feat: conversation long-term memory + fix source ENUM bug
- New: conversationSummarizer.js (LLM summary every 3 turns, loadBestSummary, persistFinalSummary) - db/index.js: conversation_summaries table, upsertConversationSummary, getSessionSummary - redisClient.js: setSummary/getSummary (TTL 2h) - nativeVoiceGateway.js: _turnCount tracking, trigger summarize, persist on close - realtimeDialogRouting.js: inject summary context, reduce history 5->3 rounds - Fix: messages source ENUM missing 'search_knowledge' causing chat DB writes to fail
This commit is contained in:
User
@@ -26,8 +26,8 @@ async function migrateSchema() {
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if (!(await columnMatchesType('messages', 'role', "'system'"))) {
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await pool.execute("ALTER TABLE `messages` MODIFY COLUMN `role` ENUM('user', 'assistant', 'tool', 'system') NOT NULL");
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}
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if (!(await columnMatchesType('messages', 'source', "'chat_bot'"))) {
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await pool.execute("ALTER TABLE `messages` MODIFY COLUMN `source` ENUM('voice_asr', 'voice_bot', 'voice_tool', 'chat_user', 'chat_bot') NOT NULL");
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if (!(await columnMatchesType('messages', 'source', "'search_knowledge'"))) {
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await pool.execute("ALTER TABLE `messages` MODIFY COLUMN `source` ENUM('voice_asr', 'voice_bot', 'voice_tool', 'chat_user', 'chat_bot', 'search_knowledge') NOT NULL");
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}
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await ensureColumnExists('messages', 'tool_name', '`tool_name` VARCHAR(64) NULL AFTER `source`');
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await ensureColumnExists('messages', 'meta_json', '`meta_json` JSON NULL AFTER `tool_name`');
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@@ -81,7 +81,7 @@ async function initialize() {
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session_id VARCHAR(128) NOT NULL,
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role ENUM('user', 'assistant', 'tool', 'system') NOT NULL,
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content TEXT NOT NULL,
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source ENUM('voice_asr', 'voice_bot', 'voice_tool', 'chat_user', 'chat_bot') NOT NULL,
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source ENUM('voice_asr', 'voice_bot', 'voice_tool', 'chat_user', 'chat_bot', 'search_knowledge') NOT NULL,
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tool_name VARCHAR(64),
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meta_json JSON,
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created_at BIGINT,
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@@ -90,6 +90,21 @@ async function initialize() {
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
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`);
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await pool.execute(`
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CREATE TABLE IF NOT EXISTS conversation_summaries (
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id INT AUTO_INCREMENT PRIMARY KEY,
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session_id VARCHAR(128) NOT NULL,
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user_id VARCHAR(128),
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summary TEXT NOT NULL,
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turn_count INT DEFAULT 0,
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topics JSON,
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created_at BIGINT,
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updated_at BIGINT,
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UNIQUE INDEX idx_session (session_id),
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INDEX idx_user_time (user_id, updated_at)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4
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`);
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await migrateSchema();
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console.log(`[DB] MySQL connected: ${dbName}, tables ready`);
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@@ -202,9 +217,31 @@ async function getSessionList(userId, limit = 50) {
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*/
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async function deleteSession(sessionId) {
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await pool.execute('DELETE FROM messages WHERE session_id = ?', [sessionId]);
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await pool.execute('DELETE FROM conversation_summaries WHERE session_id = ?', [sessionId]);
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await pool.execute('DELETE FROM sessions WHERE id = ?', [sessionId]);
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}
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// ==================== Conversation Summaries ====================
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async function upsertConversationSummary(sessionId, userId, summary, { turnCount = 0, topics = null } = {}) {
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const now = Date.now();
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const topicsJson = topics ? JSON.stringify(topics) : null;
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await pool.execute(
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`INSERT INTO conversation_summaries (session_id, user_id, summary, turn_count, topics, created_at, updated_at)
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VALUES (?, ?, ?, ?, ?, ?, ?)
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ON DUPLICATE KEY UPDATE summary=VALUES(summary), turn_count=VALUES(turn_count), topics=VALUES(topics), updated_at=VALUES(updated_at)`,
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[sessionId, userId || null, summary, turnCount, topicsJson, now, now]
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);
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}
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async function getSessionSummary(sessionId) {
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const [rows] = await pool.execute(
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'SELECT summary, turn_count, topics, updated_at FROM conversation_summaries WHERE session_id = ? LIMIT 1',
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[sessionId]
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);
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return rows[0] || null;
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}
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module.exports = {
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initialize,
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getPool,
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@@ -217,4 +254,6 @@ module.exports = {
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getHistoryForLLM,
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getSessionList,
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deleteSession,
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upsertConversationSummary,
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getSessionSummary,
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};
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425
test2/server/docs/conversation-long-term-memory.md
Normal file
425
test2/server/docs/conversation-long-term-memory.md
Normal file
@@ -0,0 +1,425 @@
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# 对话长期记忆方案：会话内摘要 + 跨会话衔接
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## 一、背景与问题
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### 1.1 现状
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当前对话记忆体系有 4 层，均为短期记忆：
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| 层级 | 机制 | 存储 | 有效窗口 | 代码位置 |
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|------|------|------|----------|----------|
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| L1 | Redis 对话历史 | 最近5轮(10条) | 30分钟TTL | `redisClient.js` HISTORY_MAX_LEN=10 |
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| L2 | MySQL 全量记录 | 无限 | 永久 | `db.addMessage()` |
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| L3 | contextKeywordTracker | 最近8个关键词 | 30分钟TTL | `contextKeywordTracker.js` |
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| L4 | KB话题记忆 | 最后1个话题 | 60秒TTL | `session._lastKbTopic` |
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| L5 | handoffSummary | 确定性摘要(最后8条) | 会话生命周期 | `loadHandoffSummaryForVoice()` |
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### 1.2 双重核心问题
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**问题 A：会话内遗忘（第6轮起丢失）**
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S2S 和 KB 检索实际使用的上下文只有最近5轮原文（`getRecentHistory(sessionId, 5)`），第6轮开始的信息完全丢失。
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**问题 B：会话重启时上下文断裂**
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| 断裂场景 | 触发条件 | 当前结果 |
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|----------|----------|----------|
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| ① 同 sessionId 快速重连 | 网络抖动/页面刷新（30min内） | Redis 历史还在，但 session 内存状态全丢（`_lastKbTopic`、`contextKeywordTracker`、`_turnCount`） |
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| ② 同 sessionId 超时重连 | 用户隔一段时间再来（30min后） | Redis TTL 过期，只剩 MySQL + `loadHandoffSummaryForVoice`（确定性摘要，只取最后8条，质量差） |
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| ③ PM2 重启/崩溃恢复 | 部署或进程崩溃 | 所有 session 内存清零，Redis 还在 |
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| ④ voice → chat 模式切换 | 用户从语音切文字 | `chat.js` 有 `loadHandoffMessages` 做交接，但也是确定性摘要 |
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> **注意**：新 sessionId = 全新对话，不需要跨 session 记忆继承。
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**关键差距**：MySQL 存了全量原文却从未被用于生成高质量摘要；`buildDeterministicHandoffSummary` 只做模式提取（"当前问题 + 上一轮关注 + 已给信息"），压缩比低、语义丢失大。
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### 1.3 受影响场景
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| 场景 | 当前体验 | 占比估算 |
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|------|----------|----------|
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| 用户聊了8轮后追问"刚才那个产品" | AI 丢失上下文，回答偏题 | ~20-30% 深度对话 |
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| 长对话中多产品对比 | 早期提到的产品信息丢失 | ~15% |
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| 网络抖动后重连继续聊 | 内存状态丢失，KB话题追踪断裂 | ~10% |
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| 隔30分钟再打开继续咨询 | Redis过期，只剩粗糙的确定性摘要 | ~10% |
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| voice→chat 切换后追问语音聊的内容 | 只有确定性摘要，细节丢失 | ~5% |
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---
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## 二、设计目标
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解决两个并列的核心问题，缺一不可：
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| 目标 | 描述 | 衡量标准 |
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|------|------|----------|
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| **G1：会话内长记忆** | 单次对话超过5轮后仍能准确关联早期话题 | 10轮后追问命中率 ≥ 80% |
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| **G2：会话重启上下文衔接** | 同sessionId重连/PM2重启后无缝延续对话 | 重连后首轮上下文关联率 ≥ 90% |
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---
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## 三、记忆分层架构（三层设计）
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```
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┌─────────────────────────────────────────────────────┐
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│ L1 热记忆（Redis） │
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│ · 最近3轮原文（精确上下文） │
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│ · 当前会话摘要（LLM生成，每3轮更新） │
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│ · TTL: 2小时 │
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├─────────────────────────────────────────────────────┤
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│ L2 温记忆（MySQL conversation_summaries 表） │
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│ · 每个 session 的最终摘要（会话结束时写入） │
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│ · 永久存储，同 sessionId 重连时可恢复 │
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├─────────────────────────────────────────────────────┤
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│ L3 冷记忆（MySQL messages 表，现有） │
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│ · 全量原文记录 │
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│ · 仅在 L1+L2 都缺失时作为降级数据源 │
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│ · 通过 buildDeterministicHandoffSummary 提取 │
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└─────────────────────────────────────────────────────┘
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```
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**加载优先级**：L1(Redis摘要) → L2(MySQL摘要) → L3(MySQL原文确定性提取)
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---
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## 四、方案详细设计
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### 4.1 支柱一：会话内摘要（解决 G1）
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#### 触发机制
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```
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用户说话 → persistUserSpeech → session._turnCount++
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AI回复 → persistAssistantSpeech ──→ _turnCount % 3 === 0 ?
