gen_ai_hub.document_grounding.models.vector module

Pydantic models for Vector API.

This module defines data models for the Vector API, which provides management and search capabilities for vector-based document collections.

Model categories:

• Collection models (collection configuration and management)

• Document and chunk models (content structure with embeddings)

• Embedding configuration models (embedding model settings)

• Search models (semantic search requests and results)

• Status models (collection creation/deletion tracking)

The Vector API enables semantic search across document collections using vector embeddings for similarity-based retrieval.

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

Bases: BaseModel

key: str

value: List[str]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

modelName: str | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

title: str | None

embeddingConfig: EmbeddingConfig

metadata: List[VectorKeyValueListPair] | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

id: str

title: str | None

embeddingConfig: EmbeddingConfig

metadata: List[VectorKeyValueListPair] | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

count: int | None

resources: List[Collection]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

content: str

metadata: List[VectorKeyValueListPair] | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

chunks: List[TextOnlyBaseChunk]

metadata: List[VectorKeyValueListPair]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

id: str

metadata: List[VectorKeyValueListPair]

model_config: ClassVar[ConfigDict] = {}

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

class gen_ai_hub.document_grounding.models.vector.Document(*, chunks: List[TextOnlyBaseChunk], metadata: List[VectorKeyValueListPair], id: str)

Bases: BaseDocument

id: str

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

documents: List[BaseDocument]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

documents: List[Document]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

documents: List[DocumentWithoutChunks]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

count: int | None

resources: List[DocumentWithoutChunks]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

collectionURL: str

status: Literal['CREATED']

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

collectionURL: str

status: Literal['DELETED']

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

Location: str

status: Literal['PENDING']

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

maxChunkCount: int | None

maxDocumentCount: int | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

key: str

value: List[str]

selectMode: List[str] | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

id: str

collectionIds: List[str]

configuration: VectorSearchConfiguration

collectionMetadata: List[VectorKeyValueListPair] | None

documentMetadata: List[VectorSearchDocumentKeyValueListPair] | None

chunkMetadata: List[VectorKeyValueListPair] | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

query: str

filters: List[VectorSearchFilter]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

id: str

content: str

metadata: List[VectorKeyValueListPair] | None

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

id: str

metadata: List[VectorKeyValueListPair] | None

chunks: List[VectorChunk]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

id: str

title: str

metadata: List[VectorKeyValueListPair] | None

documents: List[DocumentOutput]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

filterId: str

results: List[DocumentsChunk]

model_config: ClassVar[ConfigDict] = {}

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

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

Bases: BaseModel

results: List[VectorPerFilterSearchResult]

model_config: ClassVar[ConfigDict] = {}

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