Add files using upload-large-folder tool

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# -*- coding: utf-8 -*-

# Copyright 2024 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.
#

import dataclasses
from typing import List, Optional, Sequence, Union

from google.protobuf import timestamp_pb2

DEPRECATION_DATE = "June 2025"


@dataclasses.dataclass
class RagFile:
    """RAG file (output only).
    Attributes:
        name: Generated resource name. Format:
            ``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}/ragFiles/{rag_file}``
        display_name: Display name that was configured at client side.
        description: The description of the RagFile.
    """

    name: Optional[str] = None
    display_name: Optional[str] = None
    description: Optional[str] = None


@dataclasses.dataclass
class EmbeddingModelConfig:
    """EmbeddingModelConfig.
    The representation of the embedding model config. Users input a 1P embedding
    model as a Publisher model resource, or a 1P fine tuned embedding model
    as an Endpoint resource.
    Attributes:
        publisher_model: 1P publisher model resource name. Format:
            ``publishers/google/models/{model}`` or
            ``projects/{project}/locations/{location}/publishers/google/models/{model}``
        endpoint: 1P fine tuned embedding model resource name. Format:
            ``endpoints/{endpoint}`` or
            ``projects/{project}/locations/{location}/endpoints/{endpoint}``.
        model:
            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:
            Output only. Version ID of the model that is
            deployed on the endpoint. Present only when the
            endpoint is not a publisher model.
    """

    publisher_model: Optional[str] = None
    endpoint: Optional[str] = None
    model: Optional[str] = None
    model_version_id: Optional[str] = None


@dataclasses.dataclass
class VertexPredictionEndpoint:
    """VertexPredictionEndpoint.
    Attributes:
        publisher_model: 1P publisher model resource name. Format:
            ``publishers/google/models/{model}`` or
            ``projects/{project}/locations/{location}/publishers/google/models/{model}``
        endpoint: 1P fine tuned embedding model resource name. Format:
            ``endpoints/{endpoint}`` or
            ``projects/{project}/locations/{location}/endpoints/{endpoint}``.
        model:
            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:
            Output only. Version ID of the model that is
            deployed on the endpoint. Present only when the
            endpoint is not a publisher model.
    """

    endpoint: Optional[str] = None
    publisher_model: Optional[str] = None
    model: Optional[str] = None
    model_version_id: Optional[str] = None


@dataclasses.dataclass
class RagEmbeddingModelConfig:
    """RagEmbeddingModelConfig.
    Attributes:
        vertex_prediction_endpoint: The Vertex AI Prediction Endpoint resource
            name. Format:
            ``projects/{project}/locations/{location}/endpoints/{endpoint}``
    """

    vertex_prediction_endpoint: Optional[VertexPredictionEndpoint] = None


@dataclasses.dataclass
class Weaviate:
    """Weaviate.
    Attributes:
        weaviate_http_endpoint: The Weaviate DB instance HTTP endpoint
        collection_name: The corresponding Weaviate collection this corpus maps to
        api_key: The SecretManager resource name for the Weaviate DB API token. Format:
            ``projects/{project}/secrets/{secret}/versions/{version}``
    """

    weaviate_http_endpoint: Optional[str] = None
    collection_name: Optional[str] = None
    api_key: Optional[str] = None


@dataclasses.dataclass
class VertexFeatureStore:
    """VertexFeatureStore.
    Attributes:
        resource_name: The resource name of the FeatureView. Format:
            ``projects/{project}/locations/{location}/featureOnlineStores/
              {feature_online_store}/featureViews/{feature_view}``
    """

    resource_name: Optional[str] = None


@dataclasses.dataclass
class VertexVectorSearch:
    """VertexVectorSearch.
    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: Optional[str] = None
    index: Optional[str] = None


@dataclasses.dataclass
class RagManagedDb:
    """RagManagedDb."""


