structure saas with tools
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Davidson Gomes
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# -*- coding: utf-8 -*-
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# Copyright 2024 Google LLC
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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import dataclasses
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from typing import List, Optional, Sequence, Union
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from google.protobuf import timestamp_pb2
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@dataclasses.dataclass
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class RagFile:
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"""RAG file (output only).
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Attributes:
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name: Generated resource name. Format:
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``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}/ragFiles/{rag_file}``
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display_name: Display name that was configured at client side.
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description: The description of the RagFile.
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"""
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name: Optional[str] = None
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display_name: Optional[str] = None
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description: Optional[str] = None
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@dataclasses.dataclass
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class VertexPredictionEndpoint:
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"""VertexPredictionEndpoint.
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Attributes:
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publisher_model: 1P publisher model resource name. Format:
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``publishers/google/models/{model}`` or
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``projects/{project}/locations/{location}/publishers/google/models/{model}``
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endpoint: 1P fine tuned embedding model resource name. Format:
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``endpoints/{endpoint}`` or
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``projects/{project}/locations/{location}/endpoints/{endpoint}``.
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model:
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Output only. The resource name of the model that is deployed
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on the endpoint. Present only when the endpoint is not a
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publisher model. Pattern:
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``projects/{project}/locations/{location}/models/{model}``
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model_version_id:
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Output only. Version ID of the model that is
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deployed on the endpoint. Present only when the
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endpoint is not a publisher model.
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"""
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endpoint: Optional[str] = None
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publisher_model: Optional[str] = None
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model: Optional[str] = None
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model_version_id: Optional[str] = None
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@dataclasses.dataclass
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class RagEmbeddingModelConfig:
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"""RagEmbeddingModelConfig.
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Attributes:
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vertex_prediction_endpoint: The Vertex AI Prediction Endpoint resource
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name. Format:
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``projects/{project}/locations/{location}/endpoints/{endpoint}``
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"""
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vertex_prediction_endpoint: Optional[VertexPredictionEndpoint] = None
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@dataclasses.dataclass
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class Weaviate:
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"""Weaviate.
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Attributes:
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weaviate_http_endpoint: The Weaviate DB instance HTTP endpoint
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collection_name: The corresponding Weaviate collection this corpus maps to
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api_key: The SecretManager resource name for the Weaviate DB API token. Format:
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``projects/{project}/secrets/{secret}/versions/{version}``
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"""
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weaviate_http_endpoint: Optional[str] = None
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collection_name: Optional[str] = None
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api_key: Optional[str] = None
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@dataclasses.dataclass
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class VertexFeatureStore:
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"""VertexFeatureStore.
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Attributes:
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resource_name: The resource name of the FeatureView. Format:
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``projects/{project}/locations/{location}/featureOnlineStores/
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{feature_online_store}/featureViews/{feature_view}``
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"""
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resource_name: Optional[str] = None
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@dataclasses.dataclass
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class VertexVectorSearch:
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"""VertexVectorSearch.
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Attributes:
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index_endpoint (str):
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The resource name of the Index Endpoint. Format:
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``projects/{project}/locations/{location}/indexEndpoints/{index_endpoint}``
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index (str):
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The resource name of the Index. Format:
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``projects/{project}/locations/{location}/indexes/{index}``
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"""
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index_endpoint: Optional[str] = None
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index: Optional[str] = None
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@dataclasses.dataclass
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class RagManagedDb:
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"""RagManagedDb."""
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@dataclasses.dataclass
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class Pinecone:
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"""Pinecone.
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Attributes:
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index_name: The Pinecone index name.
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api_key: The SecretManager resource name for the Pinecone DB API token. Format:
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``projects/{project}/secrets/{secret}/versions/{version}``
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"""
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index_name: Optional[str] = None
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api_key: Optional[str] = None
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@dataclasses.dataclass
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class VertexAiSearchConfig:
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"""VertexAiSearchConfig.
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Attributes:
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serving_config: The resource name of the Vertex AI Search serving config.
