

DeployedIndexId string

The ID of the DeployedIndex in the above IndexEndpoint.

IndexEndpoint string

A resource name of the IndexEndpoint.

deployedIndexId String

The ID of the DeployedIndex in the above IndexEndpoint.

indexEndpoint String

A resource name of the IndexEndpoint.

deployedIndexId string

The ID of the DeployedIndex in the above IndexEndpoint.

indexEndpoint string

A resource name of the IndexEndpoint.

deployed_index_id str

The ID of the DeployedIndex in the above IndexEndpoint.

index_endpoint str

A resource name of the IndexEndpoint.

deployedIndexId String

The ID of the DeployedIndex in the above IndexEndpoint.

indexEndpoint String

A resource name of the IndexEndpoint.

ShardsCount int

The number of shards in the Index.

VectorsCount string

The number of vectors in the Index.

ShardsCount int

The number of shards in the Index.

VectorsCount string

The number of vectors in the Index.

shardsCount Integer

The number of shards in the Index.

vectorsCount String

The number of vectors in the Index.

shardsCount number

The number of shards in the Index.

vectorsCount string

The number of vectors in the Index.

shards_count int

The number of shards in the Index.

vectors_count str

The number of vectors in the Index.

shardsCount Number

The number of shards in the Index.

vectorsCount String

The number of vectors in the Index.

Configs List<GetAiIndexMetadataConfig>

The configuration of the Matching Engine Index.

ContentsDeltaUri string

Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://cloud.google.com/vertex-ai/docs/matching-engine/using-matching-engine#input-data-format

IsCompleteOverwrite bool

If this field is set together with contentsDeltaUri when calling IndexService.UpdateIndex, then existing content of the Index will be replaced by the data from the contentsDeltaUri.

Configs []GetAiIndexMetadataConfig

The configuration of the Matching Engine Index.

ContentsDeltaUri string

Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://cloud.google.com/vertex-ai/docs/matching-engine/using-matching-engine#input-data-format

IsCompleteOverwrite bool

If this field is set together with contentsDeltaUri when calling IndexService.UpdateIndex, then existing content of the Index will be replaced by the data from the contentsDeltaUri.

configs List<GetAiIndexMetadataConfig>

The configuration of the Matching Engine Index.

contentsDeltaUri String

Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://cloud.google.com/vertex-ai/docs/matching-engine/using-matching-engine#input-data-format

isCompleteOverwrite Boolean

If this field is set together with contentsDeltaUri when calling IndexService.UpdateIndex, then existing content of the Index will be replaced by the data from the contentsDeltaUri.

configs GetAiIndexMetadataConfig[]

The configuration of the Matching Engine Index.

contentsDeltaUri string

Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://cloud.google.com/vertex-ai/docs/matching-engine/using-matching-engine#input-data-format

isCompleteOverwrite boolean

If this field is set together with contentsDeltaUri when calling IndexService.UpdateIndex, then existing content of the Index will be replaced by the data from the contentsDeltaUri.

configs Sequence[GetAiIndexMetadataConfig]

The configuration of the Matching Engine Index.

contents_delta_uri str

Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://cloud.google.com/vertex-ai/docs/matching-engine/using-matching-engine#input-data-format

is_complete_overwrite bool

If this field is set together with contentsDeltaUri when calling IndexService.UpdateIndex, then existing content of the Index will be replaced by the data from the contentsDeltaUri.

configs List<Property Map>

The configuration of the Matching Engine Index.

contentsDeltaUri String

Allows inserting, updating or deleting the contents of the Matching Engine Index. The string must be a valid Cloud Storage directory path. If this field is set when calling IndexService.UpdateIndex, then no other Index field can be also updated as part of the same call. The expected structure and format of the files this URI points to is described at https://cloud.google.com/vertex-ai/docs/matching-engine/using-matching-engine#input-data-format

isCompleteOverwrite Boolean

If this field is set together with contentsDeltaUri when calling IndexService.UpdateIndex, then existing content of the Index will be replaced by the data from the contentsDeltaUri.

AlgorithmConfigs List<GetAiIndexMetadataConfigAlgorithmConfig>

The configuration with regard to the algorithms used for efficient search.

ApproximateNeighborsCount int

The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. Required if tree-AH algorithm is used.

Dimensions int

The number of dimensions of the input vectors.

DistanceMeasureType string

The distance measure used in nearest neighbor search. The value must be one of the followings:

• SQUARED_L2_DISTANCE: Euclidean (L_2) Distance
• L1_DISTANCE: Manhattan (L_1) Distance
• COSINE_DISTANCE: Cosine Distance. Defined as 1 - cosine similarity.
• DOT_PRODUCT_DISTANCE: Dot Product Distance. Defined as a negative of the dot product

FeatureNormType string

Type of normalization to be carried out on each vector. The value must be one of the followings:

• UNIT_L2_NORM: Unit L2 normalization type
• NONE: No normalization type is specified.

