Fix tests

infra_ai_service/api/ai_enhance/embedding.py

@@ -17,4 +17,4 @@ async def embed_text(input_data: TextInput):
 
 @router.get("/status/")
 async def status():
-    return await get_collection_status(collection_name)
+    return await get_collection_status()

infra_ai_service/api/ai_enhance/vector_search.py

@@ -5,6 +5,7 @@
 
 router = APIRouter()
 
+
 @router.post("/query/", response_model=SearchOutput)
 async def vector_search(input_data: SearchInput):
     return await perform_vector_search(input_data)

infra_ai_service/service/embedding_service.py

@@ -27,20 +27,21 @@ async def create_embedding(content):
             ]
         )
 
-        return EmbeddingOutput(id=point_id, embedding=embedding_vector.tolist())
+        return EmbeddingOutput(id=point_id,
+                               embedding=embedding_vector.tolist())
     except Exception as e:
         raise HTTPException(status_code=400,
                             detail=f"Error processing embedding: {e}")
 
 
-async def get_collection_status(collection_name):
+async def get_collection_status():
     try:
         collection_info = qdrant_client.get_collection(collection_name)
         return {
             "collection_name": collection_name,
             "vectors_count": collection_info.vectors_count,
-            "status": "ready" if collection_info.status == "green" else "not "
-                                                                        "ready"
+            "status": "ready" if collection_info.status == "green"
+            else "not ready"
         }
     except Exception as e:
         raise HTTPException(status_code=400,

infra_ai_service/service/search_service.py

@@ -1,7 +1,8 @@
 from fastapi import HTTPException
 import logging
 
-from infra_ai_service.model.model import SearchOutput, SearchResult, SearchInput
+from infra_ai_service.model.model import SearchOutput, SearchResult, \
+    SearchInput
 from infra_ai_service.sdk.qdrant import qdrant_client, collection_name, \
     fastembed_model
 
@@ -48,4 +49,5 @@ async def perform_vector_search(input_data: SearchInput):
         logger.error(f"Error performing vector search: {str(e)}",
                      exc_info=True)
         raise HTTPException(status_code=500,
-                            detail=f"Error performing vector search: {str(e)}")
+                            detail=f"Error performing vector search: "
+                                   f"{str(e)}")