# -*- coding: utf-8 -*- # Copyright 2025 Google LLC # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # from __future__ import annotations from typing import MutableMapping, MutableSequence import proto # type: ignore from cloudsdk.google.protobuf import timestamp_pb2 # type: ignore from googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types import api_auth as gca_api_auth from googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types import encryption_spec as gca_encryption_spec from googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types import io __protobuf__ = proto.module( package='google.cloud.aiplatform.v1beta1', manifest={ 'RagEmbeddingModelConfig', 'RagVectorDbConfig', 'FileStatus', 'VertexAiSearchConfig', 'CorpusStatus', 'RagCorpus', 'RagFile', 'RagChunk', 'RagFileChunkingConfig', 'RagFileTransformationConfig', 'RagFileParsingConfig', 'RagFileMetadataConfig', 'UploadRagFileConfig', 'ImportRagFilesConfig', 'RagManagedDbConfig', 'RagEngineConfig', }, ) class RagEmbeddingModelConfig(proto.Message): r"""Config for the embedding model to use for RAG. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: vertex_prediction_endpoint (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagEmbeddingModelConfig.VertexPredictionEndpoint): 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. This field is a member of `oneof`_ ``model_config``. hybrid_search_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagEmbeddingModelConfig.HybridSearchConfig): Configuration for hybrid search. This field is a member of `oneof`_ ``model_config``. """ class VertexPredictionEndpoint(proto.Message): r"""Config representing a model hosted on Vertex Prediction Endpoint. Attributes: endpoint (str): Required. The endpoint resource name. Format: ``projects/{project}/locations/{location}/publishers/{publisher}/models/{model}`` or ``projects/{project}/locations/{location}/endpoints/{endpoint}`` model (str): 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}`` model_version_id (str): Output only. Version ID of the model that is deployed on the endpoint. Present only when the endpoint is not a publisher model. """ endpoint: str = proto.Field( proto.STRING, number=1, ) model: str = proto.Field( proto.STRING, number=2, ) model_version_id: str = proto.Field( proto.STRING, number=3, ) class SparseEmbeddingConfig(proto.Message): r"""Configuration for sparse emebdding generation. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: bm25 (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagEmbeddingModelConfig.SparseEmbeddingConfig.Bm25): Use BM25 scoring algorithm. This field is a member of `oneof`_ ``model``. """ class Bm25(proto.Message): r"""Message for BM25 parameters. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: multilingual (bool): Optional. Use multilingual tokenizer if set to true. k1 (float): 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. This field is a member of `oneof`_ ``_k1``. b (float): 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. This field is a member of `oneof`_ ``_b``. """ multilingual: bool = proto.Field( proto.BOOL, number=1, ) k1: float = proto.Field( proto.FLOAT, number=2, optional=True, ) b: float = proto.Field( proto.FLOAT, number=3, optional=True, ) bm25: 'RagEmbeddingModelConfig.SparseEmbeddingConfig.Bm25' = proto.Field( proto.MESSAGE, number=1, oneof='model', message='RagEmbeddingModelConfig.SparseEmbeddingConfig.Bm25', ) class HybridSearchConfig(proto.Message): r"""Config for hybrid search. Attributes: sparse_embedding_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagEmbeddingModelConfig.SparseEmbeddingConfig): Optional. The configuration for sparse embedding generation. This field is optional the default behavior depends on the vector database choice on the RagCorpus. dense_embedding_model_prediction_endpoint (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagEmbeddingModelConfig.VertexPredictionEndpoint): Required. The Vertex AI Prediction Endpoint that hosts the embedding model for dense embedding generations. """ sparse_embedding_config: 'RagEmbeddingModelConfig.SparseEmbeddingConfig' = proto.Field( proto.MESSAGE, number=1, message='RagEmbeddingModelConfig.SparseEmbeddingConfig', ) dense_embedding_model_prediction_endpoint: 'RagEmbeddingModelConfig.VertexPredictionEndpoint' = proto.Field( proto.MESSAGE, number=2, message='RagEmbeddingModelConfig.VertexPredictionEndpoint', ) vertex_prediction_endpoint: VertexPredictionEndpoint = proto.Field( proto.MESSAGE, number=1, oneof='model_config', message=VertexPredictionEndpoint, ) hybrid_search_config: HybridSearchConfig = proto.Field( proto.MESSAGE, number=2, oneof='model_config', message=HybridSearchConfig, ) class RagVectorDbConfig(proto.Message): r"""Config for the Vector DB to use for RAG. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: rag_managed_db (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.RagManagedDb): The config for the RAG-managed Vector DB. This field is a member of `oneof`_ ``vector_db``. weaviate (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.Weaviate): The config for the Weaviate. This field is a member of `oneof`_ ``vector_db``. pinecone (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.Pinecone): The config for the Pinecone. This field is a member of `oneof`_ ``vector_db``. vertex_feature_store (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.VertexFeatureStore): The config for the Vertex Feature Store. This field is a member of `oneof`_ ``vector_db``. vertex_vector_search (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.VertexVectorSearch): The config for the Vertex Vector Search. This field is a member of `oneof`_ ``vector_db``. rag_managed_vertex_vector_search (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.RagManagedVertexVectorSearch): The config for the RAG-managed Vertex Vector Search 2.0. This field is a member of `oneof`_ ``vector_db``. api_auth (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.ApiAuth): Authentication config for the chosen Vector DB. rag_embedding_model_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagEmbeddingModelConfig): Optional. Immutable. The embedding model config of the Vector DB. """ class RagManagedDb(proto.Message): r"""The config for the default RAG-managed Vector DB. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: knn (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.RagManagedDb.KNN): Performs a KNN search on RagCorpus. Default choice if not specified. This field is a member of `oneof`_ ``retrieval_strategy``. ann (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig.RagManagedDb.ANN): 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. This field is a member of `oneof`_ ``retrieval_strategy``. """ class KNN(proto.Message): r"""Config for KNN search. """ class ANN(proto.Message): r"""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. Attributes: tree_depth (int): 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. leaf_count (int): 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. """ tree_depth: int = proto.Field( proto.INT32, number=1, ) leaf_count: int = proto.Field( proto.INT32, number=2, ) knn: 'RagVectorDbConfig.RagManagedDb.KNN' = proto.Field( proto.MESSAGE, number=1, oneof='retrieval_strategy', message='RagVectorDbConfig.RagManagedDb.KNN', ) ann: 'RagVectorDbConfig.RagManagedDb.ANN' = proto.Field( proto.MESSAGE, number=2, oneof='retrieval_strategy', message='RagVectorDbConfig.RagManagedDb.ANN', ) class Weaviate(proto.Message): r"""The config for the Weaviate. Attributes: http_endpoint (str): 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. collection_name (str): The corresponding collection this corpus maps to. This value cannot be changed after it's set. """ http_endpoint: str = proto.Field( proto.STRING, number=1, ) collection_name: str = proto.Field( proto.STRING, number=2, ) class Pinecone(proto.Message): r"""The config for the Pinecone. Attributes: index_name (str): Pinecone index name. This value cannot be changed after it's set. """ index_name: str = proto.Field( proto.STRING, number=1, ) class VertexFeatureStore(proto.Message): r"""The config for the Vertex Feature Store. Attributes: feature_view_resource_name (str): The resource name of the FeatureView. Format: ``projects/{project}/locations/{location}/featureOnlineStores/{feature_online_store}/featureViews/{feature_view}`` """ feature_view_resource_name: str = proto.Field( proto.STRING, number=1, ) class VertexVectorSearch(proto.Message): r"""The config for the Vertex Vector Search. Attributes: index_endpoint (str): The resource name of the Index Endpoint. Format: ``projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}`` index (str): The resource name of the Index. Format: ``projects/{project}/locations/{location}/indexes/{index}`` """ index_endpoint: str = proto.Field( proto.STRING, number=1, ) index: str = proto.Field( proto.STRING, number=2, ) class RagManagedVertexVectorSearch(proto.Message): r"""The config for the RAG-managed Vertex Vector Search 2.0. Attributes: collection_name (str): Output only. The resource name of the Vector Search 2.0 Collection that RAG Created for the corpus. Only populated after the corpus is successfully created. Format: ``projects/{project}/locations/{location}/collections/{collection_id}`` """ collection_name: str = proto.Field( proto.STRING, number=1, ) rag_managed_db: RagManagedDb = proto.Field( proto.MESSAGE, number=1, oneof='vector_db', message=RagManagedDb, ) weaviate: Weaviate = proto.Field( proto.MESSAGE, number=2, oneof='vector_db', message=Weaviate, ) pinecone: Pinecone = proto.Field( proto.MESSAGE, number=3, oneof='vector_db', message=Pinecone, ) vertex_feature_store: VertexFeatureStore = proto.Field( proto.MESSAGE, number=4, oneof='vector_db', message=VertexFeatureStore, ) vertex_vector_search: VertexVectorSearch = proto.Field( proto.MESSAGE, number=6, oneof='vector_db', message=VertexVectorSearch, ) rag_managed_vertex_vector_search: RagManagedVertexVectorSearch = proto.Field( proto.MESSAGE, number=8, oneof='vector_db', message=RagManagedVertexVectorSearch, ) api_auth: gca_api_auth.ApiAuth = proto.Field( proto.MESSAGE, number=5, message=gca_api_auth.ApiAuth, ) rag_embedding_model_config: 'RagEmbeddingModelConfig' = proto.Field( proto.MESSAGE, number=7, message='RagEmbeddingModelConfig', ) class FileStatus(proto.Message): r"""RagFile status. Attributes: state (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.FileStatus.State): Output only. RagFile state. error_status (str): Output only. Only when the ``state`` field is ERROR. """ class State(proto.Enum): r"""RagFile state. Values: STATE_UNSPECIFIED (0): RagFile state is unspecified. ACTIVE (1): RagFile resource has been created and indexed successfully. ERROR (2): RagFile resource is in a problematic state. See ``error_message`` field for details. """ STATE_UNSPECIFIED = 0 ACTIVE = 1 ERROR = 2 state: State = proto.Field( proto.ENUM, number=1, enum=State, ) error_status: str = proto.Field( proto.STRING, number=2, ) class VertexAiSearchConfig(proto.Message): r"""Config for the Vertex AI Search. Attributes: serving_config (str): 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}``. """ serving_config: str = proto.Field( proto.STRING, number=1, ) class CorpusStatus(proto.Message): r"""RagCorpus status. Attributes: state (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.CorpusStatus.State): Output only. RagCorpus life state. error_status (str): Output only. Only when the ``state`` field is ERROR. """ class State(proto.Enum): r"""RagCorpus life state. Values: UNKNOWN (0): This state is not supposed to happen. INITIALIZED (1): RagCorpus resource entry is initialized, but hasn't done validation. ACTIVE (2): RagCorpus is provisioned successfully and is ready to serve. ERROR (3): RagCorpus is in a problematic situation. See ``error_message`` field for details. """ UNKNOWN = 0 INITIALIZED = 1 ACTIVE = 2 ERROR = 3 state: State = proto.Field( proto.ENUM, number=1, enum=State, ) error_status: str = proto.Field( proto.STRING, number=2, ) class RagCorpus(proto.Message): r"""A RagCorpus is a RagFile container and a project can have multiple RagCorpora. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: vector_db_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig): Optional. Immutable. The config for the Vector DBs. This field is a member of `oneof`_ ``backend_config``. vertex_ai_search_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.VertexAiSearchConfig): Optional. Immutable. The config for the Vertex AI Search. This field is a member of `oneof`_ ``backend_config``. name (str): Output only. The resource name of the RagCorpus. display_name (str): Required. The display name of the RagCorpus. The name can be up to 128 characters long and can consist of any UTF-8 characters. description (str): Optional. The description of the RagCorpus. rag_embedding_model_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagEmbeddingModelConfig): Optional. Immutable. The embedding model config of the RagCorpus. rag_vector_db_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagVectorDbConfig): Optional. Immutable. The Vector DB config of the RagCorpus. create_time (google.protobuf.timestamp_pb2.Timestamp): Output only. Timestamp when this RagCorpus was created. update_time (google.protobuf.timestamp_pb2.Timestamp): Output only. Timestamp when this RagCorpus was last updated. corpus_status (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.CorpusStatus): Output only. RagCorpus state. rag_files_count (int): Output only. Number of RagFiles in the RagCorpus. NOTE: This field is not populated in the response of [VertexRagDataService.ListRagCorpora][google.cloud.aiplatform.v1beta1.VertexRagDataService.ListRagCorpora]. encryption_spec (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.EncryptionSpec): 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. corpus_type_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagCorpus.CorpusTypeConfig): Optional. The corpus type config of the RagCorpus. satisfies_pzs (bool): Output only. Reserved for future use. satisfies_pzi (bool): Output only. Reserved for future use. """ class CorpusTypeConfig(proto.Message): r"""The config for the corpus type of the RagCorpus. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: document_corpus (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagCorpus.CorpusTypeConfig.DocumentCorpus): Optional. Config for the document corpus. This field is a member of `oneof`_ ``corpus_type_config``. memory_corpus (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagCorpus.CorpusTypeConfig.MemoryCorpus): Optional. Config for the memory corpus. This field is a member of `oneof`_ ``corpus_type_config``. """ class DocumentCorpus(proto.Message): r"""Config for the document corpus. """ class MemoryCorpus(proto.Message): r"""Config for the memory corpus. Attributes: llm_parser (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileParsingConfig.LlmParser): The LLM parser to use for the memory corpus. """ llm_parser: 'RagFileParsingConfig.LlmParser' = proto.Field( proto.MESSAGE, number=1, message='RagFileParsingConfig.LlmParser', ) document_corpus: 'RagCorpus.CorpusTypeConfig.DocumentCorpus' = proto.Field( proto.MESSAGE, number=1, oneof='corpus_type_config', message='RagCorpus.CorpusTypeConfig.DocumentCorpus', ) memory_corpus: 'RagCorpus.CorpusTypeConfig.MemoryCorpus' = proto.Field( proto.MESSAGE, number=2, oneof='corpus_type_config', message='RagCorpus.CorpusTypeConfig.MemoryCorpus', ) vector_db_config: 'RagVectorDbConfig' = proto.Field( proto.MESSAGE, number=9, oneof='backend_config', message='RagVectorDbConfig', ) vertex_ai_search_config: 