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├─ Yes → 异步 summarize() [不阻塞]
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│ ↓
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│ LLM(旧摘要 + 最近3轮原文)
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│ ↓
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│ Redis.setSummary(sessionId)
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│ MySQL.upsertSummary(sessionId) // 双写
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└─ No → 继续
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```
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#### 滚雪球数据流
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```
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Round 1-3: 原文正常存Redis
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Round 3 完成后:
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→ LLM(Round 1-3 原文) → 生成摘要 S1 → 存 Redis + MySQL
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→ 后续上下文 = S1 + 最近3轮原文
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Round 6 完成后:
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→ LLM(S1 + Round 4-6 原文) → 生成摘要 S2 → 存 Redis + MySQL
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→ 后续上下文 = S2 + 最近3轮原文
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...以此类推（每次摘要都包含之前所有轮次的压缩信息）
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```
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#### 上下文注入
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```javascript
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// resolveReply 中
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const summary = await loadBestSummary(sessionId);
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const recentHistory = await redisClient.getRecentHistory(sessionId, 3);
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const context = [];
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if (summary) {
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context.push({ role: 'system', content: `[历史对话摘要] ${summary}` });
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}
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context.push(...recentHistory.map(item => ({ role: item.role, content: item.content })));
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```
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### 4.2 支柱二：会话重启衔接（解决 G2）
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#### 新增 MySQL 表：`conversation_summaries`
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```sql
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CREATE TABLE conversation_summaries (
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id INT AUTO_INCREMENT PRIMARY KEY,
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session_id VARCHAR(128) NOT NULL,
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user_id VARCHAR(128),
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summary TEXT NOT NULL, -- LLM 生成的摘要
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turn_count INT DEFAULT 0, -- 该 session 的总轮次
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topics JSON, -- 提取的话题标签 ["活力健","基础三合一","一成系统"]
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created_at BIGINT,
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updated_at BIGINT,
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UNIQUE INDEX idx_session (session_id),
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INDEX idx_user_time (user_id, updated_at)
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) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4;
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```
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#### 断裂场景修复方案
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| 场景 | 修复策略 | 加载顺序 |
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|------|----------|----------|
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| ① 同 sessionId 快速重连（30min内） | Redis 摘要仍在，直接使用 | L1 Redis |
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| ② 同 sessionId 超时重连（30min后） | Redis 过期，从 MySQL `conversation_summaries` 加载该 sessionId 的摘要 | L2 MySQL摘要 |
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| ③ PM2 重启 | Redis 还在（Redis 独立进程），从 Redis 加载；Redis 也丢了则走 L2 | L1 → L2 |
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| ④ voice→chat 模式切换 | `chat.js` 的 `loadHandoffMessages` 改为优先从 `conversation_summaries` 加载 LLM 摘要 | L2 MySQL摘要 |
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#### 核心函数：`loadBestSummary(sessionId)`
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```javascript
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async function loadBestSummary(sessionId) {
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// 1. 尝试 Redis（最快，~1ms）
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const redisSummary = await redisClient.getSummary(sessionId);
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if (redisSummary) return redisSummary;
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// 2. 尝试 MySQL 当前 session 的摘要（~5ms）
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const sessionSummary = await db.getSessionSummary(sessionId);
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if (sessionSummary) {
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// 回填 Redis 加速后续读取
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await redisClient.setSummary(sessionId, sessionSummary.summary);
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return sessionSummary.summary;
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}
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// 3. 降级：MySQL 原文 → 确定性摘要（现有逻辑）
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return null; // 交给现有的 loadHandoffSummaryForVoice 兜底
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}
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```
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#### 会话结束时持久化
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在 `session.client.on('close')` 中触发：
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```javascript
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// nativeVoiceGateway.js client close handler
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session.client.on('close', () => {
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// ...existing cleanup...
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// 持久化最终摘要到 MySQL（异步，不阻塞关闭流程）
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persistFinalSummary(session).catch(err => {
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console.warn('[NativeVoice] persistFinalSummary failed:', err.message);
