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/huggingface.py

@@ -0,0 +1,63 @@
+from typing import Any, Dict, List, Tuple
+
+from langchain_core.pydantic_v1 import BaseModel, Extra, Field
+
+from langchain_community.cross_encoders.base import BaseCrossEncoder
+
+DEFAULT_MODEL_NAME = "BAAI/bge-reranker-base"
+
+
+class HuggingFaceCrossEncoder(BaseModel, BaseCrossEncoder):
+    """HuggingFace cross encoder models.
+
+    Example:
+        .. code-block:: python
+
+            from langchain_community.cross_encoders import HuggingFaceCrossEncoder
+
+            model_name = "BAAI/bge-reranker-base"
+            model_kwargs = {'device': 'cpu'}
+            hf = HuggingFaceCrossEncoder(
+                model_name=model_name,
+                model_kwargs=model_kwargs
+            )
+    """
+
+    client: Any  #: :meta private:
+    model_name: str = DEFAULT_MODEL_NAME
+    """Model name to use."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Keyword arguments to pass to the model."""
+
+    def __init__(self, **kwargs: Any):
+        """Initialize the sentence_transformer."""
+        super().__init__(**kwargs)
+        try:
+            import sentence_transformers
+
+        except ImportError as exc:
+            raise ImportError(
+                "Could not import sentence_transformers python package. "
+                "Please install it with `pip install sentence-transformers`."
+            ) from exc
+
+        self.client = sentence_transformers.CrossEncoder(
+            self.model_name, **self.model_kwargs
+        )
+
+    class Config:
+        """Configuration for this pydantic object."""
+
+        extra = Extra.forbid
+
+    def score(self, text_pairs: List[Tuple[str, str]]) -> List[float]:
+        """Compute similarity scores using a HuggingFace transformer model.
+
+        Args:
+            text_pairs: The list of text text_pairs to score the similarity.
+
+        Returns:
+            List of scores, one for each pair.
+        """
+        scores = self.client.predict(text_pairs)
+        return scores