@dataclasses.dataclass
class Pinecone:
    """Pinecone.
    Attributes:
        index_name: The Pinecone index name.
        api_key: The SecretManager resource name for the Pinecone DB API token. Format:
            ``projects/{project}/secrets/{secret}/versions/{version}``
    """

    index_name: Optional[str] = None
    api_key: Optional[str] = None


@dataclasses.dataclass
class VertexAiSearchConfig:
    """VertexAiSearchConfig.
    Attributes:
        serving_config: The resource name of the Vertex AI Search serving config.
            Format:
                ``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: Optional[str] = None


@dataclasses.dataclass
class RagVectorDbConfig:
    """RagVectorDbConfig.
    Attributes:
        vector_db: Can be one of the following: Weaviate, VertexFeatureStore,
            VertexVectorSearch, Pinecone, RagManagedDb.
        rag_embedding_model_config: The embedding model config of the Vector DB.
    """

    vector_db: Optional[
        Union[Weaviate, VertexFeatureStore, VertexVectorSearch, Pinecone, RagManagedDb]
    ] = None
    rag_embedding_model_config: Optional[RagEmbeddingModelConfig] = None


@dataclasses.dataclass
class RagCorpus:
    """RAG corpus(output only).
    Attributes:
        name: Generated resource name. Format:
            ``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}``
        display_name: Display name that was configured at client side.
        description: The description of the RagCorpus.
        embedding_model_config: The embedding model config of the RagCorpus.
            Note: Deprecated. Use backend_config instead.
        vector_db: The Vector DB of the RagCorpus.
            Note: Deprecated. Use backend_config instead.
        vertex_ai_search_config: The Vertex AI Search config of the RagCorpus.
        backend_config: The backend config of the RagCorpus. It can specify a
            Vector DB and/or the embedding model config.
    """

    name: Optional[str] = None
    display_name: Optional[str] = None
    description: Optional[str] = None
    embedding_model_config: Optional[EmbeddingModelConfig] = None
    vector_db: Optional[
        Union[Weaviate, VertexFeatureStore, VertexVectorSearch, Pinecone, RagManagedDb]
    ] = None
    vertex_ai_search_config: Optional[VertexAiSearchConfig] = None
    backend_config: Optional[RagVectorDbConfig] = None


@dataclasses.dataclass
class RagResource:
    """RagResource.
    The representation of the rag source. It can be used to specify corpus only
    or ragfiles. Currently only support one corpus or multiple files from one
    corpus. In the future we may open up multiple corpora support.
    Attributes:
        rag_corpus: A Rag corpus resource name or corpus id. Format:
            ``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}``
            or ``{rag_corpus_id}``.
        rag_files_id: List of Rag file resource name or file ids in the same corpus. Format:
            ``{rag_file}``.
    """

    rag_corpus: Optional[str] = None
    rag_file_ids: Optional[List[str]] = None


@dataclasses.dataclass
class SlackChannel:
    """SlackChannel.
    Attributes:
        channel_id: The Slack channel ID.
        api_key: The SecretManager resource name for the Slack API token. Format:
            ``projects/{project}/secrets/{secret}/versions/{version}``
            See: https://api.slack.com/tutorials/tracks/getting-a-token.
        start_time: The starting timestamp for messages to import.
        end_time: The ending timestamp for messages to import.
    """

    channel_id: str
    api_key: str
    start_time: Optional[timestamp_pb2.Timestamp] = None
    end_time: Optional[timestamp_pb2.Timestamp] = None


@dataclasses.dataclass
class SlackChannelsSource:
    """SlackChannelsSource.
    Attributes:
        channels: The Slack channels.
    """

    channels: Sequence[SlackChannel]


