fix(ai): isolate heavy Ollama workloads from GCP alert lane

apps/api/src/core/config.py

@@ -500,7 +500,7 @@ class Settings(BaseSettings):
         default=False,
         description=(
             "Allow LocalCodeReviewService to fall back to Gemini when the "
-            "GCP-B/Ollama code-review lane fails. Default false to avoid "
+            "local Ollama code-review lane fails. Default false to avoid "
             "unexpected cloud spend from Gitea push/PR alerts."
         ),
     )

apps/api/src/hermes/nl_gateway.py

@@ -9,6 +9,7 @@ Layer 1 意圖路由（關鍵字正則）→ Ollama 本地模型（111）→ Tel
   debugger/vuln → deepseek-r1:14b（推理）; code agents → qwen2.5-coder:7b; 其他 → qwen2.5:7b-instruct
 """
 from __future__ import annotations
+
 import asyncio
 import re
 import time
@@ -17,7 +18,6 @@ import httpx
 import structlog
 from sqlalchemy import text
 
-from src.core.config import settings
 from src.core.redis_client import get_redis
 from src.db.base import get_db_context
 from src.hermes.agent_loader import get_agent_system_prompt
@@ -266,7 +266,9 @@ async def process_nl_message(
     success = False
     error_type: str | None = None
     try:
-        ollama_base = getattr(settings, "OLLAMA_URL", "http://34.143.170.20:11434")  # 2026-05-03 ogt: ADR-110 GCP-A Primary
+        from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
+
+        ollama_base = resolve_ollama_endpoint("hermes")
         async with httpx.AsyncClient(timeout=_OLLAMA_TIMEOUT) as _hc:
             resp = await _hc.post(
                 f"{ollama_base}/api/chat",

apps/api/src/services/decision_fusion_adapter.py

@@ -26,7 +26,7 @@ from __future__ import annotations
 
 import asyncio
 import re
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import TYPE_CHECKING, Any, Literal
 
 import httpx
@@ -125,7 +125,7 @@ class DecisionFusionAdapter:
     # Public API
     # =========================================================================
 
-    async def fuse_decision(self, event: "AiGovernanceEvent") -> FusedDecision:
+    async def fuse_decision(self, event: AiGovernanceEvent) -> FusedDecision:
         """三維融合：LLM × Playbook × MCP → FusedDecision。
 
         三個維度並行評估（asyncio.gather），任一失敗靜默降為 0.5。
@@ -226,7 +226,7 @@ class DecisionFusionAdapter:
     # =========================================================================
 
     async def _score_llm(
-        self, event: "AiGovernanceEvent"
+        self, event: AiGovernanceEvent
     ) -> tuple[float, str, dict[str, Any]]:
         """Ollama LLM 推理：治理事件情境 → 建議動作 + 信心度。
 
@@ -254,7 +254,9 @@ class DecisionFusionAdapter:
             "只輸出 CONFIDENCE 和 ACTION 兩行，不要其他解釋。"
         )
 
-        ollama_url = getattr(self._settings, "OLLAMA_URL", "http://192.168.0.111:11434")  # 2026-05-04 ogt: ADR-110 修正 — 111 primary
+        from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
+
+        ollama_url = resolve_ollama_endpoint("deep_rca")
 
         try:
             async with httpx.AsyncClient(
@@ -320,7 +322,7 @@ class DecisionFusionAdapter:
     # =========================================================================
 
     async def _score_playbook(
-        self, event: "AiGovernanceEvent"
+        self, event: AiGovernanceEvent
     ) -> tuple[float, str | None, float | None]:
         """Playbook 相似度比對 → 取最高 trust_score。
 