'VertexAiSearchConfig' = proto.Field( proto.MESSAGE, number=10, oneof='backend_config', message='VertexAiSearchConfig', ) name: str = proto.Field( proto.STRING, number=1, ) display_name: str = proto.Field( proto.STRING, number=2, ) description: str = proto.Field( proto.STRING, number=3, ) rag_embedding_model_config: 'RagEmbeddingModelConfig' = proto.Field( proto.MESSAGE, number=6, message='RagEmbeddingModelConfig', ) rag_vector_db_config: 'RagVectorDbConfig' = proto.Field( proto.MESSAGE, number=7, message='RagVectorDbConfig', ) create_time: timestamp_pb2.Timestamp = proto.Field( proto.MESSAGE, number=4, message=timestamp_pb2.Timestamp, ) update_time: timestamp_pb2.Timestamp = proto.Field( proto.MESSAGE, number=5, message=timestamp_pb2.Timestamp, ) corpus_status: 'CorpusStatus' = proto.Field( proto.MESSAGE, number=8, message='CorpusStatus', ) rag_files_count: int = proto.Field( proto.INT32, number=11, ) encryption_spec: gca_encryption_spec.EncryptionSpec = proto.Field( proto.MESSAGE, number=12, message=gca_encryption_spec.EncryptionSpec, ) corpus_type_config: CorpusTypeConfig = proto.Field( proto.MESSAGE, number=13, message=CorpusTypeConfig, ) satisfies_pzs: bool = proto.Field( proto.BOOL, number=19, ) satisfies_pzi: bool = proto.Field( proto.BOOL, number=20, ) class RagFile(proto.Message): r"""A RagFile contains user data for chunking, embedding and indexing. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: gcs_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GcsSource): Output only. Google Cloud Storage location of the RagFile. It does not support wildcards in the Cloud Storage uri for now. This field is a member of `oneof`_ ``rag_file_source``. google_drive_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GoogleDriveSource): Output only. Google Drive location. Supports importing individual files as well as Google Drive folders. This field is a member of `oneof`_ ``rag_file_source``. direct_upload_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.DirectUploadSource): Output only. The RagFile is encapsulated and uploaded in the UploadRagFile request. This field is a member of `oneof`_ ``rag_file_source``. slack_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.SlackSource): The RagFile is imported from a Slack channel. This field is a member of `oneof`_ ``rag_file_source``. jira_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.JiraSource): The RagFile is imported from a Jira query. This field is a member of `oneof`_ ``rag_file_source``. share_point_sources (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.SharePointSources): The RagFile is imported from a SharePoint source. This field is a member of `oneof`_ ``rag_file_source``. name (str): Output only. The resource name of the RagFile. display_name (str): Required. The display name of the RagFile. The name can be up to 128 characters long and can consist of any UTF-8 characters. description (str): Optional. The description of the RagFile. size_bytes (int): Output only. The size of the RagFile in bytes. rag_file_type (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFile.RagFileType): Output only. The type of the RagFile. create_time (google.protobuf.timestamp_pb2.Timestamp): Output only. Timestamp when this RagFile was created. update_time (google.protobuf.timestamp_pb2.Timestamp): Output only. Timestamp when this RagFile was last updated. file_status (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.FileStatus): Output only. State of the RagFile. user_metadata (str): Output only. The metadata for metadata search. The user_metadata Needs to be in JSON format. """ class RagFileType(proto.Enum): r"""The type of the RagFile. Values: RAG_FILE_TYPE_UNSPECIFIED (0): RagFile type is unspecified. RAG_FILE_TYPE_TXT (1): RagFile type is TXT. RAG_FILE_TYPE_PDF (2): RagFile type is PDF. """ RAG_FILE_TYPE_UNSPECIFIED = 0 RAG_FILE_TYPE_TXT = 1 RAG_FILE_TYPE_PDF = 2 gcs_source: io.GcsSource = proto.Field( proto.MESSAGE, number=8, oneof='rag_file_source', message=io.GcsSource, ) google_drive_source: io.GoogleDriveSource = proto.Field( proto.MESSAGE, number=9, oneof='rag_file_source', message=io.GoogleDriveSource, ) direct_upload_source: io.DirectUploadSource = proto.Field( proto.MESSAGE, number=10, oneof='rag_file_source', message=io.DirectUploadSource, ) slack_source: io.SlackSource = proto.Field( proto.MESSAGE, number=11, oneof='rag_file_source', message=io.SlackSource, ) jira_source: io.JiraSource = proto.Field( proto.MESSAGE, number=12, oneof='rag_file_source', message=io.JiraSource, ) share_point_sources: io.SharePointSources = proto.Field( proto.MESSAGE, number=14, oneof='rag_file_source', message=io.SharePointSources, ) name: str = proto.Field( proto.STRING, number=1, ) display_name: str = proto.Field( proto.STRING, number=2, ) description: str = proto.Field( proto.STRING, number=3, ) size_bytes: int = proto.Field( proto.INT64, number=4, ) rag_file_type: RagFileType = proto.Field( proto.ENUM, number=5, enum=RagFileType, ) create_time: timestamp_pb2.Timestamp = proto.Field( proto.MESSAGE, number=6, message=timestamp_pb2.Timestamp, ) update_time: timestamp_pb2.Timestamp = proto.Field( proto.MESSAGE, number=7, message=timestamp_pb2.Timestamp, ) file_status: 'FileStatus' = proto.Field( proto.MESSAGE, number=13, message='FileStatus', ) user_metadata: str = proto.Field( proto.STRING, number=15, ) class RagChunk(proto.Message): r"""A RagChunk includes the content of a chunk of a RagFile, and associated metadata. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: text (str): The content of the chunk. page_span (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagChunk.PageSpan): If populated, represents where the chunk starts and ends in the document. This field is a member of `oneof`_ ``_page_span``. """ class PageSpan(proto.Message): r"""Represents where the chunk starts and ends in the document. Attributes: first_page (int): Page where chunk starts in the document. Inclusive. 1-indexed. last_page (int): Page where chunk ends in the document. Inclusive. 1-indexed. """ first_page: int = proto.Field( proto.INT32, number=1, ) last_page: int = proto.Field( proto.INT32, number=2, ) text: str = proto.Field( proto.STRING, number=1, ) page_span: PageSpan = proto.Field( proto.MESSAGE, number=2, optional=True, message=PageSpan, ) class RagFileChunkingConfig(proto.Message): r"""Specifies the size and overlap of chunks for RagFiles. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: fixed_length_chunking (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileChunkingConfig.FixedLengthChunking): Specifies the fixed length chunking config. This field is a member of `oneof`_ ``chunking_config``. chunk_size (int): The size of the chunks. chunk_overlap (int): The overlap between chunks. """ class FixedLengthChunking(proto.Message): r"""Specifies the fixed length chunking config. Attributes: chunk_size (int): The size of the chunks. chunk_overlap (int): The overlap between chunks. """ chunk_size: int = proto.Field( proto.INT32, number=1, ) chunk_overlap: int = proto.Field( proto.INT32, number=2, ) fixed_length_chunking: FixedLengthChunking = proto.Field( proto.MESSAGE, number=3, oneof='chunking_config', message=FixedLengthChunking, ) chunk_size: int = proto.Field( proto.INT32, number=1, ) chunk_overlap: int = proto.Field( proto.INT32, number=2, ) class RagFileTransformationConfig(proto.Message): r"""Specifies the transformation config for RagFiles. Attributes: rag_file_chunking_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileChunkingConfig): Specifies the chunking config for RagFiles. """ rag_file_chunking_config: 'RagFileChunkingConfig' = proto.Field( proto.MESSAGE, number=1, message='RagFileChunkingConfig', ) class RagFileParsingConfig(proto.Message): r"""Specifies the parsing config for RagFiles. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: advanced_parser (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileParsingConfig.AdvancedParser): The Advanced Parser to use for RagFiles. This field is a member of `oneof`_ ``parser``. layout_parser (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileParsingConfig.LayoutParser): The Layout Parser to use for RagFiles. This field is a member of `oneof`_ ``parser``. llm_parser (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileParsingConfig.LlmParser): The LLM Parser to use for RagFiles. This field is a member of `oneof`_ ``parser``. use_advanced_pdf_parsing (bool): Whether to use advanced PDF parsing. """ class AdvancedParser(proto.Message): r"""Specifies the advanced parsing for RagFiles. Attributes: use_advanced_pdf_parsing (bool): Whether to use advanced PDF parsing. """ use_advanced_pdf_parsing: bool = proto.Field( proto.BOOL, number=1, ) class LayoutParser(proto.Message): r"""Document AI Layout Parser config. Attributes: processor_name (str): The full resource name of a Document AI processor or processor version. The processor must have type ``LAYOUT_PARSER_PROCESSOR``. If specified, the ``additional_config.parse_as_scanned_pdf`` field must be false. Format: - ``projects/{project_id}/locations/{location}/processors/{processor_id}`` - ``projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}`` max_parsing_requests_per_min (int): The maximum number of requests the job is allowed to make to the Document AI processor per minute. Consult https://cloud.google.com/document-ai/quotas and the Quota page for your project to set an appropriate value here. If unspecified, a default value of 120 QPM would be used. global_max_parsing_requests_per_min (int): The maximum number of requests the job is allowed to make to the Document AI processor per minute in this project. Consult https://cloud.google.com/document-ai/quotas and the Quota page for your project to set an appropriate value here. If this value is not specified, max_parsing_requests_per_min will be used by indexing pipeline as the global limit. """ processor_name: str = proto.Field( proto.STRING, number=1, ) max_parsing_requests_per_min: int = proto.Field( proto.INT32, number=2, ) global_max_parsing_requests_per_min: int = proto.Field( proto.INT32, number=3, ) class LlmParser(proto.Message): r"""Specifies the LLM parsing for RagFiles. Attributes: model_name (str): The name of a LLM model used for parsing. Format: - ``projects/{project_id}/locations/{location}/publishers/{publisher}/models/{model}`` max_parsing_requests_per_min (int): The maximum number of requests the job is allowed to make to the LLM model per minute. Consult https://cloud.google.com/vertex-ai/generative-ai/docs/quotas and your document size to set an appropriate value here. If unspecified, a default value of 5000 QPM would be used. global_max_parsing_requests_per_min (int): The maximum number of requests the job is allowed to make to the LLM model per minute in this project. Consult https://cloud.google.com/vertex-ai/generative-ai/docs/quotas and your document size to set an appropriate value here. If this value is not specified, max_parsing_requests_per_min will be used by indexing pipeline job as the global limit. custom_parsing_prompt (str): The prompt to use for parsing. If not specified, a default prompt will be used. """ model_name: str = proto.Field( proto.STRING, number=1, ) max_parsing_requests_per_min: int = proto.Field( proto.INT32, number=2, ) global_max_parsing_requests_per_min: int = proto.Field( proto.INT32, number=4, ) custom_parsing_prompt: str = proto.Field( proto.STRING, number=3, ) advanced_parser: AdvancedParser = proto.Field( proto.MESSAGE, number=3, oneof='parser', message=AdvancedParser, ) layout_parser: LayoutParser = proto.Field( proto.MESSAGE, number=4, oneof='parser', message=LayoutParser, ) llm_parser: LlmParser = proto.Field( proto.MESSAGE, number=5, oneof='parser', message=LlmParser, ) use_advanced_pdf_parsing: bool = proto.Field( proto.BOOL, number=2, ) class RagFileMetadataConfig(proto.Message): r"""Metadata config for RagFile. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: gcs_metadata_schema_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GcsSource): Google Cloud Storage location. Supports importing individual files as well as entire Google Cloud Storage directories. Sample formats: - ``gs://bucket_name/my_directory/object_name/metadata_schema.json`` - ``gs://bucket_name/my_directory`` If the user provides a directory, the metadata schema will be read from the files that ends with "metadata_schema.json" in the directory. This field is a member of `oneof`_ ``metadata_schema_source``. google_drive_metadata_schema_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GoogleDriveSource): Google Drive location. Supports importing individual files as well as Google Drive folders. If the user provides a folder, the metadata schema will be read from the files that ends with "metadata_schema.json" in the directory. This field is a member of `oneof`_ ``metadata_schema_source``. inline_metadata_schema_source (str): Inline metadata schema source. Must be a JSON string. This field is a member of `oneof`_ ``metadata_schema_source``. gcs_metadata_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GcsSource): Google Cloud Storage location. Supports importing individual files as well as entire Google Cloud Storage directories. Sample formats: - ``gs://bucket_name/my_directory/object_name/metadata.json`` - ``gs://bucket_name/my_directory`` If the user provides a directory, the metadata will be read from the files that ends with "metadata.json" in the directory. This field is a member of `oneof`_ ``metadata_source``. google_drive_metadata_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GoogleDriveSource): Google Drive location. Supports importing individual files as well as Google Drive folders. If the user provides a directory, the metadata will be read from the files that ends with "metadata.json" in the directory. This field is a member of `oneof`_ ``metadata_source``. inline_metadata_source (str): Inline metadata source. Must be a JSON string. This field is a member of `oneof`_ ``metadata_source``. """ gcs_metadata_schema_source: io.GcsSource = proto.Field( proto.MESSAGE, number=1, oneof='metadata_schema_source', message=io.GcsSource, ) google_drive_metadata_schema_source: io.GoogleDriveSource = proto.Field( proto.MESSAGE, number=2, oneof='metadata_schema_source', message=io.GoogleDriveSource, ) inline_metadata_schema_source: str = proto.Field( proto.STRING, number=3, oneof='metadata_schema_source', ) gcs_metadata_source: io.GcsSource = proto.Field( proto.MESSAGE, number=4, oneof='metadata_source', message=io.GcsSource, ) google_drive_metadata_source: io.GoogleDriveSource = proto.Field( proto.MESSAGE, number=5, oneof='metadata_source', message=io.GoogleDriveSource, ) inline_metadata_source: str = proto.Field( proto.STRING, number=6, oneof='metadata_source', ) class UploadRagFileConfig(proto.Message): r"""Config for uploading RagFile. Attributes: rag_file_chunking_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileChunkingConfig): Specifies the size and overlap of chunks after uploading RagFile. rag_file_transformation_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileTransformationConfig): Specifies the transformation config for RagFiles. rag_file_metadata_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileMetadataConfig): Specifies the metadata config for RagFiles. Including paths for metadata schema and metadata. Alteratively, inline metadata schema and metadata can be provided. rag_file_parsing_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileParsingConfig): Optional. Specifies the parsing config for RagFiles. RAG will use the default parser if this field is not set. """ rag_file_chunking_config: 'RagFileChunkingConfig' = proto.Field( proto.MESSAGE, number=1, message='RagFileChunkingConfig', ) rag_file_transformation_config: 'RagFileTransformationConfig' = proto.Field( proto.MESSAGE, number=3, message='RagFileTransformationConfig', ) rag_file_metadata_config: 'RagFileMetadataConfig' = proto.Field( proto.MESSAGE, number=4, message='RagFileMetadataConfig', ) rag_file_parsing_config: 'RagFileParsingConfig' = proto.Field( proto.MESSAGE, number=5, message='RagFileParsingConfig', ) class ImportRagFilesConfig(proto.Message): r"""Config for importing RagFiles. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: gcs_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GcsSource): Google Cloud Storage location. Supports importing individual files as well as entire Google Cloud Storage directories. Sample formats: - ``gs://bucket_name/my_directory/object_name/my_file.txt`` - ``gs://bucket_name/my_directory`` This field is a member of `oneof`_ ``import_source``. google_drive_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GoogleDriveSource): Google Drive location. Supports importing individual files as well as Google Drive folders. This field is a member of `oneof`_ ``import_source``. slack_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.SlackSource): Slack channels with their corresponding access tokens. This field is a member of `oneof`_ ``import_source``. jira_source (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.JiraSource): Jira queries with their corresponding authentication. This field is a member of `oneof`_ ``import_source``. share_point_sources (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.SharePointSources): SharePoint sources. This field is a member of `oneof`_ ``import_source``. partial_failure_gcs_sink (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GcsDestination): The Cloud Storage path to write partial failures to. Deprecated. Prefer to use ``import_result_gcs_sink``. This field is a member of `oneof`_ ``partial_failure_sink``. partial_failure_bigquery_sink (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.BigQueryDestination): The BigQuery destination to write partial failures to. It should be a bigquery table resource name (e.g. "bq://projectId.bqDatasetId.bqTableId"). The dataset must exist. If the table does not exist, it will be created with the expected schema. If the table exists, the schema will be validated and data will be added to this existing table. Deprecated. Prefer to use ``import_result_bq_sink``. This field is a member of `oneof`_ ``partial_failure_sink``. import_result_gcs_sink (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.GcsDestination): The Cloud Storage path to write import result to. This field is a member of `oneof`_ ``import_result_sink``. import_result_bigquery_sink (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.BigQueryDestination): The BigQuery destination to write import result to. It should be a bigquery table resource name (e.g. "bq://projectId.bqDatasetId.bqTableId"). The dataset must exist. If the table does not exist, it will be created with the expected schema. If the table exists, the schema will be validated and data will be added to this existing table. This field is a member of `oneof`_ ``import_result_sink``. rag_file_chunking_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileChunkingConfig): Specifies the size and overlap of chunks after importing RagFiles. rag_file_transformation_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileTransformationConfig): Specifies the transformation config for RagFiles. rag_file_parsing_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileParsingConfig): Optional. Specifies the parsing config for RagFiles. RAG will use the default parser if this field is not set. rag_file_metadata_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagFileMetadataConfig): Specifies the metadata config for RagFiles. Including paths for metadata schema and metadata. max_embedding_requests_per_min (int): Optional. The max number of queries per minute that this job is allowed to make to the embedding model specified on the corpus. This value is specific to this job and not shared across other import jobs. Consult the Quotas page on the project to set an appropriate value here. If unspecified, a default value of 1,000 QPM would be used. global_max_embedding_requests_per_min (int): Optional. The max number of queries per minute that the indexing pipeline job is allowed to make to the embedding model specified in the project. Please follow the quota usage guideline of the embedding model you use to set the value properly.If this value is not specified, max_embedding_requests_per_min will be used by indexing pipeline job as the global limit. rebuild_ann_index (bool): Rebuilds the ANN index to optimize for recall on the imported data. Only applicable for RagCorpora running on RagManagedDb with ``retrieval_strategy`` set to ``ANN``. The rebuild will be performed using the existing ANN config set on the RagCorpus. To change the ANN config, please use the UpdateRagCorpus API. Default is false, i.e., index is