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.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Optional. Immutable. The CMEK key name used to encrypt at-rest data related to this Corpus. Only applicable to RagManagedDb option for Vector DB. This field can only be set at corpus creation time, and cannot be updated or deleted.
"kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
},
"name": "A String", # Output only. The resource name of the RagCorpus.
"updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated.
"vectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The config for the Vector DBs.
"apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # 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}
"apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
},
},
"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.
},
"ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the Vector DB.
"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.
},
},
"ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB.
"ann": { # Config for ANN search. RagManagedDb uses a tree-based structure to partition data and facilitate faster searches. As a tradeoff, it requires longer indexing time and manual triggering of index rebuild via the ImportRagFiles and UpdateRagCorpus API. # Performs an ANN search on RagCorpus. Use this if you have a lot of files (> 10K) in your RagCorpus and want to reduce the search latency.
"leafCount": 42, # Number of leaf nodes in the tree-based structure. Each leaf node contains groups of closely related vectors along with their corresponding centroid. Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus). Default value is 500.
"treeDepth": 42, # The depth of the tree-based structure. Only depth values of 2 and 3 are supported. Recommended value is 2 if you have if you have O(10K) files in the RagCorpus and set this to 3 if more than that. Default value is 2.
},
"knn": { # Config for KNN search. # Performs a KNN search on RagCorpus. Default choice if not specified.
},
},
"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}`
},
},
"vertexAiSearchConfig": { # Config for the Vertex AI Search. # Optional. Immutable. The config for the Vertex AI Search.
"servingConfig": "A String", # Vertex AI Search Serving Config resource full name. For example, `projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config}` or `projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/servingConfigs/{serving_config}`.
},
}

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.
  "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Optional. Immutable. The CMEK key name used to encrypt at-rest data related to this Corpus. Only applicable to RagManagedDb option for Vector DB. This field can only be set at corpus creation time, and cannot be updated or deleted.
    "kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
  },
  "name": "A String", # Output only. The resource name of the RagCorpus.
  "updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated.
  "vectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The config for the Vector DBs.
    "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # 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}
        "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
      },
    },
    "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.
    },
    "ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the Vector DB.
      "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.
      },
    },
    "ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB.
      "ann": { # Config for ANN search. RagManagedDb uses a tree-based structure to partition data and facilitate faster searches. As a tradeoff, it requires longer indexing time and manual triggering of index rebuild via the ImportRagFiles and UpdateRagCorpus API. # Performs an ANN search on RagCorpus. Use this if you have a lot of files (> 10K) in your RagCorpus and want to reduce the search latency.
        "leafCount": 42, # Number of leaf nodes in the tree-based structure. Each leaf node contains groups of closely related vectors along with their corresponding centroid. Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus). Default value is 500.
        "treeDepth": 42, # The depth of the tree-based structure. Only depth values of 2 and 3 are supported. Recommended value is 2 if you have if you have O(10K) files in the RagCorpus and set this to 3 if more than that. Default value is 2.
      },
      "knn": { # Config for KNN search. # Performs a KNN search on RagCorpus. Default choice if not specified.
      },
    },
    "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}`
    },
  },
  "vertexAiSearchConfig": { # Config for the Vertex AI Search. # Optional. Immutable. The config for the Vertex AI Search.
    "servingConfig": "A String", # Vertex AI Search Serving Config resource full name. For example, `projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config}` or `projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/servingConfigs/{serving_config}`.
  },
}

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.
      "encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Optional. Immutable. The CMEK key name used to encrypt at-rest data related to this Corpus. Only applicable to RagManagedDb option for Vector DB. This field can only be set at corpus creation time, and cannot be updated or deleted.
        "kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
      },
      "name": "A String", # Output only. The resource name of the RagCorpus.
      "updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated.
      "vectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The config for the Vector DBs.
        "apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # 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}
            "apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
          },
        },
        "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.
        },
        "ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the Vector DB.
          "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.
          },
        },
        "ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB.
          "ann": { # Config for ANN search. RagManagedDb uses a tree-based structure to partition data and facilitate faster searches. As a tradeoff, it requires longer indexing time and manual triggering of index rebuild via the ImportRagFiles and UpdateRagCorpus API. # Performs an ANN search on RagCorpus. Use this if you have a lot of files (> 10K) in your RagCorpus and want to reduce the search latency.
            "leafCount": 42, # Number of leaf nodes in the tree-based structure. Each leaf node contains groups of closely related vectors along with their corresponding centroid. Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus). Default value is 500.
            "treeDepth": 42, # The depth of the tree-based structure. Only depth values of 2 and 3 are supported. Recommended value is 2 if you have if you have O(10K) files in the RagCorpus and set this to 3 if more than that. Default value is 2.
          },
          "knn": { # Config for KNN search. # Performs a KNN search on RagCorpus. Default choice if not specified.
          },
        },
        "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}`
        },
      },
      "vertexAiSearchConfig": { # Config for the Vertex AI Search. # Optional. Immutable. The config for the Vertex AI Search.
        "servingConfig": "A String", # Vertex AI Search Serving Config resource full name. For example, `projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config}` or `projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/servingConfigs/{serving_config}`.
      },
    },
  ],
}

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.
"encryptionSpec": { # Represents a customer-managed encryption key spec that can be applied to a top-level resource. # Optional. Immutable. The CMEK key name used to encrypt at-rest data related to this Corpus. Only applicable to RagManagedDb option for Vector DB. This field can only be set at corpus creation time, and cannot be updated or deleted.
"kmsKeyName": "A String", # Required. The Cloud KMS resource identifier of the customer managed encryption key used to protect a resource. Has the form: `projects/my-project/locations/my-region/keyRings/my-kr/cryptoKeys/my-key`. The key needs to be in the same region as where the compute resource is created.
},
"name": "A String", # Output only. The resource name of the RagCorpus.
"updateTime": "A String", # Output only. Timestamp when this RagCorpus was last updated.
"vectorDbConfig": { # Config for the Vector DB to use for RAG. # Optional. Immutable. The config for the Vector DBs.
"apiAuth": { # The generic reusable api auth config. Deprecated. Please use AuthConfig (google/cloud/aiplatform/master/auth.proto) instead. # 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}
"apiKeyString": "A String", # The API key string. Either this or `api_key_secret_version` must be set.
},
},
"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.
},
"ragEmbeddingModelConfig": { # Config for the embedding model to use for RAG. # Optional. Immutable. The embedding model config of the Vector DB.
"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.
},
},
"ragManagedDb": { # The config for the default RAG-managed Vector DB. # The config for the RAG-managed Vector DB.
"ann": { # Config for ANN search. RagManagedDb uses a tree-based structure to partition data and facilitate faster searches. As a tradeoff, it requires longer indexing time and manual triggering of index rebuild via the ImportRagFiles and UpdateRagCorpus API. # Performs an ANN search on RagCorpus. Use this if you have a lot of files (> 10K) in your RagCorpus and want to reduce the search latency.
"leafCount": 42, # Number of leaf nodes in the tree-based structure. Each leaf node contains groups of closely related vectors along with their corresponding centroid. Recommended value is 10 * sqrt(num of RagFiles in your RagCorpus). Default value is 500.
"treeDepth": 42, # The depth of the tree-based structure. Only depth values of 2 and 3 are supported. Recommended value is 2 if you have if you have O(10K) files in the RagCorpus and set this to 3 if more than that. Default value is 2.
},
"knn": { # Config for KNN search. # Performs a KNN search on RagCorpus. Default choice if not specified.
},
},
"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}`
},
},
"vertexAiSearchConfig": { # Config for the Vertex AI Search. # Optional. Immutable. The config for the Vertex AI Search.
"servingConfig": "A String", # Vertex AI Search Serving Config resource full name. For example, `projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config}` or `projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/servingConfigs/{serving_config}`.
},
}

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.
},
}

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