fix(memory): honor empty extraction results

conversation-memory.mjs

@@ -109,6 +109,7 @@ function buildMemoryPrompt(messages) {
     '你是 TKMind 的用户长期记忆提取器。',
     '请从下面的用户对话中提取可以长期复用的个人记忆。',
     '只记录用户明确表达或强证据支持的信息，不要猜测，不要记录临时闲聊。',
+    '不要把问题、请求、指令或待办本身当作用户事实；问句没有提供答案时不要记录。',
     '不要记录密码、密钥、身份证、手机号、银行卡等敏感信息。',
     '只返回 JSON 对象，不要 Markdown，不要解释。',
     '格式：{"memories":[{"label":"preference|habit|interest|goal|fact|experience|knowledge","text":"一句完整中文记忆","confidence":0.0-1.0}]}',
@@ -382,7 +383,9 @@ export function createConversationMemoryService(pool, options = {}) {
         warnLlmExtractionFailed(err);
       }
     }
-    if (!memories?.length) memories = fallbackMemoriesFromMessages(messages);
+    // An empty array is an authoritative LLM decision that the batch contains no
+    // durable memory. Only fall back when extraction was unavailable (`null`).
+    if (memories == null) memories = fallbackMemoriesFromMessages(messages);
     const stored = await storeMemories(userId, messages, memories);
     await markAnalyzed(messages.map((message) => message.id));
     return { ok: true, analyzed: messages.length, memories: stored };

conversation-memory.test.mjs

@@ -208,6 +208,35 @@ test('saveAndAnalyze marks messages analyzed when llm extraction fails and no me
   else process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = previous;
 });
 
+test('saveAndAnalyze still uses fallback when llm extraction is unavailable', async () => {
+  const previous = process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED;
+  process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = '1';
+  const pool = createPool();
+  const service = createConversationMemoryService(pool, {
+    now: () => 2250,
+    llmProviderService: {
+      async createChatCompletion() {
+        throw new Error('upstream unavailable');
+      },
+    },
+  });
+
+  const result = await service.saveAndAnalyze('session-fallback', 'user-fallback', [
+    {
+      id: 'm-fallback',
+      role: 'user',
+      content: [{ type: 'text', text: '我喜欢简洁的回答。' }],
+      metadata: { userVisible: true },
+    },
+  ]);
+
+  assert.equal(result.memories, 1);
+  assert.match(pool.state.memories[0].memory_text, /简洁/);
+
+  if (previous == null) delete process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED;
+  else process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = previous;
+});
+
 test('saveAndAnalyze uses admin effective env for memory extraction model', async () => {
   const previous = process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED;
   process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = '1';
@@ -284,6 +313,40 @@ test('saveAndAnalyze extracts memories through llmProviderService', async () =>
   else process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = previous;
 });
 
+test('saveAndAnalyze respects an empty llm result instead of storing a question through fallback', async () => {
+  const previous = process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED;
+  process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = '1';
+  const pool = createPool();
+  const service = createConversationMemoryService(pool, {
+    now: () => 2750,
+    llmProviderService: {
+      async createChatCompletion({ messages }) {
+        assert.match(String(messages?.[0]?.content ?? ''), /问句没有提供答案时不要记录/);
+        return {
+          ok: true,
+          reply: JSON.stringify({ memories: [] }),
+        };
+      },
+    },
+  });
+
+  const result = await service.saveAndAnalyze('session-question', 'user-question', [
+    {
+      id: 'm-question',
+      role: 'user',
+      content: [{ type: 'text', text: '用户记住的记忆召回灰度测试代号是什么？' }],
+      metadata: { userVisible: true },
+    },
+  ]);
+
+  assert.equal(result.analyzed, 1);
+  assert.equal(result.memories, 0);
+  assert.equal(pool.state.memories.length, 0);
+
+  if (previous == null) delete process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED;
+  else process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = previous;
+});
+
 test('saveAndAnalyze throttles repeated llm extraction warnings', async () => {
   const previous = process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED;
   process.env.USER_CONVERSATION_MEMORY_LLM_ENABLED = '1';

docs/regression-guards/memory-v2-candidate-and-lifecycle.md

@@ -17,6 +17,10 @@
 包含已注入的 `[Memory Context]`。提取器因此可能把旧记忆再次沉淀，而忽略用户本轮明确
 要求保存的内容，形成旧记忆自我复制。
 
+提取器返回 `{"memories":[]}` 时，旧逻辑仍会触发规则回退，可能把“……是什么”一类
+问句误存为事实。空数组必须视为成功的“无需保存”判断；只有提取不可用并返回 `null`
+时才允许规则回退。
+
 当前生产使用的本地 hash embedding 只有 3 维，且连续中文可能被视为单个 token。即使正确
 记忆已进入 pgvector，它也可能排在向量 Top-K 之外。因此 pgvector 读取必须合并有界的
 向量候选与最近候选，再以中文字符 n-gram 查询覆盖率重排；无词法重合时保持向量排序。
@@ -41,11 +45,13 @@
    原始消息查询失败时必须 fail-open 到现有可见会话，不能阻塞保存接口。
 8. pgvector 召回必须同时覆盖有界向量候选和有界最近候选，去重后只返回请求的 limit；
    中文词法重排用于弥补低维本地 hash 的排序缺陷，且候选池上限不得超过 100。
+9. LLM 提取明确返回空数组时不得再走规则回退；提取提示必须明确排除没有提供答案的
+   问题、请求、指令和待办，避免把问句本身沉淀为长期记忆。
 
 ## 回归检查
 
 ```bash
-node --test conversation-repair.test.mjs memory-v2-personal-store.test.mjs memory-v2-lifecycle.test.mjs \
+node --test conversation-memory.test.mjs conversation-repair.test.mjs memory-v2-personal-store.test.mjs memory-v2-lifecycle.test.mjs \
   memory-v2-pgvector-backfill.test.mjs memory-v2-runtime.test.mjs
 npm test
 ```