Returns the operations Resource.
Returns the ragFiles Resource.
Close httplib2 connections.
create(parent, body=None, x__xgafv=None)
Creates a RagCorpus.
delete(name, force=None, x__xgafv=None)
Deletes a RagCorpus.
Gets a RagCorpus.
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Lists RagCorpora in a Location.
Retrieves the next page of results.
patch(name, body=None, x__xgafv=None)
Updates a RagCorpus.
close()
Close httplib2 connections.
create(parent, body=None, x__xgafv=None)
Creates a RagCorpus. Args: parent: string, Required. The resource name of the Location to create the RagCorpus in. Format: `projects/{project}/locations/{location}` (required) body: object, The request body. The object takes the form of: { # A RagCorpus is a RagFile container and a project can have multiple RagCorpora. "corpusStatus": { # RagCorpus status. # Output only. RagCorpus state. "errorStatus": "A String", # Output only. Only when the `state` field is ERROR. "state": "A String", # Output only. RagCorpus life state. }, "createTime": "A String", # Output only. Timestamp when this RagCorpus was created. "description": "A String", # Optional. The description of the RagCorpus. "displayName": "A String", # Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters. "name": "A String", # Output only. The resource name of the RagCorpus. "ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the RagCorpus. "hybridSearchConfig": { # Config for hybrid search. # Configuration for hybrid search. "denseEmbeddingModelPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # Required. The Vertex AI Prediction Endpoint that hosts the embedding model for dense embedding generations. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, "sparseEmbeddingConfig": { # Configuration for sparse emebdding generation. # Optional. The configuration for sparse embedding generation. This field is optional the default behavior depends on the vector database choice on the RagCorpus. "bm25": { # Message for BM25 parameters. # Use BM25 scoring algorithm. "b": 3.14, # Optional. The parameter to control document length normalization. It determines how much the document length affects the final score. b is in the range of [0, 1]. The default value is 0.75. "k1": 3.14, # Optional. The parameter to control term frequency saturation. It determines the scaling between the matching term frequency and final score. k1 is in the range of [1.2, 3]. The default value is 1.2. "multilingual": True or False, # Optional. Use multilingual tokenizer if set to true. }, }, }, "vertexPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # The Vertex AI Prediction Endpoint that either refers to a publisher model or an endpoint that is hosting a 1P fine-tuned text embedding model. Endpoints hosting non-1P fine-tuned text embedding models are currently not supported. This is used for dense vector search. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, }, "ragVectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The Vector DB config of the RagCorpus. "apiAuth": { # The generic reusable api auth config. # Authentication config for the chosen Vector DB. "apiKeyConfig": { # The API secret. # The API secret. "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} }, }, "pinecone": { # The config for the Pinecone. # The config for the Pinecone. "indexName": "A String", # Pinecone index name. This value cannot be changed after it's set. }, "ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB. }, "vertexFeatureStore": { # The config for the Vertex Feature Store. # The config for the Vertex Feature Store. "featureViewResourceName": "A String", # The resource name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` }, "vertexVectorSearch": { # The config for the Vertex Vector Search. # The config for the Vertex Vector Search. "index": "A String", # The resource name of the Index. Format: `projects/{project}/locations/{location}/indexes/{index}` "indexEndpoint": "A String", # The resource name of the Index Endpoint. Format: `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}` }, "weaviate": { # The config for the Weaviate. # The config for the Weaviate. "collectionName": "A String", # The corresponding collection this corpus maps to. This value cannot be changed after it's set. "httpEndpoint": "A String", # Weaviate DB instance HTTP endpoint. e.g. 34.56.78.90:8080 Vertex RAG only supports HTTP connection to Weaviate. This value cannot be changed after it's set. }, }, "updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated. } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # This resource represents a long-running operation that is the result of a network API call. "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation. "code": 42, # The status code, which should be an enum value of google.rpc.Code. "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use. { "a_key": "", # Properties of the object. Contains field @type with type URL. }, ], "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. }, "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. "a_key": "", # Properties of the object. Contains field @type with type URL. }, "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. "a_key": "", # Properties of the object. Contains field @type with type URL. }, }
delete(name, force=None, x__xgafv=None)