@dataclasses.dataclass
class JiraQuery:
    """JiraQuery.
    Attributes:
        email: The Jira email address.
        jira_projects: A list of Jira projects to import in their entirety.
        custom_queries: A list of custom JQL Jira queries to import.
        api_key: The SecretManager version resource name for Jira API access. Format:
            ``projects/{project}/secrets/{secret}/versions/{version}``
            See: https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/
        server_uri: The Jira server URI. Format:
            ``{server}.atlassian.net``
    """

    email: str
    jira_projects: Sequence[str]
    custom_queries: Sequence[str]
    api_key: str
    server_uri: str


@dataclasses.dataclass
class JiraSource:
    """JiraSource.
    Attributes:
        queries: The Jira queries.
    """

    queries: Sequence[JiraQuery]


@dataclasses.dataclass
class SharePointSource:
    """SharePointSource.
    Attributes:
        sharepoint_folder_path: The path of the SharePoint folder to download
            from.
        sharepoint_folder_id: The ID of the SharePoint folder to download
            from.
        drive_name: The name of the drive to download from.
        drive_id: The ID of the drive to download from.
        client_id: The Application ID for the app registered in
            Microsoft Azure Portal. The application must
            also be configured with MS Graph permissions
            "Files.ReadAll", "Sites.ReadAll" and
            BrowserSiteLists.Read.All.
        client_secret: The application secret for the app registered
            in Azure.
        tenant_id: Unique identifier of the Azure Active
            Directory Instance.
        sharepoint_site_name: The name of the SharePoint site to download
            from. This can be the site name or the site id.
    """

    sharepoint_folder_path: Optional[str] = None
    sharepoint_folder_id: Optional[str] = None
    drive_name: Optional[str] = None
    drive_id: Optional[str] = None
    client_id: str = None
    client_secret: str = None
    tenant_id: str = None
    sharepoint_site_name: str = None


@dataclasses.dataclass
class SharePointSources:
    """SharePointSources.
    Attributes:
        share_point_sources: The SharePoint sources.
    """

    share_point_sources: Sequence[SharePointSource]


@dataclasses.dataclass
class Filter:
    """Filter.
    Attributes:
        vector_distance_threshold: Only returns contexts with vector
            distance smaller than the threshold.
        vector_similarity_threshold: Only returns contexts with vector
            similarity larger than the threshold.
        metadata_filter: String for metadata filtering.
    """

    vector_distance_threshold: Optional[float] = None
    vector_similarity_threshold: Optional[float] = None
    metadata_filter: Optional[str] = None


@dataclasses.dataclass
class HybridSearch:
    """HybridSearch.
    Attributes:
        alpha: Alpha value controls the weight between dense and
            sparse vector search results. The range is [0, 1], while 0
            means sparse vector search only and 1 means dense vector
            search only. The default value is 0.5 which balances sparse
            and dense vector search equally.
    """

    alpha: Optional[float] = None


@dataclasses.dataclass
class LlmRanker:
    """LlmRanker.
    Attributes:
        model_name: The model name used for ranking. Only Gemini models are
            supported for now.
    """

    model_name: Optional[str] = None


@dataclasses.dataclass
class RankService:
    """RankService.
    Attributes:
        model_name: The model name of the rank service. Format:
            ``semantic-ranker-512@latest``
    """

    model_name: Optional[str] = None


@dataclasses.dataclass
class Ranking:
    """Ranking.
    Attributes:
        rank_service: (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.Ranking.RankService)
                Config for Rank Service.
        llm_ranker (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.Ranking.LlmRanker):
                Config for LlmRanker.
    """

    rank_service: Optional[RankService] = None
    llm_ranker: Optional[LlmRanker] = None


@dataclasses.dataclass
class RagRetrievalConfig:
    """RagRetrievalConfig.
    Attributes:
        top_k: The number of contexts to retrieve.
        filter: Config for filters.
        hybrid_search (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.HybridSearch):
            Config for Hybrid Search.
        ranking (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.Ranking):
            Config for ranking and reranking.
    """

    top_k: Optional[int] = None
    filter: Optional[Filter] = None
    hybrid_search: Optional[HybridSearch] = None
    ranking: Optional[Ranking] = None


