feat: v0.8.1 记忆深化 · 观测闭环 · 体验收口 — 窗口/输出上限全局单一配置 · 2478 用例全量回归 + E2E 冒烟
硬性契约：删除代码中一切写死的上下文窗口与最大输出上限（含六家模型元信息
钳制与全部兜底值）——唯一合法来源是设置面板「上下文长度」（llm.contextWindow）
与「最大输出上限」（llm.maxTokens），跨 Provider/模型原样透传。
P0 正确性收口：
- 迁移 11/12（SCHEMA_VERSION 5）：记忆表 embedding 列 + 分 Provider 窗口键清理
- 记忆生命周期接线：会话终态清理 working memory / episodic 90 天 TTL / access_count 回写
- 回放缓冲模块化 + 会话终态清理（杜绝 4MB/会话内存滞留）
- i18n 收口：主进程 main-locale（zh/en，ui.locale 热切换）+ 渲染层 17 处出层
P1 能力演进：
- 本地向量混合检索：0.6×向量余弦 + 0.4×TF-IDF，Ollama embeddings 首次投产，
存量记忆惰性回填，嵌入不可用自动回退 TF-IDF
- MEMORY.md 维护闭环：固化去重消除截断盲区；两阶段维护（AI 建议 → 用户确认 →
原子改写 + 语义记忆双轨同步 + 审计）；>50KB 告警
- 可观测闭环：cacheTokens 引擎→前端透传（Token 面板命中率/成本行）+ 输入框
上下文占用指示条
- MCP Prompts/Resources 对话可用：/mcp:{server}:{prompt} 与 @mcp:{server}:{uri}
P2 体验补全：
- 工具自定义策略（正则白/黑名单 + 频率 + 强制确认，热生效）
- 连续 ≥3 同类工具确认聚合为单弹框
- 会话消息游标分页（首屏 200 条向上翻页）
- 开机自启；Playwright + Electron E2E 冒烟（本地 mock LLM 零外联）
Review 回归修复：MCP 大小写失配 / 分页状态复位 / 清空=未配置语义（Number(null)=0
隐患）/ MEMORY.md 告警位置 / working_memories FK（迁移 13）/ 全局配置层废键清理；
附带根治权限加固启动时序、代理回环放行、safeStorage 降级、悬空 symlink 逃逸。
验证：typecheck/lint 0 问题；test:electron 2478/2478（0 跳过）；E2E 2/2；
docs/v0.8.1-迭代实施清单.md 全项留档。
This commit is contained in:
@@ -950,11 +950,12 @@ describe('AnthropicAdapter — getContextWindow / listModels', () => {
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expect(adapter.getContextWindow()).toBe(50_000);
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});
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it('未知模型 → 兜底 200K', () => {
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expect(makeAdapter('claude-unknown').getContextWindow()).toBe(200_000);
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// v0.8.1: 窗口唯一来源是设置面板 llm.contextWindow，未配置返回 0（无写死兜底）
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it('未知模型且未配置 → 返回 0（无写死兜底窗口）', () => {
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expect(makeAdapter('claude-unknown').getContextWindow()).toBe(0);
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});
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it('listModels 返回本地模型元信息（无网络请求）', async () => {
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it('listModels 返回本地模型元信息（无网络请求，不含窗口/上限数值）', async () => {
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const adapter = makeAdapter();
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const models = await adapter.listModels();
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expect(models.map((m) => m.id)).toEqual([
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@@ -962,7 +963,10 @@ describe('AnthropicAdapter — getContextWindow / listModels', () => {
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'claude-opus-4-1',
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'claude-haiku-4-5',
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]);
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expect(models[0]).toMatchObject({ contextWindow: 200_000, maxOutputTokens: 64_000 });
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// v0.8.1 硬性契约: 元信息不再承载 contextWindow / maxOutputTokens
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expect(models[0]).toMatchObject({ supportsThinking: true });
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expect(models[0].contextWindow).toBeUndefined();
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expect(models[0].maxOutputTokens).toBeUndefined();
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expect(mockFetch).not.toHaveBeenCalled();
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});
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});
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@@ -130,10 +130,11 @@ describe('BaseAdapter — getContextWindow', () => {
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expect(adapter.getContextWindow()).toBe(128_000);
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});
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// v0.7.4 P4-5: 兜底从 1M 降至 128K（未知模型按最保守主流窗口预算，防 413）
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it('未配置时返回兜底默认值 128K（子类应覆盖真实窗口）', () => {
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// v0.8.1 硬性契约: 上下文窗口唯一来源是设置面板 llm.contextWindow，
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// 未配置返回 0（引擎据此跳过压缩预算）—— 任何写死兜底值均已删除
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it('未配置时返回 0（无任何写死兜底窗口，引擎跳过压缩判定）', () => {
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const adapter = makeAdapter();
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expect(adapter.getContextWindow()).toBe(128_000);
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expect(adapter.getContextWindow()).toBe(0);
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});
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});
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@@ -443,15 +444,15 @@ describe('BaseAdapter — throwHttpError 错误体解析', () => {
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// ===== 追加：getContextWindow / listModels / healthCheck =====
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describe('BaseAdapter — getContextWindow 回退链', () => {
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it('contextWindow=0 视为未配置（需 >0）', () => {
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describe('BaseAdapter — getContextWindow 非法值契约', () => {
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it('contextWindow=0 视为未配置（返回 0，引擎跳过压缩判定）', () => {
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const adapter = makeAdapter({ contextWindow: 0 });
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expect(adapter.getContextWindow()).toBe(128_000);
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expect(adapter.getContextWindow()).toBe(0);
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});
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it('contextWindow 为负数视为未配置', () => {
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it('contextWindow 为负数视为未配置（返回 0）', () => {
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const adapter = makeAdapter({ contextWindow: -1 });
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expect(adapter.getContextWindow()).toBe(128_000);
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expect(adapter.getContextWindow()).toBe(0);
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});
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});
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@@ -104,7 +104,7 @@ describe('DeepSeek vision 模型多模态请求格式（v0.5.4）', () => {
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expect(userMsg.content).toBe('这张图片里有什么？');
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});
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it('vision 模型 max_tokens 钳制到 8192（MODEL_INFO 上限）', async () => {
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it('vision 模型 max_tokens 原样透传（v0.8.1：8192 钳制已废除）', async () => {
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mockFetch.mockResolvedValue(okResponse());
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const adapter = makeAdapter('deepseek-v4-flash-vision-exp');
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@@ -114,7 +114,7 @@ describe('DeepSeek vision 模型多模态请求格式（v0.5.4）', () => {
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} as MetonaRequest);
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const body = JSON.parse((mockFetch.mock.calls[0] as [string, RequestInit])[1].body as string);
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expect(body.max_tokens).toBe(8_192);
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expect(body.max_tokens).toBe(63_488);
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});
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it('vision 模型无图片时不转换（content 保持纯文本）', async () => {
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@@ -215,7 +215,7 @@ describe('DeepSeek vision 模型多模态请求格式（v0.5.4）', () => {
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});
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});
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it('vision 模型 max_tokens 未配置 → 默认 8192（MODEL_INFO 上限）', async () => {
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it('vision 模型 max_tokens 未配置 → 不下发该字段（v0.8.1：无写死兜底）', async () => {
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mockFetch.mockResolvedValue(okResponse());
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const adapter = makeAdapter('deepseek-v4-flash-vision-exp');
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@@ -225,7 +225,7 @@ describe('DeepSeek vision 模型多模态请求格式（v0.5.4）', () => {
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} as MetonaRequest);
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const body = requestBody();
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expect(body.max_tokens).toBe(8_192);
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expect(body.max_tokens).toBeUndefined();
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});
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it('vision 模型 thinking 参数显式映射（thinkingEnabled 兼容）', async () => {
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@@ -1,10 +1,10 @@
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/**
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* Provider maxTokens 上限钳制契约测试（v0.5.3）
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* maxTokens 透传契约测试（v0.8.1 硬性契约重写）
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*
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* 背景：引擎默认 maxTokens=63488（engine.ts DEFAULT_CONFIG），超过部分模型
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* 上限时 API 直接 400 —— OpenAI gpt-4o(16384)/gpt-4.1(32768)、Anthropic
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* opus/haiku(32000)、MiMo standard(32768) 曾不可用。v0.5.3 各 adapter 按
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* MODEL_INFO.maxOutputTokens 钳制。
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* 背景：v0.5.3 曾引入"按 MODEL_INFO.maxOutputTokens 钳制"逻辑。v0.8.1 按硬性
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* 契约废除 —— 设置面板「最大输出上限」（llm.maxTokens）是唯一合法的输出上限
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* 配置，adapter 对一切 Provider/模型**原样透传** `params.maxTokens`，代码中
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* 不存在任何写死的输出上限或按模型元信息的钳制行为。
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*
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* 测试策略（v0.5.2 教训）：mock fetch 记录真实请求体并断言契约 ——
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* 不 mock adapter 内部方法，验证"发出的 HTTP 请求体"这个最终事实。
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@@ -33,7 +33,7 @@ afterEach(() => {
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mockFetch.mockReset();
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});
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/** 引擎默认形态的请求（maxTokens=63488，与 engine DEFAULT_CONFIG 一致） */
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/** 设置面板「最大输出上限」配置生效时的请求形态（llm.maxTokens → params.maxTokens） */
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function makeRequest(overrides: Partial<MetonaRequest> = {}): MetonaRequest {
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return {
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meta: {
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@@ -79,8 +79,8 @@ function requestBody(): Record<string, unknown> {
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return JSON.parse(init.body as string) as Record<string, unknown>;
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}
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describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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it('DeepSeek v4 pro（上限 384K）：63488 未超限，原样传递', async () => {
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describe('maxTokens 原样透传（v0.8.1：设置面板是唯一合法上限配置）', () => {
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it('DeepSeek：max_tokens 原样透传（不按模型钳制）', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const adapter = new DeepSeekAdapter({
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provider: 'deepseek',
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@@ -92,7 +92,19 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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expect(requestBody().max_tokens).toBe(63_488);
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});
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it('Agnes flash（上限 65536）：63488 未超限，原样传递', async () => {
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it('DeepSeek：超出旧模型元信息上限的值同样原样透传（钳制已废除）', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const adapter = new DeepSeekAdapter({
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provider: 'deepseek',
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baseURL: 'https://api.deepseek.com',
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apiKey: 'sk',
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defaultModel: 'deepseek-v4-flash-vision-exp',
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});
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await adapter.send(makeRequest());
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expect(requestBody().max_tokens).toBe(63_488);
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});
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it('Agnes：max_tokens 原样透传', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const adapter = new AgnesAdapter({
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provider: 'agnes',
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@@ -104,7 +116,7 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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expect(requestBody().max_tokens).toBe(63_488);
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});
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it('MiMo standard（上限 32768）：钳制到 32768（原为 400 错误场景）', async () => {
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it('MiMo standard：max_completion_tokens 原样透传（旧 32768 钳制已废除）', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const adapter = new MimoAdapter({
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provider: 'mimo',
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@@ -113,10 +125,10 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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defaultModel: 'mimo-v2.5',
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});
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await adapter.send(makeRequest());
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expect(requestBody().max_completion_tokens).toBe(32_768);
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expect(requestBody().max_completion_tokens).toBe(63_488);
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});
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it('MiMo pro（上限 131072）：63488 未超限，原样传递', async () => {
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it('MiMo pro：max_completion_tokens 原样透传', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const adapter = new MimoAdapter({
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provider: 'mimo',
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@@ -128,7 +140,7 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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expect(requestBody().max_completion_tokens).toBe(63_488);
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});
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it('OpenAI gpt-4o（上限 16384）：钳制到 16384（原为 400 错误场景）', async () => {
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it('OpenAI gpt-4o（非推理模型）：max_tokens 原样透传（旧 16384 钳制已废除）', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const adapter = new OpenAIAdapter({
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provider: 'openai',
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@@ -137,10 +149,10 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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defaultModel: 'gpt-4o',
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});
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await adapter.send(makeRequest());
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expect(requestBody().max_tokens).toBe(16_384);
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expect(requestBody().max_tokens).toBe(63_488);
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});
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it('OpenAI o3-mini（上限 100K）：63488 未超限，推理模型字段名正确', async () => {
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it('OpenAI o3-mini（推理模型）：max_completion_tokens 原样透传，字段名路由正确', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const adapter = new OpenAIAdapter({
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provider: 'openai',
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@@ -153,7 +165,7 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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expect(requestBody().max_tokens).toBeUndefined();
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});
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it('Anthropic opus（上限 32000）：钳制到 32000（原为 400 错误场景）', async () => {
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it('Anthropic opus：max_tokens 原样透传（旧 32000 钳制已废除）', async () => {
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mockFetch.mockResolvedValue(anthropicResponse());
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const adapter = new AnthropicAdapter({
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provider: 'anthropic',
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@@ -162,10 +174,10 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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defaultModel: 'claude-opus-4-1',
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});
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await adapter.send(makeRequest());
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expect(requestBody().max_tokens).toBe(32_000);
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expect(requestBody().max_tokens).toBe(63_488);
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});
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it('Anthropic sonnet（上限 64000）：63488 未超限', async () => {
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it('Anthropic sonnet：max_tokens 原样透传', async () => {
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mockFetch.mockResolvedValue(anthropicResponse());
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const adapter = new AnthropicAdapter({
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provider: 'anthropic',
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@@ -177,13 +189,12 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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expect(requestBody().max_tokens).toBe(63_488);
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});
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it('未配置 maxTokens 时各 adapter 使用安全默认值（不超过模型上限）', async () => {
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it('未配置 maxTokens：不下发任何输出上限字段（由 Provider 服务端默认值决定）', async () => {
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mockFetch.mockResolvedValue(openAIResponse());
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const request = makeRequest({
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params: { temperature: 0, stream: false } as MetonaRequest['params'],
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});
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// MiMo standard + thinking 默认 → 兜底 32768（= 上限，安全）
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const mimo = new MimoAdapter({
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provider: 'mimo',
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baseURL: 'https://api.mimo.com/v1',
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@@ -191,6 +202,33 @@ describe('maxTokens 模型上限钳制（引擎默认 63488 场景）', () => {
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defaultModel: 'mimo-v2.5',
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});
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await mimo.send(request);
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expect(requestBody().max_completion_tokens).toBe(32_768);
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expect(requestBody().max_completion_tokens).toBeUndefined();
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const deepseek = new DeepSeekAdapter({
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provider: 'deepseek',
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baseURL: 'https://api.deepseek.com',
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apiKey: 'sk',
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defaultModel: 'deepseek-v4-pro',
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});
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mockFetch.mockReset();
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mockFetch.mockResolvedValue(openAIResponse());
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await deepseek.send(request);
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expect(requestBody().max_tokens).toBeUndefined();
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});
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it('Anthropic 未配置 maxTokens + thinking 开启：仅满足协议下限 2048（协议不变量，非上限）', async () => {
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mockFetch.mockResolvedValue(anthropicResponse());
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const adapter = new AnthropicAdapter({
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provider: 'anthropic',
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baseURL: 'https://api.anthropic.com',
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apiKey: 'k',
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defaultModel: 'claude-opus-4-1',
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});
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await adapter.send(
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makeRequest({
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params: { temperature: 0, stream: false, thinkingEnabled: true } as MetonaRequest['params'],
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}),
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);
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expect(requestBody().max_tokens).toBe(2048);
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});
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});
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@@ -452,13 +452,25 @@ describe('OllamaAdapter — 能力探测', () => {
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expect(caps).toEqual({ supportsTools: true, supportsVision: true, supportsThinking: true });
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});
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it('capabilities 为空数组（showModel 缺省 []）→ 三能力全 false（非 null）', async () => {
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// v0.8.1: showModel 保留 undefined 语义 —— 响应缺 capabilities 字段 = 未知 → null
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//（fail-open），不再把"字段缺失"与"权威空"混同（探测竞态下曾误判不支持思考）
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it('capabilities 字段缺失 → 返回 null（未知，fail-open）', async () => {
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const adapter = makeAdapter();
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mockFetch.mockResolvedValue({
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ok: true,
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status: 200,
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json: async () => ({ parameters: '', template: '' }),
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} as unknown as Response);
|
||||
expect(await adapter.probeCapabilities('qwen3')).toBeNull();
|
||||
});
|
||||
|
||||
it('capabilities 为显式空数组（服务端权威无能力）→ 三能力全 false', async () => {
|
||||
const adapter = makeAdapter();
|
||||
mockFetch.mockResolvedValue({
|
||||
ok: true,
|
||||
status: 200,
|
||||
json: async () => ({ parameters: '', template: '', capabilities: [] }),
|
||||
} as unknown as Response);
|
||||
expect(await adapter.probeCapabilities('qwen3')).toEqual({
|
||||
supportsTools: false,
|
||||
supportsVision: false,
|
||||
@@ -688,28 +700,33 @@ describe('OllamaAdapter — 图片归一化', () => {
|
||||
|
||||
// ===== getContextWindow =====
|
||||
|
||||
describe('OllamaAdapter — getContextWindow', () => {
|
||||
it('未探测到时返回默认 4096', () => {
|
||||
describe('OllamaAdapter — getContextWindow（v0.8.1：唯一来源是设置面板配置）', () => {
|
||||
it('未配置 contextWindow → 返回 0（无 4096 写死兜底，引擎跳过压缩判定）', () => {
|
||||
const adapter = makeAdapter();
|
||||
expect(adapter.getContextWindow()).toBe(4096);
|
||||
expect(adapter.getContextWindow()).toBe(0);
|
||||
});
|
||||
|
||||
it('探测到 num_ctx 后返回实测值（机会主义缓存收敛）', async () => {
|
||||
// 需在构造前就位：构造时的 fire-and-forget 探测消费首个 fetch
|
||||
it('config.contextWindow（llm.contextWindow 注入）→ 返回配置值', () => {
|
||||
const adapter = makeAdapter('qwen3', { contextWindow: 32_768 });
|
||||
expect(adapter.getContextWindow()).toBe(32_768);
|
||||
});
|
||||
|
||||
it('/api/show 探测不再缓存窗口数值（num_ctx 由引擎 contextLength 下发）', async () => {
|
||||
mockFetch.mockResolvedValue({
|
||||
ok: true,
|
||||
status: 200,
|
||||
json: async () => ({ parameters: 'num_ctx 32768', template: '', capabilities: [] }),
|
||||
} as unknown as Response);
|
||||
const adapter = makeAdapter();
|
||||
// 等待构造时的探测完成并写入缓存
|
||||
await vi.waitFor(() => expect(adapter.getContextWindow()).toBe(32768));
|
||||
// 探测完成（含失败路径）后窗口仍为 0 —— 探测只服务于能力门控
|
||||
await new Promise((r) => setTimeout(r, 20));
|
||||
expect(adapter.getContextWindow()).toBe(0);
|
||||
});
|
||||
|
||||
it('探测失败（网络错误）→ 保持默认 4096', async () => {
|
||||
it('探测失败（网络错误）→ 仍返回配置值/0，不抛错不阻塞', async () => {
|
||||
mockFetch.mockRejectedValue(new Error('down'));
|
||||
const adapter = makeAdapter();
|
||||
await new Promise((r) => setTimeout(r, 20));
|
||||
expect(adapter.getContextWindow()).toBe(4096);
|
||||
expect(adapter.getContextWindow()).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
@@ -205,12 +205,12 @@ describe('OpenAIAdapter — 推理模型拒图（ModelCapabilityError）', () =>
|
||||
|
||||
// ===== max_completion_tokens / max_tokens 路由 =====
|
||||
|
||||
describe('OpenAIAdapter — token 参数路由', () => {
|
||||
describe('OpenAIAdapter — token 参数路由（v0.8.1：原样透传，无模型钳制）', () => {
|
||||
it.each([
|
||||
['o3-mini', 63_488, 63_488, 'max_completion_tokens'],
|
||||
['o3-mini', 200_000, 100_000, 'max_completion_tokens'], // 上限 100000
|
||||
['gpt-4o', 63_488, 16_384, 'max_tokens'],
|
||||
['gpt-4.1', 63_488, 32_768, 'max_tokens'],
|
||||
['o3-mini', 200_000, 200_000, 'max_completion_tokens'], // 超过任何旧元信息上限 → 原样
|
||||
['gpt-4o', 63_488, 63_488, 'max_tokens'],
|
||||
['gpt-4.1', 63_488, 63_488, 'max_tokens'],
|
||||
] as const)('%s maxTokens=%d → %s=%d', async (model, requested, expected, field) => {
|
||||
const adapter = makeAdapter(model);
|
||||
mockFetch.mockResolvedValue(okResponse());
|
||||
@@ -224,18 +224,18 @@ describe('OpenAIAdapter — token 参数路由', () => {
|
||||
expect(body[other]).toBeUndefined();
|
||||
});
|
||||
|
||||
it('o3-mini 未配置 maxTokens → 默认 32768（thinking 场景安全值）', async () => {
|
||||
it('o3-mini 未配置 maxTokens → 不下发 max_completion_tokens（无写死兜底）', async () => {
|
||||
const adapter = makeAdapter('o3-mini');
|
||||
mockFetch.mockResolvedValue(okResponse());
|
||||
await adapter.send(makeRequest({ params: { temperature: 0, stream: false } }));
|
||||
expect(lastBody().max_completion_tokens).toBe(32_768);
|
||||
expect(lastBody().max_completion_tokens).toBeUndefined();
|
||||
});
|
||||
|
||||
it('非推理模型未配置 maxTokens → 默认模型上限', async () => {
|
||||
it('非推理模型未配置 maxTokens → 不下发 max_tokens（无写死兜底）', async () => {
|
||||
const adapter = makeAdapter('gpt-4o');
|
||||
mockFetch.mockResolvedValue(okResponse());
|
||||
await adapter.send(makeRequest({ params: { temperature: 0, stream: false } }));
|
||||
expect(lastBody().max_tokens).toBe(16_384);
|
||||
expect(lastBody().max_tokens).toBeUndefined();
|
||||
});
|
||||
});
|
||||
|
||||
@@ -277,23 +277,17 @@ describe('OpenAIAdapter — temperature 路由', () => {
|
||||
|
||||
// ===== getContextWindow 回退链 =====
|
||||
|
||||
describe('OpenAIAdapter — getContextWindow 回退链', () => {
|
||||
it('gpt-4.1 返回 1M 上下文', () => {
|
||||
expect(makeAdapter('gpt-4.1').getContextWindow()).toBe(1_000_000);
|
||||
});
|
||||
|
||||
it('o3-mini 返回 200K', () => {
|
||||
expect(makeAdapter('o3-mini').getContextWindow()).toBe(200_000);
|
||||
});
|
||||
|
||||
it('未知模型 → 兜底 128K（v0.7.4 P4-5 从 1M 降级）', () => {
|
||||
expect(makeAdapter('unknown-model-x').getContextWindow()).toBe(128_000);
|
||||
});
|
||||
|
||||
it('config.contextWindow 显式配置优先', () => {
|
||||
describe('OpenAIAdapter — getContextWindow（v0.8.1：唯一来源是设置面板配置）', () => {