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Format:
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``projects/{project}/locations/{location}/collections/{collection}/engines/{engine}/servingConfigs/{serving_config}``
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or
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``projects/{project}/locations/{location}/collections/{collection}/dataStores/{data_store}/servingConfigs/{serving_config}``
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"""
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serving_config: Optional[str] = None
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@dataclasses.dataclass
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class RagVectorDbConfig:
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"""RagVectorDbConfig.
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Attributes:
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vector_db: Can be one of the following: RagManagedDb, Pinecone,
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VertexVectorSearch.
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rag_embedding_model_config: The embedding model config of the Vector DB.
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"""
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vector_db: Optional[
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Union[
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VertexVectorSearch,
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Pinecone,
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RagManagedDb,
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]
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] = None
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rag_embedding_model_config: Optional[RagEmbeddingModelConfig] = None
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@dataclasses.dataclass
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class RagCorpus:
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"""RAG corpus(output only).
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Attributes:
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name: Generated resource name. Format:
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``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}``
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display_name: Display name that was configured at client side.
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description: The description of the RagCorpus.
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vertex_ai_search_config: The Vertex AI Search config of the RagCorpus.
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backend_config: The backend config of the RagCorpus. It can be a data
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store and/or retrieval engine.
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"""
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name: Optional[str] = None
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display_name: Optional[str] = None
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description: Optional[str] = None
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vertex_ai_search_config: Optional[VertexAiSearchConfig] = None
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backend_config: Optional[
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Union[
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RagVectorDbConfig,
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None,
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]
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] = None
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@dataclasses.dataclass
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class RagResource:
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"""RagResource.
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The representation of the rag source. It can be used to specify corpus only
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or ragfiles. Currently only support one corpus or multiple files from one
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corpus. In the future we may open up multiple corpora support.
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Attributes:
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rag_corpus: A Rag corpus resource name or corpus id. Format:
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``projects/{project}/locations/{location}/ragCorpora/{rag_corpus_id}``
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or ``{rag_corpus_id}``.
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rag_files_id: List of Rag file resource name or file ids in the same corpus. Format:
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``{rag_file}``.
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"""
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rag_corpus: Optional[str] = None
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rag_file_ids: Optional[List[str]] = None
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@dataclasses.dataclass
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class SlackChannel:
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"""SlackChannel.
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Attributes:
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channel_id: The Slack channel ID.
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api_key: The SecretManager resource name for the Slack API token. Format:
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``projects/{project}/secrets/{secret}/versions/{version}``
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See: https://api.slack.com/tutorials/tracks/getting-a-token.
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start_time: The starting timestamp for messages to import.
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end_time: The ending timestamp for messages to import.
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"""
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channel_id: str
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api_key: str
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start_time: Optional[timestamp_pb2.Timestamp] = None
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end_time: Optional[timestamp_pb2.Timestamp] = None
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@dataclasses.dataclass
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class SlackChannelsSource:
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"""SlackChannelsSource.
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Attributes:
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channels: The Slack channels.
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"""
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channels: Sequence[SlackChannel]
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@dataclasses.dataclass
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class JiraQuery:
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"""JiraQuery.
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Attributes:
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email: The Jira email address.
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jira_projects: A list of Jira projects to import in their entirety.
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custom_queries: A list of custom JQL Jira queries to import.
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api_key: The SecretManager version resource name for Jira API access. Format:
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``projects/{project}/secrets/{secret}/versions/{version}``
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See: https://support.atlassian.com/atlassian-account/docs/manage-api-tokens-for-your-atlassian-account/
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server_uri: The Jira server URI. Format:
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``{server}.atlassian.net``
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"""
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email: str
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jira_projects: Sequence[str]
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custom_queries: Sequence[str]
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api_key: str
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server_uri: str
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@dataclasses.dataclass
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class JiraSource:
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"""JiraSource.
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Attributes:
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queries: The Jira queries.
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"""
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queries: Sequence[JiraQuery]
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@dataclasses.dataclass
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class SharePointSource:
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"""SharePointSource.
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Attributes:
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sharepoint_folder_path: The path of the SharePoint folder to download
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from.
