x

libs/langchain/langchain/evaluation/embedding_distance/base.py

@@ -14,7 +14,6 @@ from langchain_core.pydantic_v1 import Field, root_validator
 from langchain.chains.base import Chain
 from langchain.evaluation.schema import PairwiseStringEvaluator, StringEvaluator
 from langchain.schema import RUN_KEY
-from langchain_community.utils.math import cosine_similarity
 
 
 def _embedding_factory() -> Embeddings:
@@ -164,7 +163,14 @@ class _EmbeddingDistanceChainMixin(Chain):
         Returns:
             np.ndarray: The cosine distance.
         """
-
+        try:
+            from langchain_community.utils.math import cosine_similarity
+        except ImportError:
+            raise ImportError(
+                "The cosine_similarity function is required to compute cosine distance."
+                " Please install the langchain-community package using"
+                " `pip install langchain-community`."
+            )
         return 1.0 - cosine_similarity(a, b)
 
     @staticmethod

libs/langchain/langchain/retrievers/document_compressors/embeddings_filter.py

@@ -4,12 +4,22 @@ import numpy as np
 from langchain_core.callbacks.manager import Callbacks
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
-from langchain_core.pydantic_v1 import root_validator
+from langchain_core.pydantic_v1 import root_validator, Field
 
 from langchain.retrievers.document_compressors.base import (
     BaseDocumentCompressor,
 )
-from langchain_community.utils.math import cosine_similarity
+
+
+def _get_similarity_function() -> Callable:
+    try:
+        from langchain_community.utils.math import cosine_similarity
+    except ImportError:
+        raise ImportError(
+            "To use please install langchain-community "
+            "with `pip install langchain-community`."
+        )
+    return cosine_similarity
 
 
 class EmbeddingsFilter(BaseDocumentCompressor):
@@ -18,7 +28,7 @@ class EmbeddingsFilter(BaseDocumentCompressor):
 
     embeddings: Embeddings
     """Embeddings to use for embedding document contents and queries."""
-    similarity_fn: Callable = cosine_similarity
+    similarity_fn: Callable = Field(default_factory=_get_similarity_function)
     """Similarity function for comparing documents. Function expected to take as input
     two matrices (List[List[float]]) and return a matrix of scores where higher values
     indicate greater similarity."""

libs/langchain/langchain/retrievers/pupmed.py

@@ -1,8 +1,9 @@
 from typing import TYPE_CHECKING, Any
 
-from langchain._api import create_importer
 from langchain_community.retrievers import PubMedRetriever
 
+from langchain._api import create_importer
+
 if TYPE_CHECKING:
     from langchain_community.retrievers import PubMedRetriever