community: Fixing a performance issue with AzureSearch to perform batch embedding (#15594)

- **Description:** Azure Cognitive Search vector DB store performs slow
embedding as it does not utilize the batch embedding functionality. This
PR provide a fix to improve the performance of Azure Search class when
adding documents to the vector search,
  - **Issue:** #11313 ,
  - **Dependencies:** any dependencies required for this change,
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libs/community/langchain_community/vectorstores/azuresearch.py

@@ -248,7 +248,7 @@ class AzureSearch(VectorStore):
         azure_search_endpoint: str,
         azure_search_key: str,
         index_name: str,
-        embedding_function: Callable,
+        embedding_function: Union[Callable, Embeddings],
         search_type: str = "hybrid",
         semantic_configuration_name: Optional[str] = None,
         semantic_query_language: str = "en-us",
@@ -270,6 +270,12 @@ class AzureSearch(VectorStore):
         """Initialize with necessary components."""
         # Initialize base class
         self.embedding_function = embedding_function
+
+        if isinstance(self.embedding_function, Embeddings):
+            self.embed_query = self.embedding_function.embed_query
+        else:
+            self.embed_query = self.embedding_function
+
         default_fields = [
             SimpleField(
                 name=FIELDS_ID,
@@ -285,7 +291,7 @@ class AzureSearch(VectorStore):
                 name=FIELDS_CONTENT_VECTOR,
                 type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
                 searchable=True,
-                vector_search_dimensions=len(embedding_function("Text")),
+                vector_search_dimensions=len(self.embed_query("Text")),
                 vector_search_configuration="default",
             ),
             SearchableField(
@@ -329,6 +335,20 @@ class AzureSearch(VectorStore):
         """Add texts data to an existing index."""
         keys = kwargs.get("keys")
         ids = []
+
+        # batching support if embedding function is an Embeddings object
+        if isinstance(self.embedding_function, Embeddings):
+            try:
+                embeddings = self.embedding_function.embed_documents(texts)
+            except NotImplementedError:
+                embeddings = [self.embedding_function.embed_query(x) for x in texts]
+        else:
+            embeddings = [self.embedding_function(x) for x in texts]
+
+        if len(embeddings) == 0:
+            logger.debug("Nothing to insert, skipping.")
+            return []
+
         # Write data to index
         data = []
         for i, text in enumerate(texts):
@@ -344,7 +364,7 @@ class AzureSearch(VectorStore):
                 FIELDS_ID: key,
                 FIELDS_CONTENT: text,
                 FIELDS_CONTENT_VECTOR: np.array(
-                    self.embedding_function(text), dtype=np.float32
+                    embeddings[i], dtype=np.float32
                 ).tolist(),
                 FIELDS_METADATA: json.dumps(metadata),
             }
@@ -437,9 +457,7 @@ class AzureSearch(VectorStore):
             search_text="",
             vectors=[
                 Vector(
-                    value=np.array(
-                        self.embedding_function(query), dtype=np.float32
-                    ).tolist(),
+                    value=np.array(self.embed_query(query), dtype=np.float32).tolist(),
                     k=k,
                     fields=FIELDS_CONTENT_VECTOR,
                 )
@@ -508,9 +526,7 @@ class AzureSearch(VectorStore):
             search_text=query,
             vectors=[
                 Vector(
-                    value=np.array(
-                        self.embedding_function(query), dtype=np.float32
-                    ).tolist(),
+                    value=np.array(self.embed_query(query), dtype=np.float32).tolist(),
                     k=k,
                     fields=FIELDS_CONTENT_VECTOR,
                 )
@@ -600,9 +616,7 @@ class AzureSearch(VectorStore):
             search_text=query,
             vectors=[
                 Vector(
-                    value=np.array(
-                        self.embedding_function(query), dtype=np.float32
-                    ).tolist(),
+                    value=np.array(self.embed_query(query), dtype=np.float32).tolist(),
                     k=50,
                     fields=FIELDS_CONTENT_VECTOR,
                 )
@@ -684,7 +698,7 @@ class AzureSearch(VectorStore):
             azure_search_endpoint,
             azure_search_key,
             index_name,
-            embedding.embed_query,
+            embedding,
         )
         azure_search.add_texts(texts, metadatas, **kwargs)
         return azure_search