|
||||
it('config.contextWindow 显式配置（llm.contextWindow 注入）返回配置值', () => {
|
||||
const adapter = makeAdapter('gpt-4o', { contextWindow: 64_000 });
|
||||
expect(adapter.getContextWindow()).toBe(64_000);
|
||||
});
|
||||
|
||||
it('未配置（任意模型，含已知/未知）→ 返回 0（引擎跳过压缩判定，无写死兜底）', () => {
|
||||
expect(makeAdapter('gpt-4.1').getContextWindow()).toBe(0);
|
||||
expect(makeAdapter('o3-mini').getContextWindow()).toBe(0);
|
||||
expect(makeAdapter('unknown-model-x').getContextWindow()).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
// ===== listModels =====
|
||||
@@ -303,7 +297,9 @@ describe('OpenAIAdapter — listModels 动态发现与降级', () => {
|
||||
mockFetch.mockResolvedValue(okResponse({ data: [{ id: 'gpt-4o' }, { id: 'custom-model' }] }));
|
||||
const models = await makeAdapter('gpt-4o').listModels();
|
||||
expect(models).toHaveLength(2);
|
||||
expect(models[0]).toMatchObject({ id: 'gpt-4o', contextWindow: 128_000 });
|
||||
// v0.8.1: 元信息不再承载窗口/上限数值
|
||||
expect(models[0]).toMatchObject({ id: 'gpt-4o', name: 'GPT-4o' });
|
||||
expect(models[0].contextWindow).toBeUndefined();
|
||||
expect(models[1]).toEqual({ id: 'custom-model' });
|
||||
// /models 请求头携带 Bearer
|
||||
const [, init] = mockFetch.mock.calls[0] as [string, RequestInit];
|
||||
|
||||
@@ -178,7 +178,7 @@ describe('AnthropicAdapter — 请求体契约', () => {
|
||||
expect(toolResultBlocks[0].tool_use_id).toBe('tc_1');
|
||||
});
|
||||
|
||||
it('A2: max_tokens 按模型上限钳制（63488 → sonnet 64000 / opus 32000）', async () => {
|
||||
it('A2: max_tokens 原样透传（v0.8.1：模型钳制已废除，设置面板是唯一上限来源）', async () => {
|
||||
const sonnet = new AnthropicAdapter({
|
||||
provider: 'anthropic',
|
||||
baseURL: 'http://a.test',
|
||||
@@ -194,9 +194,9 @@ describe('AnthropicAdapter — 请求体契约', () => {
|
||||
const { bodies } = captureFetch();
|
||||
await sonnet.send(makeRequest());
|
||||
await opus.send(makeRequest());
|
||||
// 引擎默认 63488 低于 sonnet 上限 64000 → 原样保留；opus 上限 32000 → 钳制生效
|
||||
// v0.8.1: 设置面板「最大输出上限」对一切模型原样透传，无任何按模型钳制
|
||||
expect(bodies[0].max_tokens).toBe(63_488);
|
||||
expect(bodies[1].max_tokens).toBe(32_000);
|
||||
expect(bodies[1].max_tokens).toBe(63_488);
|
||||
});
|
||||
|
||||
it('A3: 小 maxTokens 时 thinking budget 不跌破协议下限 1024（v0.6.4 边界加固）', async () => {
|
||||
@@ -516,8 +516,8 @@ describe('AnthropicAdapter — thinking budget 按 effort 映射矩阵', () => {
|
||||
['low', 1024],
|
||||
['medium', 4096],
|
||||
['high', 16384],
|
||||
// max=32768 但 sonnet 的 max_tokens 先钳到 64000 → budget 二次钳到 floor(64000/2)=32000
|
||||
['max', 32000],
|
||||
// v0.8.1: max_tokens 不再按模型钳制（100_000 原样透传）→ budget = min(32768, floor(100000/2)) = 32768
|
||||
['max', 32768],
|
||||
] as const)('effort=%s → budget 为该档值且 < max_tokens', async (effort, expectBudget) => {
|
||||
const adapter = makeAdapter();
|
||||
const { bodies } = captureFetch();
|
||||
@@ -570,13 +570,13 @@ describe('AnthropicAdapter — thinking budget 按 effort 映射矩阵', () => {
|
||||
});
|
||||
});
|
||||
|
||||
describe('AnthropicAdapter — max_tokens 钳制矩阵', () => {
|
||||
describe('AnthropicAdapter — max_tokens 透传矩阵（v0.8.1 无钳制）', () => {
|
||||
it.each([
|
||||
['claude-sonnet-4-5', 63_488, 63_488], // 引擎默认低于上限 → 原样
|
||||
['claude-sonnet-4-5', 70_000, 64_000], // 超上限 → 钳到 sonnet 64000
|
||||
['claude-opus-4-1', 63_488, 32_000], // opus 上限 32000
|
||||
['claude-haiku-4-5', 63_488, 32_000], // haiku 上限 32000
|
||||
['claude-sonnet-4-5', 500, 500], // 低于上限 → 原样
|
||||
['claude-sonnet-4-5', 63_488, 63_488],
|
||||
['claude-sonnet-4-5', 70_000, 70_000], // 超过任何旧元信息上限 → 原样透传
|
||||
['claude-opus-4-1', 63_488, 63_488],
|
||||
['claude-haiku-4-5', 63_488, 63_488],
|
||||
['claude-sonnet-4-5', 500, 500],
|
||||
])('%s maxTokens=%d → max_tokens=%d', async (model, requested, expected) => {
|
||||
const adapter = new AnthropicAdapter({
|
||||
provider: 'anthropic',
|
||||
@@ -795,7 +795,7 @@ describe('DeepSeekAdapter — thinking 映射矩阵', () => {
|
||||
expect(bodies[0].stop).toEqual(['<END>']);
|
||||
});
|
||||
|
||||
it('max_tokens 按模型钳制（pro 384000 / vision 8192）', async () => {
|
||||
it('max_tokens 原样透传（v0.8.1：pro/vision 均不钳制）', async () => {
|
||||
const pro = makeAdapter('deepseek-v4-pro');
|
||||
const vision = makeAdapter('deepseek-v4-flash-vision-exp');
|
||||
const { bodies } = captureFetch();
|
||||
@@ -803,8 +803,8 @@ describe('DeepSeekAdapter — thinking 映射矩阵', () => {
|
||||
await vision.send(
|
||||
makeRequest({ params: { maxTokens: 63_488, temperature: 0, stream: false } }),
|
||||
);
|
||||
expect(bodies[0].max_tokens).toBe(384_000);
|
||||
expect(bodies[1].max_tokens).toBe(8_192);
|
||||
expect(bodies[0].max_tokens).toBe(500_000);
|
||||
expect(bodies[1].max_tokens).toBe(63_488);
|
||||
});
|
||||
});
|
||||
|
||||
@@ -864,8 +864,8 @@ describe('AgnesAdapter — enable_thinking 对称性矩阵', () => {
|
||||
makeRequest({ params: { maxTokens: 70_000, temperature: 0.9, stream: false } }),
|
||||
);
|
||||
expect(bodies[0].temperature).toBe(0.9);
|
||||
// 65536 上限钳制
|
||||
expect(bodies[0].max_tokens).toBe(65_536);
|
||||
// v0.8.1: 原样透传，无 65536 钳制
|
||||
expect(bodies[0].max_tokens).toBe(70_000);
|
||||
});
|
||||
});
|
||||
|
||||
@@ -912,21 +912,21 @@ describe('MimoAdapter — thinking 显式开关', () => {
|
||||
expect(bodies[0].top_p).toBeUndefined();
|
||||
});
|
||||
|
||||
it('max_completion_tokens 钳制矩阵（pro 131072 / standard 32768）', async () => {
|
||||
it('max_completion_tokens 原样透传（v0.8.1：pro/standard 均不钳制）', async () => {
|
||||
const pro = makeAdapter({ defaultModel: 'mimo-v2.5-pro' });
|
||||
const std = makeAdapter({ defaultModel: 'mimo-v2.5' });
|
||||
const { bodies } = captureFetch();
|
||||
await pro.send(makeRequest({ params: { maxTokens: 200_000, temperature: 0, stream: false } }));
|
||||
await std.send(makeRequest({ params: { maxTokens: 63_488, temperature: 0, stream: false } }));
|
||||
expect(bodies[0].max_completion_tokens).toBe(131_072);
|
||||
expect(bodies[1].max_completion_tokens).toBe(32_768);
|
||||
expect(bodies[0].max_completion_tokens).toBe(200_000);
|
||||
expect(bodies[1].max_completion_tokens).toBe(63_488);
|
||||
});
|
||||
|
||||
it('thinking 未关闭时未配置 maxTokens → 兜底 32768（思考占配额，防截断）', async () => {
|
||||
it('thinking 未关闭时未配置 maxTokens → 不下发该字段（v0.8.1：无写死兜底值）', async () => {
|
||||
const pro = makeAdapter({ defaultModel: 'mimo-v2.5-pro' });
|
||||
const { bodies } = captureFetch();
|
||||
await pro.send(makeRequest({ params: { temperature: 0, stream: false } }));
|
||||
expect(bodies[0].max_completion_tokens).toBe(32_768);
|
||||
expect(bodies[0].max_completion_tokens).toBeUndefined();
|
||||
});
|
||||
|
||||
it('enableWebSearch 且存在客户端 tools → web_search 服务端工具追加（不覆盖客户端工具）', async () => {
|
||||
@@ -1029,27 +1029,36 @@ describe('OpenAIAdapter — 推理模型字段路由（v0.6.4 P3-1）', () => {
|
||||
expect(bodies[0].reasoning_effort).toBeUndefined();
|
||||
});
|
||||
|
||||
it('gpt-4.1 长上下文 1M → getContextWindow 返回 1M（模型元信息表）', () => {
|
||||
const adapter = makeAdapter('gpt-4.1');
|
||||
it('gpt-4.1 → getContextWindow 返回设置面板配置值（v0.8.1：元信息不再承载窗口）', () => {
|
||||
const adapter = new OpenAIAdapter({
|
||||
provider: 'openai',
|
||||
baseURL: 'http://o.test',
|
||||
apiKey: 'k',
|
||||
defaultModel: 'gpt-4.1',
|
||||
contextWindow: 1_000_000,
|
||||
});
|
||||
expect(adapter.getContextWindow()).toBe(1_000_000);
|
||||
// 未配置 → 0（引擎跳过压缩判定），无任何写死兜底
|
||||
const noCfg = makeAdapter('gpt-4.1');
|
||||
expect(noCfg.getContextWindow()).toBe(0);
|
||||
});
|
||||
|
||||
it('o3-mini max_completion_tokens 钳制到 100000', async () => {
|
||||
it('o3-mini max_completion_tokens 原样透传（v0.8.1：无 100000 钳制）', async () => {
|
||||
const adapter = makeAdapter('o3-mini');
|
||||
const { bodies } = captureFetch();
|
||||
await adapter.send(
|
||||
makeRequest({ params: { maxTokens: 200_000, temperature: 0, stream: false } }),
|
||||
);
|
||||
expect(bodies[0].max_completion_tokens).toBe(100_000);
|
||||
expect(bodies[0].max_completion_tokens).toBe(200_000);
|
||||
});
|
||||
|
||||
it('gpt-4o max_tokens 钳制到 16384', async () => {
|
||||
it('gpt-4o max_tokens 原样透传（v0.8.1：无 16384 钳制）', async () => {
|
||||
const adapter = makeAdapter('gpt-4o');
|
||||
const { bodies } = captureFetch();
|
||||
await adapter.send(
|
||||
makeRequest({ params: { maxTokens: 63_488, temperature: 0, stream: false } }),
|
||||
);
|
||||
expect(bodies[0].max_tokens).toBe(16_384);
|
||||
expect(bodies[0].max_tokens).toBe(63_488);
|
||||
});
|
||||
});
|
||||
|
||||
@@ -1255,16 +1264,16 @@ describe('OllamaAdapter — options 缺省与工具定义', () => {
|
||||
|
||||
// ===== 跨 Provider maxTokens 钳制矩阵 =====
|
||||
|
||||
describe('跨 Provider — maxTokens 钳制矩阵汇总', () => {
|
||||
describe('跨 Provider — maxTokens 透传矩阵汇总（v0.8.1 无钳制）', () => {
|
||||
it.each([
|
||||
['anthropic', 'claude-opus-4-1', 100_000, 32_000],
|
||||
['anthropic', 'claude-sonnet-4-5', 100_000, 64_000],
|
||||
['deepseek', 'deepseek-v4-flash-vision-exp', 100_000, 8_192],
|
||||
['agnes', 'agnes-2.0-flash', 100_000, 65_536],
|
||||
['mimo', 'mimo-v2.5', 100_000, 32_768],
|
||||
['openai', 'gpt-4o', 100_000, 16_384],
|
||||
['anthropic', 'claude-opus-4-1', 100_000, 100_000],
|
||||
['anthropic', 'claude-sonnet-4-5', 100_000, 100_000],
|
||||
['deepseek', 'deepseek-v4-flash-vision-exp', 100_000, 100_000],
|
||||
['agnes', 'agnes-2.0-flash', 100_000, 100_000],
|
||||
['mimo', 'mimo-v2.5', 100_000, 100_000],
|
||||
['openai', 'gpt-4o', 100_000, 100_000],
|
||||
] as const)(
|
||||
'%s %s maxTokens=100000 → 钳制为 %d',
|
||||
'%s %s maxTokens=100000 → 原样透传 %d',
|
||||
async (provider, model, requested, expected) => {
|
||||
const adapterMap: Record<string, unknown> = {
|
||||
anthropic: new AnthropicAdapter({
|
||||
|
||||
@@ -56,7 +56,7 @@ function asNative(
|
||||
}
|
||||
|
||||
describe('P0-3 修订: 用户思考意图优先于模型元信息', () => {
|
||||
it('DeepSeek: vision-exp（元信息 false）+ 用户开启思考 → 照发 enabled + reasoning_effort，max_tokens 仍按模型钳制 8192', async () => {
|
||||
it('DeepSeek: vision-exp（元信息 false）+ 用户开启思考 → 照发 enabled + reasoning_effort，max_tokens 原样透传（v0.8.1 无钳制）', async () => {
|
||||
const adapter = new DeepSeekAdapter({
|
||||
provider: 'deepseek',
|
||||
baseURL: 'https://api.deepseek.com',
|
||||
@@ -66,7 +66,7 @@ describe('P0-3 修订: 用户思考意图优先于模型元信息', () => {
|
||||
const body = asNative(adapter)(makeRequest(), false);
|
||||
expect(body.thinking).toEqual({ type: 'enabled' });
|
||||
expect(body.reasoning_effort).toBe('max');
|
||||
expect(body.max_tokens).toBe(8192);
|
||||
expect(body.max_tokens).toBe(63488);
|
||||
});
|
||||
|
||||
it('DeepSeek: 用户关闭思考 → 显式 disabled', async () => {
|
||||
|
||||
@@ -23,16 +23,15 @@ export class AgnesAdapter extends OpenAICompatibleAdapter {
|
||||
readonly supportsToolCalling = true;
|
||||
readonly supportsThinking = true;
|
||||
|
||||
// H-2 修复: Agnes 模型元信息（1M 上下文，65.5K 最大输出）
|
||||
// H-2 修复: Agnes 模型元信息（v0.8.1: 仅承载展示与能力声明 —— 窗口/输出上限
|
||||
// 数值已按硬性契约删除，唯一合法来源是设置面板 llm.contextWindow / llm.maxTokens）
|
||||
private static readonly MODEL_INFO: Record<string, MetonaModelInfo> = {
|
||||
'agnes-2.0-flash': {
|
||||
id: 'agnes-2.0-flash',
|
||||
name: 'Agnes 2.0 Flash',
|
||||
contextWindow: 1_000_000,
|
||||
maxOutputTokens: 65_536,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: 'Agnes AI 快速版，1M 上下文，支持多模态图片（URL + Base64）与思考模式',
|
||||
description: 'Agnes AI 快速版，支持多模态图片（URL + Base64）与思考模式',
|
||||
},
|
||||
};
|
||||
|
||||
@@ -72,16 +71,13 @@ export class AgnesAdapter extends OpenAICompatibleAdapter {
|
||||
);
|
||||
}
|
||||
|
||||
// v0.5.3: max_tokens 按模型上限钳制（agnes-2.0-flash 上限 65536）
|
||||
const modelInfo = AgnesAdapter.MODEL_INFO[this.config.defaultModel];
|
||||
const maxOutput = modelInfo?.maxOutputTokens ?? 65_536;
|
||||
const maxTokens = Math.min(request.params.maxTokens ?? maxOutput, maxOutput);
|
||||
|
||||
// v0.8.1 硬性契约: max_tokens 原样透传设置面板「最大输出上限」（llm.maxTokens），
|
||||
// 删除了旧的按模型元信息钳制逻辑
|
||||
const body: Record<string, unknown> = {
|
||||
model: this.config.defaultModel,
|
||||
messages,
|
||||
temperature: request.params.temperature,
|
||||
max_tokens: maxTokens,
|
||||
max_tokens: request.params.maxTokens,
|
||||
stream,
|
||||
};
|
||||
|
||||
|
||||
@@ -52,21 +52,19 @@ export class AnthropicAdapter extends BaseAdapter {
|
||||
readonly supportsToolCalling = true;
|
||||
readonly supportsThinking = true;
|
||||
|
||||
// v0.8.1: 仅承载展示与能力声明 —— 窗口/输出上限数值已按硬性契约删除，
|
||||
// 唯一合法来源是设置面板 llm.contextWindow / llm.maxTokens
|
||||
private static readonly MODEL_INFO: Record<string, MetonaModelInfo> = {
|
||||
'claude-sonnet-4-5': {
|
||||
id: 'claude-sonnet-4-5',
|
||||
name: 'Claude Sonnet 4.5',
|
||||
contextWindow: 200_000,
|
||||
maxOutputTokens: 64_000,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: 'Anthropic 旗舰模型，200K 上下文，支持扩展思考与工具调用',
|
||||
description: 'Anthropic 旗舰模型，支持扩展思考与工具调用',
|
||||
},
|
||||
'claude-opus-4-1': {
|
||||
id: 'claude-opus-4-1',
|
||||
name: 'Claude Opus 4.1',
|
||||
contextWindow: 200_000,
|
||||
maxOutputTokens: 32_000,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: 'Anthropic 深度推理模型',
|
||||
@@ -74,8 +72,6 @@ export class AnthropicAdapter extends BaseAdapter {
|
||||
'claude-haiku-4-5': {
|
||||
id: 'claude-haiku-4-5',
|
||||
name: 'Claude Haiku 4.5',
|
||||
contextWindow: 200_000,
|
||||
maxOutputTokens: 32_000,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: 'Anthropic 低延迟模型',
|
||||
@@ -416,13 +412,9 @@ export class AnthropicAdapter extends BaseAdapter {
|
||||
return this.supportedModels.map((id) => AnthropicAdapter.MODEL_INFO[id] ?? { id });
|
||||
}
|
||||
|
||||
override getContextWindow(): number {
|
||||
if (typeof this.config.contextWindow === 'number' && this.config.contextWindow > 0) {
|
||||
return this.config.contextWindow;
|
||||
}
|
||||
const modelInfo = AnthropicAdapter.MODEL_INFO[this.config.defaultModel];
|
||||
return modelInfo?.contextWindow ?? 200_000;
|
||||
}
|
||||
// getContextWindow 使用基类实现 —— v0.8.1 硬性契约：唯一来源是
|
||||
// 设置面板「上下文长度」（llm.contextWindow → AdapterConfig.contextWindow），
|
||||
// 未配置返回 0，引擎据此跳过压缩预算计算。
|
||||
|
||||
// ========== 私有方法 ==========
|
||||
|
||||
@@ -531,22 +523,21 @@ export class AnthropicAdapter extends BaseAdapter {
|
||||
});
|
||||
}
|
||||
|
||||
// v0.5.3: max_tokens 按模型上限钳制（sonnet 64000 / opus 32000 / haiku 32000）—
|
||||
// 引擎默认 63488 超过 opus/haiku 上限时 API 直接 400；thinking budget 已在此值内二分
|
||||
const anthropicMaxOutput =
|
||||
AnthropicAdapter.MODEL_INFO[this.config.defaultModel]?.maxOutputTokens ?? 64_000;
|
||||
|
||||
// v0.6.4 边界加固: thinking 开启时保证 max_tokens ≥ 2048 —— 协议要求
|
||||
// budget_tokens >= 1024 且 < max_tokens。原实现当用户配置极小 maxTokens
|
||||
// （如 1500）时 Math.floor(1500/2)=750 < 1024 直接 API 400。
|
||||
const requestedMaxTokens = request.params.maxTokens ?? 8192;
|
||||
// v0.8.1 硬性契约: max_tokens 原样透传设置面板「最大输出上限」（llm.maxTokens），
|
||||
// 删除了旧的按模型元信息钳制逻辑（MODEL_INFO.maxOutputTokens 已删除）。
|
||||
// 唯一保留的协议不变量：thinking 开启时 max_tokens ≥ 2048 —— Anthropic 协议
|
||||
// 要求 budget_tokens >= 1024 且 < max_tokens，用户配置低于该下限时 API 必然
|
||||
// 400，此为协议正确性下限而非输出上限（不修改用户配置的持久化值，仅在
|
||||
// 本次请求体上满足协议约束）。
|
||||
const requestedMaxTokens = request.params.maxTokens;
|
||||
const maxTokensForRequest = thinkingRequested
|
||||
? Math.max(2048, Math.min(requestedMaxTokens, anthropicMaxOutput))
|
||||
: Math.min(requestedMaxTokens, anthropicMaxOutput);
|
||||
? Math.max(2048, requestedMaxTokens ?? 2048)
|
||||
: requestedMaxTokens;
|
||||
|
||||
const body: Record<string, unknown> = {
|
||||
model: this.config.defaultModel,
|
||||
max_tokens: maxTokensForRequest,
|
||||
// v0.8.1: 未配置「最大输出上限」时不下发 max_tokens（服务端默认值生效）
|
||||
...(maxTokensForRequest != null ? { max_tokens: maxTokensForRequest } : {}),
|
||||
messages: merged,
|
||||
stream,
|
||||
};
|
||||
@@ -581,7 +572,8 @@ export class AnthropicAdapter extends BaseAdapter {
|
||||
max: 32768,
|
||||
};
|
||||
const effortBudget = budgetMap[request.params.thinkingEffort ?? 'high'] ?? 16384;
|
||||
const budget = Math.min(effortBudget, Math.floor(maxTokensForRequest / 2));
|
||||
// thinking 路径 maxTokensForRequest 恒为数字（Math.max(2048, …) 兜底）
|
||||
const budget = Math.min(effortBudget, Math.floor((maxTokensForRequest ?? 2048) / 2));
|
||||
body.thinking = { type: 'enabled', budget_tokens: budget };
|
||||
} else {
|
||||
body.temperature = request.params.temperature;
|
||||
|
||||
@@ -69,19 +69,15 @@ export abstract class BaseAdapter implements IMetonaProviderAdapter {
|
||||
/**
|
||||
* H-2 修复: 获取上下文窗口大小（规范要求）
|
||||
*
|
||||
* 默认实现从 config 读取 contextWindow，子类可覆盖以支持动态查询。
|
||||
* Engine 用此值估算上下文使用率，决定是否触发压缩。
|
||||
*
|
||||
* @returns 上下文窗口大小（token 数）
|
||||
* v0.8.1 硬性契约：上下文窗口的唯一合法来源是设置面板 LLM 配置的「上下文长度」
|
||||
* （llm.contextWindow，经 main.ts 注入 AdapterConfig.contextWindow）。
|
||||
* 本基类与所有子类禁止携带任何写死的默认窗口值 —— 未配置时返回 0，
|
||||
* 引擎据此跳过压缩预算计算（syncContextWindow 仅在 >0 时采纳）。
|
||||
*/
|
||||
getContextWindow(): number {
|
||||
// 优先使用 AdapterConfig.contextWindow（如果存在）
|
||||
const ctx = (this.config as AdapterConfig & { contextWindow?: number }).contextWindow;
|
||||
if (typeof ctx === 'number' && ctx > 0) return ctx;
|
||||
// v0.7.4 P4-5: 兜底从 1M 降至 128K —— 旧默认值 1M 在 config 与模型元信息均缺失时
|
||||
// （如 DeepSeek 未知模型），压缩阈值按 1M 算，实际 64K/128K 模型会先 413 再压缩。
|
||||
// 128K 是当前最保守的主流窗口，未知模型按最小值预算更安全。
|
||||
return 128_000;
|
||||
return 0;
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -28,32 +28,27 @@ export class DeepSeekAdapter extends OpenAICompatibleAdapter {
|
||||
readonly supportsToolCalling = true;
|
||||
readonly supportsThinking = true;
|
||||
|
||||
// H-2 修复: DeepSeek 模型元信息（1M 上下文，384K 最大输出）
|
||||
// H-2 修复: DeepSeek 模型元信息（v0.8.1: 仅承载展示与能力声明 —— 窗口/输出上限
|
||||
// 数值已按硬性契约删除，唯一合法来源是设置面板 llm.contextWindow / llm.maxTokens）
|
||||
private static readonly MODEL_INFO: Record<string, MetonaModelInfo> = {
|
||||
'deepseek-v4-pro': {
|
||||
id: 'deepseek-v4-pro',
|
||||
name: 'DeepSeek V4 Pro',
|
||||
contextWindow: 1_000_000,
|
||||
maxOutputTokens: 384_000,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: 'DeepSeek 旗舰模型，1M 上下文，支持深度推理与工具调用',
|
||||
description: 'DeepSeek 旗舰模型，支持深度推理与工具调用',
|
||||
},
|
||||
'deepseek-v4-flash': {
|
||||
id: 'deepseek-v4-flash',
|
||||
name: 'DeepSeek V4 Flash',
|
||||
contextWindow: 1_000_000,
|
||||
maxOutputTokens: 384_000,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: 'DeepSeek 快速版，1M 上下文，低延迟推理',
|
||||
description: 'DeepSeek 快速版，低延迟推理',
|
||||
},
|
||||
// v0.5.4: DeepSeek 多模态实验模型（OpenAI image_url content parts 格式）
|
||||
'deepseek-v4-flash-vision-exp': {
|
||||
id: 'deepseek-v4-flash-vision-exp',
|
||||
name: 'DeepSeek V4 Flash Vision (Exp)',
|
||||
contextWindow: 128_000,
|
||||
maxOutputTokens: 8_192,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: false,
|
||||
description: 'DeepSeek 多模态实验模型，支持图片输入（image_url content parts）',
|
||||
@@ -173,16 +168,13 @@ export class DeepSeekAdapter extends OpenAICompatibleAdapter {
|
||||
const messages = buildOpenAICompatibleMessages(request, this.isVisionModel());
|
||||
const tools = buildOpenAICompatibleTools(request.tools);
|
||||
|
||||
// v0.5.3: max_tokens 按模型上限钳制 — 引擎默认 63488 超过部分模型上限时 API 直接 400
|
||||
const modelInfo = DeepSeekAdapter.MODEL_INFO[this.config.defaultModel];
|
||||
const maxOutput = modelInfo?.maxOutputTokens ?? 384_000;
|
||||
const maxTokens = Math.min(request.params.maxTokens ?? maxOutput, maxOutput);
|
||||
|
||||
// v0.8.1 硬性契约: max_tokens 原样透传设置面板「最大输出上限」（llm.maxTokens），
|
||||
// 删除了旧的按模型元信息钳制逻辑 —— 代码中不存在任何写死的输出上限。
|
||||
const body: Record<string, unknown> = {
|
||||
model: this.config.defaultModel,
|
||||
messages,
|
||||
temperature: request.params.temperature,
|
||||
max_tokens: maxTokens,
|
||||
max_tokens: request.params.maxTokens,
|
||||
stream,
|
||||
};
|
||||
|
||||
@@ -228,11 +220,11 @@ export class DeepSeekAdapter extends OpenAICompatibleAdapter {
|
||||
`[DeepSeek] model "${this.config.defaultModel}" metadata says thinking unsupported — sending thinking params per user config (degraded retry handles budget exhaustion)`,
|
||||
);
|
||||
}
|
||||
// v0.8.0 P0-3: 思考会占用输出预算 —— 钳制后预算过小时显式告警
|
||||
// v0.8.0 P0-3: 思考会占用输出预算 —— 用户配置的输出预算过小时显式告警
|
||||
//（思考 token 计入 max_tokens，预算过小会出现"思考耗尽正文为零"截断）
|
||||
if (maxTokens < 8192) {
|
||||
if (typeof request.params.maxTokens === 'number' && request.params.maxTokens < 8192) {
|
||||
log.warn(
|
||||
`[DeepSeek] thinking enabled with small output budget (${maxTokens} tokens after model clamp) — reasoning may consume the entire budget and truncate the answer`,
|
||||
`[DeepSeek] thinking enabled with small output budget (${request.params.maxTokens} tokens per user config) — reasoning may consume the entire budget and truncate the answer`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -24,13 +24,12 @@ export class MimoAdapter extends OpenAICompatibleAdapter {
|
||||
readonly supportsThinking = true;
|
||||
|
||||
// MiMo 模型元信息
|
||||
// mimo-v2.5-pro: 1M 上下文 / 131072 max_tokens；mimo-v2.5: 1M 上下文 / 32768 max_tokens
|
||||
// v0.8.1: 仅承载展示与能力声明 —— 窗口/输出上限数值已按硬性契约删除，
|
||||
// 唯一合法来源是设置面板 llm.contextWindow / llm.maxTokens
|
||||
private static readonly MODEL_INFO: Record<string, MetonaModelInfo> = {
|
||||
'mimo-v2.5-pro': {
|
||||
id: 'mimo-v2.5-pro',
|
||||
name: 'MiMo V2.5 Pro',
|
||||
contextWindow: 1_000_000,
|
||||
maxOutputTokens: 131_072,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: '小米 MiMo 旗舰模型，支持深度思考与工具调用',
|
||||
@@ -38,8 +37,6 @@ export class MimoAdapter extends OpenAICompatibleAdapter {
|
||||
'mimo-v2.5': {
|
||||
id: 'mimo-v2.5',
|
||||
name: 'MiMo V2.5',
|
||||
contextWindow: 1_000_000,
|
||||
maxOutputTokens: 32_768,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: '小米 MiMo 标准模型，低延迟推理',
|
||||
@@ -82,17 +79,13 @@ export class MimoAdapter extends OpenAICompatibleAdapter {
|
||||
const tools = buildOpenAICompatibleTools(request.tools);
|
||||
|
||||
// MiMo 使用 max_completion_tokens（非 max_tokens）
|
||||
// #41 修复: thinking 模式下未配置时兜底 32768（thinking 占用 token 配额，API 默认值过小会截断输出）
|
||||
// v0.5.3: 按模型上限钳制（pro 131072 / standard 32768）—
|
||||
// 引擎默认 63488 超过 standard 上限时 API 直接 400
|
||||
const mimoMaxOutput =
|
||||
MimoAdapter.MODEL_INFO[this.config.defaultModel]?.maxOutputTokens ?? 131_072;
|
||||
const mimoDefault = request.params.thinkingEnabled !== false ? 32_768 : mimoMaxOutput;
|
||||
|
||||
// v0.8.1 硬性契约: 原样透传设置面板「最大输出上限」（llm.maxTokens），删除了
|
||||
// 旧的"#41 thinking 兜底 32768"与"按模型上限钳制"逻辑 —— 代码中不存在任何
|
||||
// 写死的输出上限；未配置时该字段不下发，由服务端默认值决定。
|
||||
const body: Record<string, unknown> = {
|
||||
model: this.config.defaultModel,
|
||||
messages,
|
||||
max_completion_tokens: Math.min(request.params.maxTokens ?? mimoDefault, mimoMaxOutput),
|
||||
max_completion_tokens: request.params.maxTokens,
|
||||
stream,
|
||||
};
|
||||
|
||||
@@ -145,11 +138,11 @@ export class MimoAdapter extends OpenAICompatibleAdapter {
|
||||
`[MiMo] model "${this.config.defaultModel}" metadata says thinking unsupported — sending thinking params per user config (degraded retry handles budget exhaustion)`,
|
||||
);
|
||||
}
|
||||
// v0.8.0 P0-3: 思考占用输出预算 —— 钳制后预算过小时显式告警
|
||||
const effectiveMax = body.max_completion_tokens as number;
|
||||
// v0.8.0 P0-3: 思考占用输出预算 —— 用户配置的输出预算过小时显式告警
|
||||
const effectiveMax = body.max_completion_tokens as number | undefined;
|
||||
if (typeof effectiveMax === 'number' && effectiveMax < 8192) {
|
||||
log.warn(
|
||||
`[MiMo] thinking enabled with small output budget (${effectiveMax} tokens after model clamp) — reasoning may consume the entire budget and truncate the answer`,
|
||||
`[MiMo] thinking enabled with small output budget (${effectiveMax} tokens per user config) — reasoning may consume the entire budget and truncate the answer`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -37,18 +37,48 @@ export class OllamaAdapter extends BaseAdapter {
|
||||
readonly supportsToolCalling = true;
|
||||
readonly supportsThinking = true;
|
||||
|
||||
// H-2 修复: Ollama 本地模型默认上下文窗口（可由 options.num_ctx 覆盖）
|
||||
private static readonly DEFAULT_CONTEXT_WINDOW = 4096;
|
||||
|
||||
private baseURL: string;
|
||||
|
||||
constructor(config: ConstructorParameters<typeof BaseAdapter>[0]) {
|
||||
super(config);
|
||||
this.baseURL = config.baseURL || 'http://localhost:11434';
|
||||
// v0.6.4 P4-1: 每个适配器实例（= 每会话独立引擎）启动时做一次 /api/show 探测，
|
||||
// 把 num_ctx 实测值填充进 getContextWindow 缓存。fire-and-forget：失败静默，
|
||||
// 不阻塞/不影响首个请求；此后压缩预算基于实测窗口而非保守默认 4096。
|
||||
this.refreshContextWindow();
|
||||
// v0.8.1: 上下文窗口不再从 /api/show 探测或写死默认值获取 —— 唯一合法来源是
|
||||
// 设置面板「上下文长度」（llm.contextWindow → AdapterConfig.contextWindow，
|
||||
// 引擎侧经 contextLength=num_ctx 下发）。构造时仅探测能力（thinking/tools/vision，
|
||||
// 属协议正确性门控），不再缓存窗口数值。
|
||||
this.probeCapabilitiesOnce();
|
||||
}
|
||||
|
||||
/** /api/show 能力探测只发一次（构造函数发起），失败不重试（fail-open） */
|
||||
private probeAttempted = false;
|
||||
private probeInProgress = false;
|
||||
/**
|
||||
* /api/show capabilities 探测缓存 —— 模型是否支持思考（协议正确性门控，
|
||||
* 非窗口/输出上限语义）。null = 未探测/探测失败（fail-open 放行，与
|
||||
* listModels 能力回退策略一致）；false = 服务端明确不支持 → 不发 think 参数。
|
||||
*/
|
||||
private cachedThinkingSupport: boolean | null = null;
|
||||
|
||||
/**
|
||||
* v0.8.1: 构造时 fire-and-forget 探测一次默认模型能力（仅 thinking 门控消费）。
|
||||
* 旧实现同时缓存 num_ctx 窗口数值 —— 已按"窗口唯一来源是设置面板"契约删除。
|
||||
*/
|
||||
private probeCapabilitiesOnce(): void {
|
||||
if (this.probeAttempted) return;
|
||||
this.probeAttempted = true;
|
||||
this.probeInProgress = true;
|
||||
void this.showModel(this.config.defaultModel)
|
||||
.then((info) => {
|
||||
if (Array.isArray(info?.capabilities)) {
|
||||
this.cachedThinkingSupport = info!.capabilities.map(String).includes('thinking');
|
||||
}
|
||||
})
|
||||
.catch(() => {
|
||||
/* 模型探测失败不阻塞对话（fail-open） */
|
||||
})
|
||||
.finally(() => {
|
||||
this.probeInProgress = false;
|
||||
});
|
||||
}
|
||||
|
||||
// ===== POST /api/chat =====
|
||||
@@ -356,14 +386,13 @@ export class OllamaAdapter extends BaseAdapter {
|
||||
// 该模型回退保守 true（不可用时行为与旧实现一致，fail-open 保可用性）
|
||||
// v0.7.3 P1-4: supportsVision 随探测结果透出（undefined = 未知 → 前端保守放行），
|
||||
// 供上传入口拒绝不支持图片的本地语言模型
|
||||
// v0.8.1: 不再填充 contextWindow —— 窗口唯一来源是设置面板「上下文长度」
|
||||
const enriched = await Promise.all(
|
||||
data.models.map(async (m) => {
|
||||
const caps = await this.probeCapabilities(m.name);
|
||||
return {
|
||||
id: m.name,
|
||||
name: m.name,
|
||||
// Ollama 模型上下文窗口由 options.num_ctx 决定，此处给保守值
|
||||
contextWindow: OllamaAdapter.DEFAULT_CONTEXT_WINDOW,
|
||||
supportsToolCalling: caps ? caps.supportsTools : true,
|
||||
supportsThinking: caps ? caps.supportsThinking : true,
|
||||
supportsVision: caps ? caps.supportsVision : undefined,
|
||||
@@ -386,59 +415,15 @@ export class OllamaAdapter extends BaseAdapter {
|
||||
/**
|
||||
* H-2 修复: 获取上下文窗口大小（规范要求）
|
||||
*
|
||||
* Ollama 上下文窗口由 options.num_ctx 决定（默认 4096），
|
||||
* Engine 应通过 MetonaRequest.params.contextLength 显式设置。
|
||||
* 此处返回默认值，供 Engine 在未指定时参考。
|
||||
* v0.8.1 硬性契约: 唯一来源是设置面板「上下文长度」（llm.contextWindow），
|
||||
* 未配置返回 0 —— 删除了旧的 4096 写死默认值与 /api/show num_ctx 探测缓存。
|
||||
* Ollama 的 num_ctx 由引擎经 params.contextLength（同源配置）下发给服务端。
|
||||
*/
|
||||
override getContextWindow(): number {
|
||||
return this.cachedContextWindow ?? OllamaAdapter.DEFAULT_CONTEXT_WINDOW;
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.6.4 P4-1: 从 /api/show 的 parameters 区解析 num_ctx 真值。
|
||||
*
|
||||
* 契约约束：IMetonaProviderAdapter.getContextWindow 是同步接口（引擎压缩判定
|
||||
* 依赖同步取值），无法在内部 await。因此采用"机会主义缓存"策略：
|
||||
* send/sendStream 启动时 fire-and-forget 刷新缓存；首次请求前返回默认 4096，
|
||||
* 之后永远返回实测值。压缩预算的准确性随使用逐渐收敛到真值。
|
||||
*/
|
||||
private cachedContextWindow: number | null = null;
|
||||
private refreshingContextWindow = false;
|
||||
/** v0.8.0 P0-3: /api/show 探测只发一次（构造函数发起），失败不重试（fail-open） */
|
||||
private probeAttempted = false;
|
||||
/**
|
||||
* v0.8.0 P0-3: /api/show capabilities 探测缓存 —— 模型是否支持思考。
|
||||
* null = 未探测/探测失败（fail-open 放行，与 listModels 能力回退策略一致）；
|
||||
* false = 服务端明确不支持 → toNativeRequest 不发 think 参数。
|
||||
*/
|
||||
private cachedThinkingSupport: boolean | null = null;
|
||||
|
||||
private refreshContextWindow(): void {
|
||||
if (this.refreshingContextWindow || this.probeAttempted) return;
|
||||
this.probeAttempted = true;
|
||||
this.refreshingContextWindow = true;
|
||||
void this.showModel(this.config.defaultModel)
|
||||
.then((info) => {
|
||||
if (!info?.parameters) return;
|
||||
const match = /^num_ctx\s+(\d+)\s*$/m.exec(info.parameters);
|
||||
if (match) {
|
||||
const value = Number(match[1]);
|
||||
if (Number.isFinite(value) && value > 0) {
|
||||
this.cachedContextWindow = value;
|
||||
log.info(`[Ollama] Context window (num_ctx) detected: ${value}`);
|
||||
}
|
||||
}
|
||||
// v0.8.0 P0-3: 同一次探测顺带缓存思考能力（供 toNativeRequest 同步门控）
|
||||
if (Array.isArray(info.capabilities)) {
|
||||
this.cachedThinkingSupport = info.capabilities.map(String).includes('thinking');
|
||||
}
|
||||
})
|
||||
.catch(() => {
|
||||
/* 模型探测失败不阻塞对话 */
|
||||
})
|
||||
.finally(() => {
|
||||
this.refreshingContextWindow = false;
|
||||
});
|
||||
if (typeof this.config.contextWindow === 'number' && this.config.contextWindow > 0) {
|
||||
return this.config.contextWindow;
|
||||
}
|
||||
return 0;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -466,7 +451,7 @@ export class OllamaAdapter extends BaseAdapter {
|
||||
|
||||
async showModel(
|
||||
model: string,
|
||||
): Promise<{ parameters: string; template: string; capabilities: string[] } | null> {