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});
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});
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async function persistFinalSummary(session) {
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if (!session._turnCount || session._turnCount < 2) return; // 太短的对话不存
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// 优先用已有的 LLM 摘要
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let summary = await redisClient.getSummary(session.sessionId);
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// 如果还没生成过摘要（对话不足3轮），立刻生成一次
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if (!summary && session._turnCount >= 2) {
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const history = await redisClient.getRecentHistory(session.sessionId, 5);
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if (history && history.length >= 2) {
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summary = await summarizer.summarizeConversation(null, history);
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}
|
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}
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|
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if (summary) {
|
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await db.upsertConversationSummary(session.sessionId, session.userId, summary, {
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turnCount: session._turnCount || 0,
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topics: extractTopicTags(summary), // 从摘要中提取话题标签
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});
|
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}
|
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}
|
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```
|
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### 4.3 话题标签提取（增强跨会话关联）
|
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|
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从摘要中用正则提取产品名/系统名/话题关键词，存入 `topics` JSON 字段：
|
||||
|
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```javascript
|
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function extractTopicTags(summary) {
|
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const tags = new Set();
|
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// 复用现有的 knowledgeKeywords 和 knowledgeQueryResolver
|
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const { TRACKER_KEYWORD_GROUPS, buildKeywordRegex } = require('./knowledgeKeywords');
|
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for (const group of TRACKER_KEYWORD_GROUPS) {
|
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const regex = buildKeywordRegex(group, 'gi');
|
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const matches = summary.match(regex);
|
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if (matches) matches.forEach(m => tags.add(m));
|
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}
|
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return [...tags].slice(0, 10);
|
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}
|
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```
|
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|
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用途：未来可按 `topics` 做更精准的跨会话记忆匹配（如用户上次聊的是"活力健"，这次又提"活力健"，优先加载那次摘要）。
|
||||
|
||||
---
|
||||
|
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## 五、摘要 Prompt 设计
|
||||
|
||||
### 会话内摘要 Prompt（每3轮触发）
|
||||
|
||||
```
|
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你是对话摘要助手。将以下对话历史浓缩为简短摘要，必须保留：
|
||||
1. 用户询问过的所有产品名称和具体问题
|
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2. AI给出的关键数字（剂量、价格、数量等）
|
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3. 用户表达的偏好或关注点
|
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4. 未解决的问题或用户的疑虑
|
||||
|
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规则：只输出摘要正文，不加前缀或标题。150字以内。用"用户"和"助手"指代双方。
|
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```
|
||||
|
||||
### 跨会话注入时的格式
|
||||
|
||||
```
|
||||
用户上次（{时间差描述}）的对话摘要：{summary}
|
||||
```
|
||||
|
||||
例如：`用户上次（2小时前）的对话摘要：用户咨询了活力健和基础三合一的区别，助手介绍了两者成分差异。用户对活力健的服用方法比较关心，每天2次每次1条。用户还问了适不适合高血压人群。`
|
||||
|
||||
---
|
||||
|
||||
## 六、关键设计决策
|
||||
|
||||
| 决策点 | 建议值 | 理由 |
|
||||
|--------|--------|------|
|
||||
| **摘要触发** | `persistAssistantSpeech` 后异步 | 不阻塞对话链路，用户无感知 |
|
||||
| **摘要模型** | 复用 Seed-2.0-lite (`ep-20260320175538-lcg7g`) | 已部署，延迟低（~1-2s），成本低 |
|
||||
| **摘要 max_tokens** | 120 | 控制摘要长度，避免注入过长上下文 |
|
||||
| **摘要 thinking** | `{ type: 'enabled' }` | 异步执行不阻塞，开启推理提升摘要质量 |
|
||||
| **Redis key** | `voice:summary:{sessionId}` | 与对话历史同级 |
|
||||
| **Redis TTL** | 7200秒（2小时） | 长于对话历史的30分钟 |
|
||||
| **MySQL 双写** | 每次摘要同时写 Redis + MySQL | Redis 快读 + MySQL 持久化 |
|
||||
| **原文保留** | 摘要后仍保留最近3轮原文 | 最近对话需精确上下文 |
|
||||
| **最小对话门槛** | `_turnCount >= 2` 才持久化 | 太短的对话（打招呼就走）不值得存 |
|
||||
| **失败降级** | LLM摘要失败 → 确定性摘要 → 5轮原文 | 三级降级保证不影响主链路 |
|
||||
|
||||
---
|
||||
|
||||
## 七、涉及文件修改
|
||||
|
||||
| 文件 | 修改内容 | 复杂度 |
|
||||
|------|----------|--------|
|
||||
| **新增** `services/conversationSummarizer.js` | LLM 摘要生成、异步触发、`loadBestSummary`、`persistFinalSummary`、话题标签提取 | 中 |
|
||||
| `db/index.js` | 新增 `conversation_summaries` 建表 + `upsertConversationSummary` / `getSessionSummary` | 中 |
|
||||
| `services/redisClient.js` | 新增 `setSummary()` / `getSummary()` | 低 |
|
||||
| `services/nativeVoiceGateway.js` | session 增加 `_turnCount`；`persistUserSpeech` 计轮；`persistAssistantSpeech` 后触发摘要；`client.close` 时 `persistFinalSummary` | 低 |
|
||||
| `services/realtimeDialogRouting.js` | `resolveReply` 调用 `loadBestSummary` 注入上下文；`getRecentHistory` 轮数 5→3 | 低 |
|
||||
| `services/kbRetriever.js` | `searchAndRerank` 的 conversationHistory 也注入摘要 | 低 |
|
||||
| `routes/chat.js` | `loadHandoffMessages` 优先从 `conversation_summaries` 加载 LLM 摘要 | 低 |
|
||||
|
||||
### 新增文件结构：`conversationSummarizer.js`
|
||||
|
||||
```
|
||||
exports:
|
||||
- triggerSummarizeIfNeeded(session, sessionId) // 检查轮次 → 异步触发
|
||||
- summarizeConversation(existingSummary, messages) // 调 LLM 生成摘要
|
||||
- loadBestSummary(sessionId) // 三级降级加载最优摘要
|
||||
- persistFinalSummary(session) // 会话结束时持久化
|
||||
- extractTopicTags(text) // 从文本提取话题标签
|
||||
|
||||
constants:
|
||||
- SUMMARIZE_EVERY_N_TURNS = 3
|
||||
- SUMMARY_MAX_TOKENS = 120
|
||||
- SUMMARY_MODEL = process.env.VOLC_ARK_KB_MODEL
|
||||
- MIN_TURNS_TO_PERSIST = 2
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 八、成本与性能影响
|
||||
|
||||
### 成本
|
||||
|
||||
| 指标 | 值 |
|
||||
|------|-----|
|
||||
| 会话内摘要频率 | 每3轮1次 ≈ 平均每会话1-3次 |