Deletes a RagCorpus. Args: name: string, Required. The name of the RagCorpus resource to be deleted. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` (required) force: boolean, Optional. If set to true, any RagFiles in this RagCorpus will also be deleted. Otherwise, the request will only work if the RagCorpus has no RagFiles. x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # This resource represents a long-running operation that is the result of a network API call. "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation. "code": 42, # The status code, which should be an enum value of google.rpc.Code. "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use. { "a_key": "", # Properties of the object. Contains field @type with type URL. }, ], "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. }, "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. "a_key": "", # Properties of the object. Contains field @type with type URL. }, "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. "a_key": "", # Properties of the object. Contains field @type with type URL. }, }
get(name, x__xgafv=None)
Gets a RagCorpus. Args: name: string, Required. The name of the RagCorpus resource. Format: `projects/{project}/locations/{location}/ragCorpora/{rag_corpus}` (required) x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # A RagCorpus is a RagFile container and a project can have multiple RagCorpora. "corpusStatus": { # RagCorpus status. # Output only. RagCorpus state. "errorStatus": "A String", # Output only. Only when the `state` field is ERROR. "state": "A String", # Output only. RagCorpus life state. }, "createTime": "A String", # Output only. Timestamp when this RagCorpus was created. "description": "A String", # Optional. The description of the RagCorpus. "displayName": "A String", # Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters. "name": "A String", # Output only. The resource name of the RagCorpus. "ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the RagCorpus. "hybridSearchConfig": { # Config for hybrid search. # Configuration for hybrid search. "denseEmbeddingModelPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # Required. The Vertex AI Prediction Endpoint that hosts the embedding model for dense embedding generations. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, "sparseEmbeddingConfig": { # Configuration for sparse emebdding generation. # Optional. The configuration for sparse embedding generation. This field is optional the default behavior depends on the vector database choice on the RagCorpus. "bm25": { # Message for BM25 parameters. # Use BM25 scoring algorithm. "b": 3.14, # Optional. The parameter to control document length normalization. It determines how much the document length affects the final score. b is in the range of [0, 1]. The default value is 0.75. "k1": 3.14, # Optional. The parameter to control term frequency saturation. It determines the scaling between the matching term frequency and final score. k1 is in the range of [1.2, 3]. The default value is 1.2. "multilingual": True or False, # Optional. Use multilingual tokenizer if set to true. }, }, }, "vertexPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # The Vertex AI Prediction Endpoint that either refers to a publisher model or an endpoint that is hosting a 1P fine-tuned text embedding model. Endpoints hosting non-1P fine-tuned text embedding models are currently not supported. This is used for dense vector search. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, }, "ragVectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The Vector DB config of the RagCorpus. "apiAuth": { # The generic reusable api auth config. # Authentication config for the chosen Vector DB. "apiKeyConfig": { # The API secret. # The API secret. "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} }, }, "pinecone": { # The config for the Pinecone. # The config for the Pinecone. "indexName": "A String", # Pinecone index name. This value cannot be changed after it's set. }, "ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB. }, "vertexFeatureStore": { # The config for the Vertex Feature Store. # The config for the Vertex Feature Store. "featureViewResourceName": "A String", # The resource name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` }, "vertexVectorSearch": { # The config for the Vertex Vector Search. # The config for the Vertex Vector Search. "index": "A String", # The resource name of the Index. Format: `projects/{project}/locations/{location}/indexes/{index}` "indexEndpoint": "A String", # The resource name of the Index Endpoint. Format: `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}` }, "weaviate": { # The config for the Weaviate. # The config for the Weaviate. "collectionName": "A String", # The corresponding collection this corpus maps to. This value cannot be changed after it's set. "httpEndpoint": "A String", # Weaviate DB instance HTTP endpoint. e.g. 34.56.78.90:8080 Vertex RAG only supports HTTP connection to Weaviate. This value cannot be changed after it's set. }, }, "updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated. }
list(parent, pageSize=None, pageToken=None, x__xgafv=None)