|
||||
): Promise<{ parameters: string; template: string; capabilities?: string[] } | null> {
|
||||
try {
|
||||
const response = await fetch(`${this.baseURL}/api/show`, {
|
||||
method: 'POST',
|
||||
@@ -483,7 +468,10 @@ export class OllamaAdapter extends BaseAdapter {
|
||||
return {
|
||||
parameters: data.parameters ?? '',
|
||||
template: data.template ?? '',
|
||||
capabilities: data.capabilities ?? [],
|
||||
// v0.8.1: 保留 undefined 语义 —— 响应未携带 capabilities 字段 = 未知
|
||||
//（fail-open），显式数组（含空数组 = 服务端权威"无任何能力"）才参与门控。
|
||||
// 旧实现 `?? []` 把"字段缺失"与"权威空"混同，探测竞态下会误判不支持思考。
|
||||
capabilities: data.capabilities,
|
||||
};
|
||||
} catch {
|
||||
return null;
|
||||
@@ -707,7 +695,7 @@ export class OllamaAdapter extends BaseAdapter {
|
||||
// think 会导致每次请求 400 "does not support thinking"，而非静默忽略），
|
||||
// 故此处门控属协议正确性而非用户意图覆盖；探测为服务端实时真值（非静态
|
||||
// 元信息）。未探测/探测失败（null）fail-open 放行，与 listModels 能力回退
|
||||
// 策略一致。探测在适配器实例创建时 fire-and-forget 发起（refreshContextWindow）。
|
||||
// 策略一致。探测在适配器实例创建时 fire-and-forget 发起（probeCapabilitiesOnce）。
|
||||
if (request.params.thinkingEnabled) {
|
||||
const modelThinkingSupported = this.cachedThinkingSupport !== false;
|
||||
if (modelThinkingSupported) {
|
||||
|
||||
@@ -24,39 +24,33 @@ export class OpenAIAdapter extends OpenAICompatibleAdapter {
|
||||
readonly supportsToolCalling = true;
|
||||
readonly supportsThinking = true;
|
||||
|
||||
// v0.8.1: 仅承载展示与能力声明 —— 窗口/输出上限数值已按硬性契约删除，
|
||||
// 唯一合法来源是设置面板 llm.contextWindow / llm.maxTokens
|
||||
private static readonly MODEL_INFO: Record<string, MetonaModelInfo> = {
|
||||
'gpt-4o': {
|
||||
id: 'gpt-4o',
|
||||
name: 'GPT-4o',
|
||||
contextWindow: 128_000,
|
||||
maxOutputTokens: 16_384,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: false,
|
||||
description: 'OpenAI 旗舰多模态模型，128K 上下文',
|
||||
description: 'OpenAI 旗舰多模态模型',
|
||||
},
|
||||
'gpt-4o-mini': {
|
||||
id: 'gpt-4o-mini',
|
||||
name: 'GPT-4o mini',
|
||||
contextWindow: 128_000,
|
||||
maxOutputTokens: 16_384,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: false,
|
||||
description: 'OpenAI 高性价比模型，128K 上下文',
|
||||
description: 'OpenAI 高性价比模型',
|
||||
},
|
||||
'gpt-4.1': {
|
||||
id: 'gpt-4.1',
|
||||
name: 'GPT-4.1',
|
||||
contextWindow: 1_000_000,
|
||||
maxOutputTokens: 32_768,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: false,
|
||||
description: 'OpenAI 长上下文模型，1M 上下文',
|
||||
description: 'OpenAI 长上下文模型',
|
||||
},
|
||||
'o3-mini': {
|
||||
id: 'o3-mini',
|
||||
name: 'o3-mini',
|
||||
contextWindow: 200_000,
|
||||
maxOutputTokens: 100_000,
|
||||
supportsToolCalling: true,
|
||||
supportsThinking: true,
|
||||
description: 'OpenAI 推理模型，支持 reasoning_effort',
|
||||
@@ -81,11 +75,6 @@ export class OpenAIAdapter extends OpenAICompatibleAdapter {
|
||||
return 'OpenAI';
|
||||
}
|
||||
|
||||
// v0.6.4: OpenAI 家族兜底窗口为 128K（其余 OpenAI 兼容 Provider 为 1M）
|
||||
protected override defaultContextWindowFallback(): number {
|
||||
return 128_000;
|
||||
}
|
||||
|
||||
// ===== GET /v1/models =====
|
||||
|
||||
override async listModels(): Promise<MetonaModelInfo[]> {
|
||||
@@ -137,18 +126,13 @@ export class OpenAIAdapter extends OpenAICompatibleAdapter {
|
||||
};
|
||||
|
||||
// Token 上限参数：o 系列/gpt-5 使用 max_completion_tokens
|
||||
// v0.5.3: 按模型上限钳制（gpt-4o 16384 / gpt-4.1 32768 / o3-mini 100000）—
|
||||
// 引擎默认 63488 超过 gpt-4o/gpt-4.1 上限时 API 直接 400
|
||||
const oaMaxOutput = OpenAIAdapter.MODEL_INFO[model]?.maxOutputTokens ?? 128_000;
|
||||
const oaMaxTokens = Math.min(
|
||||
request.params.maxTokens ?? (isReasoningModel ? 32_768 : oaMaxOutput),
|
||||
oaMaxOutput,
|
||||
);
|
||||
if (oaMaxTokens) {
|
||||
// v0.8.1 硬性契约: 原样透传设置面板「最大输出上限」（llm.maxTokens），删除了
|
||||
// 旧的按模型元信息钳制逻辑与未配置时的 32_768 兜底 —— 未配置时不下发该字段。
|
||||
if (request.params.maxTokens != null) {
|
||||
if (isReasoningModel) {
|
||||
body.max_completion_tokens = oaMaxTokens;
|
||||
body.max_completion_tokens = request.params.maxTokens;
|
||||
} else {
|
||||
body.max_tokens = oaMaxTokens;
|
||||
body.max_tokens = request.params.maxTokens;
|
||||
}
|
||||
}
|
||||
|
||||
@@ -169,10 +153,10 @@ export class OpenAIAdapter extends OpenAICompatibleAdapter {
|
||||
max: 'high',
|
||||
};
|
||||
body.reasoning_effort = effortMap[request.params.thinkingEffort ?? 'high'] ?? 'high';
|
||||
// v0.8.0 P0-3: 推理 token 计入 max_completion_tokens —— 钳制后预算过小时告警
|
||||
if (oaMaxTokens < 8192) {
|
||||
// v0.8.0 P0-3: 推理 token 计入 max_completion_tokens —— 用户配置的预算过小时告警
|
||||
if (typeof request.params.maxTokens === 'number' && request.params.maxTokens < 8192) {
|
||||
log.warn(
|
||||
`[OpenAI] reasoning enabled with small output budget (${oaMaxTokens} tokens after model clamp) — reasoning may consume the entire budget and truncate the answer`,
|
||||
`[OpenAI] reasoning enabled with small output budget (${request.params.maxTokens} tokens per user config) — reasoning may consume the entire budget and truncate the answer`,
|
||||
);
|
||||
}
|
||||
} else if (!isReasoningModel) {
|
||||
|
||||
@@ -41,16 +41,9 @@ export abstract class OpenAICompatibleAdapter extends BaseAdapter {
|
||||
/** 非流式 send 的默认超时。DeepSeek/MiMo/OpenAI=120s；Agnes 历史 300s，保留其值。 */
|
||||
protected abstract sendTimeoutMs(): number;
|
||||
|
||||
/** 模型元信息表（子类持有；用于 getContextWindow 回退链与钳制） */
|
||||
/** 模型元信息表（子类持有；仅承载展示与能力声明，不含任何窗口/输出上限数值） */
|
||||
protected abstract modelInfoTable(): Record<string, MetonaModelInfo>;
|
||||
|
||||
/** getContextWindow 的最终兜底窗口（未配置且模型未知时使用） */
|
||||
// v0.7.4 P4-5: 1M → 128K（与 base-adapter 兜底对齐）——未知模型按最保守主流窗口预算，
|
||||
// 防止压缩阈值按 1M 计算导致实际小窗口模型先 413 再压缩
|
||||
protected defaultContextWindowFallback(): number {
|
||||
return 128_000;
|
||||
}
|
||||
|
||||
// ===== 认证头 =====
|
||||
|
||||
protected buildHeaders(): Record<string, string> {
|
||||
@@ -161,15 +154,16 @@ export abstract class OpenAICompatibleAdapter extends BaseAdapter {
|
||||
}
|
||||
|
||||
/**
|
||||
* 上下文窗口回退链（v0.6.3 一致化后的统一实现）：
|
||||
* config.contextWindow（用户显式配置）→ 模型元信息 → Provider 兜底。
|
||||
* 上下文窗口（v0.8.1 硬性契约单一化）：
|
||||
* 唯一合法来源是设置面板「上下文长度」（llm.contextWindow → AdapterConfig.contextWindow）。
|
||||
* 删除了旧的 config → 模型元信息 → 兜底 三级回退链 —— 模型元信息不再承载窗口数值，
|
||||
* 未配置时返回 0（引擎据此跳过压缩预算，行为与"用户未声明窗口"语义一致）。
|
||||
*/
|
||||
override getContextWindow(): number {
|
||||
if (typeof this.config.contextWindow === 'number' && this.config.contextWindow > 0) {
|
||||
return this.config.contextWindow;
|
||||
}
|
||||
const modelInfo = this.modelInfoTable()[this.config.defaultModel];
|
||||
return modelInfo?.contextWindow ?? this.defaultContextWindowFallback();
|
||||
return 0;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
@@ -84,10 +84,11 @@ const DEFAULT_CONFIG: AgentLoopConfig = {
|
||||
totalTimeoutMs: 600_000,
|
||||
enableReflection: false,
|
||||
compressionThreshold: 0.8,
|
||||
contextWindow: 128_000,
|
||||
// v0.8.1: contextWindow / maxTokens 不再携带任何写死默认值 —— 唯一合法来源是
|
||||
// 设置面板 LLM 配置（llm.contextWindow / llm.maxTokens），由 main.ts baseConfig
|
||||
// 与 applyEngineConfigKey 注入。未配置时压缩判定跳过、输出上限参数不下发。
|
||||
retryCount: 3,
|
||||
temperature: 0.0,
|
||||
maxTokens: 63488,
|
||||
thinkingEnabled: true,
|
||||
thinkingEffort: 'high',
|
||||
toolExecutionTimeoutMs: 120_000,
|
||||
@@ -185,10 +186,9 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
/**
|
||||
* #3 修复: 从 adapter 同步 contextWindow 到 Engine 配置
|
||||
*
|
||||
* Engine 的 DEFAULT_CONFIG.contextWindow 硬编码为 128_000，但各 Provider 实际支持的
|
||||
* 上下文窗口差异巨大（DeepSeek 1M / Agnes 1M / MiMo 1M / OpenAI 128K~1M /
|
||||
* Anthropic 200K / Ollama 4096 起，v0.7.4 修正注释——此前误写 64K）。
|
||||
* 不同步会导致压缩阈值（compressionThreshold * contextWindow）计算错误。
|
||||
* v0.8.1 契约: 窗口唯一来源是设置面板「上下文长度」—— 引擎 config 与 adapter
|
||||
* config 同源注入；本同步仅当 adapter 侧返回 >0（用户已配置）时采纳，
|
||||
* 返回 0（未配置）不覆盖，压缩判定按未配置语义跳过。
|
||||
*/
|
||||
private syncContextWindow(): void {
|
||||
const adapterCtx = this.adapter.getContextWindow?.();
|
||||
@@ -628,6 +628,10 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
promptTokens: event.usage.inputTokens ?? 0,
|
||||
completionTokens: event.usage.outputTokens ?? 0,
|
||||
totalTokens: event.usage.totalTokens ?? 0,
|
||||
// v0.8.1 P1-3: 透传 Prompt Cache 命中字段（前端命中率展示的数据源；
|
||||
// 此前引擎在此处丢弃 adapter 已采集的 cache 字段，观测链路断头）
|
||||
cacheHitTokens: event.usage.cacheHitTokens,
|
||||
cacheMissTokens: event.usage.cacheMissTokens,
|
||||
};
|
||||
// v0.3.18 修复: 记录最近一次 LLM 调用的真实输入 token，用于校正压缩判断
|
||||
// 估算值可能偏低（尤其中文场景），导致不压缩但 API 413
|
||||
@@ -788,10 +792,10 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
}
|
||||
|
||||
// === 上下文压缩（基于 token 使用率触发） ===
|
||||
// 有效上下文窗口：Ollama 使用 contextLength (numCtx)，其他 Provider 使用 contextWindow
|
||||
// v0.3.18 修复: 加默认值 128_000 保护，避免 config 都为 undefined 时 compressionThreshold 变 NaN 导致压缩永不触发
|
||||
const effectiveContextWindow =
|
||||
this.config.contextLength ?? this.config.contextWindow ?? 128_000;
|
||||
// 有效上下文窗口：Ollama 使用 contextLength (num_ctx)，其他 Provider 使用 contextWindow。
|
||||
// v0.8.1: 唯一来源是设置面板「上下文长度」（llm.contextWindow）—— 不再有任何写死
|
||||
// 兜底值；未配置（<=0）时跳过压缩判定（无法计算阈值，且用户未声明窗口即不预算）。
|
||||
const effectiveContextWindow = this.config.contextLength ?? this.config.contextWindow ?? 0;
|
||||
const estimatedTokens = this.estimateMessagesTokens(request.messages);
|
||||
// v0.3.18 修复: 取 max(估算值, 真实值) 作为实际占用，避免估算偏低导致不压缩但 API 413
|
||||
// 估算值用于 LLM 尚未返回 usage 时的早期判断（首轮或重试场景）
|
||||
@@ -801,7 +805,11 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
// v0.3.18 修复: 触发条件从"消息数 > 10"改为"消息数 >= 4"
|
||||
// 新压缩策略按 token 预算动态截断，不再依赖固定 10 条。
|
||||
// 至少 4 条消息（2 轮 user+assistant）才有压缩意义，否则保留区已是最小。
|
||||
if (actualTokens > compressionThreshold && request.messages.length >= 4) {
|
||||
if (
|
||||
effectiveContextWindow > 0 &&
|
||||
actualTokens > compressionThreshold &&
|
||||
request.messages.length >= 4
|
||||
) {
|
||||
await this.transitionTo(AgentLoopState.COMPRESSING);
|
||||
const compressed = await this.compressMessages(request.messages);
|
||||
if (compressed) {
|
||||
@@ -1462,9 +1470,10 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
*/
|
||||
private async compressMessages(messages: MetonaMessage[]): Promise<MetonaMessage[] | null> {
|
||||
// v0.3.18 修复: 动态计算保留预算，避免固定 10 条在超长消息场景仍超限
|
||||
// 加默认值 128_000 保护，避免 config 都为 undefined 时 keepBudget 变 NaN
|
||||
const effectiveContextWindow =
|
||||
this.config.contextLength ?? this.config.contextWindow ?? 128_000;
|
||||
// v0.8.1: 窗口唯一来源是设置面板「上下文长度」，无写死兜底 —— 未配置时无法
|
||||
// 计算保留预算，直接放弃压缩（调用方保持原数组，行为安全）
|
||||
const effectiveContextWindow = this.config.contextLength ?? this.config.contextWindow ?? 0;
|
||||
if (!(effectiveContextWindow > 0)) return null;
|
||||
const keepBudget = Math.floor(effectiveContextWindow * 0.5); // 保留区占上下文窗口 50%
|
||||
const minKeepCount = 2; // 至少保留最后 2 条（user + assistant），保证有可推理上下文
|
||||
|
||||
@@ -1623,6 +1632,16 @@ export class AgentLoopEngine extends EventEmitter {
|
||||
this.totalTokens.promptTokens += usage.promptTokens;
|
||||
this.totalTokens.completionTokens += usage.completionTokens;
|
||||
this.totalTokens.totalTokens += usage.totalTokens;
|
||||
// v0.8.1 P1-3: 累计缓存命中/未命中（未上报的 Provider 保持 undefined，
|
||||
// 不把 undefined 污染为数字 0 —— 前端以 undefined 判定"该 Provider 不上报"）
|
||||
if (usage.cacheHitTokens != null) {
|
||||
this.totalTokens.cacheHitTokens =
|
||||
(this.totalTokens.cacheHitTokens ?? 0) + usage.cacheHitTokens;
|
||||
}
|
||||
if (usage.cacheMissTokens != null) {
|
||||
this.totalTokens.cacheMissTokens =
|
||||
(this.totalTokens.cacheMissTokens ?? 0) + usage.cacheMissTokens;
|
||||
}
|
||||
}
|
||||
|
||||
private finish(reason: TerminationReason, answer?: string, error?: Error): AgentLoopOutput {
|
||||
|
||||
@@ -61,6 +61,13 @@ export interface TokenUsage {
|
||||
promptTokens: number;
|
||||
completionTokens: number;
|
||||
totalTokens: number;
|
||||
/**
|
||||
* v0.8.1 P1-3: Prompt Cache 命中/未命中 token 数（Provider 上报时透传）。
|
||||
* 供前端 TokenUsage 面板计算缓存命中率 —— 采集与展示全链路闭环，
|
||||
* 未上报的 Provider 为 undefined（UI 隐藏该行）。
|
||||
*/
|
||||
cacheHitTokens?: number;
|
||||
cacheMissTokens?: number;
|
||||
}
|
||||
|
||||
// ===== Agent Loop 配置 =====
|
||||
@@ -70,16 +77,26 @@ export interface AgentLoopConfig {
|
||||
totalTimeoutMs: number;
|
||||
enableReflection: boolean;
|
||||
compressionThreshold: number;
|
||||
contextWindow: number;
|
||||
/**
|
||||
* 上下文窗口大小（token 数）—— 唯一合法来源是设置面板 LLM 配置的「上下文长度」
|
||||
* （llm.contextWindow，经 main.ts baseConfig / applyEngineConfigKey 注入）。
|
||||
* 引擎与适配器禁止携带任何写死的默认值；未配置（undefined/0）时压缩判定跳过。
|
||||
*/
|
||||
contextWindow?: number;
|
||||
retryCount: number;
|
||||
temperature: number;
|
||||
/** 最大生成 token 数（默认 63488） */
|
||||
maxTokens: number;
|
||||
/**
|
||||
* 最大生成 token 数 —— 唯一合法来源是设置面板 LLM 配置的「最大输出上限」
|
||||
* （llm.maxTokens，经 main.ts baseConfig / applyEngineConfigKey 注入）。
|
||||
* 引擎与适配器禁止携带任何写死的默认值，也不得按模型元信息钳制；
|
||||
* 未配置时该参数不下发，由 Provider 服务端默认值决定。
|
||||
*/
|
||||
maxTokens?: number;
|
||||
/** 是否启用思考模式（默认 true） */
|
||||
thinkingEnabled: boolean;
|
||||
/** 思考强度（默认 'high'） */
|
||||
thinkingEffort: 'low' | 'medium' | 'high' | 'max';
|
||||
/** Ollama 上下文窗口大小（num_ctx），其他 Provider 忽略 */
|
||||
/** Ollama num_ctx（与「上下文长度」同源：llm.contextWindow，仅 Ollama Provider 下发） */
|
||||
contextLength?: number;
|
||||
/** 工具执行兜底超时（ms，默认 120000），实际取 max(此值, tool.timeoutMs) */
|
||||
toolExecutionTimeoutMs?: number;
|
||||
|
||||
@@ -88,6 +88,7 @@ describe('ConfirmationHook — 多窗口广播（v0.7.2 P2-7）', () => {
|
||||
}
|
||||
|
||||
it('确认请求广播到所有存活窗口（而非仅 mainWindow）', async () => {
|
||||
vi.useFakeTimers();
|
||||
const a = makeTrackedWindow();
|
||||
const b = makeTrackedWindow();
|
||||
const destroyed = makeTrackedWindow(true);
|
||||
@@ -96,6 +97,8 @@ describe('ConfirmationHook — 多窗口广播（v0.7.2 P2-7）', () => {
|
||||
const hook = new ConfirmationHook(null, null);
|
||||
hook.setToolDefs([HIGH_RISK_DEF]);
|
||||
const p = hook.beforeExecute(makeToolCall(), 'sess');
|
||||
// v0.8.1 P2-2: 聚合窗口（800ms）结束才广播 —— 推进 fake timers
|
||||
await vi.advanceTimersByTimeAsync(1000);
|
||||
|
||||
// 所有存活窗口均收到确认请求（携带 expiresAt 倒计时契约）
|
||||
expect(a.send).toHaveBeenCalledWith(
|
||||
@@ -111,20 +114,25 @@ describe('ConfirmationHook — 多窗口广播（v0.7.2 P2-7）', () => {
|
||||
|
||||
hook.resolveConfirmation(hook.getPendingConfirmations()[0].toolCallId, true, false, false);
|
||||
expect((await p).blocked).toBe(false);
|
||||
vi.useRealTimers();
|
||||
});
|
||||
|
||||
it('getAllWindows 为空时回退到注入的 mainWindow（向后兼容）', async () => {
|
||||
vi.useFakeTimers();
|
||||
const mainWin = makeMockWindow();
|
||||
getAllWindowsMock.mockReturnValue([]);
|
||||
const hook = new ConfirmationHook(mainWin, null);
|
||||
hook.setToolDefs([HIGH_RISK_DEF]);
|
||||
const p = hook.beforeExecute(makeToolCall(), 'sess');
|
||||
// v0.8.1 P2-2: 推进聚合窗口后断言广播
|
||||
await vi.advanceTimersByTimeAsync(1000);
|
||||
expect((mainWin.webContents as unknown as { send: Mock }).send).toHaveBeenCalledWith(
|
||||
'tool:confirmationRequest',
|
||||
expect.objectContaining({ toolName: 'run_command' }),
|
||||
);
|
||||
hook.resolveConfirmation(hook.getPendingConfirmations()[0].toolCallId, true, false, false);
|
||||
expect((await p).blocked).toBe(false);
|
||||
vi.useRealTimers();
|
||||
});
|
||||
|
||||
it('全部窗口不可达时 fail-closed 阻断（no main window available）', async () => {
|
||||
@@ -524,3 +532,92 @@ describe('ConfirmationHook — 跨会话隔离（v0.5.0）', () => {
|
||||
expect((await hook.beforeExecute(makeToolCall(), 'sess-b')).blocked).toBe(false);
|
||||
});
|
||||
});
|
||||
|
||||
// ===== v0.8.1 P2-2: 连续同类工具批量确认聚合 =====
|
||||
|
||||
describe('ConfirmationHook — 同类工具批量确认聚合（v0.8.1 P2-2）', () => {
|
||||
/** 单次 prompt（beforeExecute 阻塞等待确认，不消费 promise） */
|
||||
function startPrompt(hook: ConfirmationHook, id: string): Promise<unknown> {
|
||||
return hook.beforeExecute(
|
||||
{ id, name: 'run_command', args: {}, iteration: 1, timestamp: Date.now() },
|
||||
'sess',
|
||||
);
|
||||
}
|
||||
|
||||
/** 本 describe 专用的 send 追踪窗口（makeTrackedWindow 定义在上一 describe 作用域） */
|
||||
function makeWindow(): { win: BrowserWindow; send: Mock } {
|
||||
const send = vi.fn();
|
||||
const win = {
|
||||
isDestroyed: () => false,
|
||||
webContents: { send },
|
||||
} as unknown as BrowserWindow;
|
||||
return { win, send };
|
||||
}
|
||||
|
||||
function makeRunCommandDef(): MetonaToolDef {
|
||||
return {
|
||||
...HIGH_RISK_DEF,
|
||||
name: 'run_command',
|
||||
requiresPermission: true,
|
||||
};
|
||||
}
|
||||
|
||||
it('3 个同类并行请求 → 聚合为单条 batch 事件（无逐条事件）', async () => {
|
||||
vi.useFakeTimers();
|
||||
const w = makeWindow();
|
||||
getAllWindowsMock.mockReturnValue([w.win]);
|
||||
const hook = new ConfirmationHook(null, null);
|
||||
hook.setToolDefs([makeRunCommandDef()]);
|
||||
|
||||
const prompts = [
|
||||
startPrompt(hook, 'tc_1'),
|
||||
startPrompt(hook, 'tc_2'),
|
||||
startPrompt(hook, 'tc_3'),
|
||||
];
|
||||
// 达到阈值立即 flush
|
||||
await vi.advanceTimersByTimeAsync(0);
|
||||
|
||||
const batchCalls = (w.send as Mock).mock.calls.filter(
|
||||
(c) => c[0] === 'tool:confirmationRequestBatch',
|
||||
);
|
||||
expect(batchCalls).toHaveLength(1);
|
||||
expect(
|
||||
(batchCalls[0][1] as unknown[]).map((r) => (r as { toolCallId: string }).toolCallId),
|
||||
).toEqual(['tc_1', 'tc_2', 'tc_3']);
|
||||
// 不应再发送逐条事件
|
||||
const individual = (w.send as Mock).mock.calls.filter(
|
||||
(c) => c[0] === 'tool:confirmationRequest',
|
||||
);
|
||||
expect(individual).toHaveLength(0);
|
||||
|
||||
for (const id of ['tc_1', 'tc_2', 'tc_3']) {
|
||||
hook.resolveConfirmation(id, true, false, false);
|
||||
}
|
||||
await Promise.all(prompts);
|
||||
vi.useRealTimers();
|
||||
});
|
||||
|
||||
it('2 个同类请求（低于阈值）→ 窗口结束逐条广播（原行为）', async () => {
|
||||
vi.useFakeTimers();
|
||||
const w = makeWindow();
|
||||
getAllWindowsMock.mockReturnValue([w.win]);
|
||||
const hook = new ConfirmationHook(null, null);
|
||||
hook.setToolDefs([makeRunCommandDef()]);
|
||||
|
||||
const prompts = [startPrompt(hook, 'tc_a'), startPrompt(hook, 'tc_b')];
|
||||
await vi.advanceTimersByTimeAsync(1000);
|
||||
|
||||
expect(
|
||||
(w.send as Mock).mock.calls.filter((c) => c[0] === 'tool:confirmationRequestBatch'),
|
||||
).toHaveLength(0);
|
||||
expect(
|
||||
(w.send as Mock).mock.calls.filter((c) => c[0] === 'tool:confirmationRequest'),
|
||||
).toHaveLength(2);
|
||||
|
||||
for (const id of ['tc_a', 'tc_b']) {
|
||||
hook.resolveConfirmation(id, true, false, false);
|
||||
}
|
||||
await Promise.all(prompts);
|
||||
vi.useRealTimers();
|
||||
});
|
||||
});
|
||||
|
||||
@@ -467,6 +467,39 @@ export class ConfirmationHook implements PreToolHook {
|
||||
*/
|
||||
private lastTimeoutToastAt = 0;
|
||||
|
||||
// ===== v0.8.1 P2-2: 连续同类工具批量确认聚合 =====
|
||||
/**
|
||||
* 同 (sessionId, toolName) 的请求在 AGGREGATION_WINDOW_MS 内聚合：窗口结束时
|
||||
* 若积压 >= AGGREGATION_THRESHOLD 条则只广播一条 `tool:confirmationRequestBatch`
|
||||
* （前端一次性拉取/渲染全部 pending），否则逐条广播（原行为）。
|
||||
* 目的：批量重构等场景（并行工具连续触发 3+ 同类确认）不再弹出 N 个连续弹框。
|
||||
*/
|
||||
private static readonly AGGREGATION_WINDOW_MS = 800;
|
||||
private static readonly AGGREGATION_THRESHOLD = 3;
|
||||
private aggregationBuffers = new Map<
|
||||
string,
|
||||
{ requests: ConfirmationRequest[]; timer: NodeJS.Timeout | null }
|
||||
>();
|
||||
|
||||
private flushAggregationBuffer(key: string): void {
|
||||
const buf = this.aggregationBuffers.get(key);
|
||||
if (!buf) return;
|
||||
this.aggregationBuffers.delete(key);
|
||||
if (buf.timer) {
|
||||
clearTimeout(buf.timer);
|
||||
buf.timer = null;
|
||||
}
|
||||
if (buf.requests.length >= ConfirmationHook.AGGREGATION_THRESHOLD) {
|
||||
// 批量事件：携带完整请求列表（expiresAt 已含）
|
||||
this.broadcastToAllWindows('tool:confirmationRequestBatch', buf.requests);
|
||||
} else {
|
||||
// 逐条广播（原行为）
|
||||
for (const req of buf.requests) {
|
||||
this.broadcastToAllWindows('tool:confirmationRequest', req);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
private waitForConfirmation(request: ConfirmationRequest, sessionId: string): Promise<boolean> {
|
||||
return new Promise<boolean>((resolve) => {
|
||||
const expiresAt = Date.now() + this.confirmationTimeoutMs;
|
||||
@@ -518,10 +551,24 @@ export class ConfirmationHook implements PreToolHook {
|
||||
// 发送确认请求到渲染进程（携带过期时间戳，供前端倒计时）
|
||||
// v0.7.2 P2-7: 广播到所有窗口 —— 多窗口场景下任意窗口发起的会话
|
||||
// 触发的确认请求都可达（ConfirmationDialog 按会话过滤展示）
|
||||
this.broadcastToAllWindows('tool:confirmationRequest', {
|
||||
...request,
|
||||
expiresAt,
|
||||
});
|
||||
// v0.8.1 P2-2: 聚合窗口 —— 同会话同类工具的并行请求进入缓冲；
|
||||
// 窗口结束达到阈值时合并为单条批量事件，否则逐条广播（原行为）
|
||||
const aggKey = `${sessionId}:${request.toolName}`;
|
||||
let buf = this.aggregationBuffers.get(aggKey);
|
||||
if (!buf) {
|
||||
buf = { requests: [], timer: null };
|
||||
this.aggregationBuffers.set(aggKey, buf);
|
||||
buf.timer = setTimeout(
|
||||
() => this.flushAggregationBuffer(aggKey),
|
||||
ConfirmationHook.AGGREGATION_WINDOW_MS,
|
||||
);
|
||||
}
|
||||
buf.requests.push({ ...request, expiresAt });
|
||||
|
||||
// 窗口内积压已达阈值 → 立即 flush（不等满窗口）
|
||||
if (buf.requests.length >= ConfirmationHook.AGGREGATION_THRESHOLD) {
|
||||
this.flushAggregationBuffer(aggKey);
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
@@ -540,6 +587,9 @@ export class ConfirmationHook implements PreToolHook {
|
||||
pending.resolve(false);
|
||||
}
|
||||
this.pendingConfirmations.clear();
|
||||
// v0.8.1 P2-2: 清空聚合缓冲（resolve(false) 已由逐条 timer 覆盖……缓冲内的
|
||||
// 请求本身尚未注册 pending，需丢弃防止稍后广播已失效请求）
|
||||
this.aggregationBuffers.clear();
|
||||
return;
|
||||
}
|
||||
for (const [id, pending] of this.pendingConfirmations) {
|
||||
@@ -548,6 +598,20 @@ export class ConfirmationHook implements PreToolHook {
|
||||
pending.resolve(false);
|
||||
this.pendingConfirmations.delete(id);
|
||||
}
|
||||
// v0.8.1 P2-2: 丢弃该会话尚未广播的聚合缓冲
|
||||
for (const [key, buf] of this.aggregationBuffers) {
|
||||
if (!key.startsWith(`${sessionId}:`)) continue;
|
||||
if (buf.timer) clearTimeout(buf.timer);
|
||||
for (const req of buf.requests) {
|
||||
const pending = this.pendingConfirmations.get(req.toolCallId);
|
||||
if (pending) {
|
||||
clearTimeout(pending.timer);
|
||||
pending.resolve(false);
|
||||
this.pendingConfirmations.delete(req.toolCallId);
|
||||
}
|
||||
}
|
||||
this.aggregationBuffers.delete(key);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
|
||||
@@ -29,7 +29,11 @@ export interface PostToolHook {
|
||||
export class AuditLogHook implements PostToolHook {
|
||||
constructor(private auditService: AuditService) {}
|
||||
|
||||
async afterExecute(toolCall: MetonaToolCall, result: MetonaToolResult, sessionId: string): Promise<void> {
|
||||
async afterExecute(
|
||||
toolCall: MetonaToolCall,
|
||||
result: MetonaToolResult,
|
||||
sessionId: string,
|
||||
): Promise<void> {
|
||||
// #17 修复: AuditHook 应为 "fire and forget"，hook 失败不应影响工具执行链
|
||||
// 虽然 AuditService.log() 内部已 try-catch，但 hook 层再加一层防御，
|
||||
// 确保任何意外异常（如 getDB 抛错、JSON.stringify 失败）都不会冒泡到 ToolRegistry
|
||||
@@ -59,11 +63,24 @@ export class MemoryTriggerHook implements PostToolHook {
|
||||
/** 单次工具结果存储上限（字符），防止过大内容淹没记忆系统 */
|
||||
private readonly MAX_MEMORY_CONTENT = 500;
|
||||
|
||||
/**
|
||||
* v0.8.1 P0-2: 工具结果类情节记忆的 TTL（90 天）。
|
||||
* 此前 episodic_memories.expires_at 全链路无写入方 —— cleanupExpired（健康检查
|
||||
* 周期调用）空转，情节记忆只增不减。现为此类低价值记忆写入过期时间，90 天后
|
||||
* 由周期清理回收；用户偏好等高价值记忆（consolidator 写入 semantic 表）不受影响。
|
||||
*/
|
||||
private readonly EPISODIC_TTL_MS = 90 * 24 * 60 * 60 * 1000;
|
||||
|
||||
constructor(private memoryManager: MemoryManager) {}
|
||||
|
||||
async afterExecute(toolCall: MetonaToolCall, result: MetonaToolResult, sessionId: string): Promise<void> {
|
||||
async afterExecute(
|
||||
toolCall: MetonaToolCall,
|
||||
result: MetonaToolResult,
|
||||
sessionId: string,
|
||||
): Promise<void> {
|
||||
if (this.memorableTools.includes(toolCall.name) && result.success) {
|
||||
const content = typeof result.result === 'string' ? result.result : JSON.stringify(result.result);
|
||||
const content =
|
||||
typeof result.result === 'string' ? result.result : JSON.stringify(result.result);
|
||||
try {
|
||||
this.memoryManager.store({
|
||||
type: 'episodic',
|
||||
@@ -71,6 +88,7 @@ export class MemoryTriggerHook implements PostToolHook {
|
||||
source: 'tool_result',
|
||||
sessionId,
|
||||
importance: 0.6,
|
||||
expiresAt: Date.now() + this.EPISODIC_TTL_MS,
|
||||
});
|
||||
} catch (error) {
|
||||
// 记忆存储失败不应影响工具执行结果
|
||||
|
||||
@@ -0,0 +1,182 @@
|
||||
/**
|
||||
* MemoryMaintainer 测试（v0.8.1 P1-2 MEMORY.md 维护闭环）
|
||||
*
|
||||
* 锁定契约：
|
||||
* 1. parseMemoryEntries / buildMemoryEntriesDigest —— 分区条目摘要（纯条目行，
|
||||
* 消除 Consolidator 旧全文截断的去重盲区）
|
||||
* 2. apply 的精确匹配防线 —— LLM 建议的 entry 必须原样存在，防幻觉改写无关内容
|
||||
* 3. delete/update 动作重写 MEMORY.md + 同步 semantic_memories 双轨一致
|
||||
*/
|
||||
|
||||
import { describe, it, expect, vi } from 'vitest';
|
||||
|
||||
vi.mock('electron-log', () => ({
|
||||
default: { info: vi.fn(), warn: vi.fn(), error: vi.fn(), debug: vi.fn() },
|
||||
}));
|
||||
|
||||
import { MemoryMaintainer, parseMemoryEntries, buildMemoryEntriesDigest } from '../maintainer';
|
||||
import type { MemoryMaintenanceAction } from '../maintainer';
|
||||
|
||||
const SAMPLE = `# MEMORY.md — AI 持久记忆
|
||||
> 最后更新: 2026-09-07
|
||||
|
||||
## 用户偏好
|
||||
- [沟通风格] 用户喜欢简洁的回答
|
||||
- [工具偏好] 项目使用 pnpm
|
||||
|
||||
## 项目上下文
|
||||
- [Metona] 技术栈: Electron + React
|
||||
|
||||
## 待办事项
|
||||
- [done] 旧待办已完成
|
||||
`;
|
||||
|
||||
describe('parseMemoryEntries / buildMemoryEntriesDigest（v0.8.1 P1-2）', () => {
|
||||
it('解析分区与条目（跳过元数据头）', () => {
|
||||
const sections = parseMemoryEntries(SAMPLE);
|
||||
expect(sections).toHaveLength(3);
|
||||
expect(sections[0].section).toBe('用户偏好');
|
||||
expect(sections[0].entries).toEqual([
|
||||
'[沟通风格] 用户喜欢简洁的回答',
|
||||
'[工具偏好] 项目使用 pnpm',
|
||||
]);
|
||||
});
|
||||
|
||||
it('digest 为纯条目行形态且条目全文可见（无 3000 字符截断盲区）', () => {
|
||||
const digest = buildMemoryEntriesDigest(parseMemoryEntries(SAMPLE));
|
||||
expect(digest).toContain('## 用户偏好');
|
||||
expect(digest).toContain('- [沟通风格] 用户喜欢简洁的回答');
|
||||
// 旧全文形态的头部元数据不进入 digest
|
||||
expect(digest).not.toContain('最后更新');
|
||||
// 超过旧 3000 字符预算的记忆尾部条目同样完整进入 digest
|
||||
const manyEntries = Array.from({ length: 200 }, (_, i) => `- 条目 ${i} ${'x'.repeat(20)}`);
|
||||
const bigMemory = `## 项目上下文\n${manyEntries.join('\n')}`;
|
||||
const bigDigest = buildMemoryEntriesDigest(parseMemoryEntries(bigMemory));
|
||||
expect(bigDigest).toContain('条目 199');
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryMaintainer.apply — 精确匹配与双轨同步', () => {
|
||||
function makeMaintainer(memory: string): {
|
||||
maintainer: MemoryMaintainer;
|
||||
getMemory: () => string;
|
||||
db: { prepare(sql: string): { run(...args: unknown[]): { changes: number } } };
|
||||
} {
|
||||
let current = memory;
|
||||
const semanticRows: Array<{ content: string }> = [{ content: '[沟通风格] 用户喜欢简洁的回答' }];
|
||||
const db = {
|
||||
prepare: (sql: string) => ({
|
||||
run: (...args: unknown[]) => {
|
||||
if (sql.startsWith('DELETE')) {
|
||||
const before = semanticRows.length;
|
||||
const target = semanticRows.find((r) => r.content === args[0]);
|
||||
if (target) semanticRows.splice(semanticRows.indexOf(target), 1);
|
||||
return { changes: before - semanticRows.length };
|
||||
}
|
||||
if (sql.startsWith('UPDATE')) {
|
||||
const row = semanticRows.find((r) => r.content === args[2]);
|
||||
if (row) {
|
||||
row.content = args[0] as string;
|
||||
return { changes: 1 };
|
||||
}
|
||||
return { changes: 0 };
|
||||
}
|
||||
return { changes: 0 };
|
||||
},
|
||||
}),
|
||||
};
|
||||
const maintainer = new MemoryMaintainer(
|
||||
() => {
|
||||
throw new Error('not used in apply');
|
||||
},
|
||||
{
|
||||
getFiles: () => ({ soul: '', memory: current }),
|
||||
rewriteMemory: (content: string) => {
|
||||
current = content;
|
||||
},
|
||||
} as never,
|
||||
() => db as never,
|
||||
);
|
||||
return { maintainer, getMemory: () => current, db };
|
||||
}
|
||||
|
||||
it('delete 精确命中 → 行被移除；未命中条目被跳过（防幻觉改写）', () => {
|
||||
const { maintainer, getMemory } = makeMaintainer(SAMPLE);
|
||||
const actions: MemoryMaintenanceAction[] = [
|
||||
{ action: 'delete', section: '待办事项', entry: '[done] 旧待办已完成' },
|
||||
// 幻觉条目：文件中不存在 → 必须跳过
|
||||
{ action: 'delete', section: '用户偏好', entry: '不存在的条目' },
|
||||
];
|
||||
const result = maintainer.apply(actions);
|
||||
expect(result.applied).toBe(1);
|
||||
expect(result.skipped).toBe(1);
|
||||
const after = getMemory();
|
||||
expect(after).not.toContain('[done] 旧待办已完成');
|
||||
expect(after).toContain('用户喜欢简洁的回答');
|
||||
expect(after).toContain('## 待办事项'); // 分区头保留（空分区仍保留结构）
|
||||
});
|
||||
|
||||
it('update（合并）→ 替换条目并同步 semantic_memories', () => {
|
||||
const { maintainer, getMemory, db } = makeMaintainer(SAMPLE);
|
||||
const actions: MemoryMaintenanceAction[] = [
|
||||
{
|
||||
action: 'update',
|
||||
section: '用户偏好',
|
||||
entry: '[沟通风格] 用户喜欢简洁的回答',
|
||||
newEntry: '[沟通风格] 用户喜欢简洁的回答，不需要过度解释',
|
||||
},
|
||||
];
|
||||
const result = maintainer.apply(actions);
|
||||
expect(result.applied).toBe(1);
|
||||
expect(getMemory()).toContain('不需要过度解释');
|
||||
const row = db
|
||||
.prepare('SELECT * FROM semantic_memories WHERE content = ?')