ShardSize string

Index data is split into equal parts to be processed. These are called "shards". The shard size must be specified when creating an index. The value must be one of the followings:

• SHARD_SIZE_SMALL: Small (2GB)
• SHARD_SIZE_MEDIUM: Medium (20GB)
• SHARD_SIZE_LARGE: Large (50GB)

AlgorithmConfigs []GetAiIndexMetadataConfigAlgorithmConfig

The configuration with regard to the algorithms used for efficient search.

ApproximateNeighborsCount int

The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. Required if tree-AH algorithm is used.

Dimensions int

The number of dimensions of the input vectors.

DistanceMeasureType string

The distance measure used in nearest neighbor search. The value must be one of the followings:

• SQUARED_L2_DISTANCE: Euclidean (L_2) Distance
• L1_DISTANCE: Manhattan (L_1) Distance
• COSINE_DISTANCE: Cosine Distance. Defined as 1 - cosine similarity.
• DOT_PRODUCT_DISTANCE: Dot Product Distance. Defined as a negative of the dot product

FeatureNormType string

Type of normalization to be carried out on each vector. The value must be one of the followings:

• UNIT_L2_NORM: Unit L2 normalization type
• NONE: No normalization type is specified.

ShardSize string

Index data is split into equal parts to be processed. These are called "shards". The shard size must be specified when creating an index. The value must be one of the followings:

• SHARD_SIZE_SMALL: Small (2GB)
• SHARD_SIZE_MEDIUM: Medium (20GB)
• SHARD_SIZE_LARGE: Large (50GB)

algorithmConfigs List<GetAiIndexMetadataConfigAlgorithmConfig>

The configuration with regard to the algorithms used for efficient search.

approximateNeighborsCount Integer

The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. Required if tree-AH algorithm is used.

dimensions Integer

The number of dimensions of the input vectors.

distanceMeasureType String

The distance measure used in nearest neighbor search. The value must be one of the followings:

• SQUARED_L2_DISTANCE: Euclidean (L_2) Distance
• L1_DISTANCE: Manhattan (L_1) Distance
• COSINE_DISTANCE: Cosine Distance. Defined as 1 - cosine similarity.
• DOT_PRODUCT_DISTANCE: Dot Product Distance. Defined as a negative of the dot product

featureNormType String

Type of normalization to be carried out on each vector. The value must be one of the followings:

• UNIT_L2_NORM: Unit L2 normalization type
• NONE: No normalization type is specified.

shardSize String

Index data is split into equal parts to be processed. These are called "shards". The shard size must be specified when creating an index. The value must be one of the followings:

• SHARD_SIZE_SMALL: Small (2GB)
• SHARD_SIZE_MEDIUM: Medium (20GB)
• SHARD_SIZE_LARGE: Large (50GB)

algorithmConfigs GetAiIndexMetadataConfigAlgorithmConfig[]

The configuration with regard to the algorithms used for efficient search.

approximateNeighborsCount number

The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. Required if tree-AH algorithm is used.

dimensions number

The number of dimensions of the input vectors.

distanceMeasureType string

The distance measure used in nearest neighbor search. The value must be one of the followings:

• SQUARED_L2_DISTANCE: Euclidean (L_2) Distance
• L1_DISTANCE: Manhattan (L_1) Distance
• COSINE_DISTANCE: Cosine Distance. Defined as 1 - cosine similarity.
• DOT_PRODUCT_DISTANCE: Dot Product Distance. Defined as a negative of the dot product

featureNormType string

Type of normalization to be carried out on each vector. The value must be one of the followings:

• UNIT_L2_NORM: Unit L2 normalization type
• NONE: No normalization type is specified.

shardSize string

Index data is split into equal parts to be processed. These are called "shards". The shard size must be specified when creating an index. The value must be one of the followings:

• SHARD_SIZE_SMALL: Small (2GB)
• SHARD_SIZE_MEDIUM: Medium (20GB)
• SHARD_SIZE_LARGE: Large (50GB)

algorithm_configs Sequence[GetAiIndexMetadataConfigAlgorithmConfig]

The configuration with regard to the algorithms used for efficient search.

approximate_neighbors_count int

The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. Required if tree-AH algorithm is used.

dimensions int

The number of dimensions of the input vectors.

distance_measure_type str

The distance measure used in nearest neighbor search. The value must be one of the followings:

• SQUARED_L2_DISTANCE: Euclidean (L_2) Distance
• L1_DISTANCE: Manhattan (L_1) Distance
• COSINE_DISTANCE: Cosine Distance. Defined as 1 - cosine similarity.
• DOT_PRODUCT_DISTANCE: Dot Product Distance. Defined as a negative of the dot product

feature_norm_type str

Type of normalization to be carried out on each vector. The value must be one of the followings:

