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

[docs]class BedrockEmbeddings(BaseModel, Embeddings): """Bedrock embedding models. To authenticate, the AWS client uses the following methods to automatically load credentials: 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: """ 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()
[docs] 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
[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. """ embedding = self._embedding_func(text) if self.normalize: return self._normalize_vector(embedding) return embedding
[docs] 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)
[docs] 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)