`langchain_community.embeddings.cohere`.CohereEmbeddings¶

class langchain_community.embeddings.cohere.CohereEmbeddings[source]¶

Bases: BaseModel, Embeddings

Cohere embedding models.

To use, you should have the cohere python package installed, and the environment variable COHERE_API_KEY set with your API key or pass it as a named parameter to the constructor.

Example

from langchain_community.embeddings import CohereEmbeddings
cohere = CohereEmbeddings(
    model="embed-english-light-v3.0",
    cohere_api_key="my-api-key"
)

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 async_client: Any = None¶: Cohere async client.

param client: Any = None¶: Cohere client.

param cohere_api_key: Optional[str] = None¶

param max_retries: Optional[int] = None¶: Maximum number of retries to make when generating.

param model: str = 'embed-english-v2.0'¶: Model name to use.

param request_timeout: Optional[float] = None¶: Timeout in seconds for the Cohere API request.

param truncate: Optional[str] = None¶: Truncate embeddings that are too long from start or end (“NONE”|”START”|”END”)

param user_agent: str = 'langchain'¶: Identifier for the application making the request.

async aembed(texts: List[str], *, input_type: Optional[str] = None) → List[List[float]][source]¶

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

Async call out to Cohere’s embedding endpoint.

Parameters: texts – The list of texts to embed.
Returns: List of embeddings, one for each text.

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

Async call out to Cohere’s embedding endpoint.

Parameters: text – The text to embed.
Returns: Embeddings for the 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(texts: List[str], *, input_type: Optional[str] = None) → List[List[float]][source]¶

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

Embed a list of document texts.

Parameters: texts – The list of texts to embed.
Returns: List of embeddings, one for each text.

embed_query(text: str) → List[float][source]¶

Call out to Cohere’s embedding endpoint.

Parameters: text – The text to embed.
Returns: Embeddings for the 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 CohereEmbeddings¶

langchain_community.embeddings.cohere.CohereEmbeddings¶

Examples using CohereEmbeddings¶

`langchain_community.embeddings.cohere`.CohereEmbeddings¶