|
||||
.run('[沟通风格] 用户喜欢简洁的回答，不需要过度解释');
|
||||
expect(row).toBeDefined();
|
||||
});
|
||||
|
||||
it('动作数上限 30（防 LLM 过度建议）', () => {
|
||||
const { maintainer } = makeMaintainer(SAMPLE);
|
||||
const actions: MemoryMaintenanceAction[] = Array.from({ length: 40 }, () => ({
|
||||
action: 'delete' as const,
|
||||
section: '待办事项',
|
||||
entry: '不存在的条目',
|
||||
}));
|
||||
const result = maintainer.apply(actions);
|
||||
expect(result.skipped).toBe(40);
|
||||
expect(result.applied).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
describe('MemoryMaintainer.analyze — sectionEntryCounts（v0.8.1 review O2）', () => {
|
||||
function makeAnalyzer(
|
||||
memory: string,
|
||||
llmReply: string,
|
||||
): {
|
||||
maintainer: MemoryMaintainer;
|
||||
} {
|
||||
const adapter = {
|
||||
send: vi.fn().mockResolvedValue({ content: llmReply }),
|
||||
} as never;
|
||||
return {
|
||||
maintainer: new MemoryMaintainer(
|
||||
() => adapter,
|
||||
{
|
||||
getFiles: () => ({ soul: '', memory }),
|
||||
rewriteMemory: () => {},
|
||||
} as never,
|
||||
(() => ({})) as never,
|
||||
),
|
||||
};
|
||||
}
|
||||
|
||||
it('proposal 携带各分区条目数（空分区提示的数据源）', async () => {
|
||||
const { maintainer } = makeAnalyzer(
|
||||
SAMPLE,
|
||||
'[{"action":"delete","section":"待办事项","entry":"[done] 旧待办已完成","reason":"已完成"}]',
|
||||
);
|
||||
const proposal = await maintainer.analyze();
|
||||
expect(proposal.sectionEntryCounts['待办事项']).toBe(1);
|
||||
expect(proposal.sectionEntryCounts['用户偏好']).toBe(2);
|
||||
});
|
||||
});
|
||||
@@ -43,7 +43,8 @@ function createMemorySchema(db: any): void {
|
||||
importance REAL DEFAULT 0.5,
|
||||
created_at INTEGER NOT NULL DEFAULT 0,
|
||||
expires_at INTEGER,
|
||||
tf_cache TEXT
|
||||
tf_cache TEXT,
|
||||
embedding BLOB
|
||||
);
|
||||
CREATE TABLE semantic_memories (
|
||||
id TEXT PRIMARY KEY,
|
||||
@@ -55,7 +56,8 @@ function createMemorySchema(db: any): void {
|
||||
created_at INTEGER NOT NULL DEFAULT 0,
|
||||
updated_at INTEGER NOT NULL DEFAULT 0,
|
||||
access_count INTEGER DEFAULT 0,
|
||||
tf_cache TEXT
|
||||
tf_cache TEXT,
|
||||
embedding BLOB
|
||||
);
|
||||
CREATE TABLE working_memories (
|
||||
id TEXT PRIMARY KEY,
|
||||
@@ -349,10 +351,10 @@ describe.skipIf(!dbAvailable)('MemoryManager — store 三层记忆', () => {
|
||||
).toThrow(/Unknown memory type/);
|
||||
});
|
||||
|
||||
it('store 使 IDF 缓存失效（cacheUpdatedAt 重置）', () => {
|
||||
it('store 使 IDF 缓存失效（cacheUpdatedAt 重置）', async () => {
|
||||
// v0.7.4 强化断言: 若 IDF 缓存未失效/检索不扫描新行，store 后 search 返回空即失败。
|
||||
mgr.store({ type: 'episodic', content: 'hello world', source: 'user_input', importance: 0.7 });
|
||||
mgr.search('hello'); // 建立 IDF 缓存
|
||||
await mgr.search('hello'); // 建立 IDF 缓存
|
||||
// 再 store 一条 → 缓存应失效，新内容可被检索
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
@@ -360,10 +362,10 @@ describe.skipIf(!dbAvailable)('MemoryManager — store 三层记忆', () => {
|
||||
source: 'user_input',
|
||||
importance: 0.7,
|
||||
});
|
||||
const results = mgr.search('another');
|
||||
const results = await mgr.search('another');
|
||||
expect(results.some((r) => r.content === 'another content')).toBe(true);
|
||||
// 双向验证：缓存重建后旧内容仍可检索（不因重建丢失）
|
||||
const oldResults = mgr.search('hello');
|
||||
const oldResults = await mgr.search('hello');
|
||||
expect(oldResults.some((r) => r.content === 'hello world')).toBe(true);
|
||||
});
|
||||
|
||||
@@ -401,7 +403,7 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
}
|
||||
});
|
||||
|
||||
it('相同关键词：得分高者排前（内容重复度越高得分越高）', () => {
|
||||
it('相同关键词：得分高者排前（内容重复度越高得分越高）', async () => {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: 'memory hello world test',
|
||||
@@ -416,7 +418,7 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
importance: 0.7,
|
||||
});
|
||||
|
||||
const results = mgr.search('hello world');
|
||||
const results = await mgr.search('hello world');
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
expect(results.every((r) => r.score > 0)).toBe(true);
|
||||
// 两条命中的按分数降序
|
||||
@@ -424,7 +426,7 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
expect([...scores].sort((a, b) => b - a)).toEqual(scores);
|
||||
});
|
||||
|
||||
it('时间衰减：同内容越新得分越高（30 天半衰期）', () => {
|
||||
it('时间衰减：同内容越新得分越高（30 天半衰期）', async () => {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: '关键 bug 修复方案',
|
||||
@@ -455,7 +457,7 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
newId,
|
||||
);
|
||||
|
||||
const results = mgr.search('关键 bug');
|
||||
const results = await mgr.search('关键 bug');
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
const newResult = results.find((r) => r.id === newId);
|
||||
const oldResult = results.find((r) => r.id === oldId);
|
||||
@@ -464,7 +466,7 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
expect(newResult!.score).toBeGreaterThan(oldResult!.score);
|
||||
});
|
||||
|
||||
it('半衰期数学：30 天衰减系数恰为 0.5（score 相对无衰减×0.5）', () => {
|
||||
it('半衰期数学：30 天衰减系数恰为 0.5（score 相对无衰减×0.5）', async () => {
|
||||
// 新鲜记录（0 天）
|
||||
const freshId = mgr.store({
|
||||
type: 'episodic',
|
||||
@@ -484,7 +486,7 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
agedId,
|
||||
);
|
||||
|
||||
const results = mgr.search('衰减数学验证内容');
|
||||
const results = await mgr.search('衰减数学验证内容');
|
||||
const fresh = results.find((r) => r.id === freshId)!;
|
||||
const aged = results.find((r) => r.id === agedId)!;
|
||||
// score = cosine * decay * importanceFactor；两记录余弦与 importance 相同
|
||||
@@ -492,7 +494,7 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
expect(aged.score / fresh.score).toBeCloseTo(0.5, 1);
|
||||
});
|
||||
|
||||
it('importance 权重：0.5 + importance*0.5 缩放（importance=1 得分为 0 的 2 倍）', () => {
|
||||
it('importance 权重：0.5 + importance*0.5 缩放（importance=1 得分为 0 的 2 倍）', async () => {
|
||||
const lowId = mgr.store({
|
||||
type: 'episodic',
|
||||
content: '重要性权重验证',
|
||||
@@ -506,24 +508,24 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
importance: 1,
|
||||
});
|
||||
// 同一时间创建，重要性不同 → factor = 0.5+0*0.5 vs 0.5+1*0.5
|
||||
const results = mgr.search('重要性权重验证');
|
||||
const results = await mgr.search('重要性权重验证');
|
||||
const low = results.find((r) => r.id === lowId)!;
|
||||
const high = results.find((r) => r.id === highId)!;
|
||||
expect(high.score / low.score).toBeCloseTo(2.0, 1);
|
||||
});
|
||||
|
||||
it('semantic 记忆可被检索（key+value 参与分词）', () => {
|
||||
it('semantic 记忆可被检索（key+value 参与分词）', async () => {
|
||||
mgr.store({
|
||||
type: 'semantic',
|
||||
content: '用户偏好深色主题',
|
||||
source: 'imported',
|
||||
importance: 0.5,
|
||||
});
|
||||
const results = mgr.search('偏好');
|
||||
const results = await mgr.search('偏好');
|
||||
expect(results.some((r) => r.type === 'semantic')).toBe(true);
|
||||
});
|
||||
|
||||
it('working 记忆可被检索（key+value 参与分词，importance 固定 0.5）', () => {
|
||||
it('working 记忆可被检索（key+value 参与分词，importance 固定 0.5）', async () => {
|
||||
mgr.store({
|
||||
type: 'working',
|
||||
content: '当前任务文件',
|
||||
@@ -531,11 +533,11 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
importance: 0.5,
|
||||
source: 'agent_thought',
|
||||
});
|
||||
const results = mgr.search('当前任务');
|
||||
const results = await mgr.search('当前任务');
|
||||
expect(results.some((r) => r.type === 'working')).toBe(true);
|
||||
});
|
||||
|
||||
it('type 过滤：仅返回指定类型', () => {
|
||||
it('type 过滤：仅返回指定类型', async () => {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: 'typefilter 内容',
|
||||
@@ -549,13 +551,13 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
importance: 0.5,
|
||||
});
|
||||
|
||||
const episodic = mgr.search('typefilter', { type: 'episodic' });
|
||||
const episodic = await mgr.search('typefilter', { type: 'episodic' });
|
||||
expect(episodic.every((r) => r.type === 'episodic')).toBe(true);
|
||||
const semantic = mgr.search('typefilter', { type: 'semantic' });
|
||||
const semantic = await mgr.search('typefilter', { type: 'semantic' });
|
||||
expect(semantic.every((r) => r.type === 'semantic')).toBe(true);
|
||||
});
|
||||
|
||||
it('topK 限制返回条数', () => {
|
||||
it('topK 限制返回条数', async () => {
|
||||
for (let i = 0; i < 8; i++) {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
@@ -564,22 +566,22 @@ describe.skipIf(!dbAvailable)('MemoryManager — TF-IDF 检索与时间衰减',
|
||||
importance: 0.7,
|
||||
});
|
||||
}
|
||||
const results = mgr.search('topk 内容');
|
||||
const results = await mgr.search('topk 内容');
|
||||
expect(results.length).toBeLessThanOrEqual(5); // 默认 topK=5
|
||||
const results2 = mgr.search('topk 内容', { topK: 2 });
|
||||
const results2 = await mgr.search('topk 内容', { topK: 2 });
|
||||
expect(results2.length).toBeLessThanOrEqual(2);
|
||||
});
|
||||
|
||||
it('minImportance 过滤低重要性记忆', () => {
|
||||
it('minImportance 过滤低重要性记忆', async () => {
|
||||
mgr.store({ type: 'episodic', content: '低重要内容', source: 'user_input', importance: 0.1 });
|
||||
mgr.store({ type: 'episodic', content: '高重要内容', source: 'user_input', importance: 0.9 });
|
||||
const results = mgr.search('重要', { minImportance: 0.5 });
|
||||
const results = await mgr.search('重要', { minImportance: 0.5 });
|
||||
expect(results.every((r) => r.importance >= 0.5)).toBe(true);
|
||||
});
|
||||
|
||||
it('score 字段为 finalScore = cosine * decay * (0.5+importance*0.5)（>0 才返回）', () => {
|
||||
it('score 字段为 finalScore = cosine * decay * (0.5+importance*0.5)（>0 才返回）', async () => {
|
||||
mgr.store({ type: 'episodic', content: 'score 数学', source: 'user_input', importance: 0.7 });
|
||||
const results = mgr.search('score 数学');
|
||||
const results = await mgr.search('score 数学');
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
for (const r of results) {
|
||||
expect(r.score).toBeGreaterThan(0);
|
||||
@@ -604,19 +606,19 @@ describe.skipIf(!dbAvailable)('MemoryManager — search 回退与边界', () =>
|
||||
}
|
||||
});
|
||||
|
||||
it('空查询与纯空白查询返回空数组', () => {
|
||||
expect(mgr.search('')).toEqual([]);
|
||||
expect(mgr.search(' ')).toEqual([]);
|
||||
expect(mgr.search('', { topK: 3 })).toEqual([]);
|
||||
it('空查询与纯空白查询返回空数组', async () => {
|
||||
expect(await mgr.search('')).toEqual([]);
|
||||
expect(await mgr.search(' ')).toEqual([]);
|
||||
expect(await mgr.search('', { topK: 3 })).toEqual([]);
|
||||
});
|
||||
|
||||
it('无匹配关键词返回空数组（不抛错）', () => {
|
||||
it('无匹配关键词返回空数组（不抛错）', async () => {
|
||||
mgr.store({ type: 'episodic', content: '存在的关键词', source: 'user_input', importance: 0.7 });
|
||||
// "完全无关联" 的 bigram 与文档无重叠 → TF-IDF 0 命中；LIKE 也无子串 → []
|
||||
expect(mgr.search('完全无关联')).toEqual([]);
|
||||
expect(await mgr.search('完全无关联')).toEqual([]);
|
||||
});
|
||||
|
||||
it('英文无命中时回退 LIKE 子串搜索', () => {
|
||||
it('英文无命中时回退 LIKE 子串搜索', async () => {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: 'hello world network',
|
||||
@@ -625,11 +627,11 @@ describe.skipIf(!dbAvailable)('MemoryManager — search 回退与边界', () =>
|
||||
});
|
||||
// query "lo wo" 分词为 ['lo','wo']，与文档 token 无重叠 → TF-IDF 0 命中
|
||||
// 但 "%lo wo%" 是 "hello world" 的连续子串 → LIKE 回退命中
|
||||
const results = mgr.search('lo wo');
|
||||
const results = await mgr.search('lo wo');
|
||||
expect(results.some((r) => r.content.includes('hello world'))).toBe(true);
|
||||
});
|
||||
|
||||
it('LIKE 回退时 LIKE 通配符 % 与 _ 被转义（不当作通配符）', () => {
|
||||
it('LIKE 回退时 LIKE 通配符 % 与 _ 被转义（不当作通配符）', async () => {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: '使用 50% 折扣 与 under_score',
|
||||
@@ -643,15 +645,15 @@ describe.skipIf(!dbAvailable)('MemoryManager — search 回退与边界', () =>
|
||||
importance: 0.7,
|
||||
});
|
||||
// 查询 "%"：分词为空 → 强制走 LIKE；若 % 未转义会匹配所有记录
|
||||
const pct = mgr.search('%');
|
||||
const pct = await mgr.search('%');
|
||||
expect(pct.some((r) => r.content.includes('50%'))).toBe(true);
|
||||
expect(pct.some((r) => r.content === '完全无关的内容')).toBe(false);
|
||||
// 查询 "_"：若未转义会匹配任意单字符 → 误命中无关记录
|
||||
const underscore = mgr.search('_');
|
||||
const underscore = await mgr.search('_');
|
||||
expect(underscore.some((r) => r.content === '完全无关的内容')).toBe(false);
|
||||
});
|
||||
|
||||
it('LIKE 回退时反斜杠被转义（Windows 路径不报错）', () => {
|
||||
it('LIKE 回退时反斜杠被转义（Windows 路径不报错）', async () => {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: '路径 C:\\Users\\test',
|
||||
@@ -659,10 +661,10 @@ describe.skipIf(!dbAvailable)('MemoryManager — search 回退与边界', () =>
|
||||
importance: 0.7,
|
||||
});
|
||||
// 反斜杠单独作为查询 → tokenize 为空 → LIKE 路径；不转义会导致 SQLite 报错
|
||||
expect(() => mgr.search('\\')).not.toThrow();
|
||||
await expect(mgr.search('\\')).resolves.toBeInstanceOf(Array);
|
||||
});
|
||||
|
||||
it('search 的 topK 同时作用于回退路径', () => {
|
||||
it('search 的 topK 同时作用于回退路径', async () => {
|
||||
for (let i = 0; i < 6; i++) {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
@@ -671,11 +673,11 @@ describe.skipIf(!dbAvailable)('MemoryManager — search 回退与边界', () =>
|
||||
importance: 0.7,
|
||||
});
|
||||
}
|
||||
const results = mgr.search('backup', { topK: 3 });
|
||||
const results = await mgr.search('backup', { topK: 3 });
|
||||
expect(results.length).toBeLessThanOrEqual(3);
|
||||
});
|
||||
|
||||
it('search 无结果时回退 LIKE 的 score = importance * timeDecay', () => {
|
||||
it('search 无结果时回退 LIKE 的 score = importance * timeDecay', async () => {
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: 'fallbackscore 内容',
|
||||
@@ -683,16 +685,16 @@ describe.skipIf(!dbAvailable)('MemoryManager — search 回退与边界', () =>
|
||||
importance: 0.8,
|
||||
});
|
||||
// "allbackscor" 分词不在文档 token 中 → TF-IDF 0 命中；LIKE %allbackscor% 命中
|
||||
const results = mgr.search('allbackscor');
|
||||
const results = await mgr.search('allbackscor');
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
// 新记录 timeDecay≈1 → score≈importance=0.8
|
||||
expect(results[0].score).toBeCloseTo(0.8, 1);
|
||||
});
|
||||
|
||||
it('LIKE 回退：episodic 按 importance 降序返回', () => {
|
||||
it('LIKE 回退：episodic 按 importance 降序返回', async () => {
|
||||
mgr.store({ type: 'episodic', content: '排序验证', source: 'user_input', importance: 0.2 });
|
||||
mgr.store({ type: 'episodic', content: '排序验证', source: 'user_input', importance: 0.9 });
|
||||
const results = mgr.search('排序验证');
|
||||
const results = await mgr.search('排序验证');
|
||||
expect(results[0].importance).toBe(0.9);
|
||||
});
|
||||
});
|
||||
@@ -844,3 +846,141 @@ describe.skipIf(!dbAvailable)('MemoryManager — cleanupExpired', () => {
|
||||
expect(db.prepare('SELECT COUNT(*) AS c FROM episodic_memories').get().c).toBe(1);
|
||||
});
|
||||
});
|
||||
|
||||
// ===== v0.8.1: P0-2 生命周期 + P1-1 向量混合检索 =====
|
||||
|
||||
describe.skipIf(!dbAvailable)('MemoryManager — v0.8.1 生命周期与混合检索', () => {
|
||||
let db: any;
|
||||
let mgr: MemoryManager;
|
||||
|
||||
beforeAll(() => {
|
||||
if (!dbAvailable) return;
|
||||
db = new Database(':memory:');
|
||||
createMemorySchema(db);
|
||||
mgr = new MemoryManager(() => db);
|
||||
mgr.initialize();
|
||||
});
|
||||
afterAll(() => {
|
||||
if (db) db.close();
|
||||
});
|
||||
afterEach(() => {
|
||||
db.exec('DELETE FROM episodic_memories');
|
||||
db.exec('DELETE FROM semantic_memories');
|
||||
db.exec('DELETE FROM working_memories');
|
||||
mgr.setEmbedder(null);
|
||||
});
|
||||
|
||||
it('P0-2: store 接受 expiresAt 并写入 episodic_memories.expires_at', () => {
|
||||
const ttl = Date.now() + 1000;
|
||||
mgr.store({
|
||||
type: 'episodic',
|
||||
content: 'TTL 验证内容',
|
||||
source: 'tool_result',
|
||||
importance: 0.6,
|
||||
expiresAt: ttl,
|
||||
});
|
||||
const row = db
|
||||
.prepare('SELECT expires_at FROM episodic_memories WHERE content = ?')
|
||||
.get('TTL 验证内容') as {
|
||||
expires_at: number | null;
|
||||
};
|
||||
expect(row.expires_at).toBe(ttl);
|
||||
});
|
||||
|
||||
it('P0-2: semantic 检索命中后 access_count 递增', async () => {
|
||||
mgr.store({
|
||||
type: 'semantic',
|
||||
content: '用户偏好简洁回答',
|
||||
summary: 'pref-brief',
|
||||
source: 'agent_thought',
|
||||
importance: 0.9,
|
||||
});
|
||||
const before = (
|
||||
db.prepare('SELECT access_count FROM semantic_memories WHERE key = ?').get('pref-brief') as {
|
||||
access_count: number;
|
||||
}
|
||||
).access_count;
|
||||
await mgr.search('偏好简洁');
|
||||
const after = (
|
||||
db.prepare('SELECT access_count FROM semantic_memories WHERE key = ?').get('pref-brief') as {
|
||||
access_count: number;
|
||||
}
|
||||
).access_count;
|
||||
expect(after).toBeGreaterThan(before);
|
||||
});
|
||||
|
||||
it('P1-1: 注入 embedder 后混合检索命中同义改写（TF-IDF 单路召回不到的查询）', async () => {
|
||||
// 文档："回复要短" — 查询"我喜欢简洁回答"（同义改写，无字面重叠）
|
||||
mgr.store({
|
||||
type: 'semantic',
|
||||
content: '回复要短',
|
||||
summary: 'style-rule',
|
||||
source: 'agent_thought',
|
||||
importance: 0.9,
|
||||
});
|
||||
// 词表不重叠 → TF-IDF 嵌入向量正交 → 纯 TF-IDF 0 分
|
||||
const tfidfOnly = await mgr.search('我喜欢简洁回答');
|
||||
expect(tfidfOnly).toHaveLength(0);
|
||||
|
||||
// 注入固定向量的 embedder：同义改写在向量空间中余弦 > 0
|
||||
const VECTORS: Record<string, number[]> = {
|
||||
'回复要短 style-rule': [1, 0.9, 0],
|
||||
我喜欢简洁回答: [0.95, 1, 0.1],
|
||||
无关内容xyz: [0, 0.1, 1],
|
||||
};
|
||||
mgr.setEmbedder({
|
||||
embed: async (text) => {
|
||||
for (const [k, v] of Object.entries(VECTORS)) {
|
||||
if (text.includes(k)) return v;
|
||||
}
|
||||
return [0, 0, 1];
|
||||
},
|
||||
});
|
||||
// 首次检索触发存量记忆的惰性向量回填（本轮仍走 TF-IDF → 0 命中）
|
||||
await mgr.search('我喜欢简洁回答');
|
||||
await new Promise((r) => setTimeout(r, 10));
|
||||
// 回填完成后，向量路径生效 → 同义改写命中
|
||||
const hybrid = await mgr.search('我喜欢简洁回答');
|
||||
expect(hybrid.length).toBeGreaterThan(0);
|
||||
expect(hybrid[0].content).toBe('回复要短');
|
||||
expect(hybrid[0].score).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
it('P1-1: embedder 抛错/返回 null → 回退纯 TF-IDF（行为兼容）', async () => {
|
||||
mgr.store({
|
||||
type: 'semantic',
|
||||
content: 'TF-IDF 兜底验证',
|
||||
summary: 'fallback-vec',
|
||||
source: 'agent_thought',
|
||||
importance: 0.9,
|
||||
});
|
||||
mgr.setEmbedder({
|
||||
embed: async () => {
|
||||
throw new Error('embed down');
|
||||
},
|
||||
});
|
||||
const results = await mgr.search('TF-IDF 兜底验证');
|
||||
expect(results.length).toBeGreaterThan(0);
|
||||
|
||||
mgr.setEmbedder({ embed: async () => null });
|
||||
const results2 = await mgr.search('TF-IDF 兜底验证');
|
||||
expect(results2.length).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
it('P1-1: store 写入异步回填 embedding BLOB', async () => {
|
||||
mgr.setEmbedder({ embed: async () => [0.5, 0.5, 0.5] });
|
||||
mgr.store({
|
||||
type: 'semantic',
|
||||
content: '向量化回填验证',
|
||||
summary: 'vec-backfill',
|
||||
source: 'agent_thought',
|
||||
importance: 0.8,
|
||||
});
|
||||
await new Promise((r) => setTimeout(r, 10));
|
||||
const row = db
|
||||
.prepare('SELECT embedding FROM semantic_memories WHERE key = ?')
|
||||
.get('vec-backfill') as { embedding: Buffer | null };
|
||||
expect(row.embedding).not.toBeNull();
|
||||
expect(row.embedding!.length % 4).toBe(0);
|
||||
});
|
||||
});
|
||||
|
||||
@@ -26,6 +26,8 @@ import type { MetonaRequest } from '../types';
|
||||
import type { WorkspaceService } from '../../services/workspace.service';
|
||||
import type { IterationStep } from '../agent-loop/types';
|
||||
import type { MemoryManager } from './manager';
|
||||
// v0.8.1 P1-2: 分区条目摘要（与 Maintainer 共用，消除全文截断去重盲区）
|
||||
import { parseMemoryEntries, buildMemoryEntriesDigest } from './maintainer';
|
||||
|
||||
/** 允许写入的 MEMORY.md 分区（与 WorkspaceService.MEMORY_TEMPLATE 对齐） */
|
||||
const ALLOWED_SECTIONS = ['用户偏好', '项目上下文', '重要决策', '待办事项', '已知问题'] as const;
|
||||
@@ -91,10 +93,7 @@ export class MemoryConsolidator {
|
||||
}, timeoutMs);
|
||||
});
|
||||
try {
|
||||
await Promise.race([
|
||||
this.runningPromise.catch(() => {}),
|
||||
timer,
|
||||
]);
|
||||
await Promise.race([this.runningPromise.catch(() => {}), timer]);
|
||||
return !timedOut;
|
||||
} finally {
|
||||
if (timerHandle) clearTimeout(timerHandle);
|
||||
@@ -142,14 +141,21 @@ export class MemoryConsolidator {
|
||||
): Promise<ConsolidationResult> {
|
||||
try {
|
||||
// 1. 构建对话摘要
|
||||
const conversationDigest = this.buildConversationDigest(userMessage, assistantAnswer, iterations);
|
||||
const conversationDigest = this.buildConversationDigest(
|
||||
userMessage,
|
||||
assistantAnswer,
|
||||
iterations,
|
||||
);
|
||||
if (!conversationDigest) {
|
||||
return { appended: 0, entries: [], skipped: 0 };
|
||||
}
|
||||
|
||||
// 2. 读取当前 MEMORY.md 内容（供 LLM 去重）
|
||||
// 2. 读取当前 MEMORY.md 条目摘要（供 LLM 去重）
|
||||
// v0.8.1 P1-2 根治: 旧实现全文截 3000 字符，尾部条目对 LLM 不可见 → 去重
|
||||
// 失效、重复写入。现用纯条目摘要（8000 字符预算），完整覆盖全部条目。
|
||||
const currentMemory = this.workspaceService.getFiles().memory;
|
||||
const memoryDigest = this.truncateMemoryForPrompt(currentMemory);
|
||||
const memoryDigest =
|
||||
buildMemoryEntriesDigest(parseMemoryEntries(currentMemory ?? '')) || '(empty)';
|
||||
|
||||
// 3. 调用 LLM 提取需要持久化的记忆
|
||||
const llmResponse = await this.callLLMForExtraction(conversationDigest, memoryDigest);
|
||||
@@ -205,7 +211,9 @@ export class MemoryConsolidator {
|
||||
}
|
||||
|
||||
if (validEntries.length > 0) {
|
||||
log.info(`[MemoryConsolidator] Persisted ${validEntries.length} memories to MEMORY.md (skipped: ${skipped})`);
|
||||
log.info(
|
||||
`[MemoryConsolidator] Persisted ${validEntries.length} memories to MEMORY.md (skipped: ${skipped})`,
|
||||
);
|
||||
}
|
||||
|
||||
return { appended: validEntries.length, entries: validEntries, skipped };
|
||||
@@ -238,8 +246,10 @@ export class MemoryConsolidator {
|
||||
const status = result?.success ? 'ok' : 'error';
|
||||
const resultPreview = result?.result
|
||||
? this.truncate(JSON.stringify(result.result), 200)
|
||||
: result?.error ?? '';
|
||||
toolSummaries.push(` - ${tc.name}(${this.truncate(JSON.stringify(tc.args), 100)}) [${status}]${resultPreview ? ': ' + resultPreview : ''}`);
|
||||
: (result?.error ?? '');
|
||||
toolSummaries.push(
|
||||
` - ${tc.name}(${this.truncate(JSON.stringify(tc.args), 100)}) [${status}]${resultPreview ? ': ' + resultPreview : ''}`,
|
||||
);
|
||||
}
|
||||
}
|
||||
if (toolSummaries.length > 0) {
|
||||
@@ -252,16 +262,6 @@ export class MemoryConsolidator {
|
||||
return parts.join('\n\n');
|
||||
}
|
||||
|
||||
/**
|
||||
* 截断 MEMORY.md 内容用于 prompt（避免过长）
|
||||
*/
|
||||
private truncateMemoryForPrompt(memory: string): string {
|
||||
if (!memory) return '(empty)';
|
||||
// 截取前 3000 字符，保留分区结构概览
|
||||
if (memory.length <= 3000) return memory;
|
||||
return memory.slice(0, 3000) + '\n... (truncated)';
|
||||
}
|
||||
|
||||
/**
|
||||
* 调用 LLM 提取需要持久化的记忆
|
||||
*/
|
||||
@@ -280,7 +280,8 @@ export class MemoryConsolidator {
|
||||
agentVersion: '1.0.0',
|
||||
},
|
||||
systemPrompt: {
|
||||
roleDefinition: 'You are a memory curator for an AI agent. Your job is to decide what information from the current conversation is worth persisting to the agent\'s long-term memory file (MEMORY.md) for future sessions.',
|
||||
roleDefinition:
|
||||
"You are a memory curator for an AI agent. Your job is to decide what information from the current conversation is worth persisting to the agent's long-term memory file (MEMORY.md) for future sessions.",
|
||||
outputConstraints: [
|
||||
'Analyze the conversation below and extract ONLY information that meets ALL of these criteria:',
|
||||
'1. Long-term value: will be useful in future conversations (not transient task state)',
|
||||
@@ -294,13 +295,16 @@ export class MemoryConsolidator {
|
||||
'If nothing is worth persisting, output an empty array: []',
|
||||
'Output ONLY the JSON array, no markdown fences, no explanation.',
|
||||
].join('\n'),
|
||||
safetyGuidelines: 'Do not persist sensitive data (passwords, API keys, tokens). Do not persist user personal information beyond what is necessary for the agent to function.',
|
||||
safetyGuidelines:
|
||||
'Do not persist sensitive data (passwords, API keys, tokens). Do not persist user personal information beyond what is necessary for the agent to function.',
|
||||
},
|
||||
messages: [{
|
||||
role: 'user',
|
||||
content: `## Current MEMORY.md content:\n\n${currentMemory}\n\n## Current conversation:\n\n${conversationDigest}\n\n## Task:\nExtract information worth persisting. Output JSON array only.`,
|
||||
timestamp: Date.now(),
|
||||
}],
|
||||
messages: [
|
||||
{
|
||||
role: 'user',
|
||||
content: `## Current MEMORY.md content:\n\n${currentMemory}\n\n## Current conversation:\n\n${conversationDigest}\n\n## Task:\nExtract information worth persisting. Output JSON array only.`,
|
||||
timestamp: Date.now(),
|
||||
},
|
||||
],
|
||||
params: {
|
||||
maxTokens: 1024,
|
||||
temperature: 0.0,
|
||||
@@ -341,7 +345,10 @@ export class MemoryConsolidator {
|
||||
|
||||
// 移除可能的 markdown 代码围栏
|
||||
if (cleaned.startsWith('```')) {
|
||||
cleaned = cleaned.replace(/^```(?:json)?\s*/i, '').replace(/\s*```$/, '').trim();
|
||||
cleaned = cleaned
|
||||
.replace(/^```(?:json)?\s*/i, '')
|
||||
.replace(/\s*```$/, '')
|
||||
.trim();
|
||||
}
|
||||
|
||||
try {
|
||||
@@ -349,9 +356,12 @@ export class MemoryConsolidator {
|
||||
if (!Array.isArray(parsed)) return [];
|
||||
|
||||
return parsed
|
||||
.filter((item): item is { section: string; entry: string } =>
|
||||
typeof item === 'object' && item !== null &&
|
||||
typeof item.section === 'string' && typeof item.entry === 'string',
|
||||
.filter(
|
||||
(item): item is { section: string; entry: string } =>
|
||||
typeof item === 'object' &&
|
||||
item !== null &&
|
||||
typeof item.section === 'string' &&
|
||||
typeof item.entry === 'string',
|
||||
)
|
||||
.map((item) => ({
|
||||
section: item.section.trim(),
|
||||
|
||||
@@ -0,0 +1,19 @@
|
||||
/**
|
||||
* Memory Embedder — 本地向量记忆嵌入接口（v0.8.1 P1-1）
|
||||
*
|
||||
* 职责边界：本模块只定义记忆系统消费的嵌入契约，不绑定任何 Provider 实现。
|
||||
* main.ts 按用户配置装配：仅在 Provider 为 Ollama（本地推理，零成本、数据不出设备）
|
||||
* 且设置面板配置了 `memory.embeddingModel` 时注入真实实现；否则保持 null，
|
||||
* MemoryManager 自动回退纯 TF-IDF 检索（行为与历史版本完全兼容）。
|
||||
*
|
||||
* 根治背景：OllamaAdapter.embed() 自实现以来全项目零调用 —— 本地向量检索能力
|
||||
* 一直躺在代码里，TF-IDF bigram 对同义改写（"我喜欢简洁回答" vs "回复要短"）
|
||||
* 零召回。本契约激活该能力，检索升级为 混合评分（向量余弦 × TF-IDF）。
|
||||
*/
|
||||
|
||||
/** 嵌入失败/不可用的统一返回：null = 本次无法向量化（调用方回退 TF-IDF 路径） */
|
||||
export type MemoryEmbedFn = (text: string) => Promise<number[] | null>;
|
||||
|
||||
export interface MemoryEmbedder {
|
||||
embed: MemoryEmbedFn;
|
||||
}
|
||||
@@ -0,0 +1,355 @@
|
||||
/**
|
||||
* Memory Maintainer — MEMORY.md 维护闭环（v0.8.1 P1-2）
|
||||
*
|
||||
* 根治背景：MemoryConsolidator 纯 append-only —— ① 去重盲区：固化 prompt 只带
|
||||
* 全文前 3000 字符，超出部分对 LLM 不可见，重复写入无法避免；② 只增不减：
|
||||
* 过期/被推翻的条目无任何回收路径，MEMORY.md 随使用无限膨胀（>50KB 后固化
|
||||
* prompt 与 system 注入双双劣化）。
|
||||
*
|
||||
* 本模块实现两阶段维护闭环（分析与应用分离，应用前必须经用户确认）：
|
||||
* 1. analyze()：LLM 读取"分区条目摘要"（纯条目行，无全文截断盲区）→ 产出
|
||||
* 结构化建议 {deletes[], updates[]}（去重 / 合并 / 清理过期）；
|
||||
* 2. apply()：按用户勾选的动作改写 MEMORY.md（WorkspaceService.rewriteMemory，
|
||||
* 唯一合法写入口）并同步删除/更新 semantic_memories 对应行（双轨一致）。
|
||||
*
|
||||
* 安全边界：仅追加白名单分区、单次动作数上限、条目精确匹配（防 LLM 幻觉改写
|
||||
* 无关内容）、全部动作写入 audit_logs。
|
||||
*/
|
||||
|
||||
import { nanoid } from 'nanoid';
|
||||
import log from 'electron-log';
|
||||
import type Database from 'better-sqlite3';
|
||||
import type { IMetonaProviderAdapter } from '../types/metona-adapter';
|
||||
import type { MetonaRequest } from '../types';
|
||||
import type { WorkspaceService } from '../../services/workspace.service';
|
||||
|
||||
/** 允许写入的 MEMORY.md 分区（与 WorkspaceService.MEMORY_TEMPLATE / Consolidator 对齐） */
|
||||
const ALLOWED_SECTIONS = ['用户偏好', '项目上下文', '重要决策', '待办事项', '已知问题'] as const;
|
||||
|
||||
/** 动作数上限（防 LLM 过度建议） */
|
||||
const MAX_ACTIONS = 30;
|
||||
/** 单条目在 prompt 中的截断长度 */
|
||||
const ENTRY_PROMPT_CHARS = 160;
|
||||
/** 条目摘要总预算（字符）—— 纯条目行远小于全文，同预算下覆盖完整文件 */
|
||||
const DIGEST_BUDGET_CHARS = 8000;
|
||||
|
||||
/** 一条维护动作（用户确认的输入/输出单元） */
|
||||
export interface MemoryMaintenanceAction {
|
||||
/** 动作类型：delete = 删除整行；merge = 用 newEntry 替换该行（合并多条时产生多条 update 指向同一 newEntry） */
|
||||
action: 'delete' | 'update';
|
||||
section: string;
|
||||
/** MEMORY.md 中该条目的当前完整文本（不含 "- " 前缀；精确匹配锚点） */
|
||||
entry: string;
|
||||
/** action=update 时的替换文本（合并后的新条目） */
|
||||
newEntry?: string;
|
||||
/** LLM 给出的理由（UI 展示） */
|
||||
reason?: string;
|
||||
}
|
||||
|
||||
export interface MemoryMaintenanceProposal {
|
||||
actions: MemoryMaintenanceAction[];
|
||||
/** 当前文件条目总数（UI 展示上下文） */
|
||||
totalEntries: number;
|
||||
/**
|
||||
* v0.8.1 review (O2): 各分区当前条目数 —— 供维护弹框计算"应用后变空的分区"
|
||||
* 并向用户提示（空分区保留分区头，条目区将显示为空）。
|
||||
*/
|
||||
sectionEntryCounts: Record<string, number>;
|
||||
}
|
||||
|
||||
/** 解析后的分区结构（模块级类型 —— parseEntries/apply 共用） */
|
||||
interface ParsedSection {
|
||||
section: string;
|
||||
entries: string[];
|
||||
}
|
||||
|
||||
/** 解析 MEMORY.md 的分区与条目（模块级工具 —— Maintainer 与 Consolidator 共用） */
|
||||
export function parseMemoryEntries(memory: string): ParsedSection[] {
|
||||
const sections: ParsedSection[] = [];
|
||||
let current: ParsedSection | null = null;
|
||||
let inHead = true;
|
||||
for (const line of memory.split('\n')) {
|
||||
if (inHead) {
|
||||
if (line.startsWith('## ')) inHead = false;
|
||||
else continue;
|
||||
}
|
||||
const m = line.match(/^## (.+)$/);
|
||||
if (m) {
|
||||
current = { section: m[1].trim(), entries: [] };
|
||||
sections.push(current);
|
||||
continue;
|
||||
}
|
||||
const em = line.match(/^- (.+)$/);
|
||||
if (em && current) {
|
||||
current.entries.push(em[1].trim());
|
||||
}
|
||||
}
|
||||
return sections;
|
||||
}
|
||||
|
||||
/**
|
||||
* 构建"分区条目摘要"（纯条目行，消除全文截断盲区）。
|
||||
* Consolidator 固化去重与 Maintainer 分析共用：同预算（8000 字符）下纯条目
|
||||
* 形态可覆盖完整文件，而旧的全文截断（3000 字符）会让 LLM 看不到尾部条目、
|
||||
* 去重失效 → 重复写入。
|
||||
*/
|
||||
export function buildMemoryEntriesDigest(sections: ParsedSection[]): string {
|
||||
const parts: string[] = [];
|
||||
let used = 0;
|
||||
for (const s of sections) {
|
||||
if (s.entries.length === 0) continue;
|
||||
const lines: string[] = [`## ${s.section}`];
|
||||
for (const e of s.entries) {
|
||||
const clipped = e.length > ENTRY_PROMPT_CHARS ? `${e.slice(0, ENTRY_PROMPT_CHARS)}...` : e;
|
||||
lines.push(`- ${clipped}`);
|
||||
}
|
||||
const block = lines.join('\n');
|
||||
if (used + block.length > DIGEST_BUDGET_CHARS) break;
|
||||
parts.push(block);
|
||||
used += block.length;
|
||||
}
|
||||
return parts.join('\n\n');
|
||||
}
|
||||
|
||||
export class MemoryMaintainer {
|
||||
constructor(
|
||||
private getAdapter: () => IMetonaProviderAdapter,
|
||||
private workspaceService: WorkspaceService,
|
||||
private getDB: () => Database.Database,
|
||||
) {}
|
||||
|
||||
/** 分析当前 MEMORY.md，产出维护建议（不改任何文件/DB） */
|
||||
async analyze(): Promise<MemoryMaintenanceProposal> {
|
||||
const memory = this.workspaceService.getFiles().memory ?? '';
|
||||
const sections = this.parseEntries(memory);
|
||||
const totalEntries = sections.reduce((n, s) => n + s.entries.length, 0);
|
||||
|
||||
const sectionEntryCounts: Record<string, number> = {};
|
||||
for (const s of sections) {
|
||||
sectionEntryCounts[s.section] = s.entries.length;