not rebuilt. """ gcs_source: io.GcsSource = proto.Field( proto.MESSAGE, number=2, oneof='import_source', message=io.GcsSource, ) google_drive_source: io.GoogleDriveSource = proto.Field( proto.MESSAGE, number=3, oneof='import_source', message=io.GoogleDriveSource, ) slack_source: io.SlackSource = proto.Field( proto.MESSAGE, number=6, oneof='import_source', message=io.SlackSource, ) jira_source: io.JiraSource = proto.Field( proto.MESSAGE, number=7, oneof='import_source', message=io.JiraSource, ) share_point_sources: io.SharePointSources = proto.Field( proto.MESSAGE, number=13, oneof='import_source', message=io.SharePointSources, ) partial_failure_gcs_sink: io.GcsDestination = proto.Field( proto.MESSAGE, number=11, oneof='partial_failure_sink', message=io.GcsDestination, ) partial_failure_bigquery_sink: io.BigQueryDestination = proto.Field( proto.MESSAGE, number=12, oneof='partial_failure_sink', message=io.BigQueryDestination, ) import_result_gcs_sink: io.GcsDestination = proto.Field( proto.MESSAGE, number=14, oneof='import_result_sink', message=io.GcsDestination, ) import_result_bigquery_sink: io.BigQueryDestination = proto.Field( proto.MESSAGE, number=15, oneof='import_result_sink', message=io.BigQueryDestination, ) rag_file_chunking_config: 'RagFileChunkingConfig' = proto.Field( proto.MESSAGE, number=4, message='RagFileChunkingConfig', ) rag_file_transformation_config: 'RagFileTransformationConfig' = proto.Field( proto.MESSAGE, number=16, message='RagFileTransformationConfig', ) rag_file_parsing_config: 'RagFileParsingConfig' = proto.Field( proto.MESSAGE, number=8, message='RagFileParsingConfig', ) rag_file_metadata_config: 'RagFileMetadataConfig' = proto.Field( proto.MESSAGE, number=17, message='RagFileMetadataConfig', ) max_embedding_requests_per_min: int = proto.Field( proto.INT32, number=5, ) global_max_embedding_requests_per_min: int = proto.Field( proto.INT32, number=18, ) rebuild_ann_index: bool = proto.Field( proto.BOOL, number=19, ) class RagManagedDbConfig(proto.Message): r"""Configuration message for RagManagedDb used by RagEngine. This message has `oneof`_ fields (mutually exclusive fields). For each oneof, at most one member field can be set at the same time. Setting any member of the oneof automatically clears all other members. .. _oneof: https://proto-plus-python.readthedocs.io/en/stable/fields.html#oneofs-mutually-exclusive-fields Attributes: enterprise (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagManagedDbConfig.Enterprise): Sets the RagManagedDb to the Enterprise tier. This field is a member of `oneof`_ ``tier``. scaled (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagManagedDbConfig.Scaled): Sets the RagManagedDb to the Scaled tier. This is the default tier if not explicitly chosen. This field is a member of `oneof`_ ``tier``. basic (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagManagedDbConfig.Basic): Sets the RagManagedDb to the Basic tier. This field is a member of `oneof`_ ``tier``. unprovisioned (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagManagedDbConfig.Unprovisioned): Sets the RagManagedDb to the Unprovisioned tier. This field is a member of `oneof`_ ``tier``. """ class Enterprise(proto.Message): r"""Enterprise tier offers production grade performance along with autoscaling functionality. It is suitable for customers with large amounts of data or performance sensitive workloads. """ class Scaled(proto.Message): r"""Scaled tier offers production grade performance along with autoscaling functionality. It is suitable for customers with large amounts of data or performance sensitive workloads. """ class Basic(proto.Message): r"""Basic tier is a cost-effective and low compute tier suitable for the following cases: - Experimenting with RagManagedDb. - Small data size. - Latency insensitive workload. - Only using RAG Engine with external vector DBs. NOTE: This is the default tier if not explicitly chosen. """ class Unprovisioned(proto.Message): r"""Disables the RAG Engine service and deletes all your data held within this service. This will halt the billing of the service. NOTE: Once deleted the data cannot be recovered. To start using RAG Engine again, you will need to update the tier by calling the UpdateRagEngineConfig API. """ enterprise: Enterprise = proto.Field( proto.MESSAGE, number=1, oneof='tier', message=Enterprise, ) scaled: Scaled = proto.Field( proto.MESSAGE, number=4, oneof='tier', message=Scaled, ) basic: Basic = proto.Field( proto.MESSAGE, number=2, oneof='tier', message=Basic, ) unprovisioned: Unprovisioned = proto.Field( proto.MESSAGE, number=3, oneof='tier', message=Unprovisioned, ) class RagEngineConfig(proto.Message): r"""Config for RagEngine. Attributes: name (str): Identifier. The name of the RagEngineConfig. Format: ``projects/{project}/locations/{location}/ragEngineConfig`` rag_managed_db_config (googlecloudsdk.generated_clients.gapic_clients.aiplatform_v1beta1.types.RagManagedDbConfig): The config of the RagManagedDb used by RagEngine. """ name: str = proto.Field( proto.STRING, number=1, ) rag_managed_db_config: 'RagManagedDbConfig' = proto.Field( proto.MESSAGE, number=2, message='RagManagedDbConfig', ) __all__ = tuple(sorted(__protobuf__.manifest))