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sharepoint_folder_id: The ID of the SharePoint folder to download
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from.
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drive_name: The name of the drive to download from.
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drive_id: The ID of the drive to download from.
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client_id: The Application ID for the app registered in
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Microsoft Azure Portal. The application must
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also be configured with MS Graph permissions
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"Files.ReadAll", "Sites.ReadAll" and
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BrowserSiteLists.Read.All.
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client_secret: The application secret for the app registered
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in Azure.
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tenant_id: Unique identifier of the Azure Active
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Directory Instance.
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sharepoint_site_name: The name of the SharePoint site to download
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from. This can be the site name or the site id.
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"""
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sharepoint_folder_path: Optional[str] = None
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sharepoint_folder_id: Optional[str] = None
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drive_name: Optional[str] = None
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drive_id: Optional[str] = None
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client_id: str = None
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client_secret: str = None
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tenant_id: str = None
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sharepoint_site_name: str = None
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@dataclasses.dataclass
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class SharePointSources:
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"""SharePointSources.
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Attributes:
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share_point_sources: The SharePoint sources.
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"""
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share_point_sources: Sequence[SharePointSource]
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@dataclasses.dataclass
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class Filter:
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"""Filter.
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Attributes:
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vector_distance_threshold: Only returns contexts with vector
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distance smaller than the threshold.
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vector_similarity_threshold: Only returns contexts with vector
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similarity larger than the threshold.
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metadata_filter: String for metadata filtering.
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"""
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vector_distance_threshold: Optional[float] = None
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vector_similarity_threshold: Optional[float] = None
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metadata_filter: Optional[str] = None
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@dataclasses.dataclass
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class LlmRanker:
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"""LlmRanker.
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Attributes:
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model_name: The model name used for ranking. Only Gemini models are
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supported for now.
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"""
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model_name: Optional[str] = None
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@dataclasses.dataclass
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class RankService:
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"""RankService.
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Attributes:
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model_name: The model name of the rank service. Format:
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``semantic-ranker-512@latest``
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"""
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model_name: Optional[str] = None
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@dataclasses.dataclass
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class Ranking:
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"""Ranking.
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Attributes:
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rank_service: Config for Rank Service.
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llm_ranker: Config for LlmRanker.
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"""
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rank_service: Optional[RankService] = None
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llm_ranker: Optional[LlmRanker] = None
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@dataclasses.dataclass
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class RagRetrievalConfig:
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"""RagRetrievalConfig.
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Attributes:
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top_k: The number of contexts to retrieve.
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filter: Config for filters.
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ranking: Config for ranking.
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"""
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top_k: Optional[int] = None
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filter: Optional[Filter] = None
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ranking: Optional[Ranking] = None
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@dataclasses.dataclass
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class ChunkingConfig:
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"""ChunkingConfig.
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Attributes:
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chunk_size: The size of each chunk.
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chunk_overlap: The size of the overlap between chunks.
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"""
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chunk_size: int
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chunk_overlap: int
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@dataclasses.dataclass
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class TransformationConfig:
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"""TransformationConfig.
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Attributes:
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chunking_config: The chunking config.
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"""
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chunking_config: Optional[ChunkingConfig] = None
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@dataclasses.dataclass
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class LayoutParserConfig:
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"""Configuration for the Document AI Layout Parser Processor.
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Attributes:
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processor_name: The full resource name of a Document AI processor or
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processor version. The processor must have type
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`LAYOUT_PARSER_PROCESSOR`.
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Format must be one of the following:
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- `projects/{project_id}/locations/{location}/processors/{processor_id}`
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- `projects/{project_id}/locations/{location}/processors/{processor_id}/processorVersions/{processor_version_id}`
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max_parsing_requests_per_min: The maximum number of requests the job is
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allowed to make to the Document AI processor per minute. Consult
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https://cloud.google.com/document-ai/quotas and the Quota page for
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your project to set an appropriate value here. If unspecified, a
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default value of 120 QPM will be used.
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"""
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processor_name: str
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max_parsing_requests_per_min: Optional[int] = None
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