fix(api): 修復全部 lint 錯誤 (ruff --fix)
- Import sorting (I001) - Unused imports (F401) - f-string without placeholders (F541) - Loop variable unused (B007) - zip() strict parameter (B905) - Exception chaining (B904) - collections.abc imports (UP035) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
This commit is contained in:
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369
apps/api/src/services/error_analyzer_service.py
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369
apps/api/src/services/error_analyzer_service.py
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"""
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Error Analyzer Service - #39 Sentry 錯誤 AI 分析
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=================================================
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Phase 10: Sentry + OpenClaw + UI 整合
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功能:
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1. 接收 Sentry Issue + Stacktrace 數據
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2. 使用 OpenClaw LLM 進行根因分析
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3. 生成修復建議與預防措施
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遵循 leWOOOgo 積木化原則:
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- Service 層負責業務邏輯
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- 不直接存取 Redis/DB
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- 使用 DI 支援測試
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版本: v1.0
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建立: 2026-03-26 18:45 (台北時區)
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建立者: Claude Code (#39 Error Analyzer Agent)
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"""
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import json
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from typing import Protocol, runtime_checkable
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from pydantic import BaseModel, Field
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from src.core.logging import get_logger
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from src.utils.timezone import now_taipei_iso
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logger = get_logger("awoooi.error_analyzer")
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# =============================================================================
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# Error Analysis Prompt
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# =============================================================================
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ERROR_ANALYZER_SYSTEM_PROMPT = """# OpenClaw Error Analyzer - AWOOOI 錯誤分析專家
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You are a senior Software Engineer specialized in debugging and error analysis.
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## 🌐 Language Requirement (CRITICAL)
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- You MUST respond in **Traditional Chinese (繁體中文/正體中文)** for all text fields
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- FORBIDDEN: Simplified Chinese characters (简体字)
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- Use Taiwan locale conventions (台灣用語)
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## 🎯 Your Mission
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Analyze the given error from Sentry and provide:
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1. **Root Cause Analysis** - Why did this error occur?
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2. **Impact Assessment** - How serious is this error?
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3. **Fix Recommendations** - How to fix this error?
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4. **Prevention Suggestions** - How to prevent recurrence?
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## 📊 Analysis Categories
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- **CODE_BUG**: Logic error, null pointer, type error
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- **DEPENDENCY**: Third-party library issue, version conflict
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- **CONFIGURATION**: Missing env var, wrong config
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- **INFRASTRUCTURE**: Network, timeout, resource exhaustion
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- **DATA_INTEGRITY**: Corrupt data, schema mismatch
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- **EXTERNAL_SERVICE**: API failure, rate limit
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- **UNKNOWN**: Cannot determine from available information
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## ⚠️ Output Rules
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- Respond with ONLY valid JSON
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- confidence MUST be between 0.0 and 1.0
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- severity MUST be one of: LOW, MEDIUM, HIGH, CRITICAL
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- All text fields in Traditional Chinese
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## 📋 JSON Schema (REQUIRED)
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```json
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{
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"root_cause": "string - 根因分析 (繁體中文)",
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"category": "CODE_BUG|DEPENDENCY|CONFIGURATION|INFRASTRUCTURE|DATA_INTEGRITY|EXTERNAL_SERVICE|UNKNOWN",
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"severity": "LOW|MEDIUM|HIGH|CRITICAL",
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"impact_assessment": "string - 影響評估 (繁體中文)",
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"fix_recommendation": {
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"summary": "string - 修復摘要",
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"steps": ["array - 修復步驟"],
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"code_suggestion": "string | null - 建議的代碼修改"
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},
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"prevention": [
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{
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"type": "CODE_REVIEW|UNIT_TEST|MONITORING|VALIDATION|ERROR_HANDLING",
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"description": "string - 預防措施描述"
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}
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],
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"related_files": ["array - 可能相關的檔案路徑"],
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"confidence": "number - 0.0 to 1.0",
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"reasoning": "string - 分析推理過程 (繁體中文)"
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}
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```
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Now analyze the following error:
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"""
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# =============================================================================
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# Response Models
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# =============================================================================
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class FixRecommendation(BaseModel):
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"""修復建議"""
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summary: str = Field(description="修復摘要")
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steps: list[str] = Field(default_factory=list, description="修復步驟")
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code_suggestion: str | None = Field(None, description="建議的代碼修改")
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class PreventionMeasure(BaseModel):
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"""預防措施"""
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type: str = Field(description="類型 (CODE_REVIEW, UNIT_TEST, etc.)")
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description: str = Field(description="描述")
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class ErrorAnalysisResult(BaseModel):
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"""錯誤分析結果"""
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root_cause: str = Field(description="根因分析")
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category: str = Field(description="分類")
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severity: str = Field(description="嚴重度")
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impact_assessment: str = Field(description="影響評估")
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fix_recommendation: FixRecommendation = Field(description="修復建議")
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prevention: list[PreventionMeasure] = Field(
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default_factory=list, description="預防措施"
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)
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related_files: list[str] = Field(default_factory=list, description="相關檔案")
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confidence: float = Field(description="信心度")
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reasoning: str = Field(description="分析推理過程")
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# =============================================================================
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# Protocol Interface
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# =============================================================================
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@runtime_checkable
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class ILLMProvider(Protocol):
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"""LLM Provider Protocol"""
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async def call(self, prompt: str) -> tuple[str, str, bool]:
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"""
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呼叫 LLM
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Returns:
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(response, provider_name, success)
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"""
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...
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# =============================================================================
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# Error Analyzer Service
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# =============================================================================
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class ErrorAnalyzerService:
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"""
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Error Analyzer Service - Sentry 錯誤 AI 分析
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職責:
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1. 組裝分析 Prompt
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2. 呼叫 OpenClaw LLM
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3. 解析並驗證分析結果
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"""
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def __init__(self, llm_provider: ILLMProvider | None = None) -> None:
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"""
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初始化 Error Analyzer Service
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Args:
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llm_provider: LLM 提供者 (預設使用 OpenClaw)
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"""
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self._llm_provider = llm_provider
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async def _get_llm_provider(self) -> ILLMProvider:
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"""取得 LLM Provider (lazy init)"""
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if self._llm_provider is None:
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from src.services.openclaw import get_openclaw
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self._llm_provider = get_openclaw()
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return self._llm_provider
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async def analyze_error(
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self,
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issue_id: str,
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title: str,
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level: str,
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culprit: str | None,
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count: int,
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stacktrace: str,
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context: dict | None = None,
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) -> tuple[ErrorAnalysisResult | None, str, bool]:
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"""
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分析 Sentry 錯誤
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Args:
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issue_id: Sentry Issue ID
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title: 錯誤標題
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level: 嚴重度 (error, warning, etc.)
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culprit: 錯誤來源 (函數/檔案)
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count: 發生次數
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stacktrace: 堆疊追蹤
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context: 額外上下文 (browser, os, tags, etc.)
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Returns:
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(analysis_result, provider, success)
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"""
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# 組裝 Prompt
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error_context = {
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"issue_id": issue_id,
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"title": title,
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"level": level,
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"culprit": culprit,
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"occurrence_count": count,
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"stacktrace": stacktrace,
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"context": context or {},
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"analyzed_at": now_taipei_iso(),
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}
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prompt = ERROR_ANALYZER_SYSTEM_PROMPT + "\n```json\n"
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prompt += json.dumps(error_context, ensure_ascii=False, indent=2)
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prompt += "\n```"
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logger.info(
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"error_analysis_start",
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issue_id=issue_id,
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title=title,
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level=level,
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)
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# 呼叫 LLM
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try:
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llm = await self._get_llm_provider()
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response, provider, success = await llm.call(prompt)
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if not success:
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logger.error(
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"error_analysis_llm_failed",
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issue_id=issue_id,
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provider=provider,
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)
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return None, provider, False
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logger.info(
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"error_analysis_llm_response",
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issue_id=issue_id,
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provider=provider,
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response_length=len(response),
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)
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# 解析結果
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result = self._parse_analysis_result(response)
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if result:
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logger.info(
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"error_analysis_complete",
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issue_id=issue_id,
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category=result.category,
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severity=result.severity,
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confidence=result.confidence,
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)
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else:
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logger.warning(
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"error_analysis_parse_failed",
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issue_id=issue_id,
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raw_response=response[:300],
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)
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return result, provider, True
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except Exception as e:
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logger.exception(
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"error_analysis_failed",
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issue_id=issue_id,
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error=str(e),
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)
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return None, "error", False
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def _parse_analysis_result(self, raw_response: str) -> ErrorAnalysisResult | None:
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"""
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解析 LLM 回應為結構化結果
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Args:
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raw_response: LLM 原始回應
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Returns:
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解析後的 ErrorAnalysisResult，解析失敗返回 None
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"""
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try:
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# 嘗試找到 JSON 區塊
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json_str = raw_response
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# 處理可能的 markdown 包裝
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if "```json" in raw_response:
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start = raw_response.find("```json") + 7
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end = raw_response.find("```", start)
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if end > start:
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json_str = raw_response[start:end]
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elif "```" in raw_response:
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start = raw_response.find("```") + 3
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end = raw_response.find("```", start)
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if end > start:
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json_str = raw_response[start:end]
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# 解析 JSON
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data = json.loads(json_str.strip())
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# 建立 FixRecommendation
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fix_data = data.get("fix_recommendation", {})
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fix_recommendation = FixRecommendation(
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summary=fix_data.get("summary", "無建議"),
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steps=fix_data.get("steps", []),
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code_suggestion=fix_data.get("code_suggestion"),
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)
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# 建立 PreventionMeasure 列表
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prevention = []
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for p in data.get("prevention", []):
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prevention.append(PreventionMeasure(
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type=p.get("type", "UNKNOWN"),
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description=p.get("description", ""),
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))
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# 建立最終結果
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return ErrorAnalysisResult(
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root_cause=data.get("root_cause", "無法判斷根因"),
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category=data.get("category", "UNKNOWN"),
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severity=data.get("severity", "MEDIUM"),
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impact_assessment=data.get("impact_assessment", "影響評估中"),
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fix_recommendation=fix_recommendation,
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prevention=prevention,
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related_files=data.get("related_files", []),
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confidence=float(data.get("confidence", 0.5)),
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reasoning=data.get("reasoning", ""),
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)
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except json.JSONDecodeError as e:
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logger.warning(
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"error_analysis_json_decode_failed",
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error=str(e),
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raw_response=raw_response[:200],
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)
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return None
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except Exception as e:
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logger.warning(
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"error_analysis_parse_error",
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error=str(e),
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)
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return None
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# =============================================================================
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# Singleton
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# =============================================================================
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_error_analyzer_service: ErrorAnalyzerService | None = None
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def get_error_analyzer_service() -> ErrorAnalyzerService:
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"""取得 Error Analyzer Service 實例 (Singleton)"""
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global _error_analyzer_service
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if _error_analyzer_service is None:
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_error_analyzer_service = ErrorAnalyzerService()
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return _error_analyzer_service
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def set_error_analyzer_service(service: ErrorAnalyzerService) -> None:
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"""設定 Error Analyzer Service 實例 (for testing)"""
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global _error_analyzer_service
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_error_analyzer_service = service
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Reference in New Issue
Block a user