langchain_community.embeddings.baidu_qianfan_endpoint.QianfanEmbeddingsEndpoint

class langchain_community.embeddings.baidu_qianfan_endpoint.QianfanEmbeddingsEndpoint

Bases: BaseModel, Embeddings

Baidu Qianfan Embeddings embedding models.

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

param chunk_size: int = 16

Chunk size when multiple texts are input

param client: Any = None

Qianfan client

param endpoint: str = ''

Endpoint of the Qianfan Embedding, required if custom model used.

param max_retries: int = 5

Max reties times

param model: str = 'Embedding-V1'

Model name you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu

for now, we support Embedding-V1 and - Embedding-V1 （默认模型） - bge-large-en - bge-large-zh

preset models are mapping to an endpoint. model will be ignored if endpoint is set

param qianfan_ak: Optional[str] = None

Qianfan application apikey

param qianfan_sk: Optional[str] = None

Qianfan application secretkey

async aembed_documents(texts: List[str]) → List[List[float]]

Asynchronous Embed search docs.

async aembed_query(text: str) → List[float]

Asynchronous Embed query text.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, update: Optional[DictStrAny] = None, deep: bool = False) → Model

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns

new model instance

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

embed_documents(texts: List[str]) → List[List[float]]

Embeds a list of text documents using the AutoVOT algorithm.

Parameters

texts (List[str]) – A list of text documents to embed.

Returns

A list of embeddings for each document in the input list.

Each embedding is represented as a list of float values.

Return type

List[List[float]]

embed_query(text: str) → List[float]

Embed query text.

classmethod from_orm(obj: Any) → Model

json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod parse_obj(obj: Any) → Model

classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: Protocol = None, allow_pickle: bool = False) → Model

classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny

classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode

classmethod update_forward_refs(**localns: Any) → None

Try to update ForwardRefs on fields based on this Model, globalns and localns.

classmethod validate(value: Any) → Model

Examples using QianfanEmbeddingsEndpoint

• Baidu Qianfan