@@ -373,7 +375,7 @@ class DecisionFusionAdapter:
     # =========================================================================
 
     async def _score_mcp(
-        self, event: "AiGovernanceEvent"
+        self, event: AiGovernanceEvent
     ) -> tuple[float, dict[str, Any]]:
         """Prometheus 情報採集 → MCP 感官品質分數。
 

apps/api/src/services/drift_narrator_service.py

@@ -33,10 +33,11 @@ logger = structlog.get_logger(__name__)
 # ============================================================
 # 設定
 # ============================================================
-# 2026-05-03 ogt: ADR-110 GCP-A Primary — 改從 settings 讀取，不再硬編碼 111
+# 2026-05-05 Codex: 重摘要走 111 lane，避免污染 GCP alert-fast lane
 def _get_ollama_url() -> str:
-    from src.core.config import get_settings
-    return get_settings().OLLAMA_URL
+    from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
+
+    return resolve_ollama_endpoint("deep_rca")
 # D1 集中化 2026-04-11: 從 models.json providers.ollama.models.drift_summary 讀取
 NARRATOR_MODEL = get_model("ollama", "drift_summary")
 NARRATOR_TIMEOUT = 90.0  # seconds
@@ -120,8 +121,8 @@ class DriftNarratorService:
 
     async def narrate_and_notify(
         self,
-        report: "DriftReport",
-        interpretation: "DriftInterpretation | None" = None,
+        report: DriftReport,
+        interpretation: DriftInterpretation | None = None,
     ) -> None:
         """
         生成人話摘要並推送 Telegram
@@ -166,7 +167,7 @@ class DriftNarratorService:
             medium=report.medium_count,
         )
 
-    def _should_narrate(self, report: "DriftReport") -> bool:
+    def _should_narrate(self, report: DriftReport) -> bool:
         """觸發條件：high >= 1 or medium >= 3"""
         # 過濾 HPA 白名單後重算
         non_hpa_items = [
@@ -180,8 +181,8 @@ class DriftNarratorService:
 
     async def _generate_narrative_and_items(
         self,
-        report: "DriftReport",
-        interpretation: "DriftInterpretation | None",
+        report: DriftReport,
+        interpretation: DriftInterpretation | None,
     ) -> tuple[str, list[dict], dict]:
         """
         2026-04-18 ogt + Claude Opus 4.7: B 方案 — LLM 產生 narrative + 結構化 items
@@ -354,8 +355,8 @@ class DriftNarratorService:
 
     def _fallback_recommendation(
         self,
-        report: "DriftReport",
-        interpretation: "DriftInterpretation | None",
+        report: DriftReport,
+        interpretation: DriftInterpretation | None,
     ) -> dict:
         """
         2026-04-20 P0.2 ogt + Claude Opus 4.7: LLM 沒給 recommendation 時的 Python fallback
@@ -397,7 +398,7 @@ class DriftNarratorService:
 
     async def _log_ai_action_to_db(
         self,
-        report: "DriftReport",
+        report: DriftReport,
         prompt: str,
         raw_response: str | None,
         narrative: str,
@@ -416,7 +417,9 @@ class DriftNarratorService:
         - 若能找到該 drift 的 incident 關聯,設 parent_op_id
         """
         import json as _json
+
         from sqlalchemy import text as _sql
+
         from src.db.base import get_db_context
 
         input_json = _json.dumps({
@@ -511,7 +514,7 @@ class DriftNarratorService:
                 items_count=len(items),
             )
 
-    def _format_drift_for_llm(self, report: "DriftReport") -> str:
+    def _format_drift_for_llm(self, report: DriftReport) -> str:
         """
         2026-04-18 ogt + Claude Opus 4.7: B 方案 — 餵 LLM 用的 JSON 序列化
         保留更多原始 context 給 LLM 推理,不做 30 字元暴力截斷
@@ -582,7 +585,7 @@ class DriftNarratorService:
         # 一般變化
         return f"{from_val} → {to_val}"
 
-    def _fallback_items(self, report: "DriftReport") -> list[dict]:
+    def _fallback_items(self, report: DriftReport) -> list[dict]:
         """
         LLM 失敗時的 Python 智能摘要 (取代舊 str()[:30])
         - 過濾白名單
@@ -605,7 +608,7 @@ class DriftNarratorService:
             })
         return items
 
-    def _format_intent_summary(self, interpretation: "DriftInterpretation | None") -> str:
+    def _format_intent_summary(self, interpretation: DriftInterpretation | None) -> str:
         if not interpretation:
             return "無意圖分析"
         return (
@@ -616,8 +619,8 @@ class DriftNarratorService:
 
     def _fallback_narrative(
         self,
-        report: "DriftReport",
-        interpretation: "DriftInterpretation | None",
+        report: DriftReport,
+        interpretation: DriftInterpretation | None,
     ) -> str:
         """LLM 失敗時的結構化 fallback"""
         resources = list({
@@ -636,7 +639,7 @@ class DriftNarratorService:
 
     async def _send_telegram(
         self,
-        report: "DriftReport",
+        report: DriftReport,
         narrative: str,
         items: list[dict],
         recommendation: dict | None = None,
@@ -667,7 +670,7 @@ class DriftNarratorService:
         except Exception as e:
             logger.warning("drift_narrator_telegram_error", error=str(e))
 
-    def _count_nontrivial_drift(self, report: "DriftReport") -> int:
+    def _count_nontrivial_drift(self, report: DriftReport) -> int:
         """
         計算非白名單、非 trivial (K8s 自動補齊) 的 drift 數
         用於 Telegram 底部「還有 N 項」顯示實際可操作數量
@@ -704,7 +707,7 @@ class DriftNarratorService:
 
     def _render_telegram_body(
         self,
-        report: "DriftReport",
+        report: DriftReport,
         narrative: str,
         items: list[dict],
         recommendation: dict | None = None,

apps/api/src/services/embedding_service.py

@@ -21,8 +21,8 @@ from typing import Protocol
 import httpx
 import structlog
 
-from src.core.config import settings
 from src.services.model_registry import get_model as _get_model
+from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
 
 logger = structlog.get_logger(__name__)
 
@@ -98,7 +98,7 @@ class OllamaEmbeddingService:
         P1 修復 (2026-03-29): 維度配置化，支援更多模型
         """
         self._model = model
-        self._ollama_url = ollama_url or settings.OLLAMA_URL
+        self._ollama_url = ollama_url or resolve_ollama_endpoint("embedding")
         self._timeout = timeout
         self._default_dimension = default_dimension or self.MODEL_DIMENSIONS.get(
             model, self.DEFAULT_DIMENSION

apps/api/src/services/image_analysis_service.py

@@ -21,7 +21,6 @@ AWOOOI — Image Analysis Service (Phase 34, ADR-067)
 from __future__ import annotations
 
 import base64
-import os
 import time
 from pathlib import Path
 from typing import TYPE_CHECKING
@@ -29,14 +28,13 @@ from typing import TYPE_CHECKING
 import httpx
 import structlog
 
-from src.core.config import get_settings
 from src.services.model_registry import get_model
+from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
 
 if TYPE_CHECKING:
     pass
 
 logger = structlog.get_logger(__name__)
-settings = get_settings()
 
 # D1 集中化 2026-04-11: 從 models.json providers.ollama.models.image_analysis 讀取
 _MODEL = get_model("ollama", "image_analysis")
@@ -124,7 +122,7 @@ class ImageAnalysisService:
             image_b64 = base64.b64encode(image_path.read_bytes()).decode()
             http = await self._get_http()
             resp = await http.post(
-                f"{settings.OLLAMA_URL}/api/generate",
+                f"{resolve_ollama_endpoint('image_analysis')}/api/generate",
                 json={
                     "model": _MODEL,
                     "prompt": question,

apps/api/src/services/intent_classifier.py

@@ -31,8 +31,8 @@ from typing import Protocol, runtime_checkable
 import httpx
 import structlog
 
-from src.core.config import settings
 from src.services.model_registry import get_model_registry
+from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
 
 logger = structlog.get_logger(__name__)
 
@@ -549,7 +549,7 @@ class IntentClassifier:
             # 呼叫 Ollama
             async with httpx.AsyncClient() as client:
                 response = await client.post(
-                    f"{settings.OLLAMA_URL}/api/generate",
+                    f"{resolve_ollama_endpoint('hermes')}/api/generate",
                     json={
                         "model": model_name,
                         "prompt": prompt,

apps/api/src/services/knowledge_extractor_service.py

@@ -15,10 +15,11 @@ import structlog
 
 logger = structlog.get_logger(__name__)
 
-# 2026-05-03 ogt: ADR-110 GCP-A Primary — 改從 settings 讀取，不再硬編碼 111
+# 2026-05-05 Codex: KB 萃取走 111 lane，避免污染 GCP alert-fast lane
 def _get_ollama_base() -> str:
-    from src.core.config import get_settings
-    return get_settings().OLLAMA_URL
+    from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
+
+    return resolve_ollama_endpoint("deep_rca")
 _EXTRACT_MODEL = "llama3.2:3b"
 _EXTRACT_TIMEOUT = 30.0  # 秒，容忍慢速
 
@@ -117,7 +118,11 @@ class KnowledgeExtractorService:
             category = self._infer_category(incident)
 
             # 5. 建立 KB 條目
-            from src.models.knowledge import EntrySource, EntryType, KnowledgeEntryCreate
+            from src.models.knowledge import (
+                EntrySource,
+                EntryType,
+                KnowledgeEntryCreate,
+            )
             from src.services.knowledge_service import get_knowledge_service
 
             entry_data = KnowledgeEntryCreate(

apps/api/src/services/knowledge_rag_service.py

@@ -20,11 +20,10 @@ from pathlib import Path
 import httpx
 import structlog
 
-from src.core.config import get_settings
 import src.repositories.rag_chunk_repository as rag_repo
+from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
 
 logger = structlog.get_logger(__name__)
-settings = get_settings()
 
 _EMBED_MODEL = "nomic-embed-text"
 _GEN_MODEL = "qwen2.5:7b-instruct"
@@ -131,7 +130,7 @@ class KnowledgeRAGService:
         try:
             http = await self._get_http()
             resp = await http.post(
-                f"{settings.OLLAMA_URL}/api/embeddings",
+                f"{resolve_ollama_endpoint('embedding')}/api/embeddings",
                 json={"model": _EMBED_MODEL, "prompt": text},
             )
             if resp.status_code == 200:
@@ -150,7 +149,7 @@ class KnowledgeRAGService:
         try:
             http = await self._get_http()
             resp = await http.post(
-                f"{settings.OLLAMA_URL}/api/generate",
+                f"{resolve_ollama_endpoint('rag')}/api/generate",
                 json={
                     "model": _GEN_MODEL,
                     "prompt": prompt,

apps/api/src/services/local_code_review_service.py

@@ -186,7 +186,7 @@ class LocalCodeReviewService:
         )
         return {
             "review_text": (
-                "⚠️ Code Review：GCP-B/Ollama 審查未完成，"
+                "⚠️ Code Review：本地 Ollama 審查未完成，"
                 "已依成本策略跳過 Gemini fallback。"
             ),
             "issues_count": 1,

apps/api/src/services/log_summary_service.py

@@ -35,8 +35,9 @@ logger = structlog.get_logger(__name__)
 # ============================================================
 # 2026-05-03 ogt: ADR-110 GCP-A Primary — 改從 settings 讀取，不再硬編碼 111
 def _get_ollama_url() -> str:
-    from src.core.config import get_settings
-    return get_settings().OLLAMA_URL
+    from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
+
+    return resolve_ollama_endpoint("deep_rca")
 # D1 集中化 2026-04-11: 從 models.json providers.ollama.models.log_anomaly 讀取
 SUMMARY_MODEL = get_model("ollama", "log_anomaly")
 LLM_TIMEOUT = 180.0       # deepseek-r1 硬超時
@@ -145,7 +146,7 @@ class LogSummaryService:
                 self.summarize(pod_name, namespace),
                 timeout=SOFT_TIMEOUT,
             )
-        except asyncio.TimeoutError:
+        except TimeoutError:
             logger.info(
                 "log_summary_soft_timeout",
                 pod=pod_name,
@@ -182,7 +183,7 @@ class LogSummaryService:
     def _extract_anomaly_lines(self, raw_logs: str) -> list[str]:
         """過濾異常行 + 敏感資料遮蔽，取最後 ANOMALY_TAIL_LINES 行"""
         lines = raw_logs.splitlines()
-        anomaly = [l for l in lines if _ANOMALY_PATTERN.search(l)]
+        anomaly = [line for line in lines if _ANOMALY_PATTERN.search(line)]
         # 取最後 N 行
         anomaly = anomaly[-ANOMALY_TAIL_LINES:]
         # 遮蔽敏感資料