|
||||
| 会话结束摘要 | 每会话最多+1次（如果轮次不足3） |
|
||||
| 单次摘要 token | 输入~400 + 输出~120 ≈ 520 tokens |
|
||||
| Seed-2.0-lite 价格 | ~0.001元/千tokens（估算） |
|
||||
| 单会话增加成本 | ~0.001-0.004元 |
|
||||
|
||||
### 性能
|
||||
|
||||
| 指标 | 影响 |
|
||||
|------|------|
|
||||
| 对话响应延迟 | **0ms 增加**（纯异步 fire-and-forget） |
|
||||
| 会话启动延迟 | +5-10ms（`loadBestSummary` 读 Redis/MySQL） |
|
||||
| Redis 读取 | +1次 getSummary/次对话轮（~1ms） |
|
||||
| Redis 存储 | +1 key/session（~300 bytes） |
|
||||
| MySQL 存储 | +1 row/session（~500 bytes） |
|
||||
| MySQL 查询 | 新 session 启动时 1次（按 userId 查最近摘要，~5ms，有索引） |
|
||||
| LLM 调用（后台） | ~1-2s/次，不阻塞用户 |
|
||||
|
||||
---
|
||||
|
||||
## 九、风险评估
|
||||
|
||||
| 风险 | 概率 | 影响 | 缓解措施 |
|
||||
|------|------|------|----------|
|
||||
| 摘要丢失关键产品名 | 中 | 高 | Prompt 强调保留产品名+数字；话题标签做辅助索引 |
|
||||
| 摘要引入幻觉 | 低 | 高 | max_tokens=120；摘要只做压缩不做推理；`[历史对话摘要]` 前缀标识 |
|
||||
| LLM 调用失败 | 低 | 中 | 三级降级：Redis摘要 → MySQL摘要 → 确定性摘要 → 5轮原文 |
|
||||
| MySQL 写入失败 | 低 | 低 | 摘要双写 Redis+MySQL，任一成功即可；下次触发时重试 |
|
||||
| 与 contextKeywordTracker 冲突 | 低 | 低 | 互补：摘要提供语义上下文，tracker 提供精确关键词路由 |
|
||||
| 用户隐私（摘要留存） | - | - | 摘要遵循现有 messages 的同等存储策略；deleteSession 时同步删除摘要 |
|
||||
|
||||
---
|
||||
|
||||
## 十、实施计划
|
||||
|
||||
### P0 - 核心双支柱（1.5天）
|
||||
|
||||
1. `db/index.js`：新增 `conversation_summaries` 表 + CRUD 方法
|
||||
2. `redisClient.js`：新增 `setSummary` / `getSummary`
|
||||
3. **新建** `conversationSummarizer.js`：LLM 摘要 + `loadBestSummary` + `persistFinalSummary`
|
||||
4. `nativeVoiceGateway.js`：轮次计数 + 摘要触发 + close 时持久化
|
||||
5. `realtimeDialogRouting.js`：`resolveReply` 注入摘要上下文
|
||||
|
||||
### P1 - 增强（0.5天）
|
||||
|
||||
6. `kbRetriever.js`：KB 检索的 conversationHistory 也注入摘要
|
||||
7. `routes/chat.js`：voice→chat 切换时优先加载 LLM 摘要
|
||||
8. 话题标签提取 + `extractTopicTags`
|
||||
|
||||
### P2 - 高级（未来）
|
||||
|
||||
9. 摘要质量监控（定期抽检摘要 vs 原文的信息保留率）
|
||||
10. 用户画像标签聚合（高频话题、偏好产品、健康关注点）——如未来需要跨 session 记忆再启用
|
||||
|
||||
---
|
||||
|
||||
## 十一、验证方案
|
||||
|
||||
### 会话内记忆测试
|
||||
|
||||
```
|
||||
1. 模拟6轮对话 → 验证第3轮后生成摘要
|
||||
2. 验证摘要正确保留产品名和关键数字
|
||||
3. 第7轮追问第1轮的产品 → 验证上下文关联正确
|
||||
4. LLM 摘要失败时 → 验证降级到5轮原文模式
|
||||
```
|
||||
|
||||
### 跨会话衔接测试
|
||||
|
||||
```
|
||||
5. 完成6轮对话 → 断开 → 同 sessionId 重连 → 验证首轮即有摘要上下文
|
||||
6. 完成6轮对话 → 等 Redis 过期 → 重连 → 验证从 MySQL 加载摘要
|
||||
8. PM2 重启 → 重连 → 验证摘要恢复
|
||||
9. voice→chat 切换 → 验证 chat 模式拿到语音会话摘要
|
||||
```
|
||||
|
||||
### 降级测试
|
||||
|
||||
```
|
||||
10. Redis 不可用 → 验证从 MySQL 加载 + 主链路不受影响
|
||||
11. MySQL 摘要表为空 → 验证降级到确定性摘要
|
||||
12. LLM 服务不可用 → 验证降级到5轮原文模式
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 十二、配置项
|
||||
|
||||
| 环境变量 | 默认值 | 说明 |
|
||||
|----------|--------|------|
|
||||
| `ENABLE_CONVERSATION_SUMMARY` | `true` | 总开关 |
|
||||
| `SUMMARY_EVERY_N_TURNS` | `3` | 每N轮触发一次摘要 |
|
||||
| `SUMMARY_MAX_TOKENS` | `120` | 摘要最大 token 数 |
|
||||
| `SUMMARY_REDIS_TTL_SECONDS` | `7200` | 摘要 Redis TTL（秒） |
|
||||
| `SUMMARY_MODEL` | 同 `VOLC_ARK_KB_MODEL` | 摘要使用的模型 endpoint |
|
||||
| `SUMMARY_MIN_TURNS_TO_PERSIST` | `2` | 最小轮次门槛，低于此值不持久化 |
|
||||
|
||||
---
|
||||
|
||||
## 十三、与现有机制的关系
|
||||
|
||||
| 现有机制 | 变更 | 共存关系 |
|
||||
|----------|------|----------|
|
||||
| `redisClient.pushMessage` / `getRecentHistory` | `getRecentHistory` 轮数从5降到3 | 摘要覆盖早期轮次，原文只需最近3轮 |
|
||||
| `contextKeywordTracker` | 不变 | 互补：tracker 做精确关键词路由，摘要做语义上下文 |
|
||||
| `session._lastKbTopic` | 不变 | 互补：60s 窗口保护仍需要，摘要不能替代实时话题追踪 |
|
||||
| `loadHandoffSummaryForVoice` | 改为降级备选 | LLM 摘要不可用时才调用确定性摘要 |
|
||||
| `buildDeterministicHandoffSummary` | 保留作为 L3 降级 | 三级降级的最后一道防线 |
|
||||
| `withHandoffSummary` | 由 `loadBestSummary` 替代 | 新函数统一管理所有来源的摘要注入 |
|
||||
200
test2/server/services/conversationSummarizer.js
Normal file
200
test2/server/services/conversationSummarizer.js
Normal file
@@ -0,0 +1,200 @@
|
||||
const axios = require('axios');
|
||||
const redisClient = require('./redisClient');
|
||||
const db = require('../db');
|
||||
|
||||
// ============ 配置 ============
|
||||
const ENABLED = (process.env.ENABLE_CONVERSATION_SUMMARY || 'true') !== 'false';
|
||||
const SUMMARIZE_EVERY_N_TURNS = parseInt(process.env.SUMMARY_EVERY_N_TURNS) || 3;
|
||||
const SUMMARY_MAX_TOKENS = parseInt(process.env.SUMMARY_MAX_TOKENS) || 120;
|
||||
const MIN_TURNS_TO_PERSIST = parseInt(process.env.SUMMARY_MIN_TURNS_TO_PERSIST) || 2;
|
||||
const SUMMARY_MODEL = process.env.VOLC_ARK_KB_MODEL || process.env.VOLC_ARK_ENDPOINT_ID || '';
|
||||
const ARK_API_KEY = process.env.VOLC_ARK_API_KEY || process.env.VOLC_ACCESS_KEY_ID || '';
|
||||
const ARK_BASE_URL = 'https://ark.cn-beijing.volces.com/api/v3/chat/completions';
|
||||
|
||||
const SUMMARY_PROMPT = `你是对话摘要助手。将以下对话历史浓缩为简短摘要，必须保留：
|
||||
1. 用户询问过的所有产品名称和具体问题
|
||||
2. AI给出的关键数字（剂量、价格、数量等）
|
||||
3. 用户表达的偏好或关注点
|
||||
4. 未解决的问题或用户的疑虑
|
||||
|
||||
规则：只输出摘要正文，不加前缀或标题。150字以内。用"用户"和"助手"指代双方。`;
|
||||
|
||||
// ============ LLM 摘要生成 ============
|
||||
|
||||
async function summarizeConversation(existingSummary, recentMessages) {
|
||||
if (!ARK_API_KEY || !SUMMARY_MODEL) {
|
||||
console.warn('[Summarizer] missing ARK_API_KEY or SUMMARY_MODEL, skip');
|
||||
return null;
|
||||
}
|
||||
|
||||
const transcript = recentMessages
|
||||
.map((m) => `${m.role === 'user' ? '用户' : '助手'}：${(m.content || '').trim()}`)
|
||||
.filter(Boolean)
|
||||
.join('\n');
|
||||
|
||||
if (!transcript) return null;
|
||||
|
||||
const userContent = existingSummary
|
||||
? `已有摘要：${existingSummary}\n\n新增对话：\n${transcript}`
|
||||
: `对话记录：\n${transcript}`;
|
||||
|
||||
try {
|
||||
const response = await axios.post(ARK_BASE_URL, {
|
||||
model: SUMMARY_MODEL,
|
||||
messages: [
|
||||
{ role: 'system', content: SUMMARY_PROMPT },
|
||||
{ role: 'user', content: userContent },
|
||||
],
|
||||
max_tokens: SUMMARY_MAX_TOKENS,
|
||||
stream: false,
|
||||
thinking: { type: 'enabled' },
|
||||
}, {
|
||||
headers: {
|
||||
'Content-Type': 'application/json',
|
||||
'Authorization': `Bearer ${ARK_API_KEY}`,
|
||||
},
|
||||
timeout: 10000,
|
||||
});
|
||||
|
||||
const content = response.data?.choices?.[0]?.message?.content;
|
||||
return content ? content.trim() : null;
|
||||
} catch (err) {
|
||||
console.warn('[Summarizer] LLM call failed:', err.message);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
// ============ 触发检查 ============
|
||||
|
||||
function triggerSummarizeIfNeeded(session, sessionId) {
|
||||
if (!ENABLED) return;
|
||||
const turnCount = session._turnCount || 0;
|
||||
if (turnCount < SUMMARIZE_EVERY_N_TURNS) return;
|
||||
if (turnCount % SUMMARIZE_EVERY_N_TURNS !== 0) return;
|
||||
|
||||
// 异步执行，不阻塞对话
|
||||
_doSummarize(session, sessionId).catch((err) => {
|
||||
console.warn('[Summarizer] async summarize failed:', err.message);
|
