Merge branch 'langchain-ai:main' into bedrock-token-count-callbacks

libs/aws/langchain_aws/__init__.py

@@ -1,4 +1,5 @@
 from langchain_aws.chat_models import BedrockChat, ChatBedrock
+from langchain_aws.embeddings import BedrockEmbeddings
 from langchain_aws.graphs import NeptuneAnalyticsGraph, NeptuneGraph
 from langchain_aws.llms import Bedrock, BedrockLLM, SagemakerEndpoint
 from langchain_aws.retrievers import (
@@ -8,6 +9,7 @@
 
 __all__ = [
     "Bedrock",
+    "BedrockEmbeddings",
     "BedrockLLM",
     "BedrockChat",
     "ChatBedrock",

libs/aws/langchain_aws/embeddings/__init__.py

@@ -0,0 +1,3 @@
+from langchain_aws.embeddings.bedrock import BedrockEmbeddings
+
+__all__ = ["BedrockEmbeddings"]

libs/aws/langchain_aws/embeddings/bedrock.py

@@ -0,0 +1,218 @@
+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 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 "
+                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)