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# 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

    @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

        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)

        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)

        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]

        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]

        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")

        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")

        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]

        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]