gen_ai_hub.document_grounding.models package

Models subpackage for Document Grounding API.

This subpackage contains Pydantic model definitions for all Document Grounding API requests and responses. Models are organized by API domain:

• pipeline: Models for Pipeline API (document vectorization pipelines)

• retrieval: Models for Retrieval API (content retrieval from repositories)

• vector: Models for Vector API (vector collection management and search)

These models provide type-safe data structures for interacting with the Document Grounding APIs and ensure proper validation of request/response data.

class gen_ai_hub.document_grounding.models.MSSharePointPipelineCreateRequest(*, type: Literal['MSSharePoint'] = 'MSSharePoint', configuration: MSSharePointConfiguration, metadata: MetaData | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Literal['MSSharePoint']

configuration: MSSharePointConfiguration

metadata: MetaData | None

class gen_ai_hub.document_grounding.models.S3PipelineCreateRequest(*, type: Literal['S3'] = 'S3', configuration: CommonConfiguration, metadata: MetaData | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Literal['S3']

configuration: CommonConfiguration

metadata: MetaData | None

class gen_ai_hub.document_grounding.models.SFTPPipelineCreateRequest(*, type: Literal['SFTP'] = 'SFTP', configuration: CommonConfiguration, metadata: MetaData | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Literal['SFTP']

configuration: CommonConfiguration

metadata: MetaData | None

class gen_ai_hub.document_grounding.models.SearchPipelineRequest(*, dataRepositoryMetadata: List[DataRepositoryMetadataItem])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

dataRepositoryMetadata: List[DataRepositoryMetadataItem]

class gen_ai_hub.document_grounding.models.DataRepositoryMetadataItem(*, key: str, value: List[str])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

value: List[str]

class gen_ai_hub.document_grounding.models.CommonConfiguration(*, destination: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

destination: str

class gen_ai_hub.document_grounding.models.MetaData(*, destination: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

destination: str

class gen_ai_hub.document_grounding.models.MSSharePointConfiguration(*, destination: str, sharePoint: SharePointConfig)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

destination: str

sharePoint: SharePointConfig

class gen_ai_hub.document_grounding.models.SharePointConfig(*, site: SharePointSite)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

site: SharePointSite

class gen_ai_hub.document_grounding.models.SharePointSite(*, name: str, includePaths: List[str] | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

name: str

includePaths: List[str] | None

class gen_ai_hub.document_grounding.models.ManualPipelineTrigger(*, pipelineId: str, metadataOnly: bool | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

pipelineId: str

metadataOnly: bool | None

class gen_ai_hub.document_grounding.models.PipelineIdResponse(*, pipelineId: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

pipelineId: str

class gen_ai_hub.document_grounding.models.GetPipelinesResponse(*, count: int | None, resources: List[Annotated[MSSharePointPipelineGetResponse | S3PipelineGetResponse | SFTPPipelineGetResponse, FieldInfo(annotation=NoneType, required=True, discriminator='type')]])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int | None

resources: List[Annotated[MSSharePointPipelineGetResponse | S3PipelineGetResponse | SFTPPipelineGetResponse, FieldInfo(annotation=NoneType, required=True, discriminator='type')]]

class gen_ai_hub.document_grounding.models.GetPipelineStatusResponse(*, lastStarted: str | None, status: str | None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

lastStarted: str | None

status: str | None

class gen_ai_hub.document_grounding.models.PipelineExecution(*, id: str, status: PipelineExecutionStatus | None = None, createdAt: datetime | None = None, modifiedAt: datetime | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

status: PipelineExecutionStatus | None

createdAt: datetime | None

modifiedAt: datetime | None

class gen_ai_hub.document_grounding.models.GetPipelineExecutionsResponse(*, count: int | None, resources: List[PipelineExecution])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int | None

resources: List[PipelineExecution]

class gen_ai_hub.document_grounding.models.Document(*, id: str, status: DocumentStatus | None = None, viewLocation: str | None = None, downloadLocation: str | None = None, absoluteUrl: str | None = None, title: str | None = None, metadataId: str | None = None, createdTimestamp: datetime | None = None, lastUpdatedTimestamp: datetime | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

status: DocumentStatus | None

viewLocation: str | None

downloadLocation: str | None

absoluteUrl: str | None

title: str | None

metadataId: str | None

createdTimestamp: datetime | None

lastUpdatedTimestamp: datetime | None

class gen_ai_hub.document_grounding.models.DocumentsStatusResponse(*, count: int | None, resources: List[Document])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int | None

resources: List[Document]

class gen_ai_hub.document_grounding.models.MSSharePointPipelineGetResponse(*, id: str, type: Literal['MSSharePoint'] = 'MSSharePoint', metadata: MetaData | None = None, configuration: MSSharePointConfigurationGetResponse)

