fix(ci): ruff lint + format 누적 에러 78개 해결

- examples/xgen_workflow_gateway.py: SyntaxError 수정 (**kwargs 뒤 일반 param)
- ruff --fix로 36개 자동 수정 (F401 unused-import, F541 f-string, I001 imports)
- E402: pytest.importorskip 패턴에 # noqa: E402 추가 (5개)
- F841 unused-var 5개 + F811 중복 함수 1개 정리
- E501 line-too-long: 라이브러리 코드는 줄바꿈, test fixture는 file-level noqa
- ruff format 적용 (16개 파일, deterministic 재포매팅)

CI lint job이 5개 commit 동안 누적 fail 상태였음 — 이번 README 변경 PR이 그 사실을 표면화. test job은 정상 통과해 왔음.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

Author: SonAIengine

benchmarks/run_competitive.py

@@ -15,7 +15,7 @@
 import argparse
 import json
 import time
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from datetime import datetime, timezone
 
 from benchmarks.config import DATASET_REGISTRY
@@ -214,7 +214,10 @@ def print_comparison(
 def main() -> None:
     parser = argparse.ArgumentParser(description="Competitive retrieval benchmark")
     parser.add_argument(
-        "--datasets", "-d", nargs="+", default=None,
+        "--datasets",
+        "-d",
+        nargs="+",
+        default=None,
         help="Datasets to benchmark (default: all non-legacy)",
     )
     parser.add_argument("--top-k", type=int, default=5)
@@ -223,20 +226,15 @@ def main() -> None:
     parser.add_argument("--save", action="store_true", help="Save results as JSON")
     args = parser.parse_args()
 
-    dataset_names = args.datasets or [
-        k for k, v in DATASET_REGISTRY.items() if not v.get("legacy")
-    ]
+    dataset_names = args.datasets or [k for k, v in DATASET_REGISTRY.items() if not v.get("legacy")]
 
-    active_strategies = [
-        s for s in STRATEGIES
-        if not (args.no_embedding and s.embedding)
-    ]
+    active_strategies = [s for s in STRATEGIES if not (args.no_embedding and s.embedding)]
 
-    print(f"\n  Competitive Retrieval Benchmark")
+    print("\n  Competitive Retrieval Benchmark")
     print(f"  Strategies: {len(active_strategies)}")
     print(f"  Datasets: {len(dataset_names)}")
     if any(s.embedding for s in active_strategies):
-        print(f"  Embedding: ollama/qwen3-embedding:0.6b")
+        print("  Embedding: ollama/qwen3-embedding:0.6b")
     print()
 
     all_results: dict[str, dict[str, StrategyResult]] = {}
@@ -257,15 +255,21 @@ def main() -> None:
         for strategy in active_strategies:
             print(f"    → {strategy.label}...", end="", flush=True)
             result = run_strategy(
-                tg_base, strategy, gt["queries"],
-                top_k=args.top_k, verbose=args.verbose,
+                tg_base,
+                strategy,
+                gt["queries"],
+                top_k=args.top_k,
+                verbose=args.verbose,
             )
             ds_results[strategy.name] = result
             print(f" Recall={result.recall_5:.1%} MRR={result.mrr:.3f}")
 
         print_comparison(
-            gt["name"], gt.get("tool_count", len(tg_base.tools)),
-            len(gt["queries"]), ds_results, active_strategies,
+            gt["name"],
+            gt.get("tool_count", len(tg_base.tools)),
+            len(gt["queries"]),
+            ds_results,
+            active_strategies,
         )
         all_results[ds_name] = ds_results
 

examples/test_bonsai_tool_calling.py

@@ -5,6 +5,7 @@
 2. OpenAI function calling format (tools parameter)
 3. graph-tool-call retrieve + LLM 조합
 """
+# ruff: noqa: E501
 
 from __future__ import annotations
 
@@ -128,9 +129,9 @@ def chat(messages: list[dict], tools: list[dict] | None = None, **kwargs) -> dic
 
 
 def print_header(title: str) -> None:
-    print(f"\n{'='*60}")
+    print(f"\n{'=' * 60}")
     print(f"  {title}")
-    print(f"{'='*60}")
+    print(f"{'=' * 60}")
 
 
 def print_result(label: str, passed: bool, detail: str = "") -> None:
@@ -143,6 +144,7 @@ def print_result(label: str, passed: bool, detail: str = "") -> None:
 
 # ── TEST 1: SearchLLM 통합 ──────────────────────────────────────────
 
+
 def test_search_llm():
     """graph-tool-call의 OpenAICompatibleSearchLLM으로 query expansion 테스트."""
     print_header("TEST 1: SearchLLM Query Expansion & Intent Decomposition")
@@ -195,6 +197,7 @@ def test_search_llm():
 
