Retrieval and also update on russian language

services/rag/llamaindex/vector_storage.py

@@ -57,7 +57,7 @@ def initialize_vector_storage(
             base_url=ollama_base_url
         )
         # Get a test embedding to determine the correct size
-        test_embedding = embed_model.get_query_embedding("test")
+        test_embedding = embed_model.get_text_embedding("test")
         embedding_dimension = len(test_embedding)
         logger.info(f"Detected embedding dimension: {embedding_dimension}")
 
@@ -152,9 +152,9 @@ def get_vector_store_and_index() -> tuple[QdrantVectorStore, VectorStoreIndex]:
     Returns:
         Tuple of (QdrantVectorStore, VectorStoreIndex)
     """
-    # Get the embedding model from environment variables
-    embed_model = os.getenv("OLLAMA_EMBEDDING_MODEL", "qwen3-embedding:4b")
-    return initialize_vector_storage(ollama_embed_model=embed_model)
+    # Get the embedding model name from environment variables
+    embed_model_name = os.getenv("OLLAMA_EMBEDDING_MODEL", "qwen3-embedding:4b")
+    return initialize_vector_storage(ollama_embed_model=embed_model_name)
 
 
 if __name__ == "__main__":