Lists RagCorpora in a Location. Args: parent: string, Required. The resource name of the Location from which to list the RagCorpora. Format: `projects/{project}/locations/{location}` (required) pageSize: integer, Optional. The standard list page size. pageToken: string, Optional. The standard list page token. Typically obtained via ListRagCorporaResponse.next_page_token of the previous VertexRagDataService.ListRagCorpora call. x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # Response message for VertexRagDataService.ListRagCorpora. "nextPageToken": "A String", # A token to retrieve the next page of results. Pass to ListRagCorporaRequest.page_token to obtain that page. "ragCorpora": [ # List of RagCorpora in the requested page. { # A RagCorpus is a RagFile container and a project can have multiple RagCorpora. "corpusStatus": { # RagCorpus status. # Output only. RagCorpus state. "errorStatus": "A String", # Output only. Only when the `state` field is ERROR. "state": "A String", # Output only. RagCorpus life state. }, "createTime": "A String", # Output only. Timestamp when this RagCorpus was created. "description": "A String", # Optional. The description of the RagCorpus. "displayName": "A String", # Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters. "name": "A String", # Output only. The resource name of the RagCorpus. "ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the RagCorpus. "hybridSearchConfig": { # Config for hybrid search. # Configuration for hybrid search. "denseEmbeddingModelPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # Required. The Vertex AI Prediction Endpoint that hosts the embedding model for dense embedding generations. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, "sparseEmbeddingConfig": { # Configuration for sparse emebdding generation. # Optional. The configuration for sparse embedding generation. This field is optional the default behavior depends on the vector database choice on the RagCorpus. "bm25": { # Message for BM25 parameters. # Use BM25 scoring algorithm. "b": 3.14, # Optional. The parameter to control document length normalization. It determines how much the document length affects the final score. b is in the range of [0, 1]. The default value is 0.75. "k1": 3.14, # Optional. The parameter to control term frequency saturation. It determines the scaling between the matching term frequency and final score. k1 is in the range of [1.2, 3]. The default value is 1.2. "multilingual": True or False, # Optional. Use multilingual tokenizer if set to true. }, }, }, "vertexPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # The Vertex AI Prediction Endpoint that either refers to a publisher model or an endpoint that is hosting a 1P fine-tuned text embedding model. Endpoints hosting non-1P fine-tuned text embedding models are currently not supported. This is used for dense vector search. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, }, "ragVectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The Vector DB config of the RagCorpus. "apiAuth": { # The generic reusable api auth config. # Authentication config for the chosen Vector DB. "apiKeyConfig": { # The API secret. # The API secret. "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} }, }, "pinecone": { # The config for the Pinecone. # The config for the Pinecone. "indexName": "A String", # Pinecone index name. This value cannot be changed after it's set. }, "ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB. }, "vertexFeatureStore": { # The config for the Vertex Feature Store. # The config for the Vertex Feature Store. "featureViewResourceName": "A String", # The resource name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` }, "vertexVectorSearch": { # The config for the Vertex Vector Search. # The config for the Vertex Vector Search. "index": "A String", # The resource name of the Index. Format: `projects/{project}/locations/{location}/indexes/{index}` "indexEndpoint": "A String", # The resource name of the Index Endpoint. Format: `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}` }, "weaviate": { # The config for the Weaviate. # The config for the Weaviate. "collectionName": "A String", # The corresponding collection this corpus maps to. This value cannot be changed after it's set. "httpEndpoint": "A String", # Weaviate DB instance HTTP endpoint. e.g. 34.56.78.90:8080 Vertex RAG only supports HTTP connection to Weaviate. This value cannot be changed after it's set. }, }, "updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated. }, ], }
list_next()
Retrieves the next page of results. Args: previous_request: The request for the previous page. (required) previous_response: The response from the request for the previous page. (required) Returns: A request object that you can call 'execute()' on to request the next page. Returns None if there are no more items in the collection.
patch(name, body=None, x__xgafv=None)
Updates a RagCorpus. Args: name: string, Output only. The resource name of the RagCorpus. (required) body: object, The request body. The object takes the form of: { # A RagCorpus is a RagFile container and a project can have multiple RagCorpora. "corpusStatus": { # RagCorpus status. # Output only. RagCorpus state. "errorStatus": "A String", # Output only. Only when the `state` field is ERROR. "state": "A String", # Output only. RagCorpus life state. }, "createTime": "A String", # Output only. Timestamp when this RagCorpus was created. "description": "A String", # Optional. The description of the RagCorpus. "displayName": "A String", # Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters. "name": "A String", # Output only. The resource name of the RagCorpus. "ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the RagCorpus. "hybridSearchConfig": { # Config for hybrid search. # Configuration for hybrid search. "denseEmbeddingModelPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # Required. The Vertex AI Prediction Endpoint that hosts the embedding model for dense embedding generations. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, "sparseEmbeddingConfig": { # Configuration for sparse emebdding generation. # Optional. The configuration for sparse embedding generation. This field is optional the default behavior depends on the vector database choice on the RagCorpus. "bm25": { # Message for BM25 parameters. # Use BM25 scoring algorithm. "b": 3.14, # Optional. The parameter to control document length normalization. It determines how much the document length affects the final score. b is in the range of [0, 1]. The default value is 0.75. "k1": 3.14, # Optional. The parameter to control term frequency saturation. It determines the scaling between the matching term frequency and final score. k1 is in the range of [1.2, 3]. The default value is 1.2. "multilingual": True or False, # Optional. Use multilingual tokenizer if set to true. }, }, }, "vertexPredictionEndpoint": { # Config representing a model hosted on Vertex Prediction Endpoint. # The Vertex AI Prediction Endpoint that either refers to a publisher model or an endpoint that is hosting a 1P fine-tuned text embedding model. Endpoints hosting non-1P fine-tuned text embedding models are currently not supported. This is used for dense vector search. "endpoint": "A String", # Required. The endpoint resource name. Format: `projects/{project}/locations/{location}/publishers/{publisher}/models/{model}` or `projects/{project}/locations/{location}/endpoints/{endpoint}` "model": "A String", # Output only. The resource name of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. Pattern: `projects/{project}/locations/{location}/models/{model}` "modelVersionId": "A String", # Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. }, }, "ragVectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The Vector DB config of the RagCorpus. "apiAuth": { # The generic reusable api auth config. # Authentication config for the chosen Vector DB. "apiKeyConfig": { # The API secret. # The API secret. "apiKeySecretVersion": "A String", # Required. The SecretManager secret version resource name storing API key. e.g. projects/{project}/secrets/{secret}/versions/{version} }, }, "pinecone": { # The config for the Pinecone. # The config for the Pinecone. "indexName": "A String", # Pinecone index name. This value cannot be changed after it's set. }, "ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB. }, "vertexFeatureStore": { # The config for the Vertex Feature Store. # The config for the Vertex Feature Store. "featureViewResourceName": "A String", # The resource name of the FeatureView. Format: `projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}` }, "vertexVectorSearch": { # The config for the Vertex Vector Search. # The config for the Vertex Vector Search. "index": "A String", # The resource name of the Index. Format: `projects/{project}/locations/{location}/indexes/{index}` "indexEndpoint": "A String", # The resource name of the Index Endpoint. Format: `projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}` }, "weaviate": { # The config for the Weaviate. # The config for the Weaviate. "collectionName": "A String", # The corresponding collection this corpus maps to. This value cannot be changed after it's set. "httpEndpoint": "A String", # Weaviate DB instance HTTP endpoint. e.g. 34.56.78.90:8080 Vertex RAG only supports HTTP connection to Weaviate. This value cannot be changed after it's set. }, }, "updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated. } x__xgafv: string, V1 error format. Allowed values 1 - v1 error format 2 - v2 error format Returns: An object of the form: { # This resource represents a long-running operation that is the result of a network API call. "done": True or False, # If the value is `false`, it means the operation is still in progress. If `true`, the operation is completed, and either `error` or `response` is available. "error": { # The `Status` type defines a logical error model that is suitable for different programming environments, including REST APIs and RPC APIs. It is used by [gRPC](https://github.com/grpc). Each `Status` message contains three pieces of data: error code, error message, and error details. You can find out more about this error model and how to work with it in the [API Design Guide](https://cloud.google.com/apis/design/errors). # The error result of the operation in case of failure or cancellation. "code": 42, # The status code, which should be an enum value of google.rpc.Code. "details": [ # A list of messages that carry the error details. There is a common set of message types for APIs to use. { "a_key": "", # Properties of the object. Contains field @type with type URL. }, ], "message": "A String", # A developer-facing error message, which should be in English. Any user-facing error message should be localized and sent in the google.rpc.Status.details field, or localized by the client. }, "metadata": { # Service-specific metadata associated with the operation. It typically contains progress information and common metadata such as create time. Some services might not provide such metadata. Any method that returns a long-running operation should document the metadata type, if any. "a_key": "", # Properties of the object. Contains field @type with type URL. }, "name": "A String", # The server-assigned name, which is only unique within the same service that originally returns it. If you use the default HTTP mapping, the `name` should be a resource name ending with `operations/{unique_id}`. "response": { # The normal, successful response of the operation. If the original method returns no data on success, such as `Delete`, the response is `google.protobuf.Empty`. If the original method is standard `Get`/`Create`/`Update`, the response should be the resource. For other methods, the response should have the type `XxxResponse`, where `Xxx` is the original method name. For example, if the original method name is `TakeSnapshot()`, the inferred response type is `TakeSnapshotResponse`. "a_key": "", # Properties of the object. Contains field @type with type URL. }, }