langchain[minor], community[minor]: add CrossEncoderReranker with HuggingFaceCrossEncoder and SagemakerEndpointCrossEncoder (#13687)

- **Description:** Support reranking based on cross encoder models
available from HuggingFace.
      - Added `CrossEncoder` schema
- Implemented `HuggingFaceCrossEncoder` and
`SagemakerEndpointCrossEncoder`
- Implemented `CrossEncoderReranker` that performs similar functionality
to `CohereRerank`
- Added `cross-encoder-reranker.ipynb` to demonstrate how to use it.
Please let me know if anything else needs to be done to make it visible
on the table-of-contents navigation bar on the left, or on the card list
on [retrievers documentation
page](https://python.langchain.com/docs/integrations/retrievers).
  - **Issue:** N/A
  - **Dependencies:** None other than the existing ones.

---------

Co-authored-by: Kenny Choe <kchoe@amazon.com>
Co-authored-by: Bagatur <baskaryan@gmail.com>

libs/community/langchain_community/cross_encoders/sagemaker_endpoint.py

@@ -0,0 +1,151 @@
+import json
+from typing import Any, Dict, List, Optional, Tuple
+
+from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
+
+from langchain_community.cross_encoders.base import BaseCrossEncoder
+
+
+class CrossEncoderContentHandler:
+    """Content handler for CrossEncoder class."""
+
+    content_type = "application/json"
+    accepts = "application/json"
+
+    def transform_input(self, text_pairs: List[Tuple[str, str]]) -> bytes:
+        input_str = json.dumps({"text_pairs": text_pairs})
+        return input_str.encode("utf-8")
+
+    def transform_output(self, output: Any) -> List[float]:
+        response_json = json.loads(output.read().decode("utf-8"))
+        scores = response_json["scores"]
+        return scores
+
+
+class SagemakerEndpointCrossEncoder(BaseModel, BaseCrossEncoder):
+    """SageMaker Inference CrossEncoder endpoint.
+
+    To use, you must supply the endpoint name from your deployed
+    Sagemaker model & the region where it is deployed.
+
+    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 Sagemaker endpoint.
+    See: https://docs.aws.amazon.com/IAM/latest/UserGuide/access_policies.html
+    """
+
+    """
+   Example:
+       .. code-block:: python
+
+
+           from langchain.embeddings import SagemakerEndpointCrossEncoder
+           endpoint_name = (
+               "my-endpoint-name"
+           )
+           region_name = (
+               "us-west-2"
+           )
+           credentials_profile_name = (
+               "default"
+           )
+           se = SagemakerEndpointCrossEncoder(
+               endpoint_name=endpoint_name,
+               region_name=region_name,
+               credentials_profile_name=credentials_profile_name
+           )
+   """
+    client: Any  #: :meta private:
+
+    endpoint_name: str = ""
+    """The name of the endpoint from the deployed Sagemaker model.
+   Must be unique within an AWS Region."""
+
+    region_name: str = ""
+    """The aws region where the Sagemaker model is deployed, eg. `us-west-2`."""
+
+    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
+   """
+
+    content_handler: CrossEncoderContentHandler = CrossEncoderContentHandler()
+
+    model_kwargs: Optional[Dict] = None
+    """Keyword arguments to pass to the model."""
+
+    endpoint_kwargs: Optional[Dict] = None
+    """Optional attributes passed to the invoke_endpoint
+   function. See `boto3`_. docs for more info.
+   .. _boto3: <https://boto3.amazonaws.com/v1/documentation/api/latest/index.html>
+   """
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+        arbitrary_types_allowed = True
+
+    @root_validator()
+    def validate_environment(cls, values: Dict) -> Dict:
+        """Validate that AWS credentials to and python package exists in environment."""
+        try:
+            import boto3
+
+            try:
+                if values["credentials_profile_name"] is not None:
+                    session = boto3.Session(
+                        profile_name=values["credentials_profile_name"]
+                    )
+                else:
+                    # use default credentials
+                    session = boto3.Session()
+
+                values["client"] = session.client(
+                    "sagemaker-runtime", region_name=values["region_name"]
+                )
+
+            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
+
+        except ImportError:
+            raise ImportError(
+                "Could not import boto3 python package. "
+                "Please install it with `pip install boto3`."
+            )
+        return values
+
+    def score(self, text_pairs: List[Tuple[str, str]]) -> List[float]:
+        """Call out to SageMaker Inference CrossEncoder endpoint."""
+        _endpoint_kwargs = self.endpoint_kwargs or {}
+
+        body = self.content_handler.transform_input(text_pairs)
+        content_type = self.content_handler.content_type
+        accepts = self.content_handler.accepts
+
+        # send request
+        try:
+            response = self.client.invoke_endpoint(
+                EndpointName=self.endpoint_name,
+                Body=body,
+                ContentType=content_type,
+                Accept=accepts,
+                **_endpoint_kwargs,
+            )
+        except Exception as e:
+            raise ValueError(f"Error raised by inference endpoint: {e}")
+
+        return self.content_handler.transform_output(response["Body"])