fix lint

src/codegate/pipeline/codegate_context_retriever/codegate.py

@@ -36,7 +36,8 @@ async def get_objects_from_search(self, search: str) -> list[object]:
         return objects
 
     def generate_context_str(self, objects: list[object]) -> str:
-        context_str = "Please use the information about related packages to influence your answer:\n"
+        context_str = "Please use the information about related packages "
+        "to influence your answer:\n"
         for obj in objects:
             # generate dictionary from object
             package_obj = {
@@ -66,7 +67,8 @@ async def process(
             last_user_message_str, last_user_idx = last_user_message
             if "codegate" in last_user_message_str.lower():
                 # strip codegate from prompt and trim it
-                last_user_message_str = last_user_message_str.lower().replace("codegate", "").strip()
+                last_user_message_str = last_user_message_str.lower().replace(
+                    "codegate", "").strip()
                 searched_objects = await self.get_objects_from_search(last_user_message_str)
                 context_str = self.generate_context_str(searched_objects)
                 # Add a system prompt to the completion request

src/codegate/storage/storage_engine.py

@@ -42,8 +42,10 @@ def connect(self):
     def setup_schema(self):
         for class_config in self.schema_config:
             if not self.client.collections.exists(class_config['name']):
-                self.client.collections.create(class_config['name'], properties=class_config['properties'])
-                self.__logger.info(f"Weaviate schema for class {class_config['name']} setup complete.")
+                self.client.collections.create(class_config['name'],
+                                               properties=class_config['properties'])
+                self.__logger.info(
+                    f"Weaviate schema for class {class_config['name']} setup complete.")
 
     async def search(self, query: str, limit=5, distance=0.3) -> list[object]:
         """
@@ -62,7 +64,9 @@ async def search(self, query: str, limit=5, distance=0.3) -> list[object]:
         # Perform the vector search
         try:
             collection = self.client.collections.get("Package")
-            response = collection.query.near_vector(query_vector[0], limit=limit, distance=distance, return_metadata=MetadataQuery(distance=True))
+            response = collection.query.near_vector(
+                query_vector[0], limit=limit, distance=distance,
+                return_metadata=MetadataQuery(distance=True))
             if not response:
                 return []
             return response.objects

tests/test_storage.py

@@ -24,7 +24,8 @@ def mock_logger():
 @pytest.fixture
 def mock_inference_engine():
     inference_engine = AsyncMock()
-    inference_engine.embed = AsyncMock(return_value=[0.1, 0.2, 0.3])  # Adjust based on expected vector dimensions
+    inference_engine.embed = AsyncMock(
+        return_value=[0.1, 0.2, 0.3])  # Adjust based on expected vector dimensions
     return inference_engine
 
 
@@ -47,7 +48,8 @@ def test_connect(storage_engine, mock_client):
 async def test_search(storage_engine, mock_client):
     query = "test query"
     results = await storage_engine.search(query)
-    storage_engine.inference_engine.embed.assert_called_once_with("./models/all-minilm-L6-v2-q5_k_m.gguf", [query])
+    storage_engine.inference_engine.embed.assert_called_once_with(
+        "./models/all-minilm-L6-v2-q5_k_m.gguf", [query])
     assert results is not None  # Further asserts can be based on your application logic