Bases: BasePipelineResponse

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Literal['MSSharePoint']

configuration: MSSharePointConfigurationGetResponse

id: str

metadata: MetaData | None

class gen_ai_hub.document_grounding.models.S3PipelineGetResponse(*, id: str, type: Literal['S3'] = 'S3', metadata: MetaData | None = None, configuration: CommonConfiguration)

Bases: BasePipelineResponse

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Literal['S3']

configuration: CommonConfiguration

id: str

metadata: MetaData | None

class gen_ai_hub.document_grounding.models.SFTPPipelineGetResponse(*, id: str, type: Literal['SFTP'] = 'SFTP', metadata: MetaData | None = None, configuration: CommonConfiguration)

Bases: BasePipelineResponse

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

type: Literal['SFTP']

configuration: CommonConfiguration

id: str

metadata: MetaData | None

class gen_ai_hub.document_grounding.models.SearchPipelineData(*, pipelineId: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

pipelineId: str

class gen_ai_hub.document_grounding.models.SearchPipelinesResponse(*, count: int | None, resources: List[SearchPipelineData])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int | None

resources: List[SearchPipelineData]

class gen_ai_hub.document_grounding.models.PipelineExecutionStatus(*values)

Bases: str, Enum

NEW = 'NEW'

UNKNOWN = 'UNKNOWN'

INPROGRESS = 'INPROGRESS'

FINISHED = 'FINISHED'

FINISHED_WITH_ERRORS = 'FINISHEDWITHERRORS'

TIMEOUT = 'TIMEOUT'

class gen_ai_hub.document_grounding.models.DocumentStatus(*values)

Bases: str, Enum

TO_BE_PROCESSED = 'TO_BE_PROCESSED'

INDEXED = 'INDEXED'

REINDEXED = 'REINDEXED'

DEINDEXED = 'DEINDEXED'

FAILED = 'FAILED'

FAILED_TO_BE_RETRIED = 'FAILED_TO_BE_RETRIED'

TO_BE_SCHEDULED = 'TO_BE_SCHEDULED'

class gen_ai_hub.document_grounding.models.BasePipelineResponse(*, id: str, type: str, metadata: MetaData | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

type: str

metadata: MetaData | None

class gen_ai_hub.document_grounding.models.MSSharePointConfigurationGetResponse(*, destination: str, sharePoint: SharePointConfig)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

destination: str

sharePoint: SharePointConfig

class gen_ai_hub.document_grounding.models.RetrievalKeyValueListPair(*, key: str, value: List[str])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

value: List[str]

class gen_ai_hub.document_grounding.models.RetrievalDocumentKeyValueListPair(*, key: str, value: List[str], matchMode: str | None)

Bases: RetrievalKeyValueListPair

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

matchMode: str | None

key: str

value: List[str]

class gen_ai_hub.document_grounding.models.RetrievalSearchDocumentKeyValueListPair(*, key: str, value: List[str], selectMode: List[str] | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

value: List[str]

selectMode: List[str] | None

class gen_ai_hub.document_grounding.models.RetrievalChunk(*, id: str, content: str, metadata: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair] | None = <factory>)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

content: str

metadata: List[RetrievalKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.RetrievalDocument(*, id: str, metadata: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalDocumentKeyValueListPair] | None = <factory>, chunks: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalChunk])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

metadata: List[RetrievalDocumentKeyValueListPair] | None

chunks: List[RetrievalChunk]

class gen_ai_hub.document_grounding.models.DataRepository(*, id: str, title: str, type: ~typing.Literal['vector', 'help.sap.com'] | str, metadata: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair] | None = <factory>)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