||||
});
|
||||
}
|
||||
|
||||
async function _doSummarize(session, sessionId) {
|
||||
// 获取现有摘要
|
||||
const existingSummary = await redisClient.getSummary(sessionId);
|
||||
|
||||
// 获取最近3轮原文
|
||||
let recent = await redisClient.getRecentHistory(sessionId, SUMMARIZE_EVERY_N_TURNS);
|
||||
if (!recent || recent.length < 2) {
|
||||
// Redis 缺失时从 DB 降级
|
||||
try {
|
||||
recent = await db.getHistoryForLLM(sessionId, SUMMARIZE_EVERY_N_TURNS * 2);
|
||||
} catch { /* ignore */ }
|
||||
}
|
||||
if (!recent || recent.length < 2) return;
|
||||
|
||||
const summary = await summarizeConversation(existingSummary, recent);
|
||||
if (!summary) return;
|
||||
|
||||
// 双写 Redis + MySQL
|
||||
await redisClient.setSummary(sessionId, summary);
|
||||
db.upsertConversationSummary(sessionId, session.userId || null, summary, {
|
||||
turnCount: session._turnCount || 0,
|
||||
topics: extractTopicTags(summary),
|
||||
}).catch((err) => {
|
||||
console.warn('[Summarizer] MySQL upsert failed:', err.message);
|
||||
});
|
||||
|
||||
console.log(`[Summarizer] session=${sessionId} turn=${session._turnCount} summary=${summary.length}chars`);
|
||||
}
|
||||
|
||||
// ============ 三级降级加载 ============
|
||||
|
||||
async function loadBestSummary(sessionId) {
|
||||
// L1: Redis（~1ms）
|
||||
try {
|
||||
const redisSummary = await redisClient.getSummary(sessionId);
|
||||
if (redisSummary) return redisSummary;
|
||||
} catch { /* continue to L2 */ }
|
||||
|
||||
// L2: MySQL conversation_summaries（~5ms）
|
||||
try {
|
||||
const row = await db.getSessionSummary(sessionId);
|
||||
if (row && row.summary) {
|
||||
// 回填 Redis
|
||||
redisClient.setSummary(sessionId, row.summary).catch(() => {});
|
||||
return row.summary;
|
||||
}
|
||||
} catch { /* continue to L3 */ }
|
||||
|
||||
// L3: 降级到现有确定性摘要（由调用方处理）
|
||||
return null;
|
||||
}
|
||||
|
||||
// ============ 会话结束时持久化 ============
|
||||
|
||||
async function persistFinalSummary(session) {
|
||||
if (!ENABLED) return;
|
||||
if (!session._turnCount || session._turnCount < MIN_TURNS_TO_PERSIST) return;
|
||||
|
||||
const sessionId = session.sessionId;
|
||||
|
||||
// 优先用已有的 LLM 摘要
|
||||
let summary = null;
|
||||
try {
|
||||
summary = await redisClient.getSummary(sessionId);
|
||||
} catch { /* ignore */ }
|
||||
|
||||
// 如果还没生成过摘要（对话不足3轮但>=2轮），立刻生成一次
|
||||
if (!summary) {
|
||||
let history = await redisClient.getRecentHistory(sessionId, 5);
|
||||
if (!history || history.length < 2) {
|
||||
try {
|
||||
history = await db.getHistoryForLLM(sessionId, 10);
|
||||
} catch { /* ignore */ }
|
||||
}
|
||||
if (history && history.length >= 2) {
|
||||
summary = await summarizeConversation(null, history);
|
||||
}
|
||||
}
|
||||
|
||||
if (!summary) return;
|
||||
|
||||
// 写入 MySQL（Redis 无需写，会话已结束）
|
||||
await db.upsertConversationSummary(sessionId, session.userId || null, summary, {
|
||||
turnCount: session._turnCount || 0,
|
||||
topics: extractTopicTags(summary),
|
||||
});
|
||||
|
||||
console.log(`[Summarizer] persisted final summary for session=${sessionId} turns=${session._turnCount}`);
|
||||
}
|
||||
|
||||
// ============ 话题标签提取 ============
|
||||
|
||||
const PRODUCT_KEYWORDS = [
|
||||
'活力健', '基础三合一', '肽美', '小红', '大白', '小白',
|
||||
'FitLine', 'PM', '一成系统', '大沃',
|
||||
'心脏宝', '关节灵', '益力康', '免疫宝', '纤体乐',
|
||||
'奥适宝', 'Optimal Set', 'Basics', 'Activize', 'Beauty',
|
||||
'Restorate', 'PowerCocktail', 'ProShape',
|
||||
];
|
||||
|
||||
function extractTopicTags(text) {
|
||||
if (!text) return null;
|
||||
const tags = new Set();
|
||||
for (const kw of PRODUCT_KEYWORDS) {
|
||||
if (text.includes(kw)) {
|
||||
tags.add(kw);
|
||||
}
|
||||
}
|
||||
const arr = [...tags].slice(0, 10);
|
||||
return arr.length > 0 ? arr : null;
|
||||
}
|
||||
|
||||
module.exports = {
|
||||
triggerSummarizeIfNeeded,
|
||||
summarizeConversation,
|
||||
loadBestSummary,
|
||||
persistFinalSummary,
|
||||
extractTopicTags,
|
||||
SUMMARIZE_EVERY_N_TURNS,
|
||||
};
|
||||
@@ -27,21 +27,27 @@ const {
|
||||
const ToolExecutor = require('./toolExecutor');
|
||||
const {
|
||||
DEFAULT_VOICE_ASSISTANT_PROFILE,
|
||||
DEFAULT_CONSULTANT_CONTACT,
|
||||
resolveAssistantProfile,
|
||||
getAssistantDisplayName,
|
||||
buildVoiceSystemRole,
|
||||
buildVoiceGreeting,
|
||||
} = require('./assistantProfileConfig');
|
||||
const { getAssistantProfile } = require('./assistantProfileService');
|
||||
const redisClient = require('./redisClient');
|
||||
const { checkProductLinkTrigger } = require('./productLinkTrigger');
|
||||
const { triggerSummarizeIfNeeded, persistFinalSummary } = require('./conversationSummarizer');
|
||||
|
||||
const sessions = new Map();
|
||||
|
||||
const CONSULTANT_REFERRAL_PATTERN = /咨询(?:专业|你的)?顾问|健康管理顾问|联系顾问|一对一指导|咨询专业|咨询医生|咨询营养师|咨询专业人士|建议.*咨询|问问医生|问问.*营养师/;
|
||||
|
||||
const IDLE_TIMEOUT_MS = 5 * 60 * 1000;
|
||||
const AUDIO_KEEPALIVE_INTERVAL_MS = 20 * 1000;
|
||||
// 3200 bytes ≈ 66ms of silence at 24kHz s16le mono (larger frame to ensure S2S acceptance)
|
||||
const SILENT_AUDIO_FRAME = Buffer.alloc(3200, 0);
|
||||
|
||||
const DEFAULT_VOICE_BOT_NAME = DEFAULT_VOICE_ASSISTANT_PROFILE.nickname;
|
||||
const DEFAULT_VOICE_BOT_NAME = getAssistantDisplayName(DEFAULT_VOICE_ASSISTANT_PROFILE);
|
||||
|
||||
const DEFAULT_VOICE_SYSTEM_ROLE = buildVoiceSystemRole();
|
||||
|
||||
@@ -187,6 +193,7 @@ function persistUserSpeech(session, text) {
|
||||
session.lastPersistedUserAt = now;
|
||||
session.latestUserText = cleanText;
|
||||
session.latestUserTurnSeq = (session.latestUserTurnSeq || 0) + 1;
|
||||
session._turnCount = (session._turnCount || 0) + 1;
|
||||
resetIdleTimer(session);
|
||||
db.addMessage(session.sessionId, 'user', cleanText, 'voice_asr').catch((e) => console.warn('[NativeVoice][DB] add user failed:', e.message));
|
||||
redisClient.pushMessage(session.sessionId, { role: 'user', content: cleanText, source: 'voice_asr' }).catch(() => {});
|
||||
@@ -223,6 +230,24 @@ function persistAssistantSpeech(session, text, { source = 'voice_bot', toolName
|
||||
toolName,
|
||||
sequence: `native_assistant_${now}`,
|
||||
});
|
||||
// 异步触发摘要检查（每N轮）
|
||||
if (persistToDb) {
|
||||
triggerSummarizeIfNeeded(session, session.sessionId);
|
||||
}
|
||||
if (CONSULTANT_REFERRAL_PATTERN.test(cleanText)) {
|
||||
const contact = session.consultantContact || DEFAULT_CONSULTANT_CONTACT;
|
||||
if (contact.mobile || contact.wx_qr_code || contact.wechat_id) {
|
||||
console.log(`[NativeVoice] consultant referral detected session=${session.sessionId} text=${JSON.stringify(cleanText.slice(0, 80))}`);
|
||||
sendJson(session.client, {
|
||||
type: 'consultant_contact',
|
||||
name: contact.name || '大沃专业健康管理顾问',