• UNIT_L2_NORM: Unit L2 normalization type
• NONE: No normalization type is specified.

shard_size str

Index data is split into equal parts to be processed. These are called "shards". The shard size must be specified when creating an index. The value must be one of the followings:

• SHARD_SIZE_SMALL: Small (2GB)
• SHARD_SIZE_MEDIUM: Medium (20GB)
• SHARD_SIZE_LARGE: Large (50GB)

algorithmConfigs List<Property Map>

The configuration with regard to the algorithms used for efficient search.

approximateNeighborsCount Number

The default number of neighbors to find via approximate search before exact reordering is performed. Exact reordering is a procedure where results returned by an approximate search algorithm are reordered via a more expensive distance computation. Required if tree-AH algorithm is used.

dimensions Number

The number of dimensions of the input vectors.

distanceMeasureType String

The distance measure used in nearest neighbor search. The value must be one of the followings:

• SQUARED_L2_DISTANCE: Euclidean (L_2) Distance
• L1_DISTANCE: Manhattan (L_1) Distance
• COSINE_DISTANCE: Cosine Distance. Defined as 1 - cosine similarity.
• DOT_PRODUCT_DISTANCE: Dot Product Distance. Defined as a negative of the dot product

featureNormType String

Type of normalization to be carried out on each vector. The value must be one of the followings:

• UNIT_L2_NORM: Unit L2 normalization type
• NONE: No normalization type is specified.

shardSize String

Index data is split into equal parts to be processed. These are called "shards". The shard size must be specified when creating an index. The value must be one of the followings:

• SHARD_SIZE_SMALL: Small (2GB)
• SHARD_SIZE_MEDIUM: Medium (20GB)
• SHARD_SIZE_LARGE: Large (50GB)

BruteForceConfigs List<GetAiIndexMetadataConfigAlgorithmConfigBruteForceConfig>

Configuration options for using brute force search, which simply implements the standard linear search in the database for each query.

TreeAhConfigs List<GetAiIndexMetadataConfigAlgorithmConfigTreeAhConfig>

Configuration options for using the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

BruteForceConfigs []GetAiIndexMetadataConfigAlgorithmConfigBruteForceConfig

Configuration options for using brute force search, which simply implements the standard linear search in the database for each query.

TreeAhConfigs []GetAiIndexMetadataConfigAlgorithmConfigTreeAhConfig

Configuration options for using the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

bruteForceConfigs List<GetAiIndexMetadataConfigAlgorithmConfigBruteForceConfig>

Configuration options for using brute force search, which simply implements the standard linear search in the database for each query.

treeAhConfigs List<GetAiIndexMetadataConfigAlgorithmConfigTreeAhConfig>

Configuration options for using the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

bruteForceConfigs GetAiIndexMetadataConfigAlgorithmConfigBruteForceConfig[]

Configuration options for using brute force search, which simply implements the standard linear search in the database for each query.

treeAhConfigs GetAiIndexMetadataConfigAlgorithmConfigTreeAhConfig[]

Configuration options for using the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

brute_force_configs Sequence[GetAiIndexMetadataConfigAlgorithmConfigBruteForceConfig]

Configuration options for using brute force search, which simply implements the standard linear search in the database for each query.

tree_ah_configs Sequence[GetAiIndexMetadataConfigAlgorithmConfigTreeAhConfig]

Configuration options for using the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

bruteForceConfigs List<Property Map>

Configuration options for using brute force search, which simply implements the standard linear search in the database for each query.

treeAhConfigs List<Property Map>

Configuration options for using the tree-AH algorithm (Shallow tree + Asymmetric Hashing). Please refer to this paper for more details: https://arxiv.org/abs/1908.10396

LeafNodeEmbeddingCount int

Number of embeddings on each leaf node. The default value is 1000 if not set.

LeafNodesToSearchPercent int

The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set.

LeafNodeEmbeddingCount int

Number of embeddings on each leaf node. The default value is 1000 if not set.

LeafNodesToSearchPercent int

The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set.

leafNodeEmbeddingCount Integer

Number of embeddings on each leaf node. The default value is 1000 if not set.

leafNodesToSearchPercent Integer

The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set.

leafNodeEmbeddingCount number

Number of embeddings on each leaf node. The default value is 1000 if not set.

leafNodesToSearchPercent number

The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set.

leaf_node_embedding_count int

Number of embeddings on each leaf node. The default value is 1000 if not set.

leaf_nodes_to_search_percent int

The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set.

leafNodeEmbeddingCount Number

Number of embeddings on each leaf node. The default value is 1000 if not set.

leafNodesToSearchPercent Number

The default percentage of leaf nodes that any query may be searched. Must be in range 1-100, inclusive. The default value is 10 (means 10%) if not set.