Fix garbled text checks and L0 LLM handling

modules/story-summary/vector/llm/atom-extraction.js

@@ -26,7 +26,13 @@ export function isBatchCancelled() {
 
 const SYSTEM_PROMPT = `你是叙事锚点提取器。从一轮对话（用户发言+角色回复）中提取4-8个关键锚点。
 
-只输出JSON：
+输入格式：
+<round>
+  <user>...</user>
+  <assistant>...</assistant>
+</round>
+
+只输出严格JSON（不要解释，不要前后多余文字）：
 {"atoms":[{"t":"类型","s":"主体","v":"值","f":"来源"}]}
 
 类型（t）：
@@ -72,13 +78,13 @@ async function extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, op
 
     if (userMessage?.mes?.trim()) {
         const userText = filterText(userMessage.mes);
-        parts.push(`【用户：${userName}】\n${userText}`);
+        parts.push(`<user name="${userName}">\n${userText}\n</user>`);
     }
 
     const aiText = filterText(aiMessage.mes);
-    parts.push(`【角色：${aiName}】\n${aiText}`);
+    parts.push(`<assistant name="${aiName}">\n${aiText}\n</assistant>`);
 
-    const input = parts.join('\n\n---\n\n');
+    const input = `<round>\n${parts.join('\n')}\n</round>`;
 
     xbLog.info(MODULE_ID, `floor ${aiFloor} 发送输入 len=${input.length}`);
 
@@ -89,43 +95,46 @@ async function extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, op
             const response = await callLLM([
                 { role: 'system', content: SYSTEM_PROMPT },
                 { role: 'user', content: input },
+                { role: 'assistant', content: '收到，开始提取并仅输出 JSON。' },
             ], {
                 temperature: 0.2,
                 max_tokens: 500,
                 timeout,
             });
 
-            if (!response || !String(response).trim()) {
+            const rawText = String(response || '');
+            if (!rawText.trim()) {
                 xbLog.warn(MODULE_ID, `floor ${aiFloor} 解析失败：响应为空`);
                 if (attempt < RETRY_COUNT) {
                     await sleep(RETRY_DELAY);
                     continue;
                 }
-                return [];
+                return null;
             }
 
             let parsed;
             try {
-                parsed = parseJson(response);
+                parsed = parseJson(rawText);
             } catch (e) {
                 xbLog.warn(MODULE_ID, `floor ${aiFloor} 解析失败：JSON 异常`);
                 if (attempt < RETRY_COUNT) {
                     await sleep(RETRY_DELAY);
                     continue;
                 }
-                return [];
+                return null;
             }
 
             if (!parsed?.atoms || !Array.isArray(parsed.atoms)) {
+                xbLog.warn(MODULE_ID, `floor ${aiFloor} atoms 缺失，raw="${rawText.slice(0, 300)}"`);
                 xbLog.warn(MODULE_ID, `floor ${aiFloor} 解析失败：atoms 缺失`);
                 if (attempt < RETRY_COUNT) {
                     await sleep(RETRY_DELAY);
                     continue;
                 }
-                return [];
+                return null;
             }
 
-            return parsed.atoms
+            const filtered = parsed.atoms
                 .filter(a => a?.t && a?.v)
                 .map((a, idx) => ({
                     atomId: `atom-${aiFloor}-${idx}`,
@@ -136,9 +145,13 @@ async function extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, op
                     source: a.f === 'u' ? 'user' : 'ai',
                     semantic: buildSemantic(a, userName, aiName),
                 }));
+            if (!filtered.length) {
+                xbLog.warn(MODULE_ID, `floor ${aiFloor} atoms 为空，raw="${rawText.slice(0, 300)}"`);
+            }
+            return filtered;
 
         } catch (e) {
-            if (batchCancelled) return [];
+            if (batchCancelled) return null;
 
             if (attempt < RETRY_COUNT) {
                 xbLog.warn(MODULE_ID, `floor ${aiFloor} 第${attempt + 1}次失败，重试...`, e?.message);
@@ -146,11 +159,11 @@ async function extractAtomsForRoundWithRetry(userMessage, aiMessage, aiFloor, op
                 continue;
             }
             xbLog.error(MODULE_ID, `floor ${aiFloor} 失败`, e);
-            return [];
+            return null;
         }
     }
 
-    return [];
+    return null;
 }
 
 /**

modules/story-summary/vector/llm/llm-service.js

@@ -1,10 +1,12 @@
-// ═══════════════════════════════════════════════════════════════════════════
+// ═══════════════════════════════════════════════════════════════════════════
 // vector/llm/llm-service.js
 // ═══════════════════════════════════════════════════════════════════════════
-
 import { xbLog } from '../../../../core/debug-core.js';
+import { getVectorConfig } from '../../data/config.js';
 
 const MODULE_ID = 'vector-llm-service';
+const SILICONFLOW_API_URL = 'https://api.siliconflow.cn';
+const DEFAULT_L0_MODEL = 'Qwen/Qwen3-8B';
 
 // 唯一 ID 计数器
 let callCounter = 0;
@@ -36,11 +38,17 @@ export async function callLLM(messages, options = {}) {
     } = options;
 
     const mod = getStreamingModule();
-    if (!mod) throw new Error('生成模块未加载');
+    if (!mod) throw new Error('Streaming module not ready');
+
+    const cfg = getVectorConfig();
+    const apiKey = cfg?.online?.key || '';
+    if (!apiKey) {
+        throw new Error('L0 requires siliconflow API key');
+    }
 
     const top64 = b64UrlEncode(JSON.stringify(messages));
 
-    // ★ 每次调用用唯一 ID，避免 session 冲突
+    // 每次调用用唯一 ID，避免 session 冲突
     const uniqueId = generateUniqueId('l0');
 
     const args = {
@@ -50,6 +58,10 @@ export async function callLLM(messages, options = {}) {
         id: uniqueId,
         temperature: String(temperature),
         max_tokens: String(max_tokens),
+        api: 'openai',
+        apiurl: SILICONFLOW_API_URL,
+        apipassword: apiKey,
+        model: DEFAULT_L0_MODEL,
     };
 
     try {