 # ── TEST 2: OpenAI Function Calling ─────────────────────────────────
 
+
 def test_function_calling():
     """직접 tool calling format으로 호출하여 도구 선택 능력 테스트."""
     print_header("TEST 2: OpenAI Function Calling Format")
@@ -256,7 +259,11 @@ def test_function_calling():
                     func_name = called.get("function", {}).get("name", "")
                     func_args_raw = called.get("function", {}).get("arguments", "{}")
                     try:
-                        func_args = json.loads(func_args_raw) if isinstance(func_args_raw, str) else func_args_raw
+                        func_args = (
+                            json.loads(func_args_raw)
+                            if isinstance(func_args_raw, str)
+                            else func_args_raw
+                        )
                     except json.JSONDecodeError:
                         func_args = {}
 
@@ -283,6 +290,7 @@ def test_function_calling():
 
 # ── TEST 3: graph-tool-call retrieve + LLM 조합 ─────────────────────
 
+
 def test_retrieve_with_llm():
     """ToolGraph retrieve 후 LLM에게 도구 선택시키는 end-to-end 테스트."""
     print_header("TEST 3: ToolGraph Retrieve + LLM Tool Selection (E2E)")
@@ -344,7 +352,11 @@ def test_retrieve_with_llm():
                 func_name = tool_calls[0].get("function", {}).get("name", "")
                 func_args_raw = tool_calls[0].get("function", {}).get("arguments", "{}")
                 try:
-                    func_args = json.loads(func_args_raw) if isinstance(func_args_raw, str) else func_args_raw
+                    func_args = (
+                        json.loads(func_args_raw)
+                        if isinstance(func_args_raw, str)
+                        else func_args_raw
+                    )
                 except json.JSONDecodeError:
                     func_args = {}
                 passed = func_name == tc["expected_tool"]
@@ -388,6 +400,6 @@ def test_retrieve_with_llm():
     except Exception as e:
         print(f"  [!] Test 3 failed: {e}")
 
-    print(f"\n{'='*60}")
+    print(f"\n{'=' * 60}")
     print("  Done!")
-    print(f"{'='*60}\n")
+    print(f"{'=' * 60}\n")

examples/xgen_workflow_agent.py

@@ -101,12 +101,19 @@ def upload_file(file_path: str, bucket: str) -> str:
     @tool
     def query_database(sql: str) -> str:
         """Execute a SQL query on the analytics database."""
-        return f"Query result: 42 rows"
+        return "Query result: 42 rows"
 
     all_tools = [
-        search_products, get_order_detail, cancel_order, create_refund,
-        send_notification, get_user_profile, update_inventory,
-        generate_report, upload_file, query_database,
+        search_products,
+        get_order_detail,
+        cancel_order,
+        create_refund,
+        send_notification,
+        get_user_profile,
+        update_inventory,
+        generate_report,
+        upload_file,
+        query_database,
     ]
 
     # -- 핵심: filter_tools 적용 (2줄) --
@@ -199,11 +206,10 @@ def send_notification(user_id: str, message: str) -> str:
         """Send push notification to a user."""
         return f"Sent to {user_id}"
 
-    all_tools = [search_products, get_order_detail, cancel_order,
-                 create_refund, send_notification]
+    # 데모용 tool 정의 (실제 적용 코드는 아래 print 블록 참고)
+    _ = [search_products, get_order_detail, cancel_order, create_refund, send_notification]
 
     # -- 핵심: create_agent 교체 --
-    from graph_tool_call.langchain import create_agent as create_filtered_agent
 
     # query_mode="message": 기본값, 추가 LLM 호출 없음 (빠름)
     # query_mode="llm": 대화 컨텍스트에서 검색 쿼리 생성 (멀티턴 강함)
@@ -284,27 +290,70 @@ def pattern_c_gateway():
         tools (50~500개) → create_gateway_tools() → 2개 meta-tool → agent_core
     """
     import json
-    from langchain_core.tools import tool
 
     # -- 시뮬레이션: DB에서 가져온 사용자 등록 tool 50개 --
     tools = []
     tool_categories = {
-        "order": ["create_order", "get_order", "cancel_order", "update_order", "list_orders",
-                  "get_order_status", "track_shipment", "confirm_delivery", "return_order",
-                  "exchange_order"],
-        "product": ["search_products", "get_product", "create_product", "update_product",
-                    "delete_product", "get_product_reviews", "add_product_review",
-                    "get_product_inventory", "update_price", "get_categories"],
-        "user": ["get_user", "create_user", "update_user", "delete_user", "list_users",
-                "get_user_orders", "get_user_wishlist", "add_to_wishlist",
-                "get_user_notifications", "update_preferences"],
-        "payment": ["process_payment", "create_refund", "get_payment_status",
-                    "list_transactions", "get_invoice", "send_receipt",
-                    "validate_coupon", "apply_discount", "get_billing_info",
-                    "update_payment_method"],
-        "admin": ["generate_report", "get_analytics", "export_data", "import_data",
-                  "get_system_status", "clear_cache", "send_notification",
-                  "create_announcement", "get_audit_log", "manage_permissions"],
+        "order": [
+            "create_order",
+            "get_order",
+            "cancel_order",
+            "update_order",
+            "list_orders",
+            "get_order_status",
+            "track_shipment",
+            "confirm_delivery",
+            "return_order",
+            "exchange_order",
+        ],
+        "product": [
+            "search_products",
+            "get_product",
+            "create_product",
+            "update_product",
+            "delete_product",
+            "get_product_reviews",
+            "add_product_review",
+            "get_product_inventory",
+            "update_price",
+            "get_categories",
+        ],
+        "user": [
+            "get_user",
+            "create_user",
+            "update_user",
+            "delete_user",
+            "list_users",
+            "get_user_orders",
+            "get_user_wishlist",
+            "add_to_wishlist",
+            "get_user_notifications",
+            "update_preferences",
+        ],
+        "payment": [
+            "process_payment",
+            "create_refund",
+            "get_payment_status",
+            "list_transactions",
+            "get_invoice",
+            "send_receipt",
+            "validate_coupon",
+            "apply_discount",
+            "get_billing_info",
+            "update_payment_method",
+        ],
+        "admin": [
+            "generate_report",
+            "get_analytics",
+            "export_data",
+            "import_data",
+            "get_system_status",
+            "clear_cache",
+            "send_notification",
+            "create_announcement",
+            "get_audit_log",
+            "manage_permissions",
+        ],
     }
 
