Source code for langchain.embeddings.bedrock
import json
import os
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra, root_validator
from langchain.embeddings.base import Embeddings
[docs]class BedrockEmbeddings(BaseModel, Embeddings):
    """Embeddings provider to invoke 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.bedrock_embeddings import BedrockEmbeddings
            
            region_name ="us-east-1"
            credentials_profile_name = "default"
            model_id = "amazon.titan-e1t-medium"
            be = BedrockEmbeddings(
                credentials_profile_name=credentials_profile_name,
                region_name=region_name,
                model_id=model_id
            )
    """
    client: Any  #: :meta private:
    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-e1t-medium"
    """Id of the model to call, e.g., amazon.titan-e1t-medium, this is
    equivalent to the modelId property in the list-foundation-models api"""
    model_kwargs: Optional[Dict] = None
    """Key word arguments to pass to the model."""
    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"]
            values["client"] = session.client("bedrock", **client_params)
        except ImportError:
            raise ModuleNotFoundError(
                "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 "
                "profile name are valid."
            ) 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, " ")
        _model_kwargs = self.model_kwargs or {}
        input_body = {**_model_kwargs}
        input_body["inputText"] = text
        body = json.dumps(input_body)
        content_type = "application/json"
        accepts = "application/json"
        embeddings = []
        try:
            response = self.client.invoke_model(
                body=body,
                modelId=self.model_id,
                accept=accepts,
                contentType=content_type,
            )
            response_body = json.loads(response.get("body").read())
            embeddings = response_body.get("embedding")
        except Exception as e:
            raise ValueError(f"Error raised by inference endpoint: {e}")
        return embeddings
[docs]    def embed_documents(
        self, texts: List[str], chunk_size: int = 1
    ) -> List[List[float]]:
        """Compute doc embeddings using a Bedrock model.
        Args:
            texts: The list of texts to embed.
            chunk_size: Bedrock currently only allows single string
                inputs, so chunk size is always 1. This input is here
                only for compatibility with the embeddings interface.
        Returns:
            List of embeddings, one for each text.
        """
        results = []
        for text in texts:
            response = self._embedding_func(text)
            results.append(response)
        return results
[docs]    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.
        """
        return self._embedding_func(text)