@dataclasses.dataclass
class ChunkingConfig:
    """ChunkingConfig.
    Attributes:
        chunk_size: The size of each chunk.
        chunk_overlap: The size of the overlap between chunks.
    """

    chunk_size: int
    chunk_overlap: int


@dataclasses.dataclass
class TransformationConfig:
    """TransformationConfig.
    Attributes:
        chunking_config: The chunking config.
    """

    chunking_config: Optional[ChunkingConfig] = None


@dataclasses.dataclass
class LayoutParserConfig:
    """Configuration for the Document AI Layout Parser Processor.
    Attributes:
        processor_name (str):
            The full resource name of a Document AI processor or processor
            version. The processor must have type `LAYOUT_PARSER_PROCESSOR`.
            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 will be used.
    """

    processor_name: str
    max_parsing_requests_per_min: Optional[int] = None


@dataclasses.dataclass
class LlmParserConfig:
    """Configuration for the Document AI Layout Parser Processor.
    Attributes:
        model_name (str):
            The full resource name of a Vertex AI model. Format:
            -  `projects/{project_id}/locations/{location}/publishers/google/models/{model_id}`
            -  `projects/{project_id}/locations/{location}/models/{model_id}`
        max_parsing_requests_per_min (int):
            The maximum number of requests the job is allowed to make to the
            Vertex AI model per minute. Consult
            https://cloud.google.com/vertex-ai/generative-ai/docs/quotas and
            the Quota page for your project to set an appropriate value here.
            If unspecified, a default value of 120 QPM will be used.
        custom_parsing_prompt (str):
            A custom prompt to use for parsing.
    """

    model_name: str
    max_parsing_requests_per_min: Optional[int] = None
    custom_parsing_prompt: Optional[str] = None

	# -- coding: utf-8 --

	# Copyright 2024 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.
	#

	import dataclasses
	from typing import List, Optional, Sequence, Union

	from google.protobuf import timestamp_pb2

	DEPRECATION_DATE = "June 2025"


	@dataclasses.dataclass
	class RagFile:
	"""RAG file (output only).

	Attributes:
	name: Generated resource name. Format:
	``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}/ragFiles/{rag_file}``
	display_name: Display name that was configured at client side.
	description: The description of the RagFile.
	"""

	name: Optional[str] = None
	display_name: Optional[str] = None
	description: Optional[str] = None


	@dataclasses.dataclass
	class EmbeddingModelConfig:
	"""EmbeddingModelConfig.

	The representation of the embedding model config. Users input a 1P embedding
	model as a Publisher model resource, or a 1P fine tuned embedding model
	as an Endpoint resource.

	Attributes:
	publisher_model: 1P publisher model resource name. Format:
	``publishers/google/models/{model}`` or
	``projects/{project}/locations/{location}/publishers/google/models/{model}``
	endpoint: 1P fine tuned embedding model resource name. Format:
	``endpoints/{endpoint}`` or
	``projects/{project}/locations/{location}/endpoints/{endpoint}``.
	model:
	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:
	Output only. Version ID of the model that is
	deployed on the endpoint. Present only when the
	endpoint is not a publisher model.
	"""

	publisher_model: Optional[str] = None
	endpoint: Optional[str] = None
	model: Optional[str] = None
	model_version_id: Optional[str] = None


	@dataclasses.dataclass
	class VertexPredictionEndpoint:
	"""VertexPredictionEndpoint.

	Attributes:
	publisher_model: 1P publisher model resource name. Format:
	``publishers/google/models/{model}`` or
	``projects/{project}/locations/{location}/publishers/google/models/{model}``
	endpoint: 1P fine tuned embedding model resource name. Format:
	``endpoints/{endpoint}`` or
	``projects/{project}/locations/{location}/endpoints/{endpoint}``.
	model:
	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:
	Output only. Version ID of the model that is
	deployed on the endpoint. Present only when the
	endpoint is not a publisher model.
	"""

	endpoint: Optional[str] = None
	publisher_model: Optional[str] = None
	model: Optional[str] = None
	model_version_id: Optional[str] = None


	@dataclasses.dataclass
	class RagEmbeddingModelConfig:
	"""RagEmbeddingModelConfig.