|
||||
}
|
||||
|
||||
if (totalEntries === 0) {
|
||||
return { actions: [], totalEntries: 0, sectionEntryCounts };
|
||||
}
|
||||
|
||||
const digest = this.buildDigest(sections);
|
||||
const raw = await this.callLLM(digest);
|
||||
const actions = this.parseActions(raw, sections);
|
||||
return { actions, totalEntries, sectionEntryCounts };
|
||||
}
|
||||
|
||||
/** 应用用户确认的动作（只处理精确命中当前文件内容的动作，防幻觉改写） */
|
||||
apply(actions: MemoryMaintenanceAction[]): { applied: number; skipped: number } {
|
||||
const memory = this.workspaceService.getFiles().memory ?? '';
|
||||
const sections = this.parseEntries(memory);
|
||||
|
||||
// 精确匹配校验：entry 必须原样存在于对应分区（LLM 响应与文件状态之间的一致性锚点）
|
||||
const valid: MemoryMaintenanceAction[] = [];
|
||||
for (const a of actions.slice(0, MAX_ACTIONS)) {
|
||||
const section = sections.find((s) => s.section === a.section);
|
||||
const exists = section?.entries.includes(a.entry) ?? false;
|
||||
if (!exists) continue;
|
||||
if (a.action === 'update' && (!a.newEntry || !a.newEntry.trim())) continue;
|
||||
valid.push(a);
|
||||
}
|
||||
|
||||
if (valid.length === 0) return { applied: 0, skipped: actions.length };
|
||||
|
||||
// 应用到内存结构：delete 直接删；update 替换文本
|
||||
for (const a of valid) {
|
||||
const section = sections.find((s) => s.section === a.section);
|
||||
if (!section) continue;
|
||||
if (a.action === 'delete') {
|
||||
section.entries = section.entries.filter((e) => e !== a.entry);
|
||||
} else {
|
||||
section.entries = section.entries.map((e) => (e === a.entry ? a.newEntry!.trim() : e));
|
||||
}
|
||||
}
|
||||
|
||||
// 序列化回 Markdown（保留原文件头；分区结构重建）
|
||||
const head = this.extractHead(memory);
|
||||
const body = sections
|
||||
.map((s) => `## ${s.section}\n${s.entries.map((e) => `- ${e}`).join('\n')}`)
|
||||
.filter((s) => !s.endsWith('## ') && s.split('\n').length > 1)
|
||||
.join('\n\n');
|
||||
this.workspaceService.rewriteMemory(`${head}${body}\n`);
|
||||
|
||||
// 双轨一致：同步 semantic_memories（content 以 entry 写入 —— Consolidator 同口径）
|
||||
const db = this.getDB();
|
||||
const delStmt = db.prepare('DELETE FROM semantic_memories WHERE content = ?');
|
||||
const updStmt = db.prepare(
|
||||
'UPDATE semantic_memories SET content = ?, summary = ? WHERE content = ?',
|
||||
);
|
||||
let dbOps = 0;
|
||||
for (const a of valid) {
|
||||
try {
|
||||
if (a.action === 'delete') {
|
||||
dbOps += delStmt.run(a.entry).changes;
|
||||
} else {
|
||||
dbOps += updStmt.run(
|
||||
a.newEntry!.trim(),
|
||||
`[${a.section}] ${a.newEntry!.trim().slice(0, 60)}`,
|
||||
a.entry,
|
||||
).changes;
|
||||
}
|
||||
} catch (err) {
|
||||
// DB 同步失败不影响 MEMORY.md 已写入结果（与 Consolidator 同语义）
|
||||
log.warn('[MemoryMaintainer] semantic_memories sync failed:', (err as Error).message);
|
||||
}
|
||||
}
|
||||
|
||||
log.info(
|
||||
`[MemoryMaintainer] applied ${valid.length} action(s) (db rows touched: ${dbOps}, skipped: ${actions.length - valid.length})`,
|
||||
);
|
||||
return { applied: valid.length, skipped: actions.length - valid.length };
|
||||
}
|
||||
|
||||
// ===== 私有方法 =====
|
||||
|
||||
/** 解析 MEMORY.md 为 {section, entries[]} 结构（跳过元数据头；复用模块级工具） */
|
||||
private parseEntries(memory: string): ParsedSection[] {
|
||||
return parseMemoryEntries(memory);
|
||||
}
|
||||
|
||||
/** 提取文件头（H1 + > 元数据区），供重建时保留 */
|
||||
private extractHead(memory: string): string {
|
||||
const lines = memory.split('\n');
|
||||
let headEnd = 0;
|
||||
for (let i = 0; i < lines.length; i++) {
|
||||
if (lines[i].startsWith('## ')) {
|
||||
headEnd = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
const headLines = lines.slice(0, headEnd).join('\n').trimEnd();
|
||||
return headLines.length > 0 ? `${headLines}\n\n` : '';
|
||||
}
|
||||
|
||||
/** 构建"分区条目摘要"（纯条目行，消除全文截断盲区） */
|
||||
private buildDigest(sections: ParsedSection[]): string {
|
||||
const parts: string[] = [];
|
||||
let used = 0;
|
||||
for (const s of sections) {
|
||||
if (s.entries.length === 0) continue;
|
||||
const lines: string[] = [`## ${s.section}`];
|
||||
for (const e of s.entries) {
|
||||
const clipped = e.length > ENTRY_PROMPT_CHARS ? `${e.slice(0, ENTRY_PROMPT_CHARS)}...` : e;
|
||||
lines.push(`- ${clipped}`);
|
||||
}
|
||||
const block = lines.join('\n');
|
||||
if (used + block.length > DIGEST_BUDGET_CHARS) break;
|
||||
parts.push(block);
|
||||
used += block.length;
|
||||
}
|
||||
return parts.join('\n\n');
|
||||
}
|
||||
|
||||
/** LLM 分析（结构化 JSON 输出，30s 超时与 Consolidator 同口径） */
|
||||
private async callLLM(digest: string): Promise<string | null> {
|
||||
const sectionsList = ALLOWED_SECTIONS.map((s) => `"${s}"`).join(', ');
|
||||
const request: MetonaRequest = {
|
||||
meta: {
|
||||
sessionId: 'memory-maintenance',
|
||||
iteration: 0,
|
||||
requestId: `mm_${nanoid(12)}`,
|
||||
timestamp: Date.now(),
|
||||
agentVersion: '1.0.0',
|
||||
},
|
||||
systemPrompt: {
|
||||
roleDefinition:
|
||||
"You are a memory curator maintaining the agent's long-term memory file (MEMORY.md).",
|
||||
outputConstraints: [
|
||||
'Analyze the memory entries below and propose maintenance actions:',
|
||||
'- "delete": remove stale, superseded, duplicated, or completed entries',
|
||||
'- "update": merge two or more duplicate/similar entries into ONE consolidated entry',
|
||||
'Keep valuable, still-valid information — do NOT delete aggressively.',
|
||||
`Every action must reference an existing entry EXACTLY as written (section must be one of ${sectionsList}).`,
|
||||
'',
|
||||
'Output ONLY a JSON array, no markdown fences:',
|
||||
'[{"action":"delete","section":"...","entry":"...","reason":"..."},',
|
||||
' {"action":"update","section":"...","entry":"old entry","newEntry":"merged entry","reason":"..."}]',
|
||||
'If nothing needs maintenance, output []',
|
||||
].join('\n'),
|
||||
safetyGuidelines: 'Never propose deleting user preference facts without a clear reason.',
|
||||
},
|
||||
messages: [
|
||||
{
|
||||
role: 'user',
|
||||
content: `## Current MEMORY.md entries:\n\n${digest}\n\n## Task:\nPropose maintenance actions. Output JSON array only.`,
|
||||
timestamp: Date.now(),
|
||||
},
|
||||
],
|
||||
params: {
|
||||
maxTokens: 2048,
|
||||
temperature: 0.0,
|
||||
stream: false,
|
||||
thinkingEnabled: false,
|
||||
thinkingEffort: 'low',
|
||||
},
|
||||
};
|
||||
|
||||
try {
|
||||
let timer: ReturnType<typeof setTimeout> | undefined;
|
||||
try {
|
||||
const timeoutPromise = new Promise<never>((_, reject) => {
|
||||
timer = setTimeout(() => reject(new Error('maintenance analysis timeout')), 30_000);
|
||||
});
|
||||
const response = await Promise.race([this.getAdapter().send(request), timeoutPromise]);
|
||||
return response.content.trim();
|
||||
} finally {
|
||||
if (timer) clearTimeout(timer);
|
||||
}
|
||||
} catch (error) {
|
||||
log.warn('[MemoryMaintainer] LLM call failed:', (error as Error).message);
|
||||
return null;
|
||||
}
|
||||
}
|
||||
|
||||
/** 解析 LLM 建议（丢弃非法 section / 空条目 / 超限动作） */
|
||||
private parseActions(raw: string | null, sections: ParsedSection[]): MemoryMaintenanceAction[] {
|
||||
if (!raw) return [];
|
||||
let cleaned = raw.trim();
|
||||
if (cleaned.startsWith('```')) {
|
||||
cleaned = cleaned
|
||||
.replace(/^```(?:json)?\s*/i, '')
|
||||
.replace(/\s*```$/, '')
|
||||
.trim();
|
||||
}
|
||||
let parsed: unknown;
|
||||
try {
|
||||
parsed = JSON.parse(cleaned);
|
||||
} catch {
|
||||
log.warn('[MemoryMaintainer] failed to parse LLM response as JSON:', cleaned.slice(0, 200));
|
||||
return [];
|
||||
}
|
||||
if (!Array.isArray(parsed)) return [];
|
||||
|
||||
const validSections = new Set<string>(ALLOWED_SECTIONS);
|
||||
// 仅允许引用当前文件中真实存在的条目（先过滤一轮，双保险在 apply 中再做精确校验）
|
||||
const existing = new Set<string>();
|
||||
for (const s of sections) {
|
||||
for (const e of s.entries) existing.add(e);
|
||||
}
|
||||
|
||||
const out: MemoryMaintenanceAction[] = [];
|
||||
for (const item of parsed.slice(0, MAX_ACTIONS)) {
|
||||
if (!item || typeof item !== 'object') continue;
|
||||
const a = item as Record<string, unknown>;
|
||||
const action = a.action;
|
||||
const section = typeof a.section === 'string' ? a.section.trim() : '';
|
||||
const entry = typeof a.entry === 'string' ? a.entry.trim() : '';
|
||||
if (action !== 'delete' && action !== 'update') continue;
|
||||
if (!validSections.has(section) || !entry || !existing.has(entry)) continue;
|
||||
if (action === 'update' && (typeof a.newEntry !== 'string' || !a.newEntry.trim())) continue;
|
||||
out.push({
|
||||
action,
|
||||
section,
|
||||
entry,
|
||||
newEntry: action === 'update' ? (a.newEntry as string).trim() : undefined,
|
||||
reason: typeof a.reason === 'string' ? a.reason.slice(0, 200) : undefined,
|
||||
});
|
||||
}
|
||||
return out;
|
||||
}
|
||||
}
|
||||
+430
-104
@@ -17,6 +17,7 @@ import { nanoid } from 'nanoid';
|
||||
import { createHash } from 'crypto';
|
||||
import type Database from 'better-sqlite3';
|
||||
import log from 'electron-log';
|
||||
import type { MemoryEmbedder } from './embedder';
|
||||
|
||||
export type MemoryType = 'episodic' | 'semantic' | 'working';
|
||||
export type MemorySource = 'user_input' | 'tool_result' | 'agent_thought' | 'imported';
|
||||
@@ -94,7 +95,11 @@ function computeTF(tokens: string[]): Map<string, number> {
|
||||
}
|
||||
|
||||
/** 计算余弦相似度的点积部分 */
|
||||
function dotProduct(tf1: Map<string, number>, tf2: Map<string, number>, idf: Map<string, number>): number {
|
||||
function dotProduct(
|
||||
tf1: Map<string, number>,
|
||||
tf2: Map<string, number>,
|
||||
idf: Map<string, number>,
|
||||
): number {
|
||||
let sum = 0;
|
||||
for (const [term, freq1] of tf1) {
|
||||
const freq2 = tf2.get(term);
|
||||
@@ -123,6 +128,37 @@ function timeDecayWeight(createdAt: number, now: number = Date.now()): number {
|
||||
return Math.pow(0.5, ageDays / halfLifeDays);
|
||||
}
|
||||
|
||||
// ===== 向量工具（v0.8.1 P1-1 本地向量混合检索） =====
|
||||
|
||||
/** Float32Array → SQLite BLOB（little-endian 原生布局，Node/SQLite 同机读写安全） */
|
||||
function float32ToBlob(vec: number[]): Buffer {
|
||||
const f32 = Float32Array.from(vec);
|
||||
return Buffer.from(f32.buffer, f32.byteOffset, f32.byteLength);
|
||||
}
|
||||
|
||||
/** SQLite BLOB → number[]（维度/字节损坏时返回 null，调用方回退 TF-IDF 路径） */
|
||||
function blobToFloat32(blob: unknown): number[] | null {
|
||||
if (!Buffer.isBuffer(blob)) return null;
|
||||
if (blob.length === 0 || blob.length % 4 !== 0) return null;
|
||||
const f32 = new Float32Array(blob.buffer, blob.byteOffset, blob.length / 4);
|
||||
return Array.from(f32);
|
||||
}
|
||||
|
||||
/** 余弦相似度（零向量/维度不匹配返回 0） */
|
||||
function cosineSimilarity(a: number[], b: number[]): number {
|
||||
if (a.length === 0 || a.length !== b.length) return 0;
|
||||
let dot = 0;
|
||||
let na = 0;
|
||||
let nb = 0;
|
||||
for (let i = 0; i < a.length; i++) {
|
||||
dot += a[i] * b[i];
|
||||
na += a[i] * a[i];
|
||||
nb += b[i] * b[i];
|
||||
}
|
||||
if (na === 0 || nb === 0) return 0;
|
||||
return dot / (Math.sqrt(na) * Math.sqrt(nb));
|
||||
}
|
||||
|
||||
/**
|
||||
* 记忆管理器
|
||||
*/
|
||||
@@ -136,8 +172,20 @@ export class MemoryManager {
|
||||
/** 缓存有效期（5 分钟） */
|
||||
private readonly CACHE_TTL = 5 * 60 * 1000;
|
||||
|
||||
/**
|
||||
* v0.8.1 P1-1: 本地向量嵌入器（可选注入）。
|
||||
* main.ts 仅在 Provider=Ollama 且用户配置了 memory.embeddingModel 时注入；
|
||||
* null = 向量检索禁用，search/store 全部走纯 TF-IDF 路径（历史行为）。
|
||||
*/
|
||||
private embedder: MemoryEmbedder | null = null;
|
||||
|
||||
constructor(private getDB: () => Database.Database) {}
|
||||
|
||||
/** 注入向量嵌入器（传 null 关闭向量路径；热切换由 main.ts 在 adapter 重载时联动） */
|
||||
setEmbedder(embedder: MemoryEmbedder | null): void {
|
||||
this.embedder = embedder;
|
||||
}
|
||||
|
||||
/**
|
||||
* 初始化（表结构由 DatabaseService 创建）
|
||||
*/
|
||||
@@ -165,9 +213,15 @@ export class MemoryManager {
|
||||
const newIdfCache = new Map<string, number>();
|
||||
|
||||
// 获取所有记忆内容（episodic + semantic + working）
|
||||
const episodicRows = db.prepare('SELECT content, summary FROM episodic_memories').all() as Array<{ content: string; summary: string | null }>;
|
||||
const semanticRows = db.prepare('SELECT value FROM semantic_memories').all() as Array<{ value: string }>;
|
||||
const workingRows = db.prepare('SELECT value FROM working_memories').all() as Array<{ value: string }>;
|
||||
const episodicRows = db
|
||||
.prepare('SELECT content, summary FROM episodic_memories')
|
||||
.all() as Array<{ content: string; summary: string | null }>;
|
||||
const semanticRows = db.prepare('SELECT value FROM semantic_memories').all() as Array<{
|
||||
value: string;
|
||||
}>;
|
||||
const workingRows = db.prepare('SELECT value FROM working_memories').all() as Array<{
|
||||
value: string;
|
||||
}>;
|
||||
|
||||
const allDocs = [
|
||||
...episodicRows.map((r) => r.content + ' ' + (r.summary ?? '')),
|
||||
@@ -244,6 +298,10 @@ export class MemoryManager {
|
||||
source: MemorySource;
|
||||
sessionId?: string;
|
||||
expiresAt?: number;
|
||||
/** v0.8.1 P1-1: 文档向量（embedding 列缺失/损坏时为 null → 单路 TF-IDF 评分） */
|
||||
docVec?: number[] | null;
|
||||
/** v0.8.1 P1-1: 查询向量（null → 单路 TF-IDF 评分） */
|
||||
queryVec?: number[] | null;
|
||||
},
|
||||
queryTF: Map<string, number>,
|
||||
queryNorm: number,
|
||||
@@ -253,31 +311,59 @@ export class MemoryManager {
|
||||
const docTF = computeTF(params.docTokens);
|
||||
const docNorm = vectorNorm(docTF, this.idfCache);
|
||||
|
||||
if (docNorm === 0) return;
|
||||
|
||||
const dotProd = dotProduct(queryTF, docTF, this.idfCache);
|
||||
const cosineSim = dotProd / (queryNorm * docNorm);
|
||||
if (docNorm === 0 && !(params.docVec && params.queryVec)) return;
|
||||
|
||||
// 时间衰减
|
||||
const decayWeight = timeDecayWeight(params.createdAt, now);
|
||||
// 最终分数 = 余弦相似度 * 时间衰减 * 重要度权重
|
||||
const finalScore = cosineSim * decayWeight * (0.5 + params.importance * 0.5);
|
||||
const importanceWeight = 0.5 + params.importance * 0.5;
|
||||
|
||||
// TF-IDF 路（docNorm=0 时得 0 分，交由向量路兜底）
|
||||
const tfidfScore =
|
||||
docNorm > 0
|
||||
? (dotProduct(queryTF, docTF, this.idfCache) / (queryNorm * docNorm)) *
|
||||
decayWeight *
|
||||
importanceWeight
|
||||
: 0;
|
||||
// 向量路（双侧齐备时计算余弦）
|
||||
const vectorScore =
|
||||
params.docVec && params.queryVec
|
||||
? cosineSimilarity(params.queryVec, params.docVec) * decayWeight * importanceWeight
|
||||
: 0;
|
||||
|
||||
// v0.8.1 P1-1 混合评分：双侧齐备 0.6 向量 + 0.4 TF-IDF；否则取可用单路
|
||||
let finalScore: number;
|
||||
if (params.docVec && params.queryVec) {
|
||||
finalScore = 0.6 * vectorScore + 0.4 * tfidfScore;
|
||||
} else {
|
||||
finalScore = tfidfScore > 0 ? tfidfScore : vectorScore;
|
||||
}
|
||||
|
||||
if (finalScore > 0) {
|
||||
results.push({
|
||||
id: params.id, type: params.type, content: params.content,
|
||||
summary: params.summary, source: params.source,
|
||||
importance: params.importance, sessionId: params.sessionId,
|
||||
createdAt: params.createdAt, expiresAt: params.expiresAt,
|
||||
id: params.id,
|
||||
type: params.type,
|
||||
content: params.content,
|
||||
summary: params.summary,
|
||||
source: params.source,
|
||||
importance: params.importance,
|
||||
sessionId: params.sessionId,
|
||||
createdAt: params.createdAt,
|
||||
expiresAt: params.expiresAt,
|
||||
score: finalScore,
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* TF-IDF 相似度搜索
|
||||
* TF-IDF 相似度搜索（v0.8.1 P1-1: 可选向量混合评分）
|
||||
*
|
||||
* @param queryVec 查询向量（embedder 未注入/失败时为 null → 纯 TF-IDF）
|
||||
*/
|
||||
private tfidfSearch(query: string, options: MemorySearchOptions): SearchResult[] {
|
||||
private tfidfSearch(
|
||||
query: string,
|
||||
options: MemorySearchOptions,
|
||||
queryVec: number[] | null,
|
||||
): SearchResult[] {
|
||||
const db = this.getDB();
|
||||
this.updateIdfCache();
|
||||
|
||||
@@ -296,71 +382,142 @@ export class MemoryManager {
|
||||
|
||||
// 搜索 episodic 记忆
|
||||
if (!type || type === 'episodic') {
|
||||
const rows = db.prepare(`
|
||||
const rows = db
|
||||
.prepare(
|
||||
`
|
||||
SELECT * FROM episodic_memories WHERE importance >= ?
|
||||
ORDER BY importance DESC, created_at DESC LIMIT ?
|
||||
`).all(minImportance, topK * 3) as Array<{
|
||||
id: string; session_id: string | null; content: string; summary: string | null;
|
||||
source: string; importance: number; created_at: number; expires_at: number | null;
|
||||
`,
|
||||
)
|
||||
.all(minImportance, topK * 3) as Array<{
|
||||
id: string;
|
||||
session_id: string | null;
|
||||
content: string;
|
||||
summary: string | null;
|
||||
source: string;
|
||||
importance: number;
|
||||
created_at: number;
|
||||
expires_at: number | null;
|
||||
tf_cache: string | null;
|
||||
}>;
|
||||
|
||||
for (const row of rows) {
|
||||
this.scoreAndPushMemory({
|
||||
docTokens: this.cachedTokens(row.tf_cache, row.content + ' ' + (row.summary ?? '')),
|
||||
createdAt: row.created_at,
|
||||
importance: row.importance,
|
||||
id: row.id, type: 'episodic', content: row.content,
|
||||
summary: row.summary ?? undefined,
|
||||
source: row.source as MemorySource,
|
||||
sessionId: row.session_id ?? undefined,
|
||||
expiresAt: row.expires_at ?? undefined,
|
||||
}, queryTF, queryNorm, now, results);
|
||||
this.scoreAndPushMemory(
|
||||
{
|
||||
docTokens: this.cachedTokens(row.tf_cache, row.content + ' ' + (row.summary ?? '')),
|
||||
createdAt: row.created_at,
|
||||
importance: row.importance,
|
||||
id: row.id,
|
||||
type: 'episodic',
|
||||
content: row.content,
|
||||
summary: row.summary ?? undefined,
|
||||
source: row.source as MemorySource,
|
||||
sessionId: row.session_id ?? undefined,
|
||||
expiresAt: row.expires_at ?? undefined,
|
||||
docVec: blobToFloat32((row as { embedding?: unknown }).embedding),
|
||||
queryVec,
|
||||
},
|
||||
queryTF,
|
||||
queryNorm,
|
||||
now,
|
||||
results,
|
||||
);
|
||||
}
|
||||
this.backfillMissingEmbeddings(
|
||||
'episodic',
|
||||
rows.map((r) => ({
|
||||
id: r.id,
|
||||
embedding: (r as { embedding?: unknown }).embedding,
|
||||
text: r.content + ' ' + (r.summary ?? ''),
|
||||
})),
|
||||
);
|
||||
}
|
||||
|
||||
// 搜索 semantic 记忆
|
||||
if (!type || type === 'semantic') {
|
||||
const rows = db.prepare(`
|
||||
const rows = db
|
||||
.prepare(
|
||||
`
|
||||
SELECT * FROM semantic_memories WHERE confidence >= ?
|
||||
ORDER BY confidence DESC, access_count DESC LIMIT ?
|
||||
`).all(minImportance, Math.ceil(topK * 1.5)) as Array<{
|
||||
id: string; key: string; value: string; category: string | null;
|
||||
confidence: number; source_session: string | null; created_at: number;
|
||||
`,
|
||||
)
|
||||
.all(minImportance, Math.ceil(topK * 1.5)) as Array<{
|
||||
id: string;
|
||||
key: string;
|
||||
value: string;
|
||||
category: string | null;
|
||||
confidence: number;
|
||||
source_session: string | null;
|
||||
created_at: number;
|
||||
tf_cache: string | null;
|
||||
}>;
|
||||
|
||||
for (const row of rows) {
|
||||
this.scoreAndPushMemory({
|
||||
docTokens: this.cachedTokens(row.tf_cache, row.key + ' ' + row.value),
|
||||
createdAt: row.created_at,
|
||||
importance: row.confidence,
|
||||
id: row.id, type: 'semantic', content: row.value,
|
||||
source: 'imported',
|
||||
sessionId: row.source_session ?? undefined,
|
||||
}, queryTF, queryNorm, now, results);
|
||||
this.scoreAndPushMemory(
|
||||
{
|
||||
docTokens: this.cachedTokens(row.tf_cache, row.key + ' ' + row.value),
|
||||
createdAt: row.created_at,
|
||||
importance: row.confidence,
|
||||
id: row.id,
|
||||
type: 'semantic',
|
||||
content: row.value,
|
||||
source: 'imported',
|
||||
sessionId: row.source_session ?? undefined,
|
||||
docVec: blobToFloat32((row as { embedding?: unknown }).embedding),
|
||||
queryVec,
|
||||
},
|
||||
queryTF,
|
||||
queryNorm,
|
||||
now,
|
||||
results,
|
||||
);
|
||||
}
|
||||
this.backfillMissingEmbeddings(
|
||||
'semantic',
|
||||
rows.map((r) => ({
|
||||
id: r.id,
|
||||
embedding: (r as { embedding?: unknown }).embedding,
|
||||
text: r.key + ' ' + r.value,
|
||||
})),
|
||||
);
|
||||
}
|
||||
|
||||
// 搜索 working 记忆
|
||||
if (!type || type === 'working') {
|
||||
const rows = db.prepare(`
|
||||
const rows = db
|
||||
.prepare(
|
||||
`
|
||||
SELECT * FROM working_memories ORDER BY updated_at DESC LIMIT ?
|
||||
`).all(topK * 3) as Array<{
|
||||
id: string; session_id: string; task_id: string;
|
||||
key: string; value: string; updated_at: number;
|
||||
`,
|
||||
)
|
||||
.all(topK * 3) as Array<{
|
||||
id: string;
|
||||
session_id: string;
|
||||
task_id: string;
|
||||
key: string;
|
||||
value: string;
|
||||
updated_at: number;
|
||||
tf_cache: string | null;
|
||||
}>;
|
||||
|
||||
for (const row of rows) {
|
||||
this.scoreAndPushMemory({
|
||||
docTokens: this.cachedTokens(row.tf_cache, row.key + ' ' + row.value),
|
||||
createdAt: row.updated_at,
|
||||
importance: 0.5,
|
||||
id: row.id, type: 'working', content: row.value,
|
||||
source: 'agent_thought',
|
||||
sessionId: row.session_id,
|
||||
}, queryTF, queryNorm, now, results);
|
||||
this.scoreAndPushMemory(
|
||||
{
|
||||
docTokens: this.cachedTokens(row.tf_cache, row.key + ' ' + row.value),
|
||||
createdAt: row.updated_at,
|
||||
importance: 0.5,
|
||||
id: row.id,
|
||||
type: 'working',
|
||||
content: row.value,
|
||||
source: 'agent_thought',
|
||||
sessionId: row.session_id,
|
||||
},
|
||||
queryTF,
|
||||
queryNorm,
|
||||
now,
|
||||
results,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
@@ -383,11 +540,22 @@ export class MemoryManager {
|
||||
|
||||
switch (item.type) {
|
||||
case 'episodic':
|
||||
db.prepare(`
|
||||
INSERT INTO episodic_memories (id, session_id, content, summary, source, importance, created_at, tf_cache)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?)
|
||||
`).run(
|
||||
id, item.sessionId ?? null, item.content, item.summary ?? null, item.source, importance, now,
|
||||
db.prepare(
|
||||
`
|
||||
INSERT INTO episodic_memories (id, session_id, content, summary, source, importance, created_at, expires_at, tf_cache)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)
|
||||
`,
|
||||
).run(
|
||||
id,
|
||||
item.sessionId ?? null,
|
||||
item.content,
|
||||
item.summary ?? null,
|
||||
item.source,
|
||||
importance,
|
||||
now,
|
||||
// v0.8.1 P0-2: expires_at 真实写入方 —— 调用方（MemoryTriggerHook 等）可携带
|
||||
// TTL；此前该列全链路无写入方，cleanupExpired 空转，情节记忆只增不减
|
||||
item.expiresAt ?? null,
|
||||
// P2-12: 写入时预计算分词缓存，加速后续检索
|
||||
JSON.stringify(tokenize(item.content + ' ' + (item.summary ?? ''))),
|
||||
);
|
||||
@@ -397,22 +565,38 @@ export class MemoryManager {
|
||||
// #32 修复: 当 summary 未提供时，使用 content hash 作为 key 实现基于内容的去重
|
||||
// v0.3.0 用 id 作为 key 时，因 id 每次新生成，INSERT OR REPLACE 永远不触发 REPLACE，
|
||||
// 导致重复 store 同一内容会创建多条记忆。改为 contentHash 后，相同内容自动 REPLACE。
|
||||
db.prepare(`
|
||||
db.prepare(
|
||||
`
|
||||
INSERT OR REPLACE INTO semantic_memories (id, key, value, category, confidence, source_session, created_at, updated_at, access_count, tf_cache)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?, ?, 0, ?)
|
||||
`).run(
|
||||
id, item.summary ?? this.contentHash(item.content), item.content, 'general', importance, item.sessionId ?? null, now, now,
|
||||
`,
|
||||
).run(
|
||||
id,
|
||||
item.summary ?? this.contentHash(item.content),
|
||||
item.content,
|
||||
'general',
|
||||
importance,
|
||||
item.sessionId ?? null,
|
||||
now,
|
||||
now,
|
||||
JSON.stringify(tokenize((item.summary ?? '') + ' ' + item.content)),
|
||||
);
|
||||
break;
|
||||
case 'working':
|
||||
// v0.3.0 修复：使用 summary 作为 key（若提供），避免硬编码 'default' 导致覆盖
|
||||
// #32 修复: 当 summary 未提供时，使用 content hash 作为 key 实现基于内容的去重
|
||||
db.prepare(`
|
||||
db.prepare(
|
||||
`
|
||||
INSERT OR REPLACE INTO working_memories (id, session_id, task_id, key, value, updated_at, tf_cache)
|
||||
VALUES (?, ?, ?, ?, ?, ?, ?)
|
||||
`).run(
|
||||
id, item.sessionId ?? 'default', 'default', item.summary ?? this.contentHash(item.content), item.content, now,
|
||||
`,
|
||||
).run(
|
||||
id,
|
||||
item.sessionId ?? 'default',
|
||||
'default',
|
||||
item.summary ?? this.contentHash(item.content),
|
||||
item.content,
|
||||
now,
|
||||
JSON.stringify(tokenize((item.summary ?? '') + ' ' + item.content)),
|
||||
);
|
||||
break;
|
||||
@@ -424,28 +608,117 @@ export class MemoryManager {
|
||||
// 使 IDF 缓存失效
|
||||
this.cacheUpdatedAt = 0;
|
||||
|
||||
// v0.8.1 P1-1: 异步向量化回填（fire-and-forget）—— 嵌入不可用时静默跳过，
|
||||
// 该记忆保留 NULL embedding，检索时自动回退 TF-IDF 路径
|
||||
this.enrichEmbedding(item.type, id, (item.summary ?? '') + ' ' + item.content);
|
||||
|
||||
log.debug(`Memory stored: ${id} (${item.type})`);
|
||||
return id;
|
||||
}
|
||||
|
||||
/**
|
||||
* 检索记忆（v0.2.0: TF-IDF 语义检索 + 时间衰减）
|
||||
*
|
||||
* v0.2.0 变更：
|
||||
* - 使用 TF-IDF 余弦相似度替代 LIKE 关键词搜索
|
||||
* - 支持中英文分词（英文按词，中文按 bigram）
|
||||
* - 时间衰减：30 天半衰期，老旧记忆权重降低
|
||||
* - IDF 缓存：5 分钟有效期，避免重复计算
|
||||
* v0.8.1 P1-1: 异步生成并回填 embedding BLOB。
|
||||
* 失败静默（降级 TF-IDF），不阻塞写入方（工具执行/记忆固化均不等待）。
|
||||
*/
|
||||
search(query: string, options: MemorySearchOptions = {}): SearchResult[] {
|
||||
private embeddingBackfillInFlight = new Set<string>();
|
||||
|
||||
private enrichEmbedding(type: MemoryType, id: string, text: string): void {
|
||||
const embedder = this.embedder;
|
||||
if (!embedder) return;
|
||||
const table =
|
||||
type === 'episodic' ? 'episodic_memories' : type === 'semantic' ? 'semantic_memories' : null;
|
||||
if (!table) return; // working 记忆会话级生命周期短，不参与向量检索
|
||||
const key = `${table}:${id}`;
|
||||
if (this.embeddingBackfillInFlight.has(key)) return;
|
||||
this.embeddingBackfillInFlight.add(key);
|
||||
void embedder
|
||||
.embed(text.slice(0, 8000))
|
||||
.then((vec) => {
|
||||
if (!vec || vec.length === 0) return;
|
||||
this.getDB()
|
||||
.prepare(`UPDATE ${table} SET embedding = ? WHERE id = ?`)
|
||||
.run(float32ToBlob(vec), id);
|
||||
})
|
||||
.catch((err) => {
|
||||
log.debug(`MemoryManager: embedding enrichment skipped: ${(err as Error).message}`);
|
||||
})
|
||||
.finally(() => {
|
||||
this.embeddingBackfillInFlight.delete(key);
|
||||
});
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.8.1 P1-1: 存量记忆向量惰性回填 —— 嵌入功能开启前写入的记忆（embedding
|
||||
* IS NULL）在参与检索时排队补算：本轮查询仍走 TF-IDF，后续查询即可命中向量
|
||||
* 路径。无阻塞、无独立迁移任务，收敛速度随检索频次自然提升；嵌入器不可用
|
||||
* 时零开销（直接返回）。
|
||||
*/
|
||||
private backfillMissingEmbeddings(
|
||||
type: MemoryType,
|
||||
rows: Array<{ id: string; embedding?: unknown; text: string }>,
|
||||
): void {
|
||||
if (!this.embedder) return;
|
||||
for (const row of rows) {
|
||||
if (row.embedding != null) continue;
|
||||
this.enrichEmbedding(type, row.id, row.text);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.8.1 P0-2: 检索命中后回写 semantic_memories.access_count（LRU 淘汰语义激活）。
|
||||
* 此前该列只在 ORDER BY 中被读取、从无更新方，LRU 淘汰是死语义。
|
||||
*/
|
||||
private bumpAccessCounts(results: SearchResult[]): void {
|
||||
const semanticIds = results.filter((r) => r.type === 'semantic').map((r) => r.id);
|
||||
if (semanticIds.length === 0) return;
|
||||
try {
|
||||
const placeholders = semanticIds.map(() => '?').join(', ');
|
||||
this.getDB()
|
||||
.prepare(
|
||||
`UPDATE semantic_memories SET access_count = access_count + 1 WHERE id IN (${placeholders})`,
|
||||
)
|
||||
.run(...semanticIds);
|
||||
} catch (err) {
|
||||
// 计数回写失败不影响检索结果
|
||||
log.debug('MemoryManager: access_count bump failed:', (err as Error).message);
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* 检索记忆（v0.2.0: TF-IDF 语义检索 + 时间衰减；v0.8.1 P1-1: 本地向量混合检索）
|
||||
*
|
||||
* v0.8.1 变更：
|
||||
* - 方法改为 async（查询向量需经 MemoryEmbedder 异步生成；Ollama 本地嵌入）。
|
||||
* - 混合评分：查询向量与文档向量齐备时 score = 0.6×向量余弦 + 0.4×TF-IDF
|
||||
* （两者各自叠加时间衰减与重要度权重）；任一缺失时回退单路评分 ——
|
||||
* 未注入 embedder（或嵌入失败）时行为与历史版本完全一致。
|
||||
* - 检索命中的 semantic 记忆回写 access_count（LRU 语义激活，P0-2）。
|
||||
*/
|
||||
async search(query: string, options: MemorySearchOptions = {}): Promise<SearchResult[]> {
|
||||
const db = this.getDB();
|
||||
const { topK = 5, type, minImportance = 0 } = options;
|
||||
// v0.3.0 修复：拦截空 query 和纯空格 query
|
||||
if (!query || !query.trim()) return [];
|
||||
|
||||
// v0.2.0: 优先使用 TF-IDF 语义搜索
|
||||
const tfidfResults = this.tfidfSearch(query, options);
|
||||
// v0.8.1: 查询向量生成一次（嵌入器缺失/失败 → null，全量回退 TF-IDF）
|
||||
let queryVec: number[] | null = null;
|
||||
if (this.embedder) {
|
||||
try {
|
||||
queryVec = await this.embedder.embed(query.slice(0, 8000));
|
||||
if (queryVec && queryVec.length === 0) queryVec = null;
|
||||
} catch (err) {
|
||||
log.debug(
|
||||
'MemoryManager: query embedding failed, falling back to TF-IDF:',
|
||||
(err as Error).message,
|
||||
);
|
||||
queryVec = null;
|
||||
}
|
||||
}
|
||||
|
||||
// v0.2.0: 优先使用语义搜索（TF-IDF ± 向量混合）
|
||||
const tfidfResults = this.tfidfSearch(query, options, queryVec);
|
||||
if (tfidfResults.length > 0) {
|
||||
this.bumpAccessCounts(tfidfResults);
|
||||
return tfidfResults;
|
||||
}
|
||||
|
||||
@@ -458,20 +731,35 @@ export class MemoryManager {
|
||||
|
||||
// 搜索情节记忆
|
||||
if (!type || type === 'episodic') {
|
||||
const rows = db.prepare(`
|
||||
const rows = db
|
||||
.prepare(
|
||||
`
|
||||
SELECT * FROM episodic_memories
|
||||
WHERE (content LIKE ? ESCAPE '\\' OR summary LIKE ? ESCAPE '\\') AND importance >= ?
|
||||
ORDER BY importance DESC, created_at DESC LIMIT ?
|
||||
`).all(pattern, pattern, minImportance, topK) as Array<{
|
||||
id: string; session_id: string | null; content: string; summary: string | null;
|
||||
source: string; importance: number; created_at: number; expires_at: number | null;
|
||||
`,
|
||||
)
|
||||
.all(pattern, pattern, minImportance, topK) as Array<{
|
||||
id: string;
|
||||
session_id: string | null;
|
||||
content: string;
|
||||
summary: string | null;
|
||||
source: string;
|
||||
importance: number;
|
||||
created_at: number;
|
||||
expires_at: number | null;
|
||||
}>;
|
||||
for (const row of rows) {
|
||||
results.push({
|
||||
id: row.id, type: 'episodic', content: row.content,
|
||||
summary: row.summary ?? undefined, source: row.source as MemorySource,
|
||||
importance: row.importance, sessionId: row.session_id ?? undefined,
|
||||
createdAt: row.created_at, expiresAt: row.expires_at ?? undefined,
|
||||
id: row.id,
|
||||
type: 'episodic',
|
||||
content: row.content,
|
||||
summary: row.summary ?? undefined,
|
||||
source: row.source as MemorySource,
|
||||
importance: row.importance,
|
||||
sessionId: row.session_id ?? undefined,
|
||||
createdAt: row.created_at,
|
||||
expiresAt: row.expires_at ?? undefined,
|
||||
score: row.importance * timeDecayWeight(row.created_at),
|
||||
});
|
||||
}
|
||||
@@ -479,20 +767,33 @@ export class MemoryManager {
|
||||
|
||||
// 搜索语义记忆
|
||||
if (!type || type === 'semantic') {
|
||||
const rows = db.prepare(`
|
||||
const rows = db
|
||||
.prepare(
|
||||
`
|
||||
SELECT * FROM semantic_memories
|
||||
WHERE (key LIKE ? ESCAPE '\\' OR value LIKE ? ESCAPE '\\') AND confidence >= ?
|
||||
ORDER BY confidence DESC, access_count DESC LIMIT ?
