diff --git a/app/temp/qdrant_search_tool.py b/app/temp/qdrant_search_tool.py
index 66e7b5c..a2c8e32 100644
--- a/app/temp/qdrant_search_tool.py
+++ b/app/temp/qdrant_search_tool.py
@@ -54,17 +54,13 @@ class QdrantVectorSearchTool(BaseTool):
         qdrant_api_key: Authentication key for Qdrant
     """
 
-    model_config: ClassVar[dict[str, bool]] = {
-        "arbitrary_types_allowed": True
-    }  # Add ClassVar annotation
+    model_config: ClassVar[dict[str, bool]] = {"arbitrary_types_allowed": True}  # Add ClassVar annotation
     client: QdrantClient = None
     async_client: AsyncQdrantClient = None
     openai_client: Any = None  # Added for lazy initialization
     openai_async_client: Any = None  # Added for lazy initialization
     name: str = "QdrantVectorSearchTool"
-    description: str = (
-        "A tool to search the Qdrant database for relevant information on internal documents."
-    )
+    description: str = "A tool to search the Qdrant database for relevant information on internal documents."
     args_schema: type[BaseModel] = QdrantToolSchema
     query: str | None = None
     filter_by: str | None = None
@@ -146,11 +142,7 @@ def _run(
         # Create filter if filter parameters are provided
         search_filter = None
         if filter_by and filter_value:
-            search_filter = Filter(
-                must=[
-                    FieldCondition(key=filter_by, match=MatchValue(value=filter_value))
-                ]
-            )
+            search_filter = Filter(must=[FieldCondition(key=filter_by, match=MatchValue(value=filter_value))])
 
         # Search in Qdrant using the built-in query method
         query_vector = (
@@ -192,9 +184,7 @@ def _vectorize_query_sync(self, query: str, embedding_model: str) -> list[float]
         from openai import Client
 
         # Define error messages as constants
-        openai_api_key_not_set_error_msg = (
-            "OPENAI_API_KEY environment variable is not set."
-        )
+        openai_api_key_not_set_error_msg = "OPENAI_API_KEY environment variable is not set."
         # Lazy initialization of the sync client
         if not self.openai_client:
             api_key = os.getenv("OPENAI_API_KEY")
@@ -240,17 +230,11 @@ async def _arun(
         # Create filter if filter parameters are provided
         search_filter = None
         if filter_by and filter_value:
-            search_filter = Filter(
-                must=[
-                    FieldCondition(key=filter_by, match=MatchValue(value=filter_value))
-                ]
-            )
+            search_filter = Filter(must=[FieldCondition(key=filter_by, match=MatchValue(value=filter_value))])
 
         # Search in Qdrant using the built-in query method
         query_vector = (
-            await self._vectorize_query_async(
-                query, embedding_model="text-embedding-3-large"
-            )
+            await self._vectorize_query_async(query, embedding_model="text-embedding-3-large")
             if not self.custom_embedding_fn
             else self.custom_embedding_fn(query)
         )
@@ -275,9 +259,7 @@ async def _arun(
 
         return json.dumps(results, indent=2)
 
-    async def _vectorize_query_async(
-        self, query: str, embedding_model: str
-    ) -> list[float]:
+    async def _vectorize_query_async(self, query: str, embedding_model: str) -> list[float]:
         """Default async vectorization function with openai.
 
         Args:
@@ -290,9 +272,7 @@ async def _vectorize_query_async(
         from openai import AsyncClient
 
         # Define error messages as constants
-        openai_api_key_not_set_error_msg = (
-            "OPENAI_API_KEY environment variable is not set."
-        )
+        openai_api_key_not_set_error_msg = "OPENAI_API_KEY environment variable is not set."
         # Lazy initialization of the async client
         if not self.openai_async_client:
             api_key = os.getenv("OPENAI_API_KEY")