|
||||
mobile: contact.mobile || '',
|
||||
wx_qr_code: contact.wx_qr_code || '',
|
||||
wechat_id: contact.wechat_id || '',
|
||||
message: '如需个性化健康建议，可联系大沃专业健康管理顾问',
|
||||
});
|
||||
}
|
||||
}
|
||||
return true;
|
||||
}
|
||||
|
||||
@@ -250,6 +275,7 @@ function flushAssistantStream(session, { source = 'voice_bot', toolName = null,
|
||||
return persistAssistantSpeech(session, fullText, { source, toolName, meta });
|
||||
}
|
||||
|
||||
|
||||
async function loadHandoffSummaryForVoice(session) {
|
||||
try {
|
||||
const history = await db.getHistoryForLLM(session.sessionId, 10);
|
||||
@@ -404,6 +430,7 @@ function clearUpstreamSuppression(session) {
|
||||
session.pendingAssistantSource = null;
|
||||
session.pendingAssistantToolName = null;
|
||||
session.pendingAssistantMeta = null;
|
||||
session._pendingEvidencePack = null;
|
||||
session.pendingAssistantTurnSeq = 0;
|
||||
session.blockUpstreamAudio = false;
|
||||
sendJson(session.client, { type: 'assistant_pending', active: false });
|
||||
@@ -487,7 +514,18 @@ async function processReply(session, text, turnSeq = session.latestUserTurnSeq |
|
||||
if (!resolveResult) {
|
||||
resolveResult = await resolveReply(session.sessionId, session, cleanText);
|
||||
}
|
||||
const { delivery, speechText, ragItems, source, toolName, routeDecision, responseMeta } = resolveResult;
|
||||
const { delivery, speechText, ragItems, source, toolName, routeDecision, responseMeta, evidencePack } = resolveResult;
|
||||
// 产品链接触发检测：用户请求查看产品详情时推送对应链接
|
||||
const productLinkResult = checkProductLinkTrigger(cleanText);
|
||||
if (productLinkResult.triggered && productLinkResult.product) {
|
||||
console.log(`[NativeVoice] product link triggered session=${session.sessionId} product=${productLinkResult.product.name}`);
|
||||
sendJson(session.client, {
|
||||
type: 'product_link',
|
||||
product: productLinkResult.product.name,
|
||||
link: productLinkResult.product.link,
|
||||
description: productLinkResult.product.description,
|
||||
});
|
||||
}
|
||||
if (activeTurnSeq !== (session.latestUserTurnSeq || 0)) {
|
||||
console.log(`[NativeVoice] stale reply ignored session=${session.sessionId} activeTurn=${activeTurnSeq} latestTurn=${session.latestUserTurnSeq || 0}`);
|
||||
clearUpstreamSuppression(session);
|
||||
@@ -501,6 +539,7 @@ async function processReply(session, text, turnSeq = session.latestUserTurnSeq |
|
||||
}
|
||||
session.blockUpstreamAudio = false;
|
||||
session._lastPartialAt = 0;
|
||||
session._pendingEvidencePack = null;
|
||||
session.awaitingUpstreamReply = true;
|
||||
session.pendingAssistantSource = 'voice_bot';
|
||||
session.pendingAssistantToolName = null;
|
||||
@@ -515,7 +554,7 @@ async function processReply(session, text, turnSeq = session.latestUserTurnSeq |
|
||||
}
|
||||
session.discardNextAssistantResponse = true;
|
||||
sendJson(session.client, { type: 'tts_reset', reason: 'knowledge_hit' });
|
||||
const ragContent = (ragItems || []).filter((item) => item && item.content);
|
||||
const ragContent = (ragItems || []).filter((item) => item && item.content && item.kind !== 'context');
|
||||
if (ragContent.length > 0) {
|
||||
console.log(`[NativeVoice] processReply sending external_rag to S2S session=${session.sessionId} route=${routeDecision?.route || 'unknown'} items=${ragContent.length}`);
|
||||
// KB话题记忆：记录本轮用户原始问题和时间戳，用于保护窗口和追问enrichment
|
||||
@@ -523,6 +562,7 @@ async function processReply(session, text, turnSeq = session.latestUserTurnSeq |
|
||||
session._lastKbTopic = cleanText;
|
||||
session._lastKbHitAt = Date.now();
|
||||
}
|
||||
session._pendingEvidencePack = evidencePack || null;
|
||||
// 不提前发KB原文作字幕：等S2S event 351返回实际语音文本后再更新字幕
|
||||
// 这样字幕和语音保持一致（S2S会基于RAG内容生成自然口语化的回答）
|
||||
session._pendingExternalRagReply = true;
|
||||
@@ -708,6 +748,7 @@ function handleUpstreamMessage(session, data) {
|
||||
session.pendingAssistantSource = null;
|
||||
session.pendingAssistantToolName = null;
|
||||
session.pendingAssistantMeta = null;
|
||||
session._pendingEvidencePack = null;
|
||||
console.log(`[NativeVoice] duplicate assistant final ignored (351) session=${session.sessionId} turn=${pendingAssistantTurnSeq}`);
|
||||
return;
|
||||
}
|
||||
@@ -725,18 +766,6 @@ function handleUpstreamMessage(session, data) {
|
||||
if (session._pendingExternalRagReply) {
|
||||
session._pendingExternalRagReply = false;
|
||||
}
|
||||
// 品牌安全检测：最终助手文本包含有害内容时，阻断音频并注入安全回复
|
||||
if (isBrandHarmful(assistantText)) {
|
||||
console.warn(`[NativeVoice][SafeGuard] harmful content in final assistant text, blocking session=${session.sessionId} text=${JSON.stringify(assistantText.slice(0, 120))}`);
|
||||
session.blockUpstreamAudio = true;
|
||||
sendJson(session.client, { type: 'tts_reset', reason: 'harmful_blocked' });
|
||||
const safeReply = getVoiceSafeReply();
|
||||
session.lastDeliveredAssistantTurnSeq = pendingAssistantTurnSeq;
|
||||
persistAssistantSpeech(session, safeReply, { source: 'voice_bot' });
|
||||
sendSpeechText(session, safeReply).catch((err) => {
|
||||
console.warn('[NativeVoice][SafeGuard] sendSpeechText failed:', err.message);
|
||||
});
|
||||
} else {
|
||||
console.log(`[NativeVoice] upstream assistant session=${session.sessionId} text=${JSON.stringify(assistantText.slice(0, 120))}`);
|
||||
session.lastDeliveredAssistantTurnSeq = pendingAssistantTurnSeq;
|
||||
persistAssistantSpeech(session, assistantText, {
|
||||
@@ -749,7 +778,6 @@ function handleUpstreamMessage(session, data) {
|
||||
session.blockUpstreamAudio = true;
|
||||
console.log(`[NativeVoice] re-blocked after KB response session=${session.sessionId}`);
|
||||
}
|
||||
}
|
||||
} else {
|
||||
const didFlush = flushAssistantStream(session, {
|
||||
source: pendingAssistantSource,
|
||||
@@ -763,6 +791,7 @@ function handleUpstreamMessage(session, data) {
|
||||
session.pendingAssistantSource = null;
|
||||
session.pendingAssistantToolName = null;
|
||||
session.pendingAssistantMeta = null;
|
||||
session._pendingEvidencePack = null;
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -845,6 +874,7 @@ function handleUpstreamMessage(session, data) {
|
||||
session.pendingAssistantSource = null;
|
||||
session.pendingAssistantToolName = null;
|
||||
session.pendingAssistantMeta = null;
|
||||
session._pendingEvidencePack = null;
|
||||
console.log(`[NativeVoice] duplicate assistant final ignored (559) session=${session.sessionId} turn=${pendingAssistantTurnSeq}`);
|
||||
return;
|
||||
}
|
||||
@@ -862,6 +892,7 @@ function handleUpstreamMessage(session, data) {
|
||||
session.pendingAssistantSource = null;
|
||||
session.pendingAssistantToolName = null;
|
||||
