Improve MongoDB vector index handling with workspace support

- Add workspace-specific index naming
- Store index name as instance variable

lightrag/kg/mongo_impl.py

@@ -1318,6 +1318,7 @@ class MongoGraphStorage(BaseGraphStorage):
 class MongoVectorDBStorage(BaseVectorStorage):
     db: AsyncDatabase | None = field(default=None)
     _data: AsyncCollection | None = field(default=None)
+    _index_name: str = field(default="", init=False)
 
     def __init__(
         self, namespace, global_config, embedding_func, workspace=None, meta_fields=None
@@ -1355,6 +1356,14 @@ class MongoVectorDBStorage(BaseVectorStorage):
             logger.debug(f"Final namespace with workspace prefix: '{self.namespace}'")
         # When workspace is empty, keep the original namespace unchanged
 
+        # Set index name based on workspace for backward compatibility
+        if effective_workspace:
+            # Use collection-specific index name for workspaced collections to avoid conflicts
+            self._index_name = f"vector_knn_index_{self.namespace}"
+        else:
+            # Keep original index name for backward compatibility with existing deployments
+            self._index_name = "vector_knn_index"
+
         kwargs = self.global_config.get("vector_db_storage_cls_kwargs", {})
         cosine_threshold = kwargs.get("cosine_better_than_threshold")
         if cosine_threshold is None:
@@ -1384,13 +1393,11 @@ class MongoVectorDBStorage(BaseVectorStorage):
     async def create_vector_index_if_not_exists(self):
         """Creates an Atlas Vector Search index."""
         try:
-            index_name = "vector_knn_index"
-
             indexes_cursor = await self._data.list_search_indexes()
             indexes = await indexes_cursor.to_list(length=None)
             for index in indexes:
-                if index["name"] == index_name:
+                if index["name"] == self._index_name:
-                    logger.debug("vector index already exist")
+                    logger.info(f"vector index {self._index_name} already exist")
                     return
 
             search_index_model = SearchIndexModel(
@@ -1404,15 +1411,15 @@ class MongoVectorDBStorage(BaseVectorStorage):
                         }
                     ]
                 },
-                name=index_name,
+                name=self._index_name,
                 type="vectorSearch",
             )
 
             await self._data.create_search_index(search_index_model)
-            logger.info("Vector index created successfully.")
+            logger.info(f"Vector index {self._index_name} created successfully.")
 
-        except PyMongoError as _:
+        except PyMongoError as e:
-            logger.debug("vector index already exist")
+            logger.error(f"Error creating vector index {self._index_name}: {e}")
 
     async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
         logger.debug(f"Inserting {len(data)} to {self.namespace}")
@@ -1467,7 +1474,7 @@ class MongoVectorDBStorage(BaseVectorStorage):
         pipeline = [
             {
                 "$vectorSearch": {
-                    "index": "vector_knn_index",  # Ensure this matches the created index name
+                    "index": self._index_name,  # Use stored index name for consistency
                     "path": "vector",
                     "queryVector": query_vector,
                     "numCandidates": 100,  # Adjust for performance