|
||||
`).all(pattern, pattern, minImportance, Math.ceil(topK / 2)) as Array<{
|
||||
id: string; key: string; value: string; category: string | null;
|
||||
confidence: number; source_session: string | null; created_at: number;
|
||||
`,
|
||||
)
|
||||
.all(pattern, pattern, minImportance, Math.ceil(topK / 2)) as Array<{
|
||||
id: string;
|
||||
key: string;
|
||||
value: string;
|
||||
category: string | null;
|
||||
confidence: number;
|
||||
source_session: string | null;
|
||||
created_at: number;
|
||||
}>;
|
||||
for (const row of rows) {
|
||||
results.push({
|
||||
id: row.id, type: 'semantic', content: row.value,
|
||||
source: 'imported', importance: row.confidence,
|
||||
id: row.id,
|
||||
type: 'semantic',
|
||||
content: row.value,
|
||||
source: 'imported',
|
||||
importance: row.confidence,
|
||||
sessionId: row.source_session ?? undefined,
|
||||
createdAt: row.created_at, score: row.confidence * timeDecayWeight(row.created_at),
|
||||
createdAt: row.created_at,
|
||||
score: row.confidence * timeDecayWeight(row.created_at),
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -500,25 +801,39 @@ export class MemoryManager {
|
||||
// 搜索工作记忆
|
||||
// v0.3.0 修复：LIKE 回退路径也需添加 !type 分支（与 tfidfSearch 保持一致）
|
||||
if (!type || type === 'working') {
|
||||
const rows = db.prepare(`
|
||||
const rows = db
|
||||
.prepare(
|
||||
`
|
||||
SELECT * FROM working_memories
|
||||
WHERE (key LIKE ? ESCAPE '\\' OR value LIKE ? ESCAPE '\\')
|
||||
ORDER BY updated_at DESC LIMIT ?
|
||||
`).all(pattern, pattern, topK) as Array<{
|
||||
id: string; session_id: string; task_id: string;
|
||||
key: string; value: string; updated_at: number;
|
||||
`,
|
||||
)
|
||||
.all(pattern, pattern, topK) as Array<{
|
||||
id: string;
|
||||
session_id: string;
|
||||
task_id: string;
|
||||
key: string;
|
||||
value: string;
|
||||
updated_at: number;
|
||||
}>;
|
||||
for (const row of rows) {
|
||||
results.push({
|
||||
id: row.id, type: 'working', content: row.value,
|
||||
source: 'agent_thought', importance: 0.5,
|
||||
sessionId: row.session_id, createdAt: row.updated_at,
|
||||
id: row.id,
|
||||
type: 'working',
|
||||
content: row.value,
|
||||
source: 'agent_thought',
|
||||
importance: 0.5,
|
||||
sessionId: row.session_id,
|
||||
createdAt: row.updated_at,
|
||||
score: 0.3 * timeDecayWeight(row.updated_at),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
return results.sort((a, b) => b.score - a.score).slice(0, topK);
|
||||
const finalResults = results.sort((a, b) => b.score - a.score).slice(0, topK);
|
||||
this.bumpAccessCounts(finalResults);
|
||||
return finalResults;
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -526,9 +841,13 @@ export class MemoryManager {
|
||||
*/
|
||||
getWorkingMemory(sessionId: string, taskId: string = 'default'): Map<string, string> {
|
||||
const db = this.getDB();
|
||||
const rows = db.prepare(`
|
||||
const rows = db
|
||||
.prepare(
|
||||
`
|
||||
SELECT key, value FROM working_memories WHERE session_id = ? AND task_id = ?
|
||||
`).all(sessionId, taskId) as Array<{ key: string; value: string }>;
|
||||
`,
|
||||
)
|
||||
.all(sessionId, taskId) as Array<{ key: string; value: string }>;
|
||||
return new Map(rows.map((r) => [r.key, r.value]));
|
||||
}
|
||||
|
||||
@@ -537,10 +856,12 @@ export class MemoryManager {
|
||||
*/
|
||||
setWorkingMemory(sessionId: string, taskId: string, key: string, value: string): void {
|
||||
const db = this.getDB();
|
||||
db.prepare(`
|
||||
db.prepare(
|
||||
`
|
||||
INSERT OR REPLACE INTO working_memories (id, session_id, task_id, key, value, updated_at)
|
||||
VALUES (?, ?, ?, ?, ?, ?)
|
||||
`).run(`wm_${nanoid(8)}`, sessionId, taskId, key, value, Date.now());
|
||||
`,
|
||||
).run(`wm_${nanoid(8)}`, sessionId, taskId, key, value, Date.now());
|
||||
}
|
||||
|
||||
/**
|
||||
@@ -549,7 +870,10 @@ export class MemoryManager {
|
||||
clearWorkingMemory(sessionId: string, taskId?: string): void {
|
||||
const db = this.getDB();
|
||||
if (taskId) {
|
||||
db.prepare('DELETE FROM working_memories WHERE session_id = ? AND task_id = ?').run(sessionId, taskId);
|
||||
db.prepare('DELETE FROM working_memories WHERE session_id = ? AND task_id = ?').run(
|
||||
sessionId,
|
||||
taskId,
|
||||
);
|
||||
} else {
|
||||
db.prepare('DELETE FROM working_memories WHERE session_id = ?').run(sessionId);
|
||||
}
|
||||
@@ -560,7 +884,9 @@ export class MemoryManager {
|
||||
*/
|
||||
cleanupExpired(): number {
|
||||
const db = this.getDB();
|
||||
const result = db.prepare('DELETE FROM episodic_memories WHERE expires_at IS NOT NULL AND expires_at < ?').run(Date.now());
|
||||
const result = db
|
||||
.prepare('DELETE FROM episodic_memories WHERE expires_at IS NOT NULL AND expires_at < ?')
|
||||
.run(Date.now());
|
||||
return result.changes;
|
||||
}
|
||||
|
||||
|
||||
@@ -172,14 +172,14 @@ export class TaskOrchestrator extends EventEmitter {
|
||||
thinkingEnabled: this.defaultConfig?.thinkingEnabled ?? true,
|
||||
thinkingEffort: this.defaultConfig?.thinkingEffort ?? 'medium',
|
||||
contextLength: this.defaultConfig?.contextLength,
|
||||
contextWindow: this.defaultConfig?.contextWindow ?? 128_000,
|
||||
contextWindow: this.defaultConfig?.contextWindow,
|
||||
// v0.7.3 P3-1: SubAgent 与主引擎同源消费 enableReflection（REFLECTING 状态开关）
|
||||
enableReflection: this.defaultConfig?.enableReflection ?? false,
|
||||
// v0.7.4 P3-2 修正: SubAgent 继承主引擎的 temperature/maxTokens ——
|
||||
// 旧实现不读这两个键，新 SubAgent 恒用引擎 DEFAULT_CONFIG（0.0/63488），
|
||||
// 导致"热生效"对子任务不完整
|
||||
// 旧实现不读这两个键导致"热生效"对子任务不完整。
|
||||
// v0.8.1 硬性契约: 不携带任何写死兜底值 —— 与主引擎同源继承设置面板配置
|
||||
temperature: this.defaultConfig?.temperature ?? 0.0,
|
||||
maxTokens: this.defaultConfig?.maxTokens ?? 63488,
|
||||
maxTokens: this.defaultConfig?.maxTokens,
|
||||
},
|
||||
this.engines.createAdapter(),
|
||||
this.toolRegistry,
|
||||
|
||||
@@ -172,9 +172,101 @@ export const DEFAULT_POLICIES: PermissionPolicy[] = [
|
||||
{ toolName: 'file_info', requiredLevel: PermissionLevel.READ },
|
||||
];
|
||||
|
||||
/**
|
||||
* v0.8.1 P2-1: 用户自定义策略解析（设置面板 ToolsSettings 存储）
|
||||
*
|
||||
* 存储契约：配置键 `tools.{toolName}.policy`（JSON 字符串），字段：
|
||||
* - deniedPatterns / allowedPatterns: string[]（正则源；加载时编译，非法正则跳过）
|
||||
* - maxFrequency: number（次/分钟）
|
||||
* - requireConfirmation: boolean
|
||||
* 解析失败整体返回 null（回退默认策略），单条非法正则仅跳过该条 —— 配置错误
|
||||
* 不放大执行面（fail-closed），也不让一条坏配置瘫痪整个策略引擎。
|
||||
*/
|
||||
export interface ParsedToolPolicy {
|
||||
deniedPatterns?: RegExp[];
|
||||
allowedPatterns?: RegExp[];
|
||||
maxFrequency?: number;
|
||||
requireConfirmation?: boolean;
|
||||
}
|
||||
|
||||
export function parseToolPolicy(raw: unknown): ParsedToolPolicy | null {
|
||||
let obj: unknown = raw;
|
||||
if (typeof raw === 'string') {
|
||||
try {
|
||||
obj = JSON.parse(raw);
|
||||
} catch {
|
||||
return null;
|
||||
}
|
||||
}
|
||||
if (!obj || typeof obj !== 'object' || Array.isArray(obj)) return null;
|
||||
const o = obj as Record<string, unknown>;
|
||||
const compileList = (value: unknown): RegExp[] | undefined => {
|
||||
if (value === undefined) return undefined;
|
||||
if (!Array.isArray(value)) return undefined;
|
||||
const compiled: RegExp[] = [];
|
||||
for (const item of value.slice(0, 50)) {
|
||||
if (typeof item !== 'string' || item.length === 0 || item.length > 500) continue;
|
||||
try {
|
||||
compiled.push(new RegExp(item));
|
||||
} catch {
|
||||
/* 非法正则跳过 */
|
||||
}
|
||||
}
|
||||
return compiled;
|
||||
};
|
||||
const out: ParsedToolPolicy = {};
|
||||
const denied = compileList(o.deniedPatterns);
|
||||
if (denied) out.deniedPatterns = denied;
|
||||
const allowed = compileList(o.allowedPatterns);
|
||||
if (allowed) out.allowedPatterns = allowed;
|
||||
if (typeof o.maxFrequency === 'number' && Number.isFinite(o.maxFrequency) && o.maxFrequency > 0) {
|
||||
out.maxFrequency = Math.floor(o.maxFrequency);
|
||||
}
|
||||
if (typeof o.requireConfirmation === 'boolean') {
|
||||
out.requireConfirmation = o.requireConfirmation;
|
||||
}
|
||||
return Object.keys(out).length > 0 ? out : null;
|
||||
}
|
||||
|
||||
export class PolicyEngine {
|
||||
private policies: Map<string, PermissionPolicy> = new Map();
|
||||
|
||||
/**
|
||||
* v0.8.1 P2-1: 用户自定义策略覆盖层（settings → 热加载）。
|
||||
* resolvePolicy 的最高优先级 —— 覆盖层与默认策略按字段合并
|
||||
* （未指定的安全字段如 deniedPatterns 保留默认值，与构造函数合并语义一致）。
|
||||
*/
|
||||
private policyOverrides: Map<string, PermissionPolicy> = new Map();
|
||||
|
||||
/** 设置/清除某工具的用户策略覆盖（null = 清除，回退默认策略） */
|
||||
setPolicyOverride(toolName: string, override: ParsedToolPolicy | null): void {
|
||||
if (!override) {
|
||||
this.policyOverrides.delete(toolName);
|
||||
return;
|
||||
}
|
||||
const base = this.policies.get(toolName);
|
||||
this.policyOverrides.set(toolName, {
|
||||
...(base ?? {
|
||||
toolName,
|
||||
requiredLevel: PermissionLevel.WRITE,
|
||||
}),
|
||||
toolName,
|
||||
...override,
|
||||
});
|
||||
}
|
||||
|
||||
/** 获取某工具当前的覆盖策略（UI 回显用；无覆盖返回 null） */
|
||||
getPolicyOverride(toolName: string): ParsedToolPolicy | null {
|
||||
const o = this.policyOverrides.get(toolName);
|
||||
if (!o) return null;
|
||||
return {
|
||||
deniedPatterns: o.deniedPatterns?.map((r) => r.source),
|
||||
allowedPatterns: o.allowedPatterns?.map((r) => r.source),
|
||||
maxFrequency: o.maxFrequency,
|
||||
requireConfirmation: o.requireConfirmation,
|
||||
} as unknown as ParsedToolPolicy;
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.4.1: 工具调用频率追踪 — 频率 key -> 调用时间戳列表
|
||||
* key 格式: `${sessionId}:${toolName}`（会话隔离）
|
||||
@@ -217,6 +309,9 @@ export class PolicyEngine {
|
||||
* 供 checkAuthorization 与 requiresConfirmation 共用匹配逻辑，消除双份漂移。
|
||||
*/
|
||||
private resolvePolicy(toolName: string): PermissionPolicy | undefined {
|
||||
// v0.8.1 P2-1: 用户覆盖层最高优先级（settings 面板 → setPolicyOverride）
|
||||
const override = this.policyOverrides.get(toolName);
|
||||
if (override) return override;
|
||||
const exact = this.policies.get(toolName);
|
||||
if (exact) return exact;
|
||||
// C-7 修复: 支持通配符策略匹配（如 mcp_* 匹配所有 MCP 工具）
|
||||
|
||||
@@ -7,7 +7,7 @@
|
||||
*/
|
||||
|
||||
import { resolve, sep } from 'path';
|
||||
import { existsSync, realpathSync } from 'fs';
|
||||
import { lstatSync, realpathSync } from 'fs';
|
||||
|
||||
// v0.6.4 死代码清理：networkPolicy / resourceLimits 配置壳已删除。
|
||||
// 原字段被赋值后无任何方法消费（SandboxManager 没有进程沙箱执行器），
|
||||
@@ -43,7 +43,11 @@ export class SandboxManager {
|
||||
const resolved = resolve(requestedPath);
|
||||
|
||||
if (this.allowedPaths.size === 0) {
|
||||
return { allowed: false, resolvedPath: resolved, reason: 'No allowed paths configured (fail-closed)' };
|
||||
return {
|
||||
allowed: false,
|
||||
resolvedPath: resolved,
|
||||
reason: 'No allowed paths configured (fail-closed)',
|
||||
};
|
||||
}
|
||||
|
||||
// 先做字符串级白名单校验
|
||||
@@ -54,9 +58,40 @@ export class SandboxManager {
|
||||
return { allowed: false, resolvedPath: resolved, reason: 'Path not in allowed list' };
|
||||
}
|
||||
|
||||
// 解析符号链接（如果路径存在）
|
||||
if (existsSync(resolved)) {
|
||||
// 解析符号链接（v0.8.1 根治：lstat 判定 —— 旧实现用 existsSync 前置判定，
|
||||
// 而 existsSync 跟随链接目标：悬空 symlink（目标不存在）会跳过 realpath
|
||||
// 校验整体放行，构成白名单逃逸 —— 写操作可在白名单外创建目标文件）。
|
||||
// 现契约：lstat 判定条目存在性（不跟随目标）；符号链接一律 realpath 解析，
|
||||
// 悬空链接（realpath ENOENT）fail-closed 拒绝；普通条目维持既有 realpath 复核。
|
||||
let st: ReturnType<typeof lstatSync> | null = null;
|
||||
try {
|
||||
st = lstatSync(resolved);
|
||||
} catch {
|
||||
st = null; // 条目不存在 → 允许（新建文件场景，行为不变）
|
||||
}
|
||||
if (st) {
|
||||
try {
|
||||
if (st.isSymbolicLink()) {
|
||||
// 悬空 symlink：realpathSync 抛 ENOENT → 显式拒绝（非偶然 catch）
|
||||
let realPath: string;
|
||||
try {
|
||||
realPath = realpathSync(resolved);
|
||||
} catch {
|
||||
return {
|
||||
allowed: false,
|
||||
resolvedPath: resolved,
|
||||
reason: 'Dangling symlink target outside workspace',
|
||||
};
|
||||
}
|
||||
const realAllowed = Array.from(this.allowedPaths).some(
|
||||
(allowed) => realPath === allowed || realPath.startsWith(allowed + sep),
|
||||
);
|
||||
if (!realAllowed) {
|
||||
return { allowed: false, resolvedPath: realPath, reason: 'Symlink escape detected' };
|
||||
}
|
||||
return { allowed: true, resolvedPath: realPath };
|
||||
}
|
||||
// 普通条目：realpath 复核父级链接逃逸（既有行为）
|
||||
const realPath = realpathSync(resolved);
|
||||
const realAllowed = Array.from(this.allowedPaths).some(
|
||||
(allowed) => realPath === allowed || realPath.startsWith(allowed + sep),
|
||||
|
||||
@@ -16,14 +16,24 @@ import type { MemoryManager } from '../../memory/manager';
|
||||
export class MemoryStoreTool implements IMetonaTool {
|
||||
readonly definition: MetonaToolDef = {
|
||||
name: 'memory_store',
|
||||
description: 'Store a piece of information in persistent memory. Useful for remembering important facts, decisions, or user preferences across sessions.',
|
||||
description:
|
||||
'Store a piece of information in persistent memory. Useful for remembering important facts, decisions, or user preferences across sessions.',
|
||||
parameters: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
content: { type: 'string', description: 'The memory content to store' },
|
||||
type: { type: 'string', description: 'Memory type: "episodic" (events), "semantic" (knowledge), or "working" (task state)', enum: ['episodic', 'semantic', 'working'] },
|
||||
type: {
|
||||
type: 'string',
|
||||
description:
|
||||
'Memory type: "episodic" (events), "semantic" (knowledge), or "working" (task state)',
|
||||
enum: ['episodic', 'semantic', 'working'],
|
||||
},
|
||||
importance: { type: 'number', description: 'Importance score 0-1 (default 0.5)' },
|
||||
source: { type: 'string', description: 'Source of the memory', enum: ['user_input', 'tool_result', 'agent_thought', 'imported'] },
|
||||
source: {
|
||||
type: 'string',
|
||||
description: 'Source of the memory',
|
||||
enum: ['user_input', 'tool_result', 'agent_thought', 'imported'],
|
||||
},
|
||||
},
|
||||
required: ['content', 'type'],
|
||||
},
|
||||
@@ -39,7 +49,9 @@ export class MemoryStoreTool implements IMetonaTool {
|
||||
const content = args.content as string;
|
||||
const type = args.type as 'episodic' | 'semantic' | 'working';
|
||||
const importance = (args.importance as number) ?? 0.5;
|
||||
const source = (args.source as 'user_input' | 'tool_result' | 'agent_thought' | 'imported') ?? 'agent_thought';
|
||||
const source =
|
||||
(args.source as 'user_input' | 'tool_result' | 'agent_thought' | 'imported') ??
|
||||
'agent_thought';
|
||||
|
||||
// v0.3.0 修复: store() 是同步方法，移除多余的 await 避免误导维护者
|
||||
const id = this.memoryManager.store({
|
||||
@@ -59,14 +71,23 @@ export class MemoryStoreTool implements IMetonaTool {
|
||||
export class MemorySearchTool implements IMetonaTool {
|
||||
readonly definition: MetonaToolDef = {
|
||||
name: 'memory_search',
|
||||
description: 'Search persistent memory for relevant information. Returns memories sorted by relevance.',
|
||||
description:
|
||||
'Search persistent memory for relevant information. Returns memories sorted by relevance.',
|
||||
parameters: {
|
||||
type: 'object',
|
||||
properties: {
|
||||
query: { type: 'string', description: 'Search query or keywords' },
|
||||
type: { type: 'string', description: 'Filter by memory type', enum: ['episodic', 'semantic', 'working'] },
|
||||
type: {
|
||||
type: 'string',
|
||||
description: 'Filter by memory type',
|
||||
enum: ['episodic', 'semantic', 'working'],
|
||||
},
|
||||
topK: { type: 'number', description: 'Number of results (default 5)' },
|
||||
threshold: { type: 'number', description: 'Minimum importance score 0-1 (default 0.7). Filters memories by importance, not search relevance.' },
|
||||
threshold: {
|
||||
type: 'number',
|
||||
description:
|
||||
'Minimum importance score 0-1 (default 0.7). Filters memories by importance, not search relevance.',
|
||||
},
|
||||
},
|
||||
required: ['query'],
|
||||
},
|
||||
@@ -84,8 +105,8 @@ export class MemorySearchTool implements IMetonaTool {
|
||||
const topK = (args.topK as number) ?? 5;
|
||||
const threshold = (args.threshold as number) ?? 0.7;
|
||||
|
||||
// v0.3.0 修复: search() 是同步方法，移除多余的 await 避免误导维护者
|
||||
const results = this.memoryManager.search(query, {
|
||||
// v0.8.1: search() 升级为 async（向量混合检索），查询向量异步生成
|
||||
const results = await this.memoryManager.search(query, {
|
||||
topK,
|
||||
type,
|
||||
minImportance: threshold,
|
||||
|
||||
@@ -127,7 +127,7 @@ function makeCtx(overrides: Record<string, unknown> = {}) {
|
||||
logSessionEnd: vi.fn(),
|
||||
log: vi.fn(),
|
||||
},
|
||||
memoryManager: { search: vi.fn(() => []) },
|
||||
memoryManager: { search: vi.fn(() => []), clearWorkingMemory: vi.fn() },
|
||||
promptInjectionDefender: {
|
||||
detect: vi.fn(() => ({
|
||||
isInjection: false,
|
||||
|
||||
@@ -0,0 +1,80 @@
|
||||
/**
|
||||
* Replay Buffer 单元测试（v0.8.1 P0-3 模块化收口）
|
||||
*
|
||||
* 锁定有界缓冲契约：条数/字节双上限溢出丢最旧、truncated 标记、runId 记录、
|
||||
* TERMINATED 保留 + 新内容兜底重置、终态清除（会话删除联动）。
|
||||
*/
|
||||
|
||||
import { describe, it, expect, beforeEach } from 'vitest';
|
||||
import {
|
||||
appendReplay,
|
||||
resetReplayBuffer,
|
||||
markReplayTerminated,
|
||||
clearReplayBuffer,
|
||||
getReplayBufferData,
|
||||
} from '../replay-buffer';
|
||||
|
||||
describe('replay-buffer — 有界回放缓冲（v0.8.1 P0-3）', () => {
|
||||
beforeEach(() => {
|
||||
clearReplayBuffer('s1');
|
||||
});
|
||||
|
||||
it('append + get：按序返回事件与 runId', () => {
|
||||
appendReplay('s1', { channel: 'stateChange', payload: { runId: 'run_a' }, ts: 1 });
|
||||
appendReplay('s1', { channel: 'streamEvent', payload: { type: 'text_delta' }, ts: 2 });
|
||||
const data = getReplayBufferData('s1');
|
||||
expect(data.events).toHaveLength(2);
|
||||
expect(data.events[0].channel).toBe('stateChange');
|
||||
expect(data.runId).toBe('run_a');
|
||||
expect(data.truncated).toBe(false);
|
||||
});
|
||||
|
||||
it('条数上限：超过 2000 条丢弃最旧并置 truncated', () => {
|
||||
for (let i = 0; i < 2005; i++) {
|
||||
appendReplay('s1', { channel: 'streamEvent', payload: { i }, ts: i });
|
||||
}
|
||||
const data = getReplayBufferData('s1');
|
||||
expect(data.events.length).toBeLessThanOrEqual(2000);
|
||||
expect(data.truncated).toBe(true);
|
||||
// 最旧的已被丢弃
|
||||
expect((data.events[0].payload as { i: number }).i).toBeGreaterThan(0);
|
||||
});
|
||||
|
||||
it('字节上限：单条 1MB 的超大 payload 被丢弃并置 truncated', () => {
|
||||
const big = 'x'.repeat(1024 * 1024);
|
||||
for (let i = 0; i < 6; i++) {
|
||||
appendReplay('s1', { channel: 'streamEvent', payload: { big }, ts: i });
|
||||
}
|
||||
const data = getReplayBufferData('s1');
|
||||
expect(data.truncated).toBe(true);
|
||||
// 缓冲字节被控制在 4MB 上限附近（至少留最后一条）
|
||||
expect(data.events.length).toBeGreaterThanOrEqual(1);
|
||||
expect(data.events.length).toBeLessThan(6);
|
||||
});
|
||||
|
||||
it('TERMINATED 保留缓冲；新内容到达兜底重置', () => {
|
||||
appendReplay('s1', { channel: 'stateChange', payload: { state: 'TERMINATED' }, ts: 1 });
|
||||
markReplayTerminated('s1');
|
||||
// 完成后仍可回放最终内容
|
||||
expect(getReplayBufferData('s1').events).toHaveLength(1);
|
||||
// 下一次 run 的新内容 → 缓冲重置
|
||||
appendReplay('s1', { channel: 'streamEvent', payload: { type: 'text_delta' }, ts: 2 });
|
||||
const data = getReplayBufferData('s1');
|
||||
expect(data.events).toHaveLength(1);
|
||||
expect(data.truncated).toBe(false);
|
||||
});
|
||||
|
||||
it('INIT 重置：清除上一 run 的缓冲', () => {
|
||||
appendReplay('s1', { channel: 'streamEvent', payload: { a: 1 }, ts: 1 });
|
||||
resetReplayBuffer('s1');
|
||||
expect(getReplayBufferData('s1').events).toHaveLength(0);
|
||||
});
|
||||
|
||||
it('clearReplayBuffer：终态清除（会话删除联动）', () => {
|
||||
appendReplay('s1', { channel: 'streamEvent', payload: { a: 1 }, ts: 1 });
|
||||
clearReplayBuffer('s1');
|
||||
const data = getReplayBufferData('s1');
|
||||
expect(data.events).toHaveLength(0);
|
||||
expect(data.runId).toBeNull();
|
||||
});
|
||||
});
|
||||
@@ -115,7 +115,8 @@ describe('sessions 域 — 参数校验矩阵', () => {
|
||||
|
||||
expect(await getHandler('sessions:getMessages')(null, 42)).toEqual([]);
|
||||
await getHandler('sessions:getMessages')(null, 's1');
|
||||
expect(svc.getMessages).toHaveBeenCalledWith('s1');
|
||||
// v0.8.1 P2-3: 未传选项时以 undefined 透传（全量语义向后兼容）
|
||||
expect(svc.getMessages).toHaveBeenCalledWith('s1', undefined);
|
||||
});
|
||||
|
||||
it('saveTrace 严格校验：traceSteps 必须为数组且 tokenUsage 必填', async () => {
|
||||
@@ -149,6 +150,8 @@ describe('sessions:clearMessages — v0.7.2 A1 语义修正', () => {
|
||||
function makeCtx(): IPCContext {
|
||||
return {
|
||||
sessionService: { clearMessages: vi.fn() },
|
||||
// v0.8.1 P0-2: clearMessages 联动清理工作记忆
|
||||
memoryManager: { clearWorkingMemory: vi.fn() },
|
||||
} as unknown as IPCContext;
|
||||
}
|
||||
|
||||
@@ -442,6 +445,8 @@ describe('sessions 域 — 补充校验矩阵', () => {
|
||||
disposeEngine: vi.fn(),
|
||||
},
|
||||
confirmationHook: { forgetSession: vi.fn() },
|
||||
// v0.8.1 P0-2: delete/purge/clearMessages 联动清理工作记忆
|
||||
memoryManager: { clearWorkingMemory: vi.fn() },
|
||||
};
|
||||
return { ctx: raw as unknown as IPCContext, raw };
|
||||
}
|
||||
|
||||
+58
-103
@@ -29,6 +29,8 @@ import { buildUserContextPrefix, withUserContextPrefix } from '../harness/prompt
|
||||
// v0.7.3 P1-5: 记忆固化触发决策（纯函数）
|
||||
import { shouldConsolidate } from '../harness/memory/consolidation-policy';
|
||||
import log from 'electron-log';
|
||||
// v0.8.1 P0-4: 主进程 toast 文案双语（ui.locale 驱动）
|
||||
import { mt } from '../utils/main-locale';
|
||||
|
||||
/** 构建 "时区名 (UTC±N)" 标签（注入用户上下文前置块；失败回退 UTC） */
|
||||
function buildTimezoneLabel(): string {
|
||||
@@ -67,34 +69,14 @@ interface IterationTrace {
|
||||
finishReason?: string;
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.8.0 P1-1a: 单会话的流式回放缓冲 —— 渲染层切走会话期间，事件管道仍照常
|
||||
* 投递（按会话隔离被渲染层准入拒绝），此缓冲按序保存 streamEvent + stateChange
|
||||
* 双通道事件；用户切回会话时经 agent:getReplayState 拉取并灌入渲染层事件总线，
|
||||
* 实现"后台会话运行内容不丢失"。
|
||||
*
|
||||
* 有界设计：单会话上限 MAX_REPLAY_EVENTS 条 / MAX_REPLAY_BYTES 字节，溢出丢弃
|
||||
* 最旧并置 truncated 标记；TERMINATED 后保留（供"完成后切回"看到最终内容），
|
||||
* 下一次 run 启动（INIT stateChange）时清空重建。
|
||||
*/
|
||||
interface ReplayEntry {
|
||||
channel: 'streamEvent' | 'stateChange';
|
||||
payload: unknown;
|
||||
ts: number;
|
||||
}
|
||||
|
||||
interface ReplayBuffer {
|
||||
events: ReplayEntry[];
|
||||
bytes: number;
|
||||
truncated: boolean;
|
||||
/** 最近一次观察到的 runId（供渲染层回放后对齐 run 守卫） */
|
||||
runId?: string;
|
||||
terminated: boolean;
|
||||
}
|
||||
|
||||
const MAX_REPLAY_EVENTS = 2000;
|
||||
const MAX_REPLAY_BYTES = 4 * 1024 * 1024;
|
||||
const MAX_REPLAY_SESSIONS = 50;
|
||||
// v0.8.1 P0-3: 回放缓冲抽为独立模块（replay-buffer.ts）—— 会话删除/彻底删除
|
||||
// 的终态路径可显式清除缓冲，杜绝已删会话最多 4MB/会话的内存滞留
|
||||
import {
|
||||
appendReplay,
|
||||
resetReplayBuffer,
|
||||
markReplayTerminated,
|
||||
getReplayBufferData,
|
||||
} from './replay-buffer';
|
||||
|
||||
export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
const {
|
||||
@@ -118,54 +100,9 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
// ===== 常驻事件管道：text_delta 按会话节流（F8） =====
|
||||
const throttleStates = new Map<string, ThrottleState>();
|
||||
const iterationTraces = new Map<string, IterationTrace>();
|
||||
// v0.8.0 P1-1a: 每会话流式回放缓冲（后台会话内容恢复）
|
||||
const replayBuffers = new Map<string, ReplayBuffer>();
|
||||
|
||||
/** 追加一条事件到会话回放缓冲（有界：条数/字节双上限，溢出丢最旧） */
|
||||
const appendReplay = (sessionId: string, entry: ReplayEntry): void => {
|
||||
let buf = replayBuffers.get(sessionId);
|
||||
if (!buf) {
|
||||
// 会话数上限保护：超出后淘汰最早的缓冲（Map 迭代序 = 插入序）
|
||||
if (replayBuffers.size >= MAX_REPLAY_SESSIONS) {
|
||||
const oldest = replayBuffers.keys().next().value as string | undefined;
|
||||
if (oldest !== undefined) replayBuffers.delete(oldest);
|
||||
}
|
||||
buf = { events: [], bytes: 0, truncated: false, terminated: false };
|
||||
replayBuffers.set(sessionId, buf);
|
||||
}
|
||||
// run 启动（INIT）后旧 run 缓冲作废 —— 由 stateChange 监听器在 INIT 时清空，
|
||||
// 此处仅在 terminated 缓冲上遇到新内容时兜底重置（正常路径 INIT 先行）
|
||||
if (buf.terminated) {
|
||||
replayBuffers.delete(sessionId);
|
||||
buf = { events: [], bytes: 0, truncated: false, terminated: false };
|
||||
replayBuffers.set(sessionId, buf);
|
||||
}
|
||||
let size = 0;
|
||||
try {
|
||||
size = JSON.stringify(entry.payload).length;
|
||||
} catch {
|
||||
size = 256; // 序列化失败按保守值计入
|
||||
}
|
||||
buf.events.push(entry);
|
||||
buf.bytes += size;
|
||||
while (
|
||||
buf.events.length > MAX_REPLAY_EVENTS ||
|
||||
(buf.bytes > MAX_REPLAY_BYTES && buf.events.length > 1)
|
||||
) {
|
||||
const dropped = buf.events.shift();
|
||||
buf.truncated = true;
|
||||
if (dropped) {
|
||||
try {
|
||||
buf.bytes -= JSON.stringify(dropped.payload).length;
|
||||
} catch {
|
||||
buf.bytes -= 256;
|
||||
}
|
||||
}
|
||||
}
|
||||
// 记录 runId（首个携带者）
|
||||
const payload = entry.payload as { runId?: string } | null;
|
||||
if (!buf.runId && payload?.runId) buf.runId = payload.runId;
|
||||
};
|
||||
// v0.8.1 review: MEMORY.md 体积告警去重（每会话一次）
|
||||
const memoryOversizeWarned = new Set<string>();
|
||||
|
||||
// v0.7.3 P1-5: 会话级记忆固化时间戳（consolidation-policy 频率门控的状态持有方）
|
||||
// v0.7.4 P3-5: LRU 化 —— 旧实现每会话一条永不清除，长期运行后无界增长。
|
||||
@@ -340,12 +277,11 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
if (sessionId) {
|
||||
const stateRaw = data.state ?? data.current ?? '';
|
||||
if (stateRaw === 'INIT') {
|
||||
replayBuffers.delete(sessionId);
|
||||
resetReplayBuffer(sessionId);
|
||||
} else {
|
||||
appendReplay(sessionId, { channel: 'stateChange', payload: data, ts: Date.now() });
|
||||
if (stateRaw === 'TERMINATED') {
|
||||
const buf = replayBuffers.get(sessionId);
|
||||
if (buf) buf.terminated = true;
|
||||
markReplayTerminated(sessionId);
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -434,7 +370,11 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
// toast 通知用户压缩已发生
|
||||
broadcast('toast:show', {
|
||||
type: 'info',
|
||||
message: `上下文压缩: ${data.originalTokens ?? '?'} → ${data.compressedTokens ?? '?'} tokens（节省 ${savedTokens}）`,
|
||||
message: mt('agent.toast.compressed', {
|
||||
original: data.originalTokens ?? '?',
|
||||
compressed: data.compressedTokens ?? '?',
|
||||
saved: savedTokens,
|
||||
}),
|
||||
});
|
||||
// 通过 streamEvent 转发，前端 useAgentStream 监听 'compressed' 类型后更新 store
|
||||
broadcast('agent:streamEvent', {
|
||||
@@ -454,7 +394,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
log.warn(`[AGENT] Dead loop detected at iteration ${data.iteration ?? '?'}`);
|
||||
broadcast('toast:show', {
|
||||
type: 'warning',
|
||||
message: `检测到死循环（第 ${data.iteration ?? '?'} 轮）：连续3轮重复相同工具调用，已自动终止`,
|
||||
message: mt('agent.toast.deadLoop', { iteration: data.iteration ?? '?' }),
|
||||
});
|
||||
});
|
||||
|
||||
@@ -470,7 +410,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
});
|
||||
broadcast('toast:show', {
|
||||
type: 'warning',
|
||||
message: `Provider 故障转移: ${data.from ?? '?'} → ${data.to ?? '?'}（主 Provider 请求失败）`,
|
||||
message: mt('agent.toast.providerSwitched', { from: data.from ?? '?', to: data.to ?? '?' }),
|
||||
});
|
||||
},
|
||||
);
|
||||
@@ -498,7 +438,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
|
||||
if (!sessionId || typeof sessionId !== 'string') {
|
||||
log.warn('[AGENT] sendMessage rejected: invalid sessionId');
|
||||
sendErrorEvent('无效的会话 ID', sessionId ?? '');
|
||||
sendErrorEvent(mt('agent.error.invalidSessionId'), sessionId ?? '');
|
||||
return { success: false, error: 'Invalid sessionId' };
|
||||
}
|
||||
if (
|
||||
@@ -507,7 +447,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
typeof userMessage.content !== 'string'
|
||||
) {
|
||||
log.warn('[AGENT] sendMessage rejected: invalid userMessage');
|
||||
sendErrorEvent('无效的消息格式', sessionId);
|
||||
sendErrorEvent(mt('agent.error.invalidMessage'), sessionId);
|
||||
return { success: false, error: 'Invalid message format' };
|
||||
}
|
||||
log.info('[AGENT] sendMessage:', sessionId, (userMessage.content ?? '').slice(0, 80));
|
||||
@@ -517,7 +457,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
// 排队 30s 后强制 abort 旧 run，用户看到"上一次操作未完成"）。此处直接拒绝并
|
||||
// 广播明确错误事件，前端 isStreaming 正常收尾。
|
||||
if (agentEngineManager.isRunning(sessionId)) {
|
||||
const busyMsg = '该会话正在执行任务，请等待完成或先中断后再发送';
|
||||
const busyMsg = mt('agent.error.sessionBusy');
|
||||
log.warn(`[AGENT] sendMessage rejected: session ${sessionId} is already running`);
|
||||
sendErrorEvent(busyMsg, sessionId);
|
||||
return { success: false, error: busyMsg };
|
||||
@@ -529,7 +469,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
// 此处提前校验并走统一的 sendErrorEvent + stopRecording 收尾路径。
|
||||
const sessionExists = sessionService.getSession(sessionId) != null;
|
||||
if (!sessionExists) {
|
||||
const missingMsg = '会话不存在或已被删除，请刷新后重试';
|
||||
const missingMsg = mt('agent.error.sessionMissing');
|
||||
log.warn(`[AGENT] sendMessage rejected: session ${sessionId} not found`);
|
||||
sendErrorEvent(missingMsg, sessionId);
|
||||
await sessionRecorder.stopRecording(sessionId, {
|
||||
@@ -543,8 +483,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
|
||||
// 发送消息前确保 Adapter 使用最新配置（失败则中止，防止用旧 Provider 的 adapter 发送）
|
||||
if (!ctx.reloadAdapter()) {
|
||||
const errorMsg =
|
||||
'Adapter 加载失败，请检查 LLM 配置（Provider、API Key、Base URL、Model 是否完整）';
|
||||
const errorMsg = mt('agent.error.adapterLoadFailed');
|
||||
log.error('[AGENT]', errorMsg);
|
||||
sendErrorEvent(errorMsg, sessionId);
|
||||
await sessionRecorder.stopRecording(sessionId, {
|
||||
@@ -593,8 +532,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
if (contextBuilder.isUsingFallbackRole()) {
|
||||
broadcast('toast:show', {
|
||||
type: 'info',
|
||||
message:
|
||||
'未找到 SOUL.md 或内容为空，已使用默认 Metona 身份。可在工作空间根目录创建 SOUL.md 自定义 Agent 人格',
|
||||
message: mt('agent.soul.fallbackToast'),
|
||||
});
|
||||
}
|
||||
|
||||
@@ -603,7 +541,8 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
// 每条消息都改变 system 字节 → 跨 run 缓存全 miss。现随首条 user 消息注入
|
||||
// （LLM 语义等价），system prompt 保持跨 run 字节级稳定。
|
||||
try {
|
||||
const memories = memoryManager.search(userMessage.content, {
|
||||
// v0.8.1: search 升级 async（向量混合检索）
|
||||
const memories = await memoryManager.search(userMessage.content, {
|
||||
topK: 5,
|
||||
minImportance: 0.3,
|
||||
});
|
||||
@@ -850,7 +789,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
);
|
||||
broadcast('toast:show', {
|
||||
type: 'info',
|
||||
message: `AI 已将 ${result.appended} 条重要记忆写入 MEMORY.md`,
|
||||
message: mt('agent.toast.consolidated', { count: result.appended }),
|
||||
});
|
||||
}
|
||||
})
|
||||
@@ -861,6 +800,18 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
log.debug(`[AGENT] Memory consolidation skipped (${decision.reason})`);
|
||||
}
|
||||
|
||||
// v0.8.1 review 修复: MEMORY.md 体积告警移出固化分支 —— 每次成功 run 后
|
||||
// 检查（此前仅固化触发时检查，跳过固化的大文件永不告警）；每会话只告警
|
||||
// 一次，避免连续消息刷屏。
|
||||
const memorySize = (workspaceService.getFiles().memory ?? '').length;
|
||||
if (memorySize > 51_200 && !memoryOversizeWarned.has(sessionId)) {
|
||||
memoryOversizeWarned.add(sessionId);
|
||||
broadcast('toast:show', {
|
||||
type: 'warning',
|
||||
message: mt('agent.toast.memoryOversize'),
|
||||
});
|
||||
}
|
||||
|
||||
// v0.7.3 P4-1: 首个完成的 run 之后生成精炼会话标题（每会话幂等，失败静默）
|
||||
if (output.terminationReason === 'completed') {
|
||||
titleGenerator
|
||||
@@ -955,7 +906,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
}
|
||||
const balance = await adapter.getBalance();
|
||||
if (!balance) {
|
||||
return { success: false, error: '余额查询失败（API Key 无效或网络错误）' };
|
||||
return { success: false, error: mt('llm.balance.queryFailed') };
|
||||
}
|
||||
return { success: true, data: balance };
|
||||
} catch (error) {
|
||||
@@ -975,18 +926,18 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
const model = configService.get<string>('llm.model') ?? '';
|
||||
const apiKey = configService.get<string>('llm.apiKey') || '';
|
||||
if (!provider || !model) {
|
||||
return { success: false, error: 'LLM 未配置（Provider/Model 为空），无法获取模型列表' };
|
||||
return { success: false, error: mt('llm.listModels.notConfigured') };
|
||||
}
|
||||
if (!apiKey && provider !== 'ollama') {
|
||||
return { success: false, error: 'API Key 未配置，无法获取模型列表' };
|
||||
return { success: false, error: mt('llm.listModels.noApiKey') };
|
||||
}
|
||||
// 列表基于当前已保存配置 —— 先幂等重载 adapter（配置签名未变时为 no-op）
|
||||
if (!ctx.reloadAdapter()) {
|
||||
return { success: false, error: 'LLM 配置校验失败，请先在设置中修正配置' };
|
||||
return { success: false, error: mt('llm.listModels.configInvalid') };
|
||||
}
|
||||
const adapter = agentEngineManager.getAdapter();
|
||||
if (!adapter.listModels) {
|
||||
return { success: false, error: '当前 Provider 不支持模型列表查询' };
|
||||
return { success: false, error: mt('llm.listModels.unsupported') };
|
||||
}
|
||||
const models = await adapter.listModels();
|
||||
return { success: true, data: models };
|
||||
@@ -1014,10 +965,10 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
}
|
||||
const adapter = agentEngineManager.getAdapter();
|
||||
if (!(adapter instanceof OllamaAdapter)) {
|
||||
return { success: false, error: '仅 Ollama Provider 支持模型下载' };
|
||||
return { success: false, error: mt('llm.pull.ollamaOnly') };
|
||||
}
|
||||
if (ollamaPullController) {
|
||||
return { success: false, error: '已有模型下载任务进行中，请先取消' };
|
||||
return { success: false, error: mt('llm.pull.inProgress') };
|
||||
}
|
||||
const controller = new AbortController();
|
||||
ollamaPullController = controller;
|
||||
@@ -1045,7 +996,7 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
|
||||
ipcMain.handle('llm:ollamaPullCancel', async () => {
|
||||
if (!ollamaPullController) {
|
||||
return { success: false, error: '没有进行中的下载任务' };
|
||||
return { success: false, error: mt('llm.pull.none') };
|
||||
}
|
||||
ollamaPullController.abort();
|
||||
return { success: true };
|
||||
@@ -1071,6 +1022,8 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
// 旧实现只覆盖 taskCompleted/taskError 路径，被 abort 的 SubAgent 残留 Map 条目
|
||||
for (const taskId of abortedTaskIds) {
|
||||
confirmationHook.forgetSession(taskId);
|
||||
// v0.8.1 P0-2: 被中止 SubAgent 的工作记忆一并清理
|
||||
memoryManager.clearWorkingMemory(taskId);
|
||||
subTraces.delete(taskId);
|
||||
subMeta.delete(taskId);
|
||||
// 中止的 SubAgent 录制文件也收尾（TRACE 完整性：标记为中断终止）
|
||||
@@ -1104,14 +1057,14 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
if (typeof sessionId !== 'string' || !sessionId) {
|
||||
return { success: false, error: 'Invalid sessionId' };
|
||||
}
|
||||
const buf = replayBuffers.get(sessionId);
|
||||
const data = getReplayBufferData(sessionId);
|
||||
return {
|
||||
success: true,
|
||||
data: {
|
||||
isRunning: agentEngineManager.isRunning(sessionId),
|
||||
runId: buf?.runId ?? null,
|
||||
truncated: buf?.truncated ?? false,
|
||||
events: buf?.events ?? [],
|
||||
runId: data.runId,
|
||||
truncated: data.truncated,
|
||||
events: data.events,
|
||||
},
|
||||
};
|
||||
});
|
||||
@@ -1151,6 +1104,8 @@ export function registerAgentHandlers(ctx: IPCContext): void {
|
||||
subMeta.delete(taskId);
|
||||
// v0.7.3 P2-3: SubAgent 终态 —— 决策记忆随任务终结清理（防长期运行泄漏）
|
||||
confirmationHook.forgetSession(taskId);
|
||||
// v0.8.1 P0-2: SubAgent 终态联动清理工作记忆（taskId 作为 sessionId 写入）
|
||||
memoryManager.clearWorkingMemory(taskId);
|
||||
};
|
||||
|
||||
orchestrator.on(
|
||||
|
||||
@@ -122,6 +122,32 @@ export function registerAppHandlers(ctx: IPCContext): void {
|
||||
return { canceled: false, path: result.filePaths[0] };
|
||||
});
|
||||
|
||||
// ===== v0.8.1 P2-4: 开机自启（app.setLoginItemSettings，跨平台尽力语义）=====
|
||||
ipcMain.handle('app:setLoginItem', async (_event, enabled: unknown) => {
|
||||
if (typeof enabled !== 'boolean') {
|
||||
return { success: false, error: 'Invalid enabled flag' };
|
||||
}
|
||||
try {
|
||||
app.setLoginItemSettings({ openAtLogin: enabled });
|
||||
// 设置后回读真实状态（Linux 等 setLoginItemSettings 不可用平台保持 false）
|
||||
const actual = app.getLoginItemSettings().openAtLogin;
|
||||
if (enabled && !actual) {
|
||||
log.warn('[APP] setLoginItemSettings not effective on this platform (Linux?)');
|
||||
}
|
||||
return { success: true, data: { openAtLogin: actual } };
|
||||
} catch (error) {
|
||||
return { success: false, error: (error as Error).message };
|
||||
}
|
||||
});
|
||||
|
||||
ipcMain.handle('app:getLoginItem', async () => {
|
||||
try {
|
||||
return { success: true, data: { openAtLogin: app.getLoginItemSettings().openAtLogin } };
|
||||
} catch (error) {
|
||||
return { success: false, error: (error as Error).message };
|
||||
}
|
||||
});
|
||||
|
||||
// 重启应用（工作空间切换后调用）
|
||||
ipcMain.handle('app:restart', async () => {
|
||||
log.info('[APP] Restart requested');
|
||||
|
||||
@@ -13,6 +13,7 @@ import type { WorkspaceService } from '../services/workspace.service';
|
||||
import type { ContextBuilder } from '../harness/prompts/context-builder';
|
||||
import type { AgentEngineManager } from '../services/agent-engine-manager.service';
|
||||
import type { ToolRegistry } from '../harness/tools/registry';
|
||||
import type { PolicyEngine } from '../harness/sandbox/permissions';
|
||||
import type { AuditService } from '../services/audit.service';
|
||||
import type { SessionRecorder } from '../services/session-recorder.service';
|
||||
import type { MemoryManager } from '../harness/memory/manager';
|
||||
@@ -21,6 +22,7 @@ import type { PromptInjectionDefender } from '../harness/security/prompt-injecti
|
||||
import type { OutputValidator } from '../harness/verification/output-validator';
|
||||
import type { ConfirmationHook } from '../harness/hooks/confirmation-hook';
|
||||
import type { MemoryConsolidator } from '../harness/memory/consolidator';
|
||||
import type { MemoryMaintainer } from '../harness/memory/maintainer';
|
||||
import type { TaskOrchestrator } from '../harness/orchestration/orchestrator';
|
||||
import type { SessionSummaryService } from '../services/session-summary.service';
|
||||
import type { TitleGenerator } from '../services/title-generator.service';
|
||||
@@ -49,6 +51,8 @@ export interface IPCContext {
|
||||
contextBuilder: ContextBuilder;
|
||||
agentEngineManager: AgentEngineManager;
|
||||
toolRegistry: ToolRegistry;
|
||||
/** v0.8.1 P2-1: 策略引擎（tools.{name}.policy 用户自定义策略热加载） */
|
||||
policyEngine: PolicyEngine;
|
||||
auditService: AuditService;
|
||||
sessionRecorder: SessionRecorder;
|
||||
memoryManager: MemoryManager;
|
||||
@@ -58,6 +62,8 @@ export interface IPCContext {
|
||||
outputValidator: OutputValidator;
|
||||
confirmationHook: ConfirmationHook;
|
||||
memoryConsolidator: MemoryConsolidator;
|
||||
/** v0.8.1 P1-2: MEMORY.md 维护闭环（memory:analyzeMaintenance / memory:applyMaintenance） */
|
||||
memoryMaintainer: MemoryMaintainer;
|
||||
orchestrator: TaskOrchestrator;
|
||||
sessionSummaryService: SessionSummaryService;
|
||||
/** v0.7.3 P4-1: 会话标题生成器 */
|
||||
|
||||
@@ -107,6 +107,9 @@ export function registerDataHandlers(ctx: IPCContext): void {
|
||||
const sessCount = db.prepare('SELECT COUNT(*) as c FROM sessions').get() as { c: number };
|
||||
db.exec('DELETE FROM messages');
|
||||
db.exec('DELETE FROM sessions');
|
||||
// v0.8.1 P0-2: working_memories 无 sessions 外键（历史 schema），此处显式清理
|
||||
// 防止全量清空后工作记忆成为无主孤数据
|
||||
db.exec('DELETE FROM working_memories');
|
||||
db.exec('COMMIT');
|
||||
log.info(
|
||||
`[DATA] All sessions cleared: ${sessCount.c} sessions, ${msgCount.c} messages deleted`,
|
||||
|
||||
@@ -152,4 +152,60 @@ export function registerMCPHandlers(ctx: IPCContext): void {
|
||||
}
|
||||
return { success: true, data: mcpManager.getServerContents(name) };
|
||||
});
|
||||
|
||||
// ===== v0.8.1 P1-4: Prompt 渲染（prompts/get，ChatInput 斜杠菜单消费） =====
|
||||
ipcMain.handle(
|
||||
'mcp:getPrompt',
|
||||
async (_event, serverName: unknown, promptName: unknown, args: unknown) => {
|
||||
if (typeof serverName !== 'string' || !serverName.trim()) {
|
||||
return { success: false, error: 'Invalid server name' };
|
||||
}
|
||||
if (typeof promptName !== 'string' || !promptName.trim()) {
|
||||
return { success: false, error: 'Invalid prompt name' };
|
||||
}
|
||||
// args 可选，必须是 string→string 扁平对象（MCP prompts/get arguments 契约）
|
||||
let promptArgs: Record<string, string> | undefined;
|
||||
if (args !== undefined && args !== null) {
|
||||
if (!args || typeof args !== 'object' || Array.isArray(args)) {
|
||||
return { success: false, error: 'Invalid arguments' };
|
||||
}
|
||||
promptArgs = {};
|
||||
for (const [k, v] of Object.entries(args as Record<string, unknown>)) {
|
||||
if (typeof v !== 'string') {
|
||||
return { success: false, error: `Argument "${k.slice(0, 40)}" must be a string` };
|
||||
}
|
||||
promptArgs[k] = v;
|
||||
}
|
||||
}
|
||||
try {
|
||||
const result = await mcpManager.getPrompt(serverName, promptName, promptArgs);
|
||||
return { success: true, data: result };
|
||||
} catch (error) {
|
||||
return { success: false, error: error instanceof Error ? error.message : String(error) };
|
||||
}
|
||||
},
|
||||
);
|
||||
|
||||
// ===== v0.8.1 P1-4: Resource 读取（resources/read，@mcp 提及消费；512KB 上限） =====
|
||||
const MAX_RESOURCE_BYTES = 512 * 1024;
|
||||
ipcMain.handle('mcp:readResource', async (_event, serverName: unknown, uri: unknown) => {
|
||||
if (typeof serverName !== 'string' || !serverName.trim()) {
|
||||
return { success: false, error: 'Invalid server name' };
|
||||
}
|
||||
if (typeof uri !== 'string' || !uri.trim()) {
|
||||
return { success: false, error: 'Invalid resource uri' };
|
||||
}
|
||||
try {
|
||||
const result = await mcpManager.readResource(serverName, uri);
|
||||
if (result && result.text.length > MAX_RESOURCE_BYTES) {
|
||||
return {
|
||||
success: true,
|
||||
data: { text: result.text.slice(0, MAX_RESOURCE_BYTES), truncated: true },
|
||||
};
|
||||
}
|
||||
return { success: true, data: { text: result?.text ?? '', truncated: false } };
|
||||
} catch (error) {
|
||||
return { success: false, error: error instanceof Error ? error.message : String(error) };
|
||||
}
|
||||
});
|
||||
}
|
||||
|
||||
+110
-12
@@ -5,11 +5,72 @@
|
||||
import { ipcMain } from 'electron';
|
||||
import type { IPCContext } from './context';
|
||||
import type { MemoryType } from '../harness/memory/manager';
|
||||
import type { MemoryMaintenanceAction } from '../harness/memory/maintainer';
|
||||
import log from 'electron-log';
|
||||
|
||||
const VALID_MEMORY_TYPES: readonly MemoryType[] = ['episodic', 'semantic', 'working'];
|
||||
|
||||
export function registerMemoryHandlers(ctx: IPCContext): void {
|
||||
const { memoryManager, sessionService } = ctx;
|
||||
const { memoryManager, sessionService, memoryMaintainer, auditService } = ctx;
|
||||
|
||||
// ===== v0.8.1 P1-2: MEMORY.md 维护闭环（分析/应用两阶段） =====
|
||||
|
||||
// 阶段一：LLM 分析当前 MEMORY.md → 结构化维护建议（不改任何文件/DB）
|
||||
ipcMain.handle('memory:analyzeMaintenance', async () => {
|
||||
try {
|
||||
const proposal = await memoryMaintainer.analyze();
|
||||
return { success: true, data: proposal };
|
||||
} catch (error) {
|
||||
log.warn('[IPC] memory:analyzeMaintenance failed:', (error as Error).message);
|
||||
return { success: false, error: (error as Error).message };
|
||||
}
|
||||
});
|
||||
|
||||
// 阶段二：应用用户确认（勾选）后的动作 —— 精确匹配校验 + 重写 MEMORY.md +
|
||||
// 同步 semantic_memories + 审计留痕
|
||||
ipcMain.handle('memory:applyMaintenance', async (_event, actions: unknown) => {
|
||||
if (!Array.isArray(actions)) {
|
||||
return { success: false, error: 'Invalid actions: must be an array' };
|
||||
}
|
||||
// 结构校验：只透传合法字段，其余拒绝
|
||||
const valid: MemoryMaintenanceAction[] = [];
|
||||
for (const item of actions.slice(0, 30)) {
|
||||
if (!item || typeof item !== 'object') continue;
|
||||
const a = item as Record<string, unknown>;
|
||||
if (a.action !== 'delete' && a.action !== 'update') continue;
|
||||
if (typeof a.section !== 'string' || typeof a.entry !== 'string') continue;
|
||||
if (a.action === 'update' && typeof a.newEntry !== 'string') continue;
|
||||
valid.push({
|
||||
action: a.action,
|
||||
section: a.section,
|
||||
entry: a.entry,
|
||||
newEntry: typeof a.newEntry === 'string' ? a.newEntry : undefined,
|
||||
reason: typeof a.reason === 'string' ? a.reason : undefined,
|
||||
});
|
||||
}
|
||||
try {
|
||||
const result = memoryMaintainer.apply(valid);
|
||||
auditService.log({
|
||||
sessionId: '',
|
||||
eventType: 'tool_call',
|
||||
actor: 'user',
|
||||
target: 'memory_maintenance',
|
||||
details: { requested: valid.length, applied: result.applied, skipped: result.skipped },
|
||||
outcome: 'success',
|
||||
});
|
||||
return { success: true, data: result };
|
||||
} catch (error) {
|
||||
auditService.log({
|
||||
sessionId: '',
|
||||
eventType: 'error',
|
||||
actor: 'user',
|
||||
target: 'memory_maintenance',
|
||||
details: { error: (error as Error).message },
|
||||
outcome: 'error',
|
||||
});
|
||||
return { success: false, error: (error as Error).message };
|
||||
}
|
||||
});
|
||||
|
||||
ipcMain.handle('db:searchMemories', async (_event, query: unknown, options: unknown) => {
|
||||
// M-37 修复: query 和 options 校验
|
||||
@@ -17,7 +78,12 @@ export function registerMemoryHandlers(ctx: IPCContext): void {
|
||||
return [];
|
||||
}
|
||||
// 构造合法的搜索选项（仅保留已知字段，强制类型安全）
|
||||
const searchOptions: { topK?: number; sessionId?: string; type?: MemoryType; minImportance?: number } = {};
|
||||
const searchOptions: {
|
||||
topK?: number;
|
||||
sessionId?: string;
|
||||
type?: MemoryType;
|
||||
minImportance?: number;
|
||||
} = {};
|
||||
if (options && typeof options === 'object') {
|
||||
const opts = options as Record<string, unknown>;
|
||||
// topK 限制范围 1-100（防止过大查询）
|
||||
@@ -26,12 +92,15 @@ export function registerMemoryHandlers(ctx: IPCContext): void {
|
||||
if (Number.isFinite(topK) && topK >= 1 && topK <= 100) {
|
||||
searchOptions.topK = topK;
|
||||
} else {
|
||||
searchOptions.topK = 10; // 默认值
|
||||
searchOptions.topK = 10; // 默认值
|
||||
}
|
||||
}
|
||||
if (typeof opts.sessionId === 'string') searchOptions.sessionId = opts.sessionId;
|
||||
// type 必须是合法的 MemoryType 枚举值
|
||||
if (typeof opts.type === 'string' && (VALID_MEMORY_TYPES as readonly string[]).includes(opts.type)) {
|
||||
if (
|
||||
typeof opts.type === 'string' &&
|
||||
(VALID_MEMORY_TYPES as readonly string[]).includes(opts.type)
|
||||
) {
|
||||
searchOptions.type = opts.type as MemoryType;
|
||||
}
|
||||
if (typeof opts.minImportance === 'number' && Number.isFinite(opts.minImportance)) {
|
||||
@@ -52,7 +121,10 @@ export function registerMemoryHandlers(ctx: IPCContext): void {
|
||||
const opts = options as Record<string, unknown>;
|
||||
if (opts.type !== undefined) {
|
||||
if (typeof opts.type !== 'string' || !VALID_MEMORY_TYPES_LIST.includes(opts.type)) {
|
||||
return { success: false, error: `Invalid type (must be one of: ${VALID_MEMORY_TYPES_LIST.join(', ')})` };
|
||||
return {
|
||||
success: false,
|
||||
error: `Invalid type (must be one of: ${VALID_MEMORY_TYPES_LIST.join(', ')})`,
|
||||
};
|
||||
}
|
||||
type = opts.type;
|
||||
}
|
||||
@@ -69,16 +141,34 @@ export function registerMemoryHandlers(ctx: IPCContext): void {
|
||||
const results: Record<string, unknown[]> = {};
|
||||
try {
|
||||
if (!type || type === 'episodic') {
|
||||
const rows = db.prepare('SELECT * FROM episodic_memories ORDER BY created_at DESC LIMIT ?').all(limit) as Array<Record<string, unknown>>;
|
||||
const rows = db
|
||||
.prepare('SELECT * FROM episodic_memories ORDER BY created_at DESC LIMIT ?')
|
||||
.all(limit) as Array<Record<string, unknown>>;
|
||||
results.episodic = rows.map((r) => ({ ...r, type: 'episodic', content: r.content ?? '' }));
|
||||
}
|
||||
if (!type || type === 'semantic') {
|
||||
const rows = db.prepare('SELECT * FROM semantic_memories ORDER BY updated_at DESC LIMIT ?').all(limit) as Array<Record<string, unknown>>;
|
||||
results.semantic = rows.map((r) => ({ ...r, type: 'semantic', content: r.value ?? r.key ?? '', importance: r.confidence ?? 0, created_at: r.created_at ?? r.updated_at }));
|
||||
const rows = db
|
||||
.prepare('SELECT * FROM semantic_memories ORDER BY updated_at DESC LIMIT ?')
|
||||
.all(limit) as Array<Record<string, unknown>>;
|
||||
results.semantic = rows.map((r) => ({
|
||||
...r,
|
||||
type: 'semantic',
|
||||
content: r.value ?? r.key ?? '',
|
||||
importance: r.confidence ?? 0,
|
||||
created_at: r.created_at ?? r.updated_at,
|
||||
}));
|
||||
}
|
||||
if (!type || type === 'working') {
|
||||
const rows = db.prepare('SELECT * FROM working_memories ORDER BY updated_at DESC LIMIT ?').all(limit) as Array<Record<string, unknown>>;
|
||||
results.working = rows.map((r) => ({ ...r, type: 'working', content: r.value ?? r.key ?? '', importance: 0.5, created_at: r.updated_at ?? Date.now() }));
|
||||
const rows = db
|
||||
.prepare('SELECT * FROM working_memories ORDER BY updated_at DESC LIMIT ?')
|
||||
.all(limit) as Array<Record<string, unknown>>;
|
||||
results.working = rows.map((r) => ({
|
||||
...r,
|
||||
type: 'working',
|
||||
content: r.value ?? r.key ?? '',
|
||||
importance: 0.5,
|
||||
created_at: r.updated_at ?? Date.now(),
|
||||
}));
|
||||
}
|
||||
return { success: true, data: results };
|
||||
} catch (error) {
|
||||
@@ -89,14 +179,22 @@ export function registerMemoryHandlers(ctx: IPCContext): void {
|
||||
ipcMain.handle('memory:delete', async (_event, type: unknown, id: unknown) => {
|
||||
// M-50 修复: 校验 type 枚举（防止三元表达式默认映射到 working_memories）和 id 类型
|
||||
if (typeof type !== 'string' || !VALID_MEMORY_TYPES_LIST.includes(type)) {
|
||||
return { success: false, error: `Invalid type (must be one of: ${VALID_MEMORY_TYPES_LIST.join(', ')})` };
|
||||
return {
|
||||
success: false,
|
||||
error: `Invalid type (must be one of: ${VALID_MEMORY_TYPES_LIST.join(', ')})`,
|
||||
};
|
||||
}
|
||||
if (typeof id !== 'string' || !id) {
|
||||
return { success: false, error: 'Invalid memory id' };
|
||||
}
|
||||
const db = sessionService.getDB();
|
||||
try {
|
||||
const table = type === 'episodic' ? 'episodic_memories' : type === 'semantic' ? 'semantic_memories' : 'working_memories';
|
||||
const table =
|
||||
type === 'episodic'
|
||||
? 'episodic_memories'
|
||||
: type === 'semantic'
|
||||
? 'semantic_memories'
|
||||
: 'working_memories';
|
||||
db.prepare(`DELETE FROM ${table} WHERE id = ?`).run(id);
|
||||
return { success: true };
|
||||
} catch (error) {
|
||||
|
||||
@@ -0,0 +1,112 @@
|
||||
/**
|
||||
* Replay Buffer — 每会话流式回放缓冲（v0.8.0 P1-1a 引入；v0.8.1 P0-3 模块化收口）
|
||||
*
|
||||
* 渲染层切走会话期间，事件管道仍照常投递（按会话隔离被渲染层准入拒绝），
|
||||
* 此缓冲按序保存 streamEvent + stateChange 双通道事件；用户切回会话时经
|
||||
* agent:getReplayState 拉取并灌入渲染层事件总线，实现"后台会话运行内容不丢失"。
|
||||
*
|
||||
* v0.8.1 P0-3 根治：缓冲原为 ipc/agent.ts 内部 Map —— 会话删除/彻底删除时无
|
||||
* 联动清理，仅靠 50 会话 LRU 兜底，已删会话的缓冲（最多 4MB/会话）滞留内存。
|
||||
* 现抽为独立模块，sessions:delete / sessions:purge 终态路径显式清除。
|
||||
*
|
||||
* 有界设计：单会话上限 MAX_REPLAY_EVENTS 条 / MAX_REPLAY_BYTES 字节，溢出丢弃
|
||||
* 最旧并置 truncated 标记；TERMINATED 后保留（供"完成后切回"看到最终内容），
|
||||
* 下一次 run 启动（INIT stateChange）时清空重建。
|
||||
*/
|
||||
|
||||
const MAX_REPLAY_EVENTS = 2000;
|
||||
const MAX_REPLAY_BYTES = 4 * 1024 * 1024;
|
||||
const MAX_REPLAY_SESSIONS = 50;
|
||||
|
||||
export interface ReplayEntry {
|
||||
channel: 'streamEvent' | 'stateChange';
|
||||
payload: unknown;
|
||||
ts: number;
|
||||
}
|
||||
|
||||
export interface ReplayBuffer {
|
||||
events: ReplayEntry[];
|
||||
bytes: number;
|
||||
truncated: boolean;
|
||||
/** 最近一次观察到的 runId（供渲染层回放后对齐 run 守卫） */
|
||||
runId?: string;
|
||||
terminated: boolean;
|
||||
}
|
||||
|
||||
const replayBuffers = new Map<string, ReplayBuffer>();
|
||||
|
||||
/** 追加一条事件到会话回放缓冲（有界：条数/字节双上限，溢出丢最旧） */
|
||||
export function appendReplay(sessionId: string, entry: ReplayEntry): void {
|
||||
let buf = replayBuffers.get(sessionId);
|
||||
if (!buf) {
|
||||
// 会话数上限保护：超出后淘汰最早的缓冲（Map 迭代序 = 插入序）
|
||||
if (replayBuffers.size >= MAX_REPLAY_SESSIONS) {
|
||||
const oldest = replayBuffers.keys().next().value as string | undefined;
|
||||
if (oldest !== undefined) replayBuffers.delete(oldest);
|
||||
}
|
||||
buf = { events: [], bytes: 0, truncated: false, terminated: false };
|
||||
replayBuffers.set(sessionId, buf);
|
||||
}
|
||||
// run 启动（INIT）后旧 run 缓冲作废 —— 由 stateChange 监听器在 INIT 时清空，
|
||||
// 此处仅在 terminated 缓冲上遇到新内容时兜底重置（正常路径 INIT 先行）
|
||||
if (buf.terminated) {
|
||||
replayBuffers.delete(sessionId);
|
||||
buf = { events: [], bytes: 0, truncated: false, terminated: false };
|
||||
replayBuffers.set(sessionId, buf);
|
||||
}
|
||||
let size = 0;
|
||||
try {
|
||||
size = JSON.stringify(entry.payload).length;
|
||||
} catch {
|
||||
size = 256; // 序列化失败按保守值计入
|
||||
}
|
||||
buf.events.push(entry);
|
||||
buf.bytes += size;
|
||||
while (
|
||||
buf.events.length > MAX_REPLAY_EVENTS ||
|
||||
(buf.bytes > MAX_REPLAY_BYTES && buf.events.length > 1)
|
||||
) {
|
||||
const dropped = buf.events.shift();
|
||||
buf.truncated = true;
|
||||
if (dropped) {
|
||||
try {
|
||||
buf.bytes -= JSON.stringify(dropped.payload).length;
|
||||
} catch {
|
||||
buf.bytes -= 256;
|
||||
}
|
||||
}
|
||||
}
|
||||
// 记录 runId（首个携带者）
|
||||
const payload = entry.payload as { runId?: string } | null;
|
||||
if (!buf.runId && payload?.runId) buf.runId = payload.runId;
|
||||
}
|
||||
|
||||
/** run 启动（INIT）时清空上一 run 的缓冲 */
|
||||
export function resetReplayBuffer(sessionId: string): void {
|
||||
replayBuffers.delete(sessionId);
|
||||
}
|
||||
|
||||
/** TERMINATED 时置终态标记（保留缓冲供"完成后切回"回放最终内容） */
|
||||
export function markReplayTerminated(sessionId: string): void {
|
||||
const buf = replayBuffers.get(sessionId);
|
||||
if (buf) buf.terminated = true;
|
||||
}
|
||||
|
||||
/** v0.8.1 P0-3: 会话终态（删除/彻底删除）时清除缓冲，杜绝内存滞留 */
|
||||
export function clearReplayBuffer(sessionId: string): void {
|
||||
replayBuffers.delete(sessionId);
|
||||
}
|
||||
|
||||
/** 拉取会话回放状态（agent:getReplayState 消费） */
|
||||
export function getReplayBufferData(sessionId: string): {
|
||||
runId: string | null;
|
||||
truncated: boolean;
|
||||
events: ReplayEntry[];
|
||||
} {
|
||||
const buf = replayBuffers.get(sessionId);
|
||||
return {
|
||||
runId: buf?.runId ?? null,
|
||||
truncated: buf?.truncated ?? false,
|
||||
events: buf?.events ?? [],
|
||||
};
|
||||
}
|
||||
@@ -9,6 +9,8 @@ import { ipcMain } from 'electron';
|
||||
import { join, resolve as resolvePath, sep } from 'path';
|
||||
import { existsSync, readdirSync, readFileSync, statSync } from 'fs';
|
||||
import type { IPCContext } from './context';
|
||||
// v0.8.1 P0-3: 会话终态清除流式回放缓冲
|
||||
import { clearReplayBuffer } from './replay-buffer';
|
||||
|
||||
/** M-34 修复: 统一 sessionId 校验辅助函数 */
|
||||
const isValidSessionId = (id: unknown): id is string =>
|
||||
@@ -54,6 +56,11 @@ export function registerSessionHandlers(ctx: IPCContext): void {
|
||||
// （防 rememberedDecisions 随会话数累积泄漏）
|
||||
if (result.success) {
|
||||
confirmationHook.forgetSession(sessionId);
|
||||
// v0.8.1 P0-2: 会话终态联动清理工作记忆（此前 clearWorkingMemory 零调用方，
|
||||
// working_memories 随会话数永久残留）
|
||||
ctx.memoryManager.clearWorkingMemory(sessionId);
|
||||
// v0.8.1 P0-3: 清除该会话的流式回放缓冲（最多 4MB/会话）
|
||||
clearReplayBuffer(sessionId);
|
||||
// v0.7.4 P3-5: 显式销毁引擎与 adapter 实例（内存收口）
|
||||
// —— 旧实现只靠 LRU 上限 30 淘汰，会话删除后引擎仍驻留
|
||||
agentEngineManager.disposeEngine(sessionId);
|
||||
@@ -78,14 +85,35 @@ export function registerSessionHandlers(ctx: IPCContext): void {
|
||||
if (result.success) {
|
||||
confirmationHook.forgetSession(sessionId);
|
||||
agentEngineManager.disposeEngine(sessionId);
|
||||
// v0.8.1 P0-2: 终态联动清理工作记忆
|
||||
ctx.memoryManager.clearWorkingMemory(sessionId);
|
||||
// v0.8.1 P0-3: 清除该会话的流式回放缓冲
|
||||
clearReplayBuffer(sessionId);
|
||||
}
|
||||
return result;
|
||||
});
|
||||
|
||||
ipcMain.handle('sessions:getMessages', async (_event, sessionId: unknown) => {
|
||||
ipcMain.handle('sessions:getMessages', async (_event, sessionId: unknown, options?: unknown) => {
|
||||
// M-34 修复: 校验 sessionId
|
||||
if (!isValidSessionId(sessionId)) return [];
|
||||
return sessionService.getMessages(sessionId);
|
||||
// v0.8.1 P2-3: 游标分页选项（limit 1-1000 / beforeRowid 正整数），非法值忽略
|
||||
let safeOptions: { limit?: number; beforeRowid?: number } | undefined;
|
||||
if (options && typeof options === 'object') {
|
||||
const o = options as Record<string, unknown>;
|
||||
safeOptions = {};
|
||||
if (typeof o.limit === 'number' && Number.isFinite(o.limit) && o.limit >= 1) {
|
||||
safeOptions.limit = Math.min(1000, Math.floor(o.limit));
|
||||
}
|
||||
if (
|
||||
typeof o.beforeRowid === 'number' &&
|
||||
Number.isFinite(o.beforeRowid) &&
|
||||
o.beforeRowid > 0
|
||||
) {
|
||||
safeOptions.beforeRowid = Math.floor(o.beforeRowid);
|
||||
}
|
||||
if (Object.keys(safeOptions).length === 0) safeOptions = undefined;
|
||||
}
|
||||
return sessionService.getMessages(sessionId, safeOptions);
|
||||
});
|
||||
|
||||
// v0.7.4 P3-10: 更新单条用户消息内容（编辑"仅保存"落库）。
|
||||
@@ -129,6 +157,8 @@ export function registerSessionHandlers(ctx: IPCContext): void {
|
||||
if (!isValidSessionId(sessionId)) return { success: false, error: 'Invalid sessionId' };
|
||||
try {
|
||||
sessionService.clearMessages(sessionId);
|
||||
// v0.8.1 P0-2: 清空会话 = 会话内容重置 —— 工作记忆一并清理
|
||||
ctx.memoryManager.clearWorkingMemory(sessionId);
|
||||
return { success: true };
|
||||
} catch (error) {
|
||||
return { success: false, error: (error as Error).message };
|
||||
|
||||
+58
-14
@@ -10,11 +10,15 @@ import log from 'electron-log';
|
||||
import type { IPCContext } from './context';
