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# Source code for langchain_community.embeddings.bedrock

    import asyncio
    import json
    import os
    from typing import Any, Dict, List, Optional

    import numpy as np
    from langchain_core.embeddings import Embeddings
    from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
    from langchain_core.runnables.config import run_in_executor

    class BedrockEmbeddings(BaseModel, Embeddings):
        """Bedrock embedding models.

        To authenticate, the AWS client uses the following methods to
        automatically load credentials:
        https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html

        If a specific credential profile should be used, you must pass
        the name of the profile from the ~/.aws/credentials file that is to be used.

        Make sure the credentials / roles used have the required policies to
        access the Bedrock service.
        """

        """
        Example:
            .. code-block:: python

                from langchain_community.bedrock_embeddings import BedrockEmbeddings

                region_name ="us-east-1"
                credentials_profile_name = "default"
                model_id = "amazon.titan-embed-text-v1"

                be = BedrockEmbeddings(
                    credentials_profile_name=credentials_profile_name,
                    region_name=region_name,
                    model_id=model_id
                )
        """

        client: Any  #: :meta private:
        """Bedrock client."""
        region_name: Optional[str] = None
        """The aws region e.g., `us-west-2`. Fallsback to AWS_DEFAULT_REGION env variable
        or region specified in ~/.aws/config in case it is not provided here.
        """

        credentials_profile_name: Optional[str] = None
        """The name of the profile in the ~/.aws/credentials or ~/.aws/config files, which
        has either access keys or role information specified.
        If not specified, the default credential profile or, if on an EC2 instance,
        credentials from IMDS will be used.
        See: https://boto3.amazonaws.com/v1/documentation/api/latest/guide/credentials.html
        """

        model_id: str = "amazon.titan-embed-text-v1"
        """Id of the model to call, e.g., amazon.titan-embed-text-v1, this is
        equivalent to the modelId property in the list-foundation-models api"""

        model_kwargs: Optional[Dict] = None
        """Keyword arguments to pass to the model."""

        endpoint_url: Optional[str] = None
        """Needed if you don't want to default to us-east-1 endpoint"""

        normalize: bool = False
        """Whether the embeddings should be normalized to unit vectors"""

        class Config:
            """Configuration for this pydantic object."""

            extra = Extra.forbid

        @root_validator()
        def validate_environment(cls, values: Dict) -> Dict:
            """Validate that AWS credentials to and python package exists in environment."""

            if values["client"] is not None:
                return values

            try:
                import boto3

                if values["credentials_profile_name"] is not None:
                    session = boto3.Session(profile_name=values["credentials_profile_name"])
                else:
                    # use default credentials
                    session = boto3.Session()

                client_params = {}
                if values["region_name"]:
                    client_params["region_name"] = values["region_name"]

                if values["endpoint_url"]:
                    client_params["endpoint_url"] = values["endpoint_url"]

                values["client"] = session.client("bedrock-runtime", **client_params)

            except ImportError:
                raise ImportError(
                    "Could not import boto3 python package. "
                    "Please install it with `pip install boto3`."
                )
            except Exception as e:
                raise ValueError(
                    "Could not load credentials to authenticate with AWS client. "
                    "Please check that credentials in the specified "
                    f"profile name are valid. Bedrock error: {e}"
                ) from e

            return values

        def _embedding_func(self, text: str) -> List[float]:
            """Call out to Bedrock embedding endpoint."""
            # replace newlines, which can negatively affect performance.
            text = text.replace(os.linesep, " ")

            # format input body for provider
            provider = self.model_id.split(".")[0]
            _model_kwargs = self.model_kwargs or {}
            input_body = {**_model_kwargs}
            if provider == "cohere":
                if "input_type" not in input_body.keys():
                    input_body["input_type"] = "search_document"
                input_body["texts"] = [text]
            else:
                # includes common provider == "amazon"
                input_body["inputText"] = text
            body = json.dumps(input_body)

            try:
                # invoke bedrock API
                response = self.client.invoke_model(
                    body=body,
                    modelId=self.model_id,
                    accept="application/json",
                    contentType="application/json",
                )

                # format output based on provider
                response_body = json.loads(response.get("body").read())
                if provider == "cohere":
                    return response_body.get("embeddings")[0]
                else:
                    # includes common provider == "amazon"
                    return response_body.get("embedding")
            except Exception as e:
                raise ValueError(f"Error raised by inference endpoint: {e}")

        def _normalize_vector(self, embeddings: List[float]) -> List[float]:
            """Normalize the embedding to a unit vector."""
            emb = np.array(embeddings)
            norm_emb = emb / np.linalg.norm(emb)
            return norm_emb.tolist()

        def embed_documents(self, texts: List[str]) -> List[List[float]]:
            """Compute doc embeddings using a Bedrock model.

            Args:
                texts: The list of texts to embed

            Returns:
                List of embeddings, one for each text.
            """
            results = []
            for text in texts:
                response = self._embedding_func(text)

                if self.normalize:
                    response = self._normalize_vector(response)

                results.append(response)

            return results

        def embed_query(self, text: str) -> List[float]:
            """Compute query embeddings using a Bedrock model.

            Args:
                text: The text to embed.

            Returns:
                Embeddings for the text.
            """
            embedding = self._embedding_func(text)

            if self.normalize:
                return self._normalize_vector(embedding)

            return embedding

        async def aembed_query(self, text: str) -> List[float]:
            """Asynchronous compute query embeddings using a Bedrock model.

            Args:
                text: The text to embed.

            Returns:
                Embeddings for the text.
            """

            return await run_in_executor(None, self.embed_query, text)

        async def aembed_documents(self, texts: List[str]) -> List[List[float]]:
            """Asynchronous compute doc embeddings using a Bedrock model.

            Args:
                texts: The list of texts to embed

            Returns:
                List of embeddings, one for each text.
            """

            result = await asyncio.gather(*[self.aembed_query(text) for text in texts])

            return list(result)