     for category, tool_names in tool_categories.items():
@@ -410,7 +459,7 @@ def bonus_multiturn_scenario():
         top_name = results[0].name if results else "없음"
         match = "✓" if top_name == s["expected"] else "✗"
 
-        print(f"  턴 {s['turn']}: \"{s['message']}\"")
+        print(f'  턴 {s["turn"]}: "{s["message"]}"')
         print(f"    → message 모드 Top-1: {top_name} {match}")
         if "note" in s:
             print(f"    ※ {s['note']}")

examples/xgen_workflow_gateway.py

@@ -24,13 +24,13 @@
 import asyncio
 import json
 
-
 # =====================================================================
 # 방법 1: MCP 서버에서 tool 수집 → gateway
 # =====================================================================
 # 실제 MCP 서버(Slack, GitHub, Jira 등)에서 tool을 가져와서
 # gateway 2개로 축약하는 패턴. xgen-workflow 실 적용 코드와 동일.
 
+
 async def example_mcp_gateway():
     """MCP 서버에서 tool 수집 후 gateway agent 구성."""
     from langchain_openai import ChatOpenAI
@@ -66,8 +66,9 @@ async def example_mcp_gateway():
                 # MCP tool → LangChain StructuredTool 변환
                 for t in response.tools:
                     from langchain_core.tools import StructuredTool
+
                     tool = StructuredTool.from_function(
-                        func=lambda **kwargs, _s=session, _n=t.name: asyncio.run(
+                        func=lambda _s=session, _n=t.name, **kwargs: asyncio.run(
                             _s.call_tool(_n, kwargs)
                         ),
                         name=t.name,
@@ -97,14 +98,14 @@ async def example_mcp_gateway():
 # Swagger/OpenAPI에서 tool을 자동 생성하고 gateway에 넣는 패턴.
 # 사내 API가 OpenAPI spec으로 문서화되어 있을 때 유용.
 
+
 def example_openapi_gateway():
     """OpenAPI spec에서 tool 생성 후 gateway agent 구성."""
     from langchain_openai import ChatOpenAI
     from langgraph.prebuilt import create_react_agent
 
     from graph_tool_call import ToolGraph
     from graph_tool_call.langchain import create_gateway_tools
-    from graph_tool_call.langchain.tools import tool_schema_to_openai_function
 
     # ── 1. OpenAPI spec에서 ToolGraph 구축 ───────────────────────
     # URL 또는 파일 경로 모두 가능
@@ -127,12 +128,15 @@ def example_openapi_gateway():
         def make_fn(tool_schema=schema):
             def fn(**kwargs):
                 req = build_request(tool_schema, kwargs)
-                return json.dumps({
-                    "method": req.method,
-                    "url": req.url,
-                    "body": req.body,
-                    "note": "실제 환경에서는 requests.request()로 호출",
-                })
+                return json.dumps(
+                    {
+                        "method": req.method,
+                        "url": req.url,
+                        "body": req.body,
+                        "note": "실제 환경에서는 requests.request()로 호출",
+                    }
+                )
+
             return fn
 
         tool = StructuredTool.from_function(
@@ -163,6 +167,7 @@ def fn(**kwargs):
 # 이미 LangChain tool이 있는 프로젝트에서 gateway 또는 filter를
 # 한 줄로 적용하는 패턴. 기존 코드 변경 최소화.
 
+
 def example_langchain_integration():
     """기존 LangChain tool에 gateway/filter 적용."""
     from langchain_community.tools import DuckDuckGoSearchRun
@@ -214,8 +219,14 @@ def send_slack_message(channel: str, message: str) -> str:
         return json.dumps({"ok": True, "channel": channel})
 
     all_tools = [
-        search, get_weather, send_email, create_calendar_event,
-        list_files, read_file, query_database, create_jira_issue,
+        search,
+        get_weather,
+        send_email,
+        create_calendar_event,
+        list_files,
+        read_file,
+        query_database,
+        create_jira_issue,
         send_slack_message,
     ]
     print(f"  기존 tool {len(all_tools)}개")