	Attributes:
	vertex_prediction_endpoint: The Vertex AI Prediction Endpoint resource
	name. Format:
	``projects/{project}/locations/{location}/endpoints/{endpoint}``
	"""

	vertex_prediction_endpoint: Optional[VertexPredictionEndpoint] = None


	@dataclasses.dataclass
	class Weaviate:
	"""Weaviate.

	Attributes:
	weaviate_http_endpoint: The Weaviate DB instance HTTP endpoint
	collection_name: The corresponding Weaviate collection this corpus maps to
	api_key: The SecretManager resource name for the Weaviate DB API token. Format:
	``projects/{project}/secrets/{secret}/versions/{version}``
	"""

	weaviate_http_endpoint: Optional[str] = None
	collection_name: Optional[str] = None
	api_key: Optional[str] = None


	@dataclasses.dataclass
	class VertexFeatureStore:
	"""VertexFeatureStore.

	Attributes:
	resource_name: The resource name of the FeatureView. Format:
	``projects/{project}/locations/{location}/featureOnlineStores/
	{feature_online_store}/featureViews/{feature_view}``
	"""

	resource_name: Optional[str] = None


	@dataclasses.dataclass
	class VertexVectorSearch:
	"""VertexVectorSearch.

	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: Optional[str] = None
	index: Optional[str] = None


	@dataclasses.dataclass
	class RagManagedDb:
	"""RagManagedDb."""


	@dataclasses.dataclass
	class Pinecone:
	"""Pinecone.

	Attributes:
	index_name: The Pinecone index name.
	api_key: The SecretManager resource name for the Pinecone DB API token. Format:
	``projects/{project}/secrets/{secret}/versions/{version}``
	"""

	index_name: Optional[str] = None
	api_key: Optional[str] = None


	@dataclasses.dataclass
	class VertexAiSearchConfig:
	"""VertexAiSearchConfig.

	Attributes:
	serving_config: The resource name of the Vertex AI Search serving config.
	Format:
	``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: Optional[str] = None


	@dataclasses.dataclass
	class RagVectorDbConfig:
	"""RagVectorDbConfig.

	Attributes:
	vector_db: Can be one of the following: Weaviate, VertexFeatureStore,
	VertexVectorSearch, Pinecone, RagManagedDb.
	rag_embedding_model_config: The embedding model config of the Vector DB.
	"""

	vector_db: Optional[
	Union[Weaviate, VertexFeatureStore, VertexVectorSearch, Pinecone, RagManagedDb]
	] = None
	rag_embedding_model_config: Optional[RagEmbeddingModelConfig] = None


	@dataclasses.dataclass
	class RagCorpus:
	"""RAG corpus(output only).

	Attributes:
	name: Generated resource name. Format:
	``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}``
	display_name: Display name that was configured at client side.
	description: The description of the RagCorpus.
	embedding_model_config: The embedding model config of the RagCorpus.
	Note: Deprecated. Use backend_config instead.
	vector_db: The Vector DB of the RagCorpus.
	Note: Deprecated. Use backend_config instead.
	vertex_ai_search_config: The Vertex AI Search config of the RagCorpus.
	backend_config: The backend config of the RagCorpus. It can specify a
	Vector DB and/or the embedding model config.
	"""

	name: Optional[str] = None
	display_name: Optional[str] = None
	description: Optional[str] = None
	embedding_model_config: Optional[EmbeddingModelConfig] = None
	vector_db: Optional[
	Union[Weaviate, VertexFeatureStore, VertexVectorSearch, Pinecone, RagManagedDb]
	] = None
	vertex_ai_search_config: Optional[VertexAiSearchConfig] = None
	backend_config: Optional[RagVectorDbConfig] = None


	@dataclasses.dataclass
	class RagResource:
	"""RagResource.