title: str

type: Literal['vector', 'help.sap.com'] | str

metadata: List[RetrievalKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.DataRepositoryWithDocuments(*, id: str, title: str, metadata: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair] | None = <factory>, documents: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalDocument])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

title: str

metadata: List[RetrievalKeyValueListPair] | None

documents: List[RetrievalDocument]

class gen_ai_hub.document_grounding.models.RetrievalSearchConfiguration(*, maxChunkCount: int | None = None, maxDocumentCount: int | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

maxChunkCount: int | None

maxDocumentCount: int | None

class gen_ai_hub.document_grounding.models.RetrievalSearchFilter(*, id: str, dataRepositoryType: ~typing.Literal['vector', 'help.sap.com'] | str, searchConfiguration: ~gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchConfiguration | None = <factory>, dataRepositories: ~typing.List[str] | None = <factory>, dataRepositoryMetadata: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair] | None = <factory>, documentMetadata: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalSearchDocumentKeyValueListPair] | None = <factory>, chunkMetadata: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalKeyValueListPair] | None = <factory>)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

dataRepositoryType: Literal['vector', 'help.sap.com'] | str

searchConfiguration: RetrievalSearchConfiguration | None

dataRepositories: List[str] | None

dataRepositoryMetadata: List[RetrievalKeyValueListPair] | None

documentMetadata: List[RetrievalSearchDocumentKeyValueListPair] | None

chunkMetadata: List[RetrievalKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.RetrievalSearchInput(*, query: str, filters: List[RetrievalSearchFilter])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

query: str

filters: List[RetrievalSearchFilter]

class gen_ai_hub.document_grounding.models.RetrievalDataRepositorySearchResult(*, dataRepository: DataRepositoryWithDocuments)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

dataRepository: DataRepositoryWithDocuments

class gen_ai_hub.document_grounding.models.RetrievalPerFilterSearchResult(*, filterId: str, results: ~typing.List[~gen_ai_hub.document_grounding.models.retrieval.RetrievalDataRepositorySearchResult] = <factory>)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

filterId: str

results: List[RetrievalDataRepositorySearchResult]

class gen_ai_hub.document_grounding.models.RetrievalPerFilterSearchResultError(*, message: str)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

message: str

class gen_ai_hub.document_grounding.models.RetrievalPerFilterSearchResultWithError(*, filterId: str, error: RetrievalPerFilterSearchResultError)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

filterId: str

error: RetrievalPerFilterSearchResultError

class gen_ai_hub.document_grounding.models.RetrievalSearchResults(*, results: List[RetrievalPerFilterSearchResult | RetrievalPerFilterSearchResultWithError])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

results: List[RetrievalPerFilterSearchResult | RetrievalPerFilterSearchResultWithError]

class gen_ai_hub.document_grounding.models.DataRepositories(*, count: int | None = None, resources: List[DataRepository])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int | None

resources: List[DataRepository]

class gen_ai_hub.document_grounding.models.VectorKeyValueListPair(*, key: str, value: List[str])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

value: List[str]

class gen_ai_hub.document_grounding.models.EmbeddingConfig(*, modelName: str | None = 'text-embedding-3-large')

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

modelName: str | None

class gen_ai_hub.document_grounding.models.CollectionCreateRequest(*, title: str | None = None, embeddingConfig: EmbeddingConfig, metadata: List[VectorKeyValueListPair] | None = [])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

title: str | None

embeddingConfig: EmbeddingConfig

metadata: List[VectorKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.Collection(*, id: str, title: str | None = None, embeddingConfig: EmbeddingConfig, metadata: List[VectorKeyValueListPair] | None = [])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

title: str | None

embeddingConfig: EmbeddingConfig

metadata: List[VectorKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.CollectionsListResponse(*, count: int | None = None, resources: List[Collection])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int | None

resources: List[Collection]

class gen_ai_hub.document_grounding.models.TextOnlyBaseChunk(*, content: str, metadata: List[VectorKeyValueListPair] | None = [])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

content: str

metadata: List[VectorKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.BaseDocument(*, chunks: List[TextOnlyBaseChunk], metadata: List[VectorKeyValueListPair])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

chunks: List[TextOnlyBaseChunk]

metadata: List[VectorKeyValueListPair]

