fix: use lazy %s formatting in all logger calls for consistent deferred evaluation

autogpt_platform/backend/backend/api/features/store/content_handlers.py

@@ -179,7 +179,7 @@ def _get_enabled_blocks() -> dict[str, AnyBlockSchema]:
         try:
             instance = block_cls()
         except Exception as e:
-            logger.warning(f"Skipping block {block_id}: init failed: {e}")
+            logger.warning("Skipping block %s: init failed: %s", block_id, e)
             continue
         if not instance.disabled:
             enabled[block_id] = instance
@@ -227,7 +227,8 @@ class BlockHandler(ContentHandler):
                 # Build searchable text from block metadata
                 if not block.name:
                     logger.warning(
-                        f"Block {block_id} has no name — using block_id as fallback"
+                        "Block %s has no name — using block_id as fallback",
+                        block_id,
                     )
                 display_name = split_camelcase(block.name) if block.name else ""
                 parts = []
@@ -282,7 +283,7 @@ class BlockHandler(ContentHandler):
                     )
                 )
             except Exception as e:
-                logger.warning(f"Failed to process block {block_id}: {e}")
+                logger.warning("Failed to process block %s: %s", block_id, e)
                 continue
 
         return items
@@ -367,7 +368,7 @@ class DocumentationHandler(ContentHandler):
             # If no title found, use filename
             return file_path.stem.replace("-", " ").replace("_", " ").title()
         except Exception as e:
-            logger.warning(f"Failed to read title from {file_path}: {e}")
+            logger.warning("Failed to read title from %s: %s", file_path, e)
             return file_path.stem.replace("-", " ").replace("_", " ").title()
 
     def _chunk_markdown_by_headings(
@@ -387,7 +388,7 @@ class DocumentationHandler(ContentHandler):
         try:
             content = file_path.read_text(encoding="utf-8")
         except Exception as e:
-            logger.warning(f"Failed to read {file_path}: {e}")
+            logger.warning("Failed to read %s: %s", file_path, e)
             return []
 
         lines = content.split("\n")
@@ -512,7 +513,7 @@ class DocumentationHandler(ContentHandler):
         docs_root = self._get_docs_root()
 
         if not docs_root.exists():
-            logger.warning(f"Documentation root not found: {docs_root}")
+            logger.warning("Documentation root not found: %s", docs_root)
             return []
 
         # Find all .md and .mdx files
@@ -588,7 +589,7 @@ class DocumentationHandler(ContentHandler):
                     )
                 )
             except Exception as e:
-                logger.warning(f"Failed to process section {content_id}: {e}")
+                logger.warning("Failed to process section %s: %s", content_id, e)
                 continue
 
         return items

autogpt_platform/backend/backend/api/features/store/hybrid_search.py

@@ -190,8 +190,9 @@ async def unified_hybrid_search(
         query_embedding = await embed_query(query)
     except Exception as e:
         logger.warning(
-            f"Failed to generate query embedding - falling back to lexical-only search: {e}. "
-            "Check that openai_internal_api_key is configured and OpenAI API is accessible."
+            "Failed to generate query embedding - falling back to lexical-only search: %s. "
+            "Check that openai_internal_api_key is configured and OpenAI API is accessible.",
+            e,
         )
         query_embedding = [0.0] * EMBEDDING_DIM
         # Redistribute semantic weight to lexical
@@ -382,7 +383,7 @@ async def unified_hybrid_search(
     for result in results:
         result.pop("total_count", None)
 
-    logger.info(f"Unified hybrid search: {len(results)} results, {total} total")
+    logger.info("Unified hybrid search: %d results, %d total", len(results), total)
 
     return results, total
 
@@ -471,7 +472,8 @@ async def hybrid_search(
         query_embedding = await embed_query(query)
     except Exception as e:
         logger.warning(
-            f"Failed to generate query embedding - falling back to lexical-only search: {e}"
+            "Failed to generate query embedding - falling back to lexical-only search: %s",
+            e,
         )
         query_embedding = [0.0] * EMBEDDING_DIM
         total_non_semantic = (
@@ -711,7 +713,9 @@ async def hybrid_search(
         result.pop("total_count", None)
         result.pop("searchable_text", None)
 
-    logger.info(f"Hybrid search (store agents): {len(results)} results, {total} total")
+    logger.info(
+        "Hybrid search (store agents): %d results, %d total", len(results), total
+    )
 
     return results, total
 
@@ -795,15 +799,20 @@ async def _log_vector_error_diagnostics(error: Exception) -> None:
             diagnostics["pgvector_extension"] = f"Error: {e}"
 
         logger.error(
-            f"Vector type error diagnostics:\n"
-            f"  Error: {error}\n"
-            f"  search_path: {diagnostics.get('search_path')}\n"
-            f"  current_schema: {diagnostics.get('current_schema')}\n"
-            f"  user_info: {diagnostics.get('user_info')}\n"
-            f"  pgvector_extension: {diagnostics.get('pgvector_extension')}"
+            "Vector type error diagnostics:\n"
+            "  Error: %s\n"
+            "  search_path: %s\n"
+            "  current_schema: %s\n"
+            "  user_info: %s\n"
+            "  pgvector_extension: %s",
+            error,
+            diagnostics.get("search_path"),
+            diagnostics.get("current_schema"),
+            diagnostics.get("user_info"),
+            diagnostics.get("pgvector_extension"),
         )
     except Exception as diag_error:
-        logger.error(f"Failed to collect vector error diagnostics: {diag_error}")
+        logger.error("Failed to collect vector error diagnostics: %s", diag_error)
 
 
 # Backward compatibility alias - HybridSearchWeights maps to StoreAgentSearchWeights