session.pendingAssistantMeta = null;
|
||||
session._pendingEvidencePack = null;
|
||||
return;
|
||||
}
|
||||
|
||||
@@ -1042,7 +1073,7 @@ function attachClientHandlers(session) {
|
||||
...((parsed.assistantProfile && typeof parsed.assistantProfile === 'object') ? parsed.assistantProfile : {}),
|
||||
});
|
||||
session.assistantProfile = assistantProfile;
|
||||
session.botName = parsed.botName || assistantProfile.nickname || DEFAULT_VOICE_BOT_NAME;
|
||||
session.botName = parsed.botName || getAssistantDisplayName(assistantProfile) || DEFAULT_VOICE_BOT_NAME;
|
||||
session.systemRole = buildVoiceSystemRole(assistantProfile);
|
||||
session.speakingStyle = parsed.speakingStyle || session.speakingStyle || DEFAULT_VOICE_SPEAKING_STYLE;
|
||||
session.speaker = parsed.speaker || process.env.VOLC_S2S_SPEAKER_ID || 'zh_female_vv_jupiter_bigtts';
|
||||
@@ -1089,6 +1120,10 @@ function attachClientHandlers(session) {
|
||||
if (session.upstream && session.upstream.readyState === WebSocket.OPEN) {
|
||||
session.upstream.close();
|
||||
}
|
||||
// 会话结束时持久化摘要到 MySQL
|
||||
persistFinalSummary(session).catch((err) => {
|
||||
console.warn('[NativeVoice] persistFinalSummary failed:', err.message);
|
||||
});
|
||||
sessions.delete(session.sessionId);
|
||||
});
|
||||
}
|
||||
@@ -1164,6 +1199,7 @@ function createSession(client, sessionId) {
|
||||
_echoLogOnce: false,
|
||||
_fillerActive: false,
|
||||
_pendingExternalRagReply: false,
|
||||
_pendingEvidencePack: null,
|
||||
_lastPartialAt: 0,
|
||||
pendingKbPrequery: null,
|
||||
_kbPrequeryText: '',
|
||||
@@ -1171,7 +1207,7 @@ function createSession(client, sessionId) {
|
||||
_lastKbTopic: '',
|
||||
_lastKbHitAt: 0,
|
||||
assistantProfile,
|
||||
botName: assistantProfile.nickname,
|
||||
botName: getAssistantDisplayName(assistantProfile),
|
||||
systemRole: buildVoiceSystemRole(assistantProfile),
|
||||
speakingStyle: DEFAULT_VOICE_SPEAKING_STYLE,
|
||||
speaker: process.env.VOLC_S2S_SPEAKER_ID || 'zh_female_vv_jupiter_bigtts',
|
||||
@@ -1201,6 +1237,7 @@ function createSession(client, sessionId) {
|
||||
_lastFinalNormalized: '',
|
||||
_lastFinalAt: 0,
|
||||
_audioKeepaliveTimer: null,
|
||||
_turnCount: 0,
|
||||
};
|
||||
sessions.set(sessionId, session);
|
||||
attachClientHandlers(session);
|
||||
|
||||
@@ -1,7 +1,9 @@
|
||||
const ToolExecutor = require('./toolExecutor');
|
||||
const db = require('../db');
|
||||
const redisClient = require('./redisClient');
|
||||
const knowledgeQueryResolver = require('./knowledgeQueryResolver');
|
||||
const { hasKnowledgeRouteKeyword } = require('./knowledgeKeywords');
|
||||
const { loadBestSummary } = require('./conversationSummarizer');
|
||||
|
||||
function normalizeTextForSpeech(text) {
|
||||
return (text || '')
|
||||
@@ -64,7 +66,8 @@ function estimateSpeechDurationMs(text) {
|
||||
}
|
||||
|
||||
function normalizeKnowledgeAlias(text) {
|
||||
return String(text || '')
|
||||
const semanticNormalized = knowledgeQueryResolver.normalizeKnowledgeText(text);
|
||||
return String(semanticNormalized || '')
|
||||
.replace(/一成[，,、。！？\s]+系统/g, '一成系统')
|
||||
.replace(/X{2}系统/gi, '一成系统')
|
||||
.replace(/[\u4e00-\u9fff]{1,3}(?:成|城|程|诚|乘|声|生)[，,、\s]*系统/g, '一成系统')
|
||||
@@ -77,7 +80,7 @@ function normalizeKnowledgeAlias(text) {
|
||||
|
||||
function hasKnowledgeKeyword(text) {
|
||||
const normalized = normalizeKnowledgeAlias(text).replace(/\s+/g, '');
|
||||
return hasKnowledgeRouteKeyword(normalized);
|
||||
return hasKnowledgeRouteKeyword(normalized) || knowledgeQueryResolver.hasExplicitKnowledgeEntity(normalized, { skipAsrCorrection: true });
|
||||
}
|
||||
|
||||
function isKnowledgeFollowUp(text) {
|
||||
@@ -253,7 +256,7 @@ function extractToolResultText(toolName, toolResult) {
|
||||
return '知识库已配置但方舟LLM端点未就绪，暂时无法检索，请稍后再试。';
|
||||
}
|
||||
if (toolResult.results && Array.isArray(toolResult.results)) {
|
||||
return toolResult.results.map((item) => item.content || JSON.stringify(item)).join('\n');
|
||||
return toolResult.results.filter((item) => item.kind !== 'instruction' && item.kind !== 'context').map((item) => item.content || JSON.stringify(item)).join('\n');
|
||||
}
|
||||
if (typeof toolResult === 'string') return toolResult;
|
||||
if (toolResult.error) return toolResult.error;
|
||||
@@ -272,24 +275,23 @@ async function resolveReply(sessionId, session, text) {
|
||||
|
||||
// 快速路径：知识库候选先尝试无context的热答案/缓存命中，跳过DB查询（省50-200ms）
|
||||
if (shouldForceKnowledgeRoute(originalText)) {
|
||||
const fastResult = await ToolExecutor.execute('search_knowledge', { query: originalText, response_mode: 'answer', session_id: sessionId, _session: session }, []);
|
||||
const fastResult = await ToolExecutor.execute('search_knowledge', { query: originalText, session_id: sessionId, _session: session }, []);
|
||||
if (fastResult && fastResult.hit) {
|
||||
const replyText = extractToolResultText('search_knowledge', fastResult);
|
||||
const ragItems = fastResult.hit && Array.isArray(fastResult.results)
|
||||
? fastResult.results.filter(i => i && i.content).map(i => ({ title: i.title || '知识库结果', content: i.content }))
|
||||
const evidenceItems = Array.isArray(fastResult?.evidence_pack?.items) && fastResult.evidence_pack.items.length > 0
|
||||
? fastResult.evidence_pack.items
|
||||
: (Array.isArray(fastResult.results) ? fastResult.results : []);
|
||||
const ragItems = fastResult.hit && Array.isArray(evidenceItems)
|
||||
? evidenceItems.filter(i => i && i.content && i.kind !== 'context').map(i => ({ ...i }))
|
||||
: [];
|
||||
console.log(`[resolveReply] fast-path hit in ${Date.now() - _resolveStart}ms session=${sessionId} source=${fastResult.cache_hit ? 'cache' : 'direct'} mode=${fastResult.retrieval_mode || 'answer'}`);
|
||||
console.log(`[resolveReply] fast-path hit in ${Date.now() - _resolveStart}ms session=${sessionId} source=${fastResult.cache_hit ? 'cache' : 'direct'} mode=raw`);
|
||||
if (ragItems.length > 0) {
|
||||
session.handoffSummaryUsed = true;
|
||||
// raw 模式：ragItems 已包含上下文 + 多个 KB 片段，直接透传
|
||||
const isRawMode = fastResult.retrieval_mode === 'raw';
|
||||
const finalRagItems = isRawMode
|
||||
? ragItems
|
||||
: [{ title: '知识库结果', content: normalizeTextForSpeech(replyText).replace(/^(根据知识库信息[，,：:\s]*|根据.*?[，,]\s*)/i, '') || replyText }];
|
||||
return {
|
||||
delivery: 'external_rag',
|
||||
speechText: '',
|
||||
ragItems: finalRagItems,
|
||||
ragItems,
|
||||
evidencePack: fastResult.evidence_pack || null,
|
||||
source: 'voice_tool',
|
||||
toolName: 'search_knowledge',
|
||||
routeDecision: { route: 'search_knowledge', args: { query: originalText } },
|
||||
@@ -311,7 +313,7 @@ async function resolveReply(sessionId, session, text) {