class gen_ai_hub.document_grounding.models.DocumentWithoutChunks(*, id: str, metadata: List[VectorKeyValueListPair])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

metadata: List[VectorKeyValueListPair]

gen_ai_hub.document_grounding.models.VectorDocument

alias of Document

class gen_ai_hub.document_grounding.models.DocumentsCreateRequest(*, documents: List[BaseDocument])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

documents: List[BaseDocument]

class gen_ai_hub.document_grounding.models.DocumentsUpdateRequest(*, documents: List[Document])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

documents: List[Document]

class gen_ai_hub.document_grounding.models.DocumentsListResponse(*, documents: List[DocumentWithoutChunks])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

documents: List[DocumentWithoutChunks]

class gen_ai_hub.document_grounding.models.DocumentsResponse(*, count: int | None = None, resources: List[DocumentWithoutChunks])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

count: int | None

resources: List[DocumentWithoutChunks]

class gen_ai_hub.document_grounding.models.CollectionCreatedResponse(*, collectionUrl: str, status: Literal['CREATED'] = 'CREATED')

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

collectionURL: str

status: Literal['CREATED']

class gen_ai_hub.document_grounding.models.CollectionDeletedResponse(*, collectionUrl: str, status: Literal['DELETED'] = 'DELETED')

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

collectionURL: str

status: Literal['DELETED']

class gen_ai_hub.document_grounding.models.CollectionPendingResponse(*, location: str, status: Literal['PENDING'] = 'PENDING')

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

Location: str

status: Literal['PENDING']

class gen_ai_hub.document_grounding.models.VectorSearchConfiguration(*, maxChunkCount: int | None = None, maxDocumentCount: int | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

maxChunkCount: int | None

maxDocumentCount: int | None

class gen_ai_hub.document_grounding.models.VectorSearchDocumentKeyValueListPair(*, key: str, value: List[str], selectMode: List[str] | None = None)

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

key: str

value: List[str]

selectMode: List[str] | None

class gen_ai_hub.document_grounding.models.VectorSearchFilter(*, id: str, collectionIds: List[str], configuration: VectorSearchConfiguration, collectionMetadata: List[VectorKeyValueListPair] | None = [], documentMetadata: List[VectorSearchDocumentKeyValueListPair] | None = [], chunkMetadata: List[VectorKeyValueListPair] | None = [])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

collectionIds: List[str]

configuration: VectorSearchConfiguration

collectionMetadata: List[VectorKeyValueListPair] | None

documentMetadata: List[VectorSearchDocumentKeyValueListPair] | None

chunkMetadata: List[VectorKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.TextSearchRequest(*, query: str, filters: List[VectorSearchFilter])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

query: str

filters: List[VectorSearchFilter]

class gen_ai_hub.document_grounding.models.VectorChunk(*, id: str, content: str, metadata: List[VectorKeyValueListPair] | None = [])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

content: str

metadata: List[VectorKeyValueListPair] | None

class gen_ai_hub.document_grounding.models.DocumentOutput(*, id: str, metadata: List[VectorKeyValueListPair] | None = [], chunks: List[VectorChunk])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

metadata: List[VectorKeyValueListPair] | None

chunks: List[VectorChunk]

class gen_ai_hub.document_grounding.models.DocumentsChunk(*, id: str, title: str, metadata: List[VectorKeyValueListPair] | None = [], documents: List[DocumentOutput])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

id: str

title: str

metadata: List[VectorKeyValueListPair] | None

documents: List[DocumentOutput]

class gen_ai_hub.document_grounding.models.VectorPerFilterSearchResult(*, filterId: str, results: List[DocumentsChunk])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

filterId: str

results: List[DocumentsChunk]

class gen_ai_hub.document_grounding.models.VectorSearchResults(*, results: List[VectorPerFilterSearchResult])

Bases: BaseModel

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

results: List[VectorPerFilterSearchResult]

Submodules

• gen_ai_hub.document_grounding.models.pipeline module
  • MetaData
    • MetaData.destination
    • MetaData.model_config
  • SharePointSite
    • SharePointSite.name
    • SharePointSite.includePaths
    • SharePointSite.model_config
  • SharePointConfig
    • SharePointConfig.site
    • SharePointConfig.model_config
  • MSSharePointConfiguration
    • MSSharePointConfiguration.destination
    • MSSharePointConfiguration.sharePoint
    • MSSharePointConfiguration.model_config
  • CommonConfiguration
    • CommonConfiguration.destination
    • CommonConfiguration.model_config
  • MSSharePointPipelineCreateRequest
    • MSSharePointPipelineCreateRequest.type
    • MSSharePointPipelineCreateRequest.configuration
    • MSSharePointPipelineCreateRequest.metadata
    • MSSharePointPipelineCreateRequest.model_config
  • S3PipelineCreateRequest
    • S3PipelineCreateRequest.type
    • S3PipelineCreateRequest.configuration
    • S3PipelineCreateRequest.metadata
    • S3PipelineCreateRequest.model_config
  • SFTPPipelineCreateRequest
    • SFTPPipelineCreateRequest.type
    • SFTPPipelineCreateRequest.configuration
    • SFTPPipelineCreateRequest.metadata
    • SFTPPipelineCreateRequest.model_config
  • PipelineIdResponse
    • PipelineIdResponse.pipelineId
    • PipelineIdResponse.model_config
  • BasePipelineResponse
    • BasePipelineResponse.id
    • BasePipelineResponse.type
    • BasePipelineResponse.metadata
    • BasePipelineResponse.model_config
  • MSSharePointConfigurationGetResponse
    • MSSharePointConfigurationGetResponse.destination
    • MSSharePointConfigurationGetResponse.sharePoint
    • MSSharePointConfigurationGetResponse.model_config
  • MSSharePointPipelineGetResponse
    • MSSharePointPipelineGetResponse.type
    • MSSharePointPipelineGetResponse.configuration
    • MSSharePointPipelineGetResponse.model_config
    • MSSharePointPipelineGetResponse.id
    • MSSharePointPipelineGetResponse.metadata
  • S3PipelineGetResponse
    • S3PipelineGetResponse.type
    • S3PipelineGetResponse.configuration
    • S3PipelineGetResponse.model_config
    • S3PipelineGetResponse.id
    • S3PipelineGetResponse.metadata
  • SFTPPipelineGetResponse
    • SFTPPipelineGetResponse.type
    • SFTPPipelineGetResponse.configuration
    • SFTPPipelineGetResponse.model_config
    • SFTPPipelineGetResponse.id
    • SFTPPipelineGetResponse.metadata
  • GetPipelinesResponse
    • GetPipelinesResponse.count
    • GetPipelinesResponse.resources
    • GetPipelinesResponse.model_config
  • GetPipelineStatusResponse
    • GetPipelineStatusResponse.lastStarted
    • GetPipelineStatusResponse.status
    • GetPipelineStatusResponse.model_config
  • DataRepositoryMetadataItem
    • DataRepositoryMetadataItem.key
    • DataRepositoryMetadataItem.value
    • DataRepositoryMetadataItem.model_config
  • SearchPipelineRequest
    • SearchPipelineRequest.dataRepositoryMetadata
    • SearchPipelineRequest.model_config
  • SearchPipelineData
    • SearchPipelineData.pipelineId
    • SearchPipelineData.model_config
  • SearchPipelinesResponse
    • SearchPipelinesResponse.count
    • SearchPipelinesResponse.resources
    • SearchPipelinesResponse.model_config
  • PipelineExecutionStatus
    • PipelineExecutionStatus.NEW
    • PipelineExecutionStatus.UNKNOWN
    • PipelineExecutionStatus.INPROGRESS
    • PipelineExecutionStatus.FINISHED
    • PipelineExecutionStatus.FINISHED_WITH_ERRORS
    • PipelineExecutionStatus.TIMEOUT
  • PipelineExecution
    • PipelineExecution.id
    • PipelineExecution.status
    • PipelineExecution.createdAt
    • PipelineExecution.modifiedAt
    • PipelineExecution.model_config
  • GetPipelineExecutionsResponse
    • GetPipelineExecutionsResponse.count
