diff --git a/graphrag/index/operations/embed_text/embed_text.py b/graphrag/index/operations/embed_text/embed_text.py
index 96e6111e9e..93813cb391 100644
--- a/graphrag/index/operations/embed_text/embed_text.py
+++ b/graphrag/index/operations/embed_text/embed_text.py
@@ -12,7 +12,7 @@
 
 from graphrag.cache.pipeline_cache import PipelineCache
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.embeddings import create_collection_name
+from graphrag.utils.api import get_collection_name
 from graphrag.index.operations.embed_text.strategies.typing import TextEmbeddingStrategy
 from graphrag.vector_stores.base import BaseVectorStore, VectorStoreDocument
 from graphrag.vector_stores.factory import VectorStoreFactory
@@ -49,7 +49,7 @@ async def embed_text(
     vector_store_config = strategy.get("vector_store")
 
     if vector_store_config:
-        collection_name = _get_collection_name(vector_store_config, embedding_name)
+        collection_name = get_collection_name(vector_store_config, embedding_name)
         vector_store: BaseVectorStore = _create_vector_store(
             vector_store_config, collection_name
         )
@@ -197,15 +197,6 @@ def _create_vector_store(
     return vector_store
 
 
-def _get_collection_name(vector_store_config: dict, embedding_name: str) -> str:
-    container_name = vector_store_config.get("container_name", "default")
-    collection_name = create_collection_name(container_name, embedding_name)
-
-    msg = f"using vector store {vector_store_config.get('type')} with container_name {container_name} for embedding {embedding_name}: {collection_name}"
-    logger.info(msg)
-    return collection_name
-
-
 def load_strategy(strategy: TextEmbedStrategyType) -> TextEmbeddingStrategy:
     """Load strategy method definition."""
     match strategy:
diff --git a/graphrag/utils/api.py b/graphrag/utils/api.py
index 484a279b1f..e5aa1a94aa 100644
--- a/graphrag/utils/api.py
+++ b/graphrag/utils/api.py
@@ -20,6 +20,8 @@
     VectorStoreSearchResult,
 )
 from graphrag.vector_stores.factory import VectorStoreFactory
+import logging
+logger = logging.getLogger(__name__)
 
 
 class MultiVectorStore(BaseVectorStore):
@@ -103,9 +105,7 @@ def get_embedding_store(
     index_names = []
     for index, store in config_args.items():
         vector_store_type = store["type"]
-        collection_name = create_collection_name(
-            store.get("container_name", "default"), embedding_name
-        )
+        collection_name = get_collection_name(store, embedding_name)
         embedding_store = VectorStoreFactory().create_vector_store(
             vector_store_type=vector_store_type,
             kwargs={**store, "collection_name": collection_name},
@@ -119,6 +119,21 @@ def get_embedding_store(
     return MultiVectorStore(embedding_stores, index_names)
 
 
+def get_collection_name(vector_store_config: dict, embedding_name: str) -> str:
+    collection_name = vector_store_config.get("collection_name")
+    if collection_name:
+        msg = f"using vector store {vector_store_config.get('type')} with user provided collection_name {collection_name} for embedding {embedding_name}"
+        logger.info(msg)
+        return collection_name
+
+    container_name = vector_store_config.get("container_name", "default")
+    collection_name = create_collection_name(container_name, embedding_name)
+
+    msg = f"using vector store {vector_store_config.get('type')} with container_name {container_name} for embedding {embedding_name}: {collection_name}"
+    logger.info(msg)
+    return collection_name
+
+
 def reformat_context_data(context_data: dict) -> dict:
     """
     Reformats context_data for all query responses.