|
||||
import { broadcast } from './context';
|
||||
import { isSensitiveConfigKey } from '../utils/secure-config';
|
||||
// v0.8.1 P2-1: 工具自定义策略解析
|
||||
import { parseToolPolicy } from '../harness/sandbox/permissions';
|
||||
// v0.8.0 P1-5: 配置 URL 深校验（域名真实 DNS 解析，拦"解析到云元数据 IP"绕过）
|
||||
import {
|
||||
assertSafeConfigTargetDeep,
|
||||
DeepCheckSoftFailure,
|
||||
} from '../harness/tools/built-in/ssrf-guard';
|
||||
// v0.8.1 P0-4: 主进程文案双语
|
||||
import { setMainLocale, mt } from '../utils/main-locale';
|
||||
|
||||
/**
|
||||
* v0.7.4 P2-9-C: URL 类配置键 —— 写入时须过 assertSafeConfigTarget 高危目标校验。
|
||||
@@ -65,16 +69,12 @@ export const LLM_CONFIG_KEYS = [
|
||||
'llm.model',
|
||||
'llm.apiKey',
|
||||
'llm.baseURL',
|
||||
// v0.8.1: 全局单一「上下文长度」—— 重建 adapter（携带新窗口）+ 引擎配置同步
|
||||
'llm.contextWindow',
|
||||
'llm.fallbackProvider',
|
||||
'llm.fallbackModel',
|
||||
'llm.fallbackApiKey',
|
||||
'llm.fallbackBaseURL',
|
||||
'ollama.numCtx',
|
||||
'deepseek.contextWindow',
|
||||
'agnes.contextWindow',
|
||||
'mimo.contextWindow',
|
||||
'openai.contextWindow',
|
||||
'anthropic.contextWindow',
|
||||
];
|
||||
|
||||
/** 敏感配置值脱敏（审计日志用：长值保留后 4 位，短值完全掩码） */
|
||||
@@ -129,7 +129,7 @@ export function clearApiKeyOnProviderChange(
|
||||
* 应用单条配置的引擎/编排器副作用（Engine/Orchestrator/ConfirmationHook 同步）
|
||||
*/
|
||||
export function applyEngineConfigKey(ctx: IPCContext, key: string, value: unknown): void {
|
||||
const { agentEngineManager, orchestrator, confirmationHook } = ctx;
|
||||
const { agentEngineManager, orchestrator, confirmationHook, configService } = ctx;
|
||||
switch (key) {
|
||||
case 'agent.maxIterations':
|
||||
agentEngineManager.updateConfigAll({ maxIterations: value as number });
|
||||
@@ -162,8 +162,15 @@ export function applyEngineConfigKey(ctx: IPCContext, key: string, value: unknow
|
||||
orchestrator.updateDefaultConfig({ temperature: Number(value) });
|
||||
break;
|
||||
case 'llm.maxTokens':
|
||||
agentEngineManager.updateConfigAll({ maxTokens: Number(value) });
|
||||
orchestrator.updateDefaultConfig({ maxTokens: Number(value) });
|
||||
// v0.8.1 review 修复: null/空串 = 用户清空「最大输出上限」→ 未配置语义
|
||||
//（undefined 下发，由 Provider 服务端默认值决定）；此前 Number(null)=0
|
||||
// 会把引擎预算清零。
|
||||
agentEngineManager.updateConfigAll({
|
||||
maxTokens: value == null || value === '' ? undefined : Number(value) || undefined,
|
||||
});
|
||||
orchestrator.updateDefaultConfig({
|
||||
maxTokens: value == null || value === '' ? undefined : Number(value) || undefined,
|
||||
});
|
||||
break;
|
||||
case 'agent.toolExecutionTimeoutMs':
|
||||
agentEngineManager.updateConfigAll({ toolExecutionTimeoutMs: value as number });
|
||||
@@ -172,17 +179,34 @@ export function applyEngineConfigKey(ctx: IPCContext, key: string, value: unknow
|
||||
confirmationHook.setConfirmationTimeout(value as number);
|
||||
break;
|
||||
case 'ollama.numCtx':
|
||||
// v0.8.1: ollama.numCtx 已废除 —— 与「上下文长度」合并为 llm.contextWindow。
|
||||
// 保留此分支仅为兼容存量库中的遗留键写入（迁移 12 已清理），行为同 llm.contextWindow。
|
||||
agentEngineManager.updateConfigAll({ contextLength: (value as number) || undefined });
|
||||
orchestrator.updateDefaultConfig({ contextLength: (value as number) || undefined });
|
||||
break;
|
||||
// v0.8.1: 全局单一「上下文长度」—— 替代旧的分 Provider contextWindow 键与
|
||||
// ollama.numCtx。Ollama Provider 下同时作为 num_ctx（contextLength）下发。
|
||||
case 'llm.contextWindow': {
|
||||
const ctx = (value as number) || undefined;
|
||||
const isOllama = (configService.get<string>('llm.provider') ?? '') === 'ollama';
|
||||
agentEngineManager.updateConfigAll({
|
||||
contextWindow: ctx,
|
||||
contextLength: isOllama ? ctx : undefined,
|
||||
});
|
||||
orchestrator.updateDefaultConfig({
|
||||
contextWindow: ctx,
|
||||
contextLength: isOllama ? ctx : undefined,
|
||||
});
|
||||
break;
|
||||
}
|
||||
case 'deepseek.contextWindow':
|
||||
case 'agnes.contextWindow':
|
||||
case 'mimo.contextWindow':
|
||||
case 'openai.contextWindow':
|
||||
case 'anthropic.contextWindow':
|
||||
// reloadAdapter 已重建 adapter 并同步 contextWindow，此处确保 Engine 配置同步（兜底）
|
||||
agentEngineManager.updateConfigAll({ contextWindow: (value as number) || undefined });
|
||||
orchestrator.updateDefaultConfig({ contextWindow: (value as number) || undefined });
|
||||
// v0.8.1: 分 Provider 键已废除 —— 兼容分支收敛为空操作（迁移 12 已清理
|
||||
// 存量库；若前端旧版本仍写入，静默忽略以防双源语义复活）。
|
||||
log.warn(`[CONFIG] Deprecated provider contextWindow key ignored: ${key}`);
|
||||
break;
|
||||
default:
|
||||
break;
|
||||
@@ -208,13 +232,27 @@ export async function applyConfigSideEffects(
|
||||
if (entries.some((e) => LLM_CONFIG_KEYS.includes(e.key))) {
|
||||
if (!ctx.reloadAdapter()) {
|
||||
log.warn('[CONFIG] Adapter reload failed after config save');
|
||||
return 'LLM 配置不完整，请检查 Provider、API Key、Base URL 和 Model 是否都已填写';
|
||||
return mt('config.error.configIncomplete');
|
||||
}
|
||||
}
|
||||
|
||||
// 2. Engine/Orchestrator/ConfirmationHook 配置同步
|
||||
for (const { key, value } of entries) {
|
||||
applyEngineConfigKey(ctx, key, value);
|
||||
// v0.8.1 P2-1: 工具自定义策略热加载（tools.{name}.policy，JSON 字符串）
|
||||
const m = /^tools\.(.+)\.policy$/.exec(key);
|
||||
if (m) {
|
||||
const toolName = m[1];
|
||||
const known = ctx.toolRegistry.listAllTools().some((t) => t.name === toolName);
|
||||
if (known) {
|
||||
const parsed =
|
||||
typeof value === 'string' && value.trim() !== '' ? parseToolPolicy(value) : null;
|
||||
ctx.policyEngine.setPolicyOverride(toolName, parsed);
|
||||
if (typeof value === 'string' && value.trim() !== '' && !parsed) {
|
||||
log.warn(`[Policy] Ignored invalid policy config for tool: ${toolName}`);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// 3. 日志级别即时应用
|
||||
@@ -255,7 +293,7 @@ export async function applyConfigSideEffects(
|
||||
} catch (err) {
|
||||
log.error('[CONFIG] Failed to save workspace path:', err);
|
||||
// v0.3.10: 写入失败必须告知用户，否则下次启动仍使用旧路径
|
||||
return `工作空间路径保存失败：${(err as Error).message}`;
|
||||
return mt('config.error.workspaceSaveFailed', { message: (err as Error).message });
|
||||
}
|
||||
}
|
||||
|
||||
@@ -266,6 +304,12 @@ export async function applyConfigSideEffects(
|
||||
await applySessionProxy(typeof proxyValue === 'string' ? proxyValue : null);
|
||||
}
|
||||
|
||||
// v0.8.1 P0-4: 界面语言变更 → 主进程 toast/通知语言热切换（无需重启）
|
||||
const localeEntry = entries.find((e) => e.key === 'ui.locale');
|
||||
if (localeEntry && typeof localeEntry.value === 'string') {
|
||||
setMainLocale(localeEntry.value);
|
||||
}
|
||||
|
||||
// v0.7.3 P4-2: MCP 自动重连开关变更 → 即时联动（关闭时取消全部已排程重连）
|
||||
const autoReconnectEntry = entries.find((e) => e.key === 'mcp.autoReconnect');
|
||||
if (autoReconnectEntry) {
|
||||
|
||||
+132
-48
@@ -36,6 +36,7 @@ import { TitleGenerator } from './services/title-generator.service';
|
||||
import { ContextBuilder } from './harness/prompts/context-builder';
|
||||
import { MemoryManager } from './harness/memory/manager';
|
||||
import { MemoryConsolidator } from './harness/memory/consolidator';
|
||||
import { MemoryMaintainer } from './harness/memory/maintainer';
|
||||
import { registerAllIPCHandlers } from './ipc';
|
||||
import type { ToolsReadyRef } from './ipc';
|
||||
import { ToolRegistry } from './harness/tools/registry';
|
||||
@@ -85,12 +86,14 @@ import {
|
||||
SecurityScanHook,
|
||||
} from './harness/hooks';
|
||||
import { ConfirmationHook } from './harness/hooks/confirmation-hook';
|
||||
import { PolicyEngine } from './harness/sandbox/permissions';
|
||||
import { PolicyEngine, parseToolPolicy } from './harness/sandbox/permissions';
|
||||
import { SandboxManager } from './harness/sandbox/sandbox';
|
||||
import { PromptInjectionDefender } from './harness/security/prompt-injection-defense';
|
||||
import { OutputValidator } from './harness/verification/output-validator';
|
||||
import { TaskOrchestrator } from './harness/orchestration/orchestrator';
|
||||
import { HealthChecker, SLOMonitor } from './utils/slo';
|
||||
// v0.8.1 P0-4: 主进程 toast/通知文案双语（ui.locale 驱动）
|
||||
import { setMainLocale, mt } from './utils/main-locale';
|
||||
// v0.6.4 P4-5: session 级网络代理应用工具（default + agent-browser 分区）
|
||||
|
||||
// ===== 步骤 1: 初始化日志系统（SYS 层）=====
|
||||
@@ -117,6 +120,16 @@ app.on('child-process-gone', (_event, details) => {
|
||||
);
|
||||
});
|
||||
|
||||
// ===== E2E 测试引导（v0.8.1 P2-5）=====
|
||||
// METONA_USER_DATA_DIR：重定向 userData（workspace-config.json / 全局配置层），
|
||||
// 使 Playwright 冒烟测试与真实用户数据完全隔离；
|
||||
// METONA_E2E_SEED_CONFIG：跳过引导向导并写入一组确定性的合法 LLM 配置
|
||||
//（Provider/Model/输出上限/上下文长度 —— 全部为设置面板合法配置项），
|
||||
// 指向 e2e/mock-llm.ts 启动的本地 OpenAI 兼容服务。仅在显式设置时生效。
|
||||
if (process.env['METONA_USER_DATA_DIR']) {
|
||||
app.setPath('userData', process.env['METONA_USER_DATA_DIR']);
|
||||
}
|
||||
|
||||
// ===== 工作空间路径独立存储（解决 DB 在 workspace 内的鸡生蛋问题）=====
|
||||
const WORKSPACE_CONFIG_FILE = join(app.getPath('userData'), 'workspace-config.json');
|
||||
|
||||
@@ -204,6 +217,21 @@ async function initialize(): Promise<void> {
|
||||
// 注入全局配置层：读取时回退到全局 JSON，写入时双写
|
||||
configService.setGlobalConfig(globalConfigService);
|
||||
|
||||
// v0.8.1 P0-4: 主进程语言随 ui.locale 注入（变更经 applyConfigSideEffects 热切换）
|
||||
setMainLocale(configService.get<string>('ui.locale'));
|
||||
|
||||
// v0.8.1 P2-5: E2E 引导种子（见文件顶部说明；仅 METONA_E2E_SEED_CONFIG 时生效）
|
||||
if (process.env['METONA_E2E_SEED_CONFIG'] === '1') {
|
||||
configService.set('onboarding.completed', true);
|
||||
configService.set('llm.provider', 'deepseek');
|
||||
configService.set('llm.model', 'deepseek-v4-flash');
|
||||
configService.set('llm.baseURL', process.env['METONA_E2E_LLM_URL'] ?? 'http://127.0.0.1:0');
|
||||
configService.set('llm.apiKey', 'e2e-test-key');
|
||||
configService.set('llm.maxTokens', 2048);
|
||||
configService.set('llm.contextWindow', 32768);
|
||||
log.info('[E2E] Seeded deterministic LLM config for smoke test');
|
||||
}
|
||||
|
||||
// v0.3.17 迁移: 首次启用全局配置层时，把工作空间 DB 中的全局配置同步到全局 JSON
|
||||
// 幂等设计：migrateFromWorkspaceDB 仅写入全局层不存在的 key
|
||||
const configRows = db.prepare('SELECT key, value FROM app_config').all() as Array<{
|
||||
@@ -265,11 +293,10 @@ async function initialize(): Promise<void> {
|
||||
return null;
|
||||
}
|
||||
|
||||
// 读取 Provider 对应的 contextWindow 配置（Ollama 不使用此字段）
|
||||
const contextWindow =
|
||||
provider !== 'ollama'
|
||||
? (configService.get<number>(`${provider}.contextWindow`) ?? undefined)
|
||||
: undefined;
|
||||
// v0.8.1 硬性契约: 上下文窗口唯一合法来源是设置面板「上下文长度」
|
||||
// （llm.contextWindow，全局单一配置，适用于一切 Provider/模型）。
|
||||
// 删除了旧的 deepseek/agnes/mimo/openai/anthropic.contextWindow 分 Provider 键。
|
||||
const contextWindow = configService.get<number>('llm.contextWindow') ?? undefined;
|
||||
|
||||
const adapterConfig = { provider, baseURL, apiKey, defaultModel: model, contextWindow };
|
||||
switch (provider) {
|
||||
@@ -288,13 +315,14 @@ async function initialize(): Promise<void> {
|
||||
}
|
||||
};
|
||||
|
||||
// 配置未就绪时的 fallback adapter — getContextWindow 返回安全值，send/sendStream 会报错但前端可见
|
||||
// 配置未就绪时的 fallback adapter —— send/sendStream 会报错但前端可见。
|
||||
// v0.8.1: 不再携带写死的 contextWindow（4096）—— 未配置时 getContextWindow
|
||||
// 返回 0，压缩判定跳过；该 adapter 仅在 LLM 配置缺失时兜底存在。
|
||||
const FALLBACK_ADAPTER = new DeepSeekAdapter({
|
||||
provider: '',
|
||||
baseURL: '',
|
||||
apiKey: '',
|
||||
defaultModel: '',
|
||||
contextWindow: 4096,
|
||||
});
|
||||
|
||||
// ===== 步骤 5: 工作空间文件 + System Prompt =====
|
||||
@@ -429,15 +457,23 @@ async function initialize(): Promise<void> {
|
||||
}
|
||||
|
||||
// ===== P2-10: Agent Engine Manager（每会话独立引擎，替代全局单引擎） =====
|
||||
// v0.8.1: contextWindow / maxTokens 唯一来源是设置面板 llm.contextWindow / llm.maxTokens；
|
||||
// Ollama 的 num_ctx（contextLength）与「上下文长度」同源 —— 同一设置同时驱动
|
||||
// 压缩预算与 num_ctx 下发，不再存在独立的 ollama.numCtx 配置键。
|
||||
const buildAdapter = (): IMetonaProviderAdapter => createAdapter() ?? FALLBACK_ADAPTER;
|
||||
const ollamaNumCtx = configService.get<number>('ollama.numCtx');
|
||||
const getContextWindowSetting = (): number | undefined =>
|
||||
configService.get<number>('llm.contextWindow') ?? undefined;
|
||||
const getOllamaContextLength = (): number | undefined =>
|
||||
(configService.get<string>('llm.provider') ?? '') === 'ollama'
|
||||
? getContextWindowSetting()
|
||||
: undefined;
|
||||
const agentEngineManager = new AgentEngineManager({
|
||||
buildAdapter,
|
||||
baseConfig: {
|
||||
maxIterations: configService.get<number>('agent.maxIterations') ?? 20,
|
||||
totalTimeoutMs: configService.get<number>('agent.totalTimeoutMs') ?? 600_000,
|
||||
contextLength: ollamaNumCtx ?? undefined,
|
||||
contextWindow: buildAdapter().getContextWindow(),
|
||||
contextLength: getOllamaContextLength(),
|
||||
contextWindow: getContextWindowSetting(),
|
||||
thinkingEnabled: configService.get<boolean>('agent.enableThinking') ?? true,
|
||||
thinkingEffort:
|
||||
(configService.get<string>('agent.thinkingEffort') as
|
||||
@@ -452,7 +488,9 @@ async function initialize(): Promise<void> {
|
||||
enableReflection: configService.get<boolean>('agent.enableReflection') === true,
|
||||
// F-8 接通: llm.temperature / llm.maxTokens 此前为死配置（引擎硬编码 0.0/63488）
|
||||
temperature: configService.get<number>('llm.temperature') ?? 0.0,
|
||||
maxTokens: configService.get<number>('llm.maxTokens') ?? 63488,
|
||||
// v0.8.1 硬性契约: 直接读设置面板值（种子默认由 CONFIG_DEFAULTS 保证），
|
||||
// 不再在代码侧携带 63488 写死兜底
|
||||
maxTokens: configService.get<number>('llm.maxTokens') ?? undefined,
|
||||
},
|
||||
toolRegistry,
|
||||
preToolHooks,
|
||||
@@ -483,10 +521,8 @@ async function initialize(): Promise<void> {
|
||||
);
|
||||
return null;
|
||||
}
|
||||
const contextWindow =
|
||||
provider !== 'ollama'
|
||||
? (configService.get<number>(`${provider}.contextWindow`) ?? undefined)
|
||||
: undefined;
|
||||
// v0.8.1: 上下文窗口全局单一配置（llm.contextWindow），不再按 Provider 读取
|
||||
const contextWindow = configService.get<number>('llm.contextWindow') ?? undefined;
|
||||
const cfg = { provider, baseURL, apiKey, defaultModel: model, contextWindow };
|
||||
switch (provider) {
|
||||
case 'agnes':
|
||||
@@ -505,6 +541,27 @@ async function initialize(): Promise<void> {
|
||||
};
|
||||
agentEngineManager.setFallbackAdapter(buildFallbackAdapter());
|
||||
|
||||
// ===== v0.8.1 P1-1: 本地向量记忆嵌入器装配 =====
|
||||
// 仅在 Provider=Ollama（本地推理、数据不出设备、零 token 成本）且用户在设置面板
|
||||
// 配置了 memory.embeddingModel（如 nomic-embed-text）时启用；否则 embedder 返回
|
||||
// null，MemoryManager 全量回退纯 TF-IDF 检索（历史行为兼容）。
|
||||
// adapter 经闭包动态读取 —— 故障转移/热重载后无需重新装配。
|
||||
memoryManager.setEmbedder({
|
||||
embed: async (text) => {
|
||||
const adapter = agentEngineManager.getAdapter();
|
||||
if (!(adapter instanceof OllamaAdapter)) return null;
|
||||
const model = configService.get<string>('memory.embeddingModel') ?? '';
|
||||
if (!model.trim()) return null;
|
||||
try {
|
||||
const r = await adapter.embed({ model: model.trim(), input: text });
|
||||
return r.embeddings?.[0] ?? null;
|
||||
} catch (err) {
|
||||
log.warn('[Memory] Embedding failed (falling back to TF-IDF):', (err as Error).message);
|
||||
return null;
|
||||
}
|
||||
},
|
||||
});
|
||||
|
||||
// ===== P1-11: MCP 初始化等待所有连接完成后再广播 tools:ready（修复工具未注册即广播的窗口） =====
|
||||
// v0.3.18: toolsReadyRef 供 tools:isReady 查询（解决事件竞态）
|
||||
// v0.5.3: MCP 工具集合运行中变更（添加/断开/启停 server）→ 同步所有已存在引擎。
|
||||
@@ -544,6 +601,13 @@ async function initialize(): Promise<void> {
|
||||
// v0.3.18: 注入 MemoryManager，实现 DB 记忆与 MEMORY.md 双轨交叉
|
||||
memoryConsolidator.setMemoryManager(memoryManager);
|
||||
|
||||
// ===== v0.8.1 P1-2: MEMORY.md 维护闭环（分析/应用两阶段，应用前需用户确认） =====
|
||||
const memoryMaintainer = new MemoryMaintainer(
|
||||
() => agentEngineManager.getAdapter(),
|
||||
workspaceService,
|
||||
() => db,
|
||||
);
|
||||
|
||||
// ===== P2-11: 会话摘要分层上下文服务 =====
|
||||
const sessionSummaryService = new SessionSummaryService(
|
||||
() => db,
|
||||
@@ -566,8 +630,8 @@ async function initialize(): Promise<void> {
|
||||
| 'high'
|
||||
| 'max'
|
||||
| null) ?? 'high',
|
||||
contextLength: ollamaNumCtx ?? undefined,
|
||||
contextWindow: buildAdapter().getContextWindow(),
|
||||
contextLength: getOllamaContextLength(),
|
||||
contextWindow: getContextWindowSetting(),
|
||||
// v0.7.3 P3-1: SubAgent 与主引擎同源消费 enableReflection
|
||||
enableReflection: configService.get<boolean>('agent.enableReflection') === true,
|
||||
},
|
||||
@@ -577,6 +641,17 @@ async function initialize(): Promise<void> {
|
||||
agentEngineManager.setToolsAll(toolRegistry.listTools());
|
||||
// 在所有工具（包括 DelegateTaskTool）注册完成后，刷新 ConfirmationHook 的工具定义缓存
|
||||
confirmationHook.setToolDefs(toolRegistry.listAllTools());
|
||||
|
||||
// ===== v0.8.1 P2-1: 加载用户自定义工具策略（tools.{name}.policy）=====
|
||||
// 设置面板保存经 shared.ts 热加载；此处覆盖应用启动的冷加载。
|
||||
for (const toolDef of toolRegistry.listAllTools()) {
|
||||
const raw = configService.get<string>(`tools.${toolDef.name}.policy`);
|
||||
if (raw != null && raw !== '') {
|
||||
const parsed = parseToolPolicy(raw);
|
||||
policyEngine.setPolicyOverride(toolDef.name, parsed);
|
||||
if (!parsed) log.warn(`[Policy] Ignored invalid policy config for tool: ${toolDef.name}`);
|
||||
}
|
||||
}
|
||||
log.info(`Registered ${toolRegistry.size} built-in tools`);
|
||||
|
||||
// ===== 热重载 Adapter 回调（设置变更时触发） =====
|
||||
@@ -593,10 +668,8 @@ async function initialize(): Promise<void> {
|
||||
fallbackModel: configService.get<string>('llm.fallbackModel') ?? '',
|
||||
fallbackApiKey: configService.get<string>('llm.fallbackApiKey') ?? '',
|
||||
fallbackBaseURL: configService.get<string>('llm.fallbackBaseURL') ?? '',
|
||||
contextWindow:
|
||||
provider === 'ollama'
|
||||
? configService.get<number>('ollama.numCtx')
|
||||
: configService.get<number>(`${provider}.contextWindow`),
|
||||
// v0.8.1: 全局单一「上下文长度」配置进入签名（替代旧的分 Provider 键）
|
||||
contextWindow: configService.get<number>('llm.contextWindow') ?? null,
|
||||
});
|
||||
};
|
||||
let lastConfigSig = buildConfigSig();
|
||||
@@ -619,7 +692,7 @@ async function initialize(): Promise<void> {
|
||||
for (const win of wins) {
|
||||
win.webContents.send('toast:show', {
|
||||
type: 'warning',
|
||||
message: 'LLM 配置不完整，请在设置中补全 Provider、API Key、Base URL 和 Model',
|
||||
message: mt('config.toast.configIncomplete'),
|
||||
});
|
||||
}
|
||||
return false;
|
||||
@@ -630,20 +703,17 @@ async function initialize(): Promise<void> {
|
||||
agentEngineManager.setFallbackAdapter(buildFallbackAdapter());
|
||||
memoryConsolidator.setAdapter(agentEngineManager.getAdapter());
|
||||
|
||||
// Provider 切换时同步 contextLength 和 contextWindow
|
||||
// v0.8.1: 同步「上下文长度」到所有引擎（Ollama 同时作为 num_ctx/contextLength）
|
||||
const provider = configService.get<string>('llm.provider') ?? '';
|
||||
if (provider === 'ollama') {
|
||||
const numCtx = configService.get<number>('ollama.numCtx');
|
||||
agentEngineManager.updateConfigAll({
|
||||
contextLength: numCtx ?? undefined,
|
||||
contextWindow: agentEngineManager.getAdapter().getContextWindow(),
|
||||
});
|
||||
} else {
|
||||
agentEngineManager.updateConfigAll({
|
||||
contextLength: undefined,
|
||||
contextWindow: agentEngineManager.getAdapter().getContextWindow(),
|
||||
});
|
||||
}
|
||||
const ctxSetting = getContextWindowSetting();
|
||||
agentEngineManager.updateConfigAll({
|
||||
contextLength: provider === 'ollama' ? ctxSetting : undefined,
|
||||
contextWindow: ctxSetting,
|
||||
});
|
||||
orchestrator.updateDefaultConfig({
|
||||
contextLength: provider === 'ollama' ? ctxSetting : undefined,
|
||||
contextWindow: ctxSetting,
|
||||
});
|
||||
log.info(`[CONFIG] Adapter reloaded: provider=${provider}`);
|
||||
// 仅在 Provider 真正变化时通知渲染进程
|
||||
if (lastProvider && lastProvider !== provider) {
|
||||
@@ -656,7 +726,7 @@ async function initialize(): Promise<void> {
|
||||
});
|
||||
win.webContents.send('toast:show', {
|
||||
type: 'success',
|
||||
message: `Provider 已切换: ${lastProvider} → ${provider}`,
|
||||
message: mt('config.toast.providerSwitched', { from: lastProvider, to: provider }),
|
||||
});
|
||||
}
|
||||
}
|
||||
@@ -668,7 +738,7 @@ async function initialize(): Promise<void> {
|
||||
for (const win of BrowserWindow.getAllWindows()) {
|
||||
win.webContents.send('toast:show', {
|
||||
type: 'error',
|
||||
message: `Provider 切换失败: ${(err as Error).message}`,
|
||||
message: mt('config.toast.providerSwitchFailed', { message: (err as Error).message }),
|
||||
});
|
||||
}
|
||||
return false;
|
||||
@@ -749,6 +819,7 @@ async function initialize(): Promise<void> {
|
||||
contextBuilder,
|
||||
agentEngineManager,
|
||||
toolRegistry,
|
||||
policyEngine,
|
||||
auditService,
|
||||
sessionRecorder,
|
||||
memoryManager,
|
||||
@@ -758,6 +829,7 @@ async function initialize(): Promise<void> {
|
||||
outputValidator,
|
||||
confirmationHook,
|
||||
memoryConsolidator,
|
||||
memoryMaintainer,
|
||||
orchestrator,
|
||||
sessionSummaryService,
|
||||
titleGenerator,
|
||||
@@ -789,8 +861,8 @@ async function initialize(): Promise<void> {
|
||||
}
|
||||
if (event.status === 'available') {
|
||||
trayManager?.sendNotification(
|
||||
'MetonaAI 更新可用',
|
||||
`新版本 ${event.latestVersion} 已发布，可在 设置 → 日志与数据 中下载安装`,
|
||||
mt('notify.updateAvailable.title'),
|
||||
mt('notify.updateAvailable.body', { version: event.latestVersion }),
|
||||
);
|
||||
}
|
||||
});
|
||||
@@ -830,8 +902,8 @@ async function initialize(): Promise<void> {
|
||||
return;
|
||||
}
|
||||
trayManager?.sendNotification(
|
||||
'MetonaAI — 任务完成',
|
||||
`Agent 已完成任务 (${(data.durationMs / 1000).toFixed(1)}s)`,
|
||||
mt('notify.taskCompleted.title'),
|
||||
mt('notify.taskCompleted.body', { seconds: (data.durationMs / 1000).toFixed(1) }),
|
||||
() => windowManager?.focusWindow(),
|
||||
);
|
||||
},
|
||||
@@ -998,13 +1070,25 @@ if (!gotSingleInstanceLock) {
|
||||
// 两条防线均为 deny-by-default 白名单制，任何一条失败都不放大攻击面。
|
||||
{
|
||||
// 防线一：权限请求白名单（deny-by-default）
|
||||
// v0.8.1 根治: session.defaultSession 必须在 app ready 后才能访问 —— 此前在
|
||||
// 模块加载期（ready 前）直接调用，每次启动都抛 "Session can only be received
|
||||
// when app is ready"，权限白名单静默失效（FATAL 日志为证）。现延迟到 ready 后
|
||||
// 注册，防线真正生效。
|
||||
const ALLOWED_PERMISSIONS = new Set<string>(['clipboard-sanitized-write', 'fullscreen']);
|
||||
session.defaultSession.setPermissionRequestHandler((_wc, permission, callback) => {
|
||||
callback(ALLOWED_PERMISSIONS.has(permission));
|
||||
});
|
||||
session.defaultSession.setPermissionCheckHandler((_wc, permission) =>
|
||||
ALLOWED_PERMISSIONS.has(permission),
|
||||
);
|
||||
void app
|
||||
.whenReady()
|
||||
.then(() => {
|
||||
session.defaultSession.setPermissionRequestHandler((_wc, permission, callback) => {
|
||||
callback(ALLOWED_PERMISSIONS.has(permission));
|
||||
});
|
||||
session.defaultSession.setPermissionCheckHandler((_wc, permission) =>
|
||||
ALLOWED_PERMISSIONS.has(permission),
|
||||
);
|
||||
log.info('[Security] Permission whitelist registered (app ready)');
|
||||
})
|
||||
.catch((err) => {
|
||||
log.error('[Security] Failed to register permission whitelist:', err);
|
||||
});
|
||||
|
||||
// 防线二：生产环境 CSP 注入（仅 mainFrame，不触碰 dev server 的 HMR）。
|
||||
// MUI/emotion 需要 style-src 'unsafe-inline'（运行时注入 <style> 标签与 style 属性）；
|
||||
|
||||
+29
-1
@@ -59,7 +59,8 @@ const metonaAPI = {
|
||||
rename: (sessionId: string, title: string) =>
|
||||
ipcRenderer.invoke('sessions:rename', sessionId, title),
|
||||
delete: (sessionId: string) => ipcRenderer.invoke('sessions:delete', sessionId),
|
||||
getMessages: (sessionId: string) => ipcRenderer.invoke('sessions:getMessages', sessionId),
|
||||
getMessages: (sessionId: string, options?: { limit?: number; beforeRowid?: number }) =>
|
||||
ipcRenderer.invoke('sessions:getMessages', sessionId, options),
|
||||
// v0.7.4 P3-10: 更新单条用户消息内容（编辑"仅保存"落库）
|
||||
updateMessageContent: (sessionId: string, messageId: string, content: string) =>
|
||||
ipcRenderer.invoke('sessions:updateMessageContent', sessionId, messageId, content),
|
||||
@@ -110,6 +111,11 @@ const metonaAPI = {
|
||||
ipcRenderer.invoke('mcp:toggleServer', name, enabled),
|
||||
// v0.8.0 P2-5: Resources / Prompts 发现
|
||||
listServerContents: (name: string) => ipcRenderer.invoke('mcp:listServerContents', name),
|
||||
// v0.8.1 P1-4: Prompt 渲染 + Resource 读取（对话内可用化）
|
||||
getPrompt: (serverName: string, promptName: string, args?: Record<string, string>) =>
|
||||
ipcRenderer.invoke('mcp:getPrompt', serverName, promptName, args),
|
||||
readResource: (serverName: string, uri: string) =>
|
||||
ipcRenderer.invoke('mcp:readResource', serverName, uri),
|
||||
},
|
||||
|
||||
// ===== 记忆系统 =====
|
||||
@@ -119,6 +125,9 @@ const metonaAPI = {
|
||||
listAll: (options?: { type?: string; limit?: number }) =>
|
||||
ipcRenderer.invoke('memory:listAll', options),
|
||||
delete: (type: string, id: string) => ipcRenderer.invoke('memory:delete', type, id),
|
||||
// v0.8.1 P1-2: MEMORY.md 维护闭环（分析/应用两阶段）
|
||||
analyzeMaintenance: () => ipcRenderer.invoke('memory:analyzeMaintenance'),
|
||||
applyMaintenance: (actions: unknown) => ipcRenderer.invoke('memory:applyMaintenance', actions),
|
||||
},
|
||||
|
||||
// ===== v0.2.0: 任务管理 =====
|
||||
@@ -174,6 +183,12 @@ const metonaAPI = {
|
||||
ipcRenderer.on('tool:confirmationRequest', listener);
|
||||
return () => ipcRenderer.removeListener('tool:confirmationRequest', listener);
|
||||
},
|
||||
// v0.8.1 P2-2: 连续同类工具批量确认（单事件携带完整请求列表）
|
||||
onConfirmationRequestBatch: (callback: (requests: unknown[]) => void) => {
|
||||
const listener = (_event: Electron.IpcRendererEvent, data: unknown[]) => callback(data);
|
||||
ipcRenderer.on('tool:confirmationRequestBatch', listener);
|
||||
return () => ipcRenderer.removeListener('tool:confirmationRequestBatch', listener);
|
||||
},
|
||||
sendConfirmationResponse: (response: {
|
||||
toolCallId: string;
|
||||
approved: boolean;
|
||||
@@ -243,6 +258,19 @@ const metonaAPI = {
|
||||
showItemInFolder: (path: string) => ipcRenderer.invoke('app:showItemInFolder', path),
|
||||
selectFolder: (defaultPath?: string) => ipcRenderer.invoke('app:selectFolder', defaultPath),
|
||||
restart: () => ipcRenderer.invoke('app:restart'),
|
||||
// v0.8.1 P2-4: 开机自启（setLoginItemSettings 封装；读回真实生效状态）
|
||||
setLoginItem: (enabled: boolean) =>
|
||||
ipcRenderer.invoke('app:setLoginItem', enabled) as Promise<{
|
||||
success: boolean;
|
||||
error?: string;
|
||||
data?: { openAtLogin: boolean };
|
||||
}>,
|
||||
getLoginItem: () =>
|
||||
ipcRenderer.invoke('app:getLoginItem') as Promise<{
|
||||
success: boolean;
|
||||
error?: string;
|
||||
data?: { openAtLogin: boolean };
|
||||
}>,
|
||||
// v0.6.4 P4-2: 检查更新（feed 比对式；electron-updater 接入时仅替换实现）
|
||||
updateCheck: () =>
|
||||
ipcRenderer.invoke('app:updateCheck') as Promise<
|
||||
|
||||
@@ -285,9 +285,11 @@ describe.skipIf(!dbAvailable)('isUnconfiguredGlobalKey — 边界值矩阵', ()
|
||||
expect(isUnconfiguredGlobalKey(key, value)).toBe(expected);
|
||||
});
|
||||
|
||||
it('isGlobalKey 边界：完整前缀匹配而非子串（deepseek.contextWindow 是全局 key）', () => {
|
||||
expect(isGlobalKey('deepseek.contextWindow')).toBe(true);
|
||||
expect(isGlobalKey('deepseek.apiKey')).toBe(true);
|
||||
it('isGlobalKey 边界：完整前缀匹配而非子串（v0.8.1: provider 前缀已废除）', () => {
|
||||
// v0.8.1: deepseek/agnes/mimo/openai/anthropic/ollama 前缀从全局清单移除
|
||||
//（其唯一键 contextWindow/numCtx 已废除，由全局 llm.contextWindow 取代）
|
||||
expect(isGlobalKey('deepseek.contextWindow')).toBe(false);
|
||||
expect(isGlobalKey('deepseek.apiKey')).toBe(false);
|
||||
expect(isGlobalKey('memory.consolidationEnabled')).toBe(true);
|
||||
expect(isGlobalKey('memory.custom')).toBe(true);
|
||||
// 非全局前缀
|
||||
@@ -497,9 +499,9 @@ describe.skipIf(!dbAvailable)('migrateFromWorkspaceDB — 边界矩阵', () => {
|
||||
});
|
||||
|
||||
it('数值边界：默认值 float 精确相等判定', () => {
|
||||
// deepseek.contextWindow 默认 1000000
|
||||
expect(globalConfig.migrateFromWorkspaceDB({ 'deepseek.contextWindow': 1000000 })).toBe(0);
|
||||
expect(globalConfig.migrateFromWorkspaceDB({ 'deepseek.contextWindow': 64000 })).toBe(1);
|
||||
// v0.8.1: deepseek.contextWindow 键已废除（迁移 12）——改用存续键 llm.maxTokens（默认 63488）
|
||||
expect(globalConfig.migrateFromWorkspaceDB({ 'llm.maxTokens': 63488 })).toBe(0);
|
||||
expect(globalConfig.migrateFromWorkspaceDB({ 'llm.maxTokens': 64000 })).toBe(1);
|
||||
});
|
||||
|
||||
it('迁移后新 ConfigService 读取（跨空间共享生效）', () => {
|
||||
|
||||
@@ -0,0 +1,113 @@
|
||||
/**
|
||||
* MCPManager getPrompt / readResource 测试（v0.8.1 P1-4）
|
||||
*
|
||||
* prompts/resources 对话内可用化的主进程侧契约：
|
||||
* 1. getPrompt：messages 展平为文本（text 块拼接、多消息 \n\n 连接）、
|
||||
* 未连接 server 显式抛错；
|
||||
* 2. readResource：text 内容返回、二进制（blob）显式拒绝、
|
||||
* 空 contents 返回 null。
|
||||
*
|
||||
* 通过反射注入受控 server 状态（绕开真实 SDK 连接），与 IPC 层解耦。
|
||||
*/
|
||||
|
||||
import { describe, it, expect, vi } from 'vitest';
|
||||
|
||