    • GetPipelineExecutionsResponse.resources
    • GetPipelineExecutionsResponse.model_config
  • DocumentStatus
    • DocumentStatus.TO_BE_PROCESSED
    • DocumentStatus.INDEXED
    • DocumentStatus.REINDEXED
    • DocumentStatus.DEINDEXED
    • DocumentStatus.FAILED
    • DocumentStatus.FAILED_TO_BE_RETRIED
    • DocumentStatus.TO_BE_SCHEDULED
  • Document
    • Document.id
    • Document.status
    • Document.viewLocation
    • Document.downloadLocation
    • Document.absoluteUrl
    • Document.title
    • Document.metadataId
    • Document.createdTimestamp
    • Document.lastUpdatedTimestamp
    • Document.model_config
  • DocumentsStatusResponse
    • DocumentsStatusResponse.count
    • DocumentsStatusResponse.resources
    • DocumentsStatusResponse.model_config
  • ManualPipelineTrigger
    • ManualPipelineTrigger.pipelineId
    • ManualPipelineTrigger.metadataOnly
    • ManualPipelineTrigger.model_config
• gen_ai_hub.document_grounding.models.retrieval module
  • RetrievalKeyValueListPair
    • RetrievalKeyValueListPair.key
    • RetrievalKeyValueListPair.value
    • RetrievalKeyValueListPair.model_config
  • RetrievalDocumentKeyValueListPair
    • RetrievalDocumentKeyValueListPair.matchMode
    • RetrievalDocumentKeyValueListPair.model_config
    • RetrievalDocumentKeyValueListPair.key
    • RetrievalDocumentKeyValueListPair.value
  • RetrievalSearchDocumentKeyValueListPair
    • RetrievalSearchDocumentKeyValueListPair.key
    • RetrievalSearchDocumentKeyValueListPair.value
    • RetrievalSearchDocumentKeyValueListPair.selectMode
    • RetrievalSearchDocumentKeyValueListPair.model_config
  • RetrievalChunk
    • RetrievalChunk.id
    • RetrievalChunk.content
    • RetrievalChunk.metadata
    • RetrievalChunk.model_config
  • RetrievalDocument
    • RetrievalDocument.id
    • RetrievalDocument.metadata
    • RetrievalDocument.chunks
    • RetrievalDocument.model_config
  • DataRepository
    • DataRepository.id
    • DataRepository.title
    • DataRepository.type
    • DataRepository.metadata
    • DataRepository.model_config
  • DataRepositoryWithDocuments
    • DataRepositoryWithDocuments.id
    • DataRepositoryWithDocuments.title
    • DataRepositoryWithDocuments.metadata
    • DataRepositoryWithDocuments.documents
    • DataRepositoryWithDocuments.model_config
  • RetrievalSearchConfiguration
    • RetrievalSearchConfiguration.maxChunkCount
    • RetrievalSearchConfiguration.maxDocumentCount
    • RetrievalSearchConfiguration.model_config
  • RetrievalSearchFilter
    • RetrievalSearchFilter.id
    • RetrievalSearchFilter.dataRepositoryType
    • RetrievalSearchFilter.searchConfiguration
    • RetrievalSearchFilter.dataRepositories
    • RetrievalSearchFilter.dataRepositoryMetadata
    • RetrievalSearchFilter.documentMetadata
    • RetrievalSearchFilter.chunkMetadata
    • RetrievalSearchFilter.model_config
  • RetrievalSearchInput
    • RetrievalSearchInput.query
    • RetrievalSearchInput.filters
    • RetrievalSearchInput.model_config
  • RetrievalDataRepositorySearchResult
    • RetrievalDataRepositorySearchResult.dataRepository
    • RetrievalDataRepositorySearchResult.model_config
  • RetrievalPerFilterSearchResult
    • RetrievalPerFilterSearchResult.filterId
    • RetrievalPerFilterSearchResult.results
    • RetrievalPerFilterSearchResult.model_config
  • RetrievalPerFilterSearchResultError
    • RetrievalPerFilterSearchResultError.message
    • RetrievalPerFilterSearchResultError.model_config
  • RetrievalPerFilterSearchResultWithError
    • RetrievalPerFilterSearchResultWithError.filterId
    • RetrievalPerFilterSearchResultWithError.error
    • RetrievalPerFilterSearchResultWithError.model_config
  • RetrievalSearchResults
    • RetrievalSearchResults.results
    • RetrievalSearchResults.model_config
