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src/knowledge_search_mcp/clients/vector_search.py

@@ -1,4 +1,3 @@
-# ruff: noqa: INP001
 """Google Cloud Vector Search client."""
 
 import asyncio
@@ -6,8 +5,9 @@ from collections.abc import Sequence
 
 from gcloud.aio.auth import Token
 
-from ..logging import log_structured_entry
-from ..models import SearchResult, SourceNamespace
+from knowledge_search_mcp.logging import log_structured_entry
+from knowledge_search_mcp.models import SearchResult, SourceNamespace
+
 from .base import BaseGoogleCloudClient
 from .storage import GoogleCloudFileStorage
 
@@ -94,7 +94,7 @@ class GoogleCloudVectorSearch(BaseGoogleCloudClient):
             log_structured_entry(
                 "Vector search query failed - endpoint not configured",
                 "ERROR",
-                {"error": msg}
+                {"error": msg},
             )
             raise RuntimeError(msg)
 
@@ -113,8 +113,8 @@ class GoogleCloudVectorSearch(BaseGoogleCloudClient):
                 "deployed_index_id": deployed_index_id,
                 "neighbor_count": limit,
                 "endpoint_id": endpoint_id,
-                "embedding_dimension": len(query)
-            }
+                "embedding_dimension": len(query),
+            },
         )
 
         datapoint: dict = {"feature_vector": list(query)}
@@ -149,10 +149,10 @@ class GoogleCloudVectorSearch(BaseGoogleCloudClient):
                         {
                             "status": response.status,
                             "response_body": body,
-                            "deployed_index_id": deployed_index_id
-                        }
+                            "deployed_index_id": deployed_index_id,
+                        },
                     )
-                    raise RuntimeError(msg)
+                    raise RuntimeError(msg)  # noqa: TRY301
                 data = await response.json()
 
             neighbors = data.get("nearestNeighbors", [{}])[0].get("neighbors", [])
@@ -161,15 +161,15 @@ class GoogleCloudVectorSearch(BaseGoogleCloudClient):
                 "INFO",
                 {
                     "neighbors_found": len(neighbors),
-                    "deployed_index_id": deployed_index_id
-                }
+                    "deployed_index_id": deployed_index_id,
+                },
             )
 
             if not neighbors:
                 log_structured_entry(
                     "No neighbors found in vector search",
                     "WARNING",
-                    {"deployed_index_id": deployed_index_id}
+                    {"deployed_index_id": deployed_index_id},
                 )
                 return []
 
@@ -185,7 +185,7 @@ class GoogleCloudVectorSearch(BaseGoogleCloudClient):
             log_structured_entry(
                 "Fetching content for search results",
                 "INFO",
-                {"file_count": len(content_tasks)}
+                {"file_count": len(content_tasks)},
             )
 
             file_streams = await asyncio.gather(*content_tasks)
@@ -206,12 +206,9 @@ class GoogleCloudVectorSearch(BaseGoogleCloudClient):
             log_structured_entry(
                 "Vector search completed successfully",
                 "INFO",
-                {
-                    "results_count": len(results),
-                    "deployed_index_id": deployed_index_id
-                }
+                {"results_count": len(results), "deployed_index_id": deployed_index_id},
             )
-            return results
+            return results  # noqa: TRY300
 
         except Exception as e:
             log_structured_entry(
@@ -220,7 +217,7 @@ class GoogleCloudVectorSearch(BaseGoogleCloudClient):
                 {
                     "error": str(e),
                     "error_type": type(e).__name__,
-                    "deployed_index_id": deployed_index_id
-                }
+                    "deployed_index_id": deployed_index_id,
+                },
             )
             raise