Source code for langchain_community.embeddings.cohere

from typing import Any, Dict, List, Optional

from langchain_core._api.deprecation import deprecated
from langchain_core.embeddings import Embeddings
from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
from langchain_core.utils import get_from_dict_or_env

from langchain_community.llms.cohere import _create_retry_decorator


[docs]@deprecated( since="0.0.30", removal="0.3.0", alternative_import="langchain_cohere.CohereEmbeddings", ) class CohereEmbeddings(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: .. code-block:: python from langchain_community.embeddings import CohereEmbeddings cohere = CohereEmbeddings( model="embed-english-light-v3.0", cohere_api_key="my-api-key" ) """ client: Any #: :meta private: """Cohere client.""" async_client: Any #: :meta private: """Cohere async client.""" model: str = "embed-english-v2.0" """Model name to use.""" truncate: Optional[str] = None """Truncate embeddings that are too long from start or end ("NONE"|"START"|"END")""" cohere_api_key: Optional[str] = None max_retries: int = 3 """Maximum number of retries to make when generating.""" request_timeout: Optional[float] = None """Timeout in seconds for the Cohere API request.""" user_agent: str = "langchain" """Identifier for the application making the request.""" class Config: """Configuration for this pydantic object.""" extra = Extra.forbid @root_validator() def validate_environment(cls, values: Dict) -> Dict: """Validate that api key and python package exists in environment.""" cohere_api_key = get_from_dict_or_env( values, "cohere_api_key", "COHERE_API_KEY" ) request_timeout = values.get("request_timeout") try: import cohere client_name = values["user_agent"] values["client"] = cohere.Client( cohere_api_key, timeout=request_timeout, client_name=client_name, ) values["async_client"] = cohere.AsyncClient( cohere_api_key, timeout=request_timeout, client_name=client_name, ) except ImportError: raise ImportError( "Could not import cohere python package. " "Please install it with `pip install cohere`." ) return values
[docs] def embed_with_retry(self, **kwargs: Any) -> Any: """Use tenacity to retry the embed call.""" retry_decorator = _create_retry_decorator(self.max_retries) @retry_decorator def _embed_with_retry(**kwargs: Any) -> Any: return self.client.embed(**kwargs) return _embed_with_retry(**kwargs)
[docs] def aembed_with_retry(self, **kwargs: Any) -> Any: """Use tenacity to retry the embed call.""" retry_decorator = _create_retry_decorator(self.max_retries) @retry_decorator async def _embed_with_retry(**kwargs: Any) -> Any: return await self.async_client.embed(**kwargs) return _embed_with_retry(**kwargs)
[docs] def embed( self, texts: List[str], *, input_type: Optional[str] = None ) -> List[List[float]]: embeddings = self.embed_with_retry( model=self.model, texts=texts, input_type=input_type, truncate=self.truncate, ).embeddings return [list(map(float, e)) for e in embeddings]
[docs] async def aembed( self, texts: List[str], *, input_type: Optional[str] = None ) -> List[List[float]]: embeddings = ( await self.aembed_with_retry( model=self.model, texts=texts, input_type=input_type, truncate=self.truncate, ) ).embeddings return [list(map(float, e)) for e in embeddings]
[docs] def embed_documents(self, texts: List[str]) -> List[List[float]]: """Embed a list of document texts. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ return self.embed(texts, input_type="search_document")
[docs] async def aembed_documents(self, texts: List[str]) -> List[List[float]]: """Async call out to Cohere's embedding endpoint. Args: texts: The list of texts to embed. Returns: List of embeddings, one for each text. """ return await self.aembed(texts, input_type="search_document")
[docs] def embed_query(self, text: str) -> List[float]: """Call out to Cohere's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. """ return self.embed([text], input_type="search_query")[0]
[docs] async def aembed_query(self, text: str) -> List[float]: """Async call out to Cohere's embedding endpoint. Args: text: The text to embed. Returns: Embeddings for the text. """ return (await self.aembed([text], input_type="search_query"))[0]