Add CI

src/knowledge_search_mcp/__main__.py

@@ -1,4 +1,3 @@
-# ruff: noqa: INP001
 """MCP server for semantic search over Vertex AI Vector Search."""
 
 import time
@@ -9,7 +8,11 @@ from .config import _args
 from .logging import log_structured_entry
 from .models import AppContext, SourceNamespace
 from .server import lifespan
-from .services.search import filter_search_results, format_search_results, generate_query_embedding
+from .services.search import (
+    filter_search_results,
+    format_search_results,
+    generate_query_embedding,
+)
 
 mcp = FastMCP(
     "knowledge-search",
@@ -44,7 +47,7 @@ async def knowledge_search(
     log_structured_entry(
         "knowledge_search request received",
         "INFO",
-        {"query": query[:100]}  # Log first 100 chars of query
+        {"query": query[:100]},  # Log first 100 chars of query
     )
 
     try:
@@ -61,7 +64,7 @@ async def knowledge_search(
         log_structured_entry(
             "Query embedding generated successfully",
             "INFO",
-            {"time_ms": round((t_embed - t0) * 1000, 1)}
+            {"time_ms": round((t_embed - t0) * 1000, 1)},
         )
 
         # Perform vector search
@@ -74,17 +77,13 @@ async def knowledge_search(
                 source=source,
             )
             t_search = time.perf_counter()
-        except Exception as e:
+        except Exception as e:  # noqa: BLE001
             log_structured_entry(
                 "Vector search failed",
                 "ERROR",
-                {
-                    "error": str(e),
-                    "error_type": type(e).__name__,
-                    "query": query[:100]
-                }
+                {"error": str(e), "error_type": type(e).__name__, "query": query[:100]},
             )
-            return f"Error performing vector search: {str(e)}"
+            return f"Error performing vector search: {e!s}"
 
         # Apply similarity filtering
         filtered_results = filter_search_results(search_results)
@@ -98,32 +97,26 @@ async def knowledge_search(
                 "total_ms": f"{round((t_search - t0) * 1000, 1)}ms",
                 "source_filter": source.value if source is not None else None,
                 "results_count": len(filtered_results),
-                "chunks": [s["id"] for s in filtered_results]
-            }
+                "chunks": [s["id"] for s in filtered_results],
+            },
         )
 
         # Format and return results
         if not filtered_results:
             log_structured_entry(
-                "No results found for query",
-                "INFO",
-                {"query": query[:100]}
+                "No results found for query", "INFO", {"query": query[:100]}
             )
 
         return format_search_results(filtered_results)
 
-    except Exception as e:
+    except Exception as e:  # noqa: BLE001
         # Catch-all for any unexpected errors
         log_structured_entry(
             "Unexpected error in knowledge_search",
             "ERROR",
-            {
-                "error": str(e),
-                "error_type": type(e).__name__,
-                "query": query[:100]
-            }
+            {"error": str(e), "error_type": type(e).__name__, "query": query[:100]},
         )
-        return f"Unexpected error during search: {str(e)}"
+        return f"Unexpected error during search: {e!s}"
 
 
 def main() -> None: