refactor: improve comment formatting and streamline AI handling in findings processing
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+17
-30
@@ -11,7 +11,6 @@ const LEVELS = ['critical', 'warning', 'info'];
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export async function analyzeWithRole(role, diff) {
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console.log(` [${role.name}] 開始分析...`);
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const findings = await chatJSON(role.system_prompt, `以下是 Git Diff 內容:\n\n${diff}`);
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// 確保每筆都有必要欄位,並標記為新問題
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const valid = findings.filter(f => f.level && f.role && f.location && f.suggestion)
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.map(f => ({ ...f, is_new: true }));
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console.log(` [${role.name}] 找到 ${valid.length} 個問題`);
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@@ -40,7 +39,6 @@ export function loadOldFindings(workspace) {
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/**
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* 合併新舊 findings,以 (role + location + suggestion前50字) 為 key 去除重複
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* 舊問題保留,新問題若與舊問題重複則捨棄
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*/
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export function mergeFindings(oldFindings, newFindings) {
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const key = f => `${f.role}|${f.location}|${String(f.suggestion).slice(0, 50)}`;
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@@ -63,8 +61,17 @@ export function sortByLevel(findings) {
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}
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/**
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* 呼叫 LLM 進行語意去重,回傳去重後的 findings
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* 失敗時降級回傳原始 findings
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* AI 呼叫失敗時的統一降級處理
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*/
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function fallback(label, findings, e) {
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const status = e.response?.status;
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const reason = (status === 402 || status === 429) ? `${status} 額度/限流` : e.message;
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console.log(` ⚠️ ${label}失敗(${reason}),降級:保留所有問題`);
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return findings;
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}
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/**
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* 呼叫 LLM 進行語意去重,失敗時降級回傳原始 findings
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*/
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export async function deduplicateWithAI(findings) {
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if (findings.length === 0) return findings;
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@@ -74,29 +81,20 @@ export async function deduplicateWithAI(findings) {
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保留等級較高的版本,優先保留 critical > warning > info。
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只回傳去重後的 JSON 陣列,不要有其他文字。`;
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const userContent = `以下是問題清單,請去除語意重複的項目:\n\n${JSON.stringify(findings, null, 2)}`;
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try {
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const result = await chatJSON(systemPrompt, userContent);
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const result = await chatJSON(systemPrompt, `以下是問題清單,請去除語意重複的項目:\n\n${JSON.stringify(findings, null, 2)}`);
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if (Array.isArray(result) && result.length > 0) {
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console.log(` AI 去重: ${findings.length} -> ${result.length} 筆`);
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return result;
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}
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throw new Error('AI 回傳空陣列');
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} catch (e) {
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const status = e.response?.status;
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if (status === 402 || status === 429) {
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console.log(` ⚠️ AI 去重失敗(${status} 額度/限流),降級:保留所有問題`);
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} else {
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console.log(` ⚠️ AI 去重失敗(${e.message}),降級:保留所有問題`);
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}
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return findings;
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return fallback('AI 去重', findings, e);
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}
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}
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/**
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* 讀取排除問題檔案(從 workspace 的 EXCLUSIONS_PATH)
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* 格式:[{ role, location, suggestion }],欄位可部分省略,省略表示萬用
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*/
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export function loadExclusions(workspace) {
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const fullPath = path.join(workspace, EXCLUSIONS_PATH);
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@@ -125,17 +123,14 @@ export function applyExclusions(findings, exclusions) {
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const filtered = findings.filter(f => !exclusions.some(ex => {
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const fPath = String(f.location).split(':')[0];
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const exPath = ex.location ? String(ex.location).split(':')[0] : null;
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return (!exPath || fPath === exPath) &&
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(!ex.role || ex.role === f.role);
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return (!exPath || fPath === exPath) && (!ex.role || ex.role === f.role);
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}));
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console.log(` 排除過濾: ${before} -> ${filtered.length} 筆(排除 ${before - filtered.length} 筆)`);
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return filtered;
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}
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/**
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* 呼叫 AI 判斷哪些問題是誤報或不需處理,回傳需保留的 findings
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* exclusions 為已知誤報清單,供 AI 參考判斷
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* 失敗時降級回傳原始 findings
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* 呼叫 AI 判斷哪些問題是誤報或不需處理,失敗時降級回傳原始 findings
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*/
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export async function filterFalsePositivesWithAI(findings, exclusions = []) {
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if (findings.length === 0) return findings;
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@@ -152,22 +147,14 @@ export async function filterFalsePositivesWithAI(findings, exclusions = []) {
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3. 與已知誤報清單語意相近的問題(檔案路徑相同且建議內容相似)
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只回傳需要保留的問題 JSON 陣列,不要有其他文字。${exclusionHint}`;
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const userContent = `請判斷以下問題清單,移除誤報或不需處理的問題:\n\n${JSON.stringify(findings, null, 2)}`;
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try {
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const result = await chatJSON(systemPrompt, userContent);
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const result = await chatJSON(systemPrompt, `請判斷以下問題清單,移除誤報或不需處理的問題:\n\n${JSON.stringify(findings, null, 2)}`);
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if (Array.isArray(result) && result.length > 0) {
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console.log(` AI 誤報過濾: ${findings.length} -> ${result.length} 筆`);
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return result;
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}
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throw new Error('AI 回傳空陣列或非陣列');
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} catch (e) {
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const status = e.response?.status;
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if (status === 402 || status === 429) {
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console.log(` ⚠️ AI 誤報過濾失敗(${status} 額度/限流),降級:保留所有問題`);
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} else {
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console.log(` ⚠️ AI 誤報過濾失敗(${e.message}),降級:保留所有問題`);
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}
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return findings;
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return fallback('AI 誤報過濾', findings, e);
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}
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}
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