community[patch]: Fixing some caching issues for AzureCosmosDBSemanticCache (#18884)

Fixing some issues for AzureCosmosDBSemanticCache
- Added the entry for "AzureCosmosDBSemanticCache" which was missing in
langchain/cache.py
- Added application name when creating the MongoClient for the
AzureCosmosDBVectorSearch, for tracking purposes.

@baskaryan, can you please review this PR, we need this to go in asap.
These are just small fixes which we found today in our testing.

libs/community/langchain_community/cache.py

@@ -1879,6 +1879,7 @@ class AzureCosmosDBSemanticCache(BaseCache):
         ef_construction: int = 64,
         ef_search: int = 40,
         score_threshold: Optional[float] = None,
+        application_name: str = "LANGCHAIN_CACHING_PYTHON",
     ):
         """
         Args:
@@ -1920,6 +1921,7 @@ class AzureCosmosDBSemanticCache(BaseCache):
                        (40 by default). A higher value provides better
                        recall at the cost of speed.
             score_threshold: Maximum score used to filter the vector search documents.
+            application_name: Application name for the client for tracking and logging
         """
 
         self._validate_enum_value(similarity, CosmosDBSimilarityType)
@@ -1942,6 +1944,7 @@ class AzureCosmosDBSemanticCache(BaseCache):
         self.ef_search = ef_search
         self.score_threshold = score_threshold
         self._cache_dict: Dict[str, AzureCosmosDBVectorSearch] = {}
+        self.application_name = application_name
 
     def _index_name(self, llm_string: str) -> str:
         hashed_index = _hash(llm_string)
@@ -1972,6 +1975,7 @@ class AzureCosmosDBSemanticCache(BaseCache):
                 namespace=namespace,
                 embedding=self.embedding,
                 index_name=index_name,
+                application_name=self.application_name,
             )
 
         # create index for the vectorstore