refactor: Improve code readability by formatting long lines and updating subprocess path

Author: Programmer-RD-AI

app/temp/qdrant_search_tool.py

@@ -56,7 +56,9 @@ class QdrantVectorSearchTool(BaseTool):
     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
@@ -97,7 +99,7 @@ def __init__(self, **kwargs):
             ):
                 import subprocess
 
-                subprocess.run(["uv", "add", "qdrant-client"], check=True)
+                subprocess.run(["/usr/bin/uv", "add", "qdrant-client"], check=True)
             else:
                 raise ImportError(
                     "The 'qdrant-client' package is required to use the QdrantVectorSearchTool. "
@@ -130,7 +132,11 @@ 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 = (
@@ -214,11 +220,17 @@ 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)
         )
@@ -243,7 +255,9 @@ 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: