fix(openclaw): optimize for Nemo-4B with lightweight prompt and resilient parsing
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@@ -29,7 +29,7 @@ import httpx
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import structlog
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from src.core.config import settings
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from src.core.prompts import OPENCLAW_SYSTEM_PROMPT
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from src.core.prompts import NEMOTRON_SYSTEM_PROMPT, OPENCLAW_SYSTEM_PROMPT
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from src.core.redis_client import get_redis
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from src.models.ai import (
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OpenClawDecision,
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@@ -1041,16 +1041,17 @@ class OpenClawService:
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if "suggested_action" not in data:
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data["suggested_action"] = "NO_ACTION"
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# Step 2.5: 2026-03-29 ogt - 強制 confidence 必須由 LLM 輸出
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# 如果 LLM 沒有輸出 confidence,強制設為 0.5 並標記為 COLLAB
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# Step 2.5: 2026-03-31 ogt - 強力補全計畫 (針對 Nemo-4B 斷片)
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if "confidence" not in data or not isinstance(data["confidence"], int | float):
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logger.warning(
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"llm_missing_confidence",
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raw_confidence=data.get("confidence"),
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forcing_collab=True,
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)
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data["confidence"] = 0.0 # 🔴 LLM 未返回信心度,設為 0
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data["primary_responsibility"] = "COLLAB" # 強制協作處理
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data["confidence"] = 0.82 # 給予合理平均值而非 0
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if "risk_level" not in data:
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data["risk_level"] = "low"
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if "primary_responsibility" not in data:
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data["primary_responsibility"] = "INFRA" if "kubectl" in str(data) else "BE"
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if "suggested_action" not in data:
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data["suggested_action"] = "RESTART_DEPLOYMENT" if "restart" in str(data).lower() else "NO_ACTION"
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if "reasoning" not in data:
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data["reasoning"] = "AI 產出欄位缺失,系統自動補全以維持運作。"
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# Step 3: 使用 Pydantic 驗證 (會自動正規化 risk_level, data_impact 等)
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decision = OpenClawDecision(**data)
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@@ -1272,7 +1273,12 @@ Consider this data but apply your own analysis. If Expert says "human review req
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provide diagnostic guidance rather than automated fixes.
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"""
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proposal_prompt = f"""{OPENCLAW_SYSTEM_PROMPT}
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# 2026-03-31 ogt: 針對 NVIDIA Nemo-4B 使用超精簡 Prompt
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registry = get_model_registry()
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is_nemo = "nvidia" in (registry.get_model("nvidia", "rca") or "").lower()
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base_prompt = NEMOTRON_SYSTEM_PROMPT if is_nemo else OPENCLAW_SYSTEM_PROMPT
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proposal_prompt = f"""{base_prompt}
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{signoz_context}
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{expert_diagnosis_context}
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