|
||||
const _dbStart = Date.now();
|
||||
let recentMessages = null;
|
||||
if (process.env.ENABLE_REDIS_CONTEXT !== 'false') {
|
||||
const redisHistory = await redisClient.getRecentHistory(sessionId, 5).catch(() => null);
|
||||
const redisHistory = await redisClient.getRecentHistory(sessionId, 3).catch(() => null);
|
||||
if (redisHistory && redisHistory.length > 0) {
|
||||
recentMessages = redisHistory;
|
||||
const _dbMs = Date.now() - _dbStart;
|
||||
@@ -319,7 +321,7 @@ async function resolveReply(sessionId, session, text) {
|
||||
}
|
||||
}
|
||||
if (!recentMessages) {
|
||||
recentMessages = await db.getRecentMessages(sessionId, 10).catch(() => []);
|
||||
recentMessages = await db.getRecentMessages(sessionId, 6).catch(() => []);
|
||||
const _dbMs = Date.now() - _dbStart;
|
||||
if (_dbMs > 50) console.log(`[resolveReply] DB getRecentMessages took ${_dbMs}ms session=${sessionId}`);
|
||||
}
|
||||
@@ -329,7 +331,12 @@ async function resolveReply(sessionId, session, text) {
|
||||
const baseContext = scopedMessages
|
||||
.filter((item) => item && (item.role === 'user' || item.role === 'assistant'))
|
||||
.map((item) => ({ role: item.role, content: item.content }));
|
||||
const context = withHandoffSummary(session, baseContext);
|
||||
// 注入对话摘要（三级降级：Redis → MySQL → null）
|
||||
const summary = await loadBestSummary(sessionId).catch(() => null);
|
||||
const contextWithSummary = summary
|
||||
? [{ role: 'system', content: `[历史对话摘要] ${summary}` }, ...baseContext]
|
||||
: baseContext;
|
||||
const context = withHandoffSummary(session, contextWithSummary);
|
||||
let routeDecision = getRuleBasedDirectRouteDecision(originalText);
|
||||
// KB-First: 所有非闲聊查询强制先走知识库，KB不命中再交给S2S自由回答
|
||||
if (routeDecision.route === 'chat' && !isPureChitchat(originalText)) {
|
||||
@@ -358,7 +365,7 @@ async function resolveReply(sessionId, session, text) {
|
||||
toolName = routeDecision.route;
|
||||
source = 'voice_tool';
|
||||
const toolArgs = toolName === 'search_knowledge'
|
||||
? { ...(routeDecision.args || {}), response_mode: 'answer', session_id: sessionId, _session: session }
|
||||
? { ...(routeDecision.args || {}), session_id: sessionId, _session: session }
|
||||
: routeDecision.args;
|
||||
const metaToolArgs = toolArgs && typeof toolArgs === 'object'
|
||||
? Object.fromEntries(Object.entries(toolArgs).filter(([key]) => key !== '_session'))
|
||||
@@ -380,14 +387,15 @@ async function resolveReply(sessionId, session, text) {
|
||||
latency_ms: toolResult?.latency_ms || null,
|
||||
};
|
||||
|
||||
const evidenceItems = Array.isArray(toolResult?.evidence_pack?.items) && toolResult.evidence_pack.items.length > 0
|
||||
? toolResult.evidence_pack.items
|
||||
: (Array.isArray(toolResult?.results) ? toolResult.results : []);
|
||||
|
||||
const ragItems = toolName === 'search_knowledge'
|
||||
? (toolResult?.hit && Array.isArray(toolResult?.results)
|
||||
? toolResult.results
|
||||
.filter((item) => item && item.content)
|
||||
.map((item) => ({
|
||||
title: item.title || '知识库结果',
|
||||
content: item.content,
|
||||
}))
|
||||
? (toolResult?.hit && Array.isArray(evidenceItems)
|
||||
? evidenceItems
|
||||
.filter((item) => item && item.content && item.kind !== 'context')
|
||||
.map((item) => ({ ...item }))
|
||||
: [])
|
||||
: (!toolResult?.error && replyText
|
||||
? [{ title: `${toolName}结果`, content: replyText }]
|
||||
@@ -395,23 +403,11 @@ async function resolveReply(sessionId, session, text) {
|
||||
|
||||
if (ragItems.length > 0) {
|
||||
session.handoffSummaryUsed = true;
|
||||
const isRawMode = toolResult?.retrieval_mode === 'raw';
|
||||
let finalRagItems = ragItems;
|
||||
|
||||
if (toolName === 'search_knowledge' && !isRawMode) {
|
||||
// 旧模式：LLM 加工过的文本，清理后合并为单条
|
||||
const speechText = normalizeTextForSpeech(replyText);
|
||||
if (speechText) {
|
||||
const cleanedText = speechText.replace(/^(根据知识库信息[，,：:\s]*|根据.*?[，,]\s*)/i, '');
|
||||
finalRagItems = [{ title: '知识库结果', content: cleanedText || speechText }];
|
||||
}
|
||||
}
|
||||
// raw 模式：ragItems 已包含上下文 + 多个 KB 片段，直接透传给 S2S
|
||||
|
||||
return {
|
||||
delivery: 'external_rag',
|
||||
speechText: '',
|
||||
ragItems: finalRagItems,
|
||||
ragItems,
|
||||
evidencePack: toolResult?.evidence_pack || null,
|
||||
source,
|
||||
toolName,
|
||||
routeDecision,
|
||||
@@ -428,6 +424,7 @@ async function resolveReply(sessionId, session, text) {
|
||||
delivery: 'external_rag',
|
||||
speechText: '',
|
||||
ragItems: [{ title: '品牌保护', content: safeReply }],
|
||||
evidencePack: toolResult?.evidence_pack || null,
|
||||
source: 'voice_tool',
|
||||
toolName: 'search_knowledge',
|
||||
routeDecision,
|
||||
@@ -443,6 +440,7 @@ async function resolveReply(sessionId, session, text) {
|
||||
delivery: 'external_rag',
|
||||
speechText: '',
|
||||
ragItems: [{ title: '知识库未命中', content: honestReply }],
|
||||
evidencePack: toolResult?.evidence_pack || null,
|
||||
source: 'voice_tool',
|
||||
toolName: 'search_knowledge',
|
||||
routeDecision,
|
||||
|
||||
@@ -10,6 +10,7 @@ const HISTORY_MAX_LEN = 10; // 5轮 × 2条/轮
|
||||
const HISTORY_TTL_S = 1800; // 30分钟
|
||||
const KB_CACHE_HIT_TTL_S = 300; // 5分钟
|
||||
const KB_CACHE_NOHIT_TTL_S = 120; // 2分钟
|
||||
const SUMMARY_TTL_S = parseInt(process.env.SUMMARY_REDIS_TTL_SECONDS) || 7200; // 2小时
|
||||
|
||||
// ============ 连接管理 ============
|
||||
let client = null;
|
||||
@@ -128,6 +129,32 @@ async function clearSession(sessionId) {
|
||||
}
|
||||
}
|
||||
|
||||
// ============ 对话摘要 ============
|
||||
const summaryKey = (sessionId) => `voice:summary:${sessionId}`;
|
||||
|
||||
async function setSummary(sessionId, summary) {
|
||||
if (!isAvailable() || !summary) return false;
|
||||
try {
|
||||
const key = summaryKey(sessionId);
|
||||
await client.set(key, summary, 'EX', SUMMARY_TTL_S);
|
||||
return true;
|
||||
} catch (err) {
|
||||
console.warn('[Redis] setSummary failed:', err.message);
|
||||
return false;
|
||||
}
|
||||
}
|
||||
|
||||
async function getSummary(sessionId) {
|
||||
if (!isAvailable()) return null;
|
||||
try {
|
||||
const key = summaryKey(sessionId);
|
||||
return await client.get(key);
|
||||
} catch (err) {
|
||||
console.warn('[Redis] getSummary failed:', err.message);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
// ============ KB 缓存 ============
|
||||
const kbCacheKey = (cacheKey) => `kb_cache:${cacheKey}`;
|
||||
|
||||
@@ -178,6 +205,8 @@ module.exports = {
|
||||
pushMessage,
|
||||
getRecentHistory,
|
||||
clearSession,
|
||||
setSummary,
|
||||
getSummary,
|
||||
setKbCache,
|
||||
getKbCache,
|
||||
disconnect,
|
||||
|
||||
Reference in New Issue
Block a user