	The representation of the rag source. It can be used to specify corpus only
	or ragfiles. Currently only support one corpus or multiple files from one
	corpus. In the future we may open up multiple corpora support.

	Attributes:
	rag_corpus: A Rag corpus resource name or corpus id. Format:
	``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}``
	or ``{rag_corpus_id}``.
	rag_files_id: List of Rag file resource name or file ids in the same corpus. Format:
	``{rag_file}``.
	"""

	rag_corpus: Optional[str] = None
	rag_file_ids: Optional[List[str]] = None


	@dataclasses.dataclass
	class SlackChannel:
	"""SlackChannel.

	Attributes:
	channel_id: The Slack channel ID.
	api_key: The SecretManager resource name for the Slack API token. Format:
	``projects/{project}/secrets/{secret}/versions/{version}``
	See: https://api.slack.com/tutorials/tracks/getting-a-token.
	start_time: The starting timestamp for messages to import.
	end_time: The ending timestamp for messages to import.
	"""

	channel_id: str
	api_key: str
	start_time: Optional[timestamp_pb2.Timestamp] = None
	end_time: Optional[timestamp_pb2.Timestamp] = None


	@dataclasses.dataclass
	class SlackChannelsSource:
	"""SlackChannelsSource.

	Attributes:
	channels: The Slack channels.
	"""

	channels: Sequence[SlackChannel]


	@dataclasses.dataclass
	class JiraQuery:
	"""JiraQuery.

	Attributes:
	email: The Jira email address.
	jira_projects: A list of Jira projects to import in their entirety.
	custom_queries: A list of custom JQL Jira queries to import.
	api_key: The SecretManager version resource name for Jira API access. Format:
	``projects/{project}/secrets/{secret}/versions/{version}``
	See: https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/
	server_uri: The Jira server URI. Format:
	``{server}.atlassian.net``
	"""

	email: str
	jira_projects: Sequence[str]
	custom_queries: Sequence[str]
	api_key: str
	server_uri: str


	@dataclasses.dataclass
	class JiraSource:
	"""JiraSource.

	Attributes:
	queries: The Jira queries.
	"""

	queries: Sequence[JiraQuery]


	@dataclasses.dataclass
	class SharePointSource:
	"""SharePointSource.

	Attributes:
	sharepoint_folder_path: The path of the SharePoint folder to download
	from.
	sharepoint_folder_id: The ID of the SharePoint folder to download
	from.
	drive_name: The name of the drive to download from.
	drive_id: The ID of the drive to download from.
	client_id: The Application ID for the app registered in
	Microsoft Azure Portal. The application must
	also be configured with MS Graph permissions
	"Files.ReadAll", "Sites.ReadAll" and
	BrowserSiteLists.Read.All.
	client_secret: The application secret for the app registered
	in Azure.
	tenant_id: Unique identifier of the Azure Active
	Directory Instance.
	sharepoint_site_name: The name of the SharePoint site to download
	from. This can be the site name or the site id.
	"""

	sharepoint_folder_path: Optional[str] = None
	sharepoint_folder_id: Optional[str] = None
	drive_name: Optional[str] = None
	drive_id: Optional[str] = None
	client_id: str = None
	client_secret: str = None
	tenant_id: str = None
	sharepoint_site_name: str = None


	@dataclasses.dataclass
	class SharePointSources:
	"""SharePointSources.

	Attributes:
	share_point_sources: The SharePoint sources.
	"""

	share_point_sources: Sequence[SharePointSource]


	@dataclasses.dataclass
	class Filter:
	"""Filter.