apps/api/src/services/ollama_endpoint_resolver.py

@@ -16,27 +16,37 @@ from src.core.config import settings
 OllamaWorkloadType = Literal[
     "interactive",
     "healthcheck",
+    "alert_fast",
     "batch",
     "embedding",
     "rag",
     "code_review",
     "shadow",
     "canary",
+    "deep_rca",
+    "image_analysis",
+    "hermes",
     "local_required",
     "privacy_sensitive",
     "dr",
 ]
 
-_GCP_B_PREFERRED_WORKLOADS = {
+_GCP_A_PREFERRED_WORKLOADS = {
+    "interactive",
+    "healthcheck",
+    "alert_fast",
+}
+
+_LOCAL_PREFERRED_WORKLOADS = {
     "batch",
     "embedding",
     "rag",
     "code_review",
     "shadow",
     "canary",
-}
-
-_LOCAL_PREFERRED_WORKLOADS = {
+    "deep_rca",
+    "image_analysis",
+    "hermes",
     "local_required",
     "privacy_sensitive",
     "dr",
@@ -68,20 +78,28 @@ def resolve_ollama_selection(
     secondary = cfg.OLLAMA_SECONDARY_URL
     fallback = cfg.OLLAMA_FALLBACK_URL
 
-    if workload_type in _GCP_B_PREFERRED_WORKLOADS and secondary:
+    if workload_type in _LOCAL_PREFERRED_WORKLOADS:
+        if fallback:
+            return OllamaEndpointSelection(
+                url=fallback,
+                provider_name="ollama_local",
+                workload_type=workload_type,
+                reason="local_heavy_or_privacy_lane",
+            )
+        if secondary:
+            return OllamaEndpointSelection(
+                url=secondary,
+                provider_name="ollama_gcp_b",
+                workload_type=workload_type,
+                reason="local_missing_gcp_b_fallback",
+            )
+
+    if workload_type not in _GCP_A_PREFERRED_WORKLOADS and secondary:
         return OllamaEndpointSelection(
             url=secondary,
             provider_name="ollama_gcp_b",
             workload_type=workload_type,
-            reason="gcp_b_batch_lane",
-        )
-
-    if workload_type in _LOCAL_PREFERRED_WORKLOADS and fallback:
-        return OllamaEndpointSelection(
-            url=fallback,
-            provider_name="ollama_local",
-            workload_type=workload_type,
-            reason="local_privacy_or_dr_lane",
+            reason="gcp_b_default_non_alert_lane",
         )
 
     return OllamaEndpointSelection(

apps/api/src/services/playbook_rag.py

@@ -29,9 +29,9 @@ from typing import Any
 import httpx
 import structlog
 
-from src.core.config import settings
 from src.models.playbook import Playbook, SymptomPattern
 from src.repositories.interfaces import IEmbeddingCacheRepository
+from src.services.ollama_endpoint_resolver import resolve_ollama_endpoint
 
 logger = structlog.get_logger(__name__)
 
@@ -146,7 +146,7 @@ class PlaybookRAGService:
         """
         self._http_client = http_client
         self._embedding_cache = embedding_cache
-        self.ollama_url = settings.OLLAMA_URL
+        self.ollama_url = resolve_ollama_endpoint("embedding")
         self.embedding_model = EMBEDDING_MODEL
 
     # =========================================================================