  • DataRepositories
    • DataRepositories.count
    • DataRepositories.resources
    • DataRepositories.model_config
• gen_ai_hub.document_grounding.models.vector module
  • VectorKeyValueListPair
    • VectorKeyValueListPair.key
    • VectorKeyValueListPair.value
    • VectorKeyValueListPair.model_config
  • EmbeddingConfig
    • EmbeddingConfig.modelName
    • EmbeddingConfig.model_config
  • CollectionCreateRequest
    • CollectionCreateRequest.title
    • CollectionCreateRequest.embeddingConfig
    • CollectionCreateRequest.metadata
    • CollectionCreateRequest.model_config
  • Collection
    • Collection.id
    • Collection.title
    • Collection.embeddingConfig
    • Collection.metadata
    • Collection.model_config
  • CollectionsListResponse
    • CollectionsListResponse.count
    • CollectionsListResponse.resources
    • CollectionsListResponse.model_config
  • TextOnlyBaseChunk
    • TextOnlyBaseChunk.content
    • TextOnlyBaseChunk.metadata
    • TextOnlyBaseChunk.model_config
  • BaseDocument
    • BaseDocument.chunks
    • BaseDocument.metadata
    • BaseDocument.model_config
  • DocumentWithoutChunks
    • DocumentWithoutChunks.id
    • DocumentWithoutChunks.metadata
    • DocumentWithoutChunks.model_config
  • Document
    • Document.id
    • Document.model_config
  • DocumentsCreateRequest
    • DocumentsCreateRequest.documents
    • DocumentsCreateRequest.model_config
  • DocumentsUpdateRequest
    • DocumentsUpdateRequest.documents
    • DocumentsUpdateRequest.model_config
  • DocumentsListResponse
    • DocumentsListResponse.documents
    • DocumentsListResponse.model_config
  • DocumentsResponse
    • DocumentsResponse.count
    • DocumentsResponse.resources
    • DocumentsResponse.model_config
  • CollectionCreatedResponse
    • CollectionCreatedResponse.collectionURL
    • CollectionCreatedResponse.status
    • CollectionCreatedResponse.model_config
  • CollectionDeletedResponse
    • CollectionDeletedResponse.collectionURL
    • CollectionDeletedResponse.status
    • CollectionDeletedResponse.model_config
  • CollectionPendingResponse
    • CollectionPendingResponse.Location
    • CollectionPendingResponse.status
    • CollectionPendingResponse.model_config
  • VectorSearchConfiguration
    • VectorSearchConfiguration.maxChunkCount
    • VectorSearchConfiguration.maxDocumentCount
    • VectorSearchConfiguration.model_config
  • VectorSearchDocumentKeyValueListPair
    • VectorSearchDocumentKeyValueListPair.key
    • VectorSearchDocumentKeyValueListPair.value
    • VectorSearchDocumentKeyValueListPair.selectMode
    • VectorSearchDocumentKeyValueListPair.model_config
  • VectorSearchFilter
    • VectorSearchFilter.id
    • VectorSearchFilter.collectionIds
    • VectorSearchFilter.configuration
    • VectorSearchFilter.collectionMetadata
    • VectorSearchFilter.documentMetadata
    • VectorSearchFilter.chunkMetadata
    • VectorSearchFilter.model_config
  • TextSearchRequest
    • TextSearchRequest.query
    • TextSearchRequest.filters
    • TextSearchRequest.model_config
  • VectorChunk
    • VectorChunk.id
    • VectorChunk.content
    • VectorChunk.metadata
    • VectorChunk.model_config
  • DocumentOutput
    • DocumentOutput.id
    • DocumentOutput.metadata
    • DocumentOutput.chunks
    • DocumentOutput.model_config
  • DocumentsChunk
    • DocumentsChunk.id
    • DocumentsChunk.title
    • DocumentsChunk.metadata
    • DocumentsChunk.documents
    • DocumentsChunk.model_config
  • VectorPerFilterSearchResult
    • VectorPerFilterSearchResult.filterId
    • VectorPerFilterSearchResult.results
    • VectorPerFilterSearchResult.model_config
  • VectorSearchResults
    • VectorSearchResults.results
    • VectorSearchResults.model_config