	Attributes:
	vector_distance_threshold: Only returns contexts with vector
	distance smaller than the threshold.
	vector_similarity_threshold: Only returns contexts with vector
	similarity larger than the threshold.
	metadata_filter: String for metadata filtering.
	"""

	vector_distance_threshold: Optional[float] = None
	vector_similarity_threshold: Optional[float] = None
	metadata_filter: Optional[str] = None


	@dataclasses.dataclass
	class HybridSearch:
	"""HybridSearch.

	Attributes:
	alpha: Alpha value controls the weight between dense and
	sparse vector search results. The range is [0, 1], while 0
	means sparse vector search only and 1 means dense vector
	search only. The default value is 0.5 which balances sparse
	and dense vector search equally.
	"""

	alpha: Optional[float] = None


	@dataclasses.dataclass
	class LlmRanker:
	"""LlmRanker.

	Attributes:
	model_name: The model name used for ranking. Only Gemini models are
	supported for now.
	"""

	model_name: Optional[str] = None


	@dataclasses.dataclass
	class RankService:
	"""RankService.

	Attributes:
	model_name: The model name of the rank service. Format:
	``semantic-ranker-512@latest``
	"""

	model_name: Optional[str] = None


	@dataclasses.dataclass
	class Ranking:
	"""Ranking.

	Attributes:
	rank_service: (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.Ranking.RankService)
	Config for Rank Service.
	llm_ranker (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.Ranking.LlmRanker):
	Config for LlmRanker.
	"""

	rank_service: Optional[RankService] = None
	llm_ranker: Optional[LlmRanker] = None


	@dataclasses.dataclass
	class RagRetrievalConfig:
	"""RagRetrievalConfig.

	Attributes:
	top_k: The number of contexts to retrieve.
	filter: Config for filters.
	hybrid_search (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.HybridSearch):
	Config for Hybrid Search.
	ranking (google.cloud.aiplatform_v1beta1.types.RagRetrievalConfig.Ranking):
	Config for ranking and reranking.
	"""

	top_k: Optional[int] = None
	filter: Optional[Filter] = None
	hybrid_search: Optional[HybridSearch] = None
	ranking: Optional[Ranking] = None


	@dataclasses.dataclass
	class ChunkingConfig:
	"""ChunkingConfig.

	Attributes:
	chunk_size: The size of each chunk.
	chunk_overlap: The size of the overlap between chunks.
	"""

	chunk_size: int
	chunk_overlap: int


	@dataclasses.dataclass
	class TransformationConfig:
	"""TransformationConfig.

	Attributes:
	chunking_config: The chunking config.
	"""

	chunking_config: Optional[ChunkingConfig] = None


	@dataclasses.dataclass
	class LayoutParserConfig:
	"""Configuration for the Document AI Layout Parser Processor.

	Attributes:
	processor_name (str):
	The full resource name of a Document AI processor or processor
	version. The processor must have type `LAYOUT_PARSER_PROCESSOR`.
	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 will be used.
	"""

	processor_name: str
	max_parsing_requests_per_min: Optional[int] = None


	@dataclasses.dataclass
	class LlmParserConfig:
	"""Configuration for the Document AI Layout Parser Processor.

	Attributes:
	model_name (str):
	The full resource name of a Vertex AI model. Format:
	- `projects/{project_id}/locations/{location}/publishers/google/models/{model_id}`
	- `projects/{project_id}/locations/{location}/models/{model_id}`
	max_parsing_requests_per_min (int):
	The maximum number of requests the job is allowed to make to the
	Vertex AI model per minute. Consult
	https://cloud.google.com/vertex-ai/generative-ai/docs/quotas and
	the Quota page for your project to set an appropriate value here.
	If unspecified, a default value of 120 QPM will be used.
	custom_parsing_prompt (str):
	A custom prompt to use for parsing.
	"""

	model_name: str
	max_parsing_requests_per_min: Optional[int] = None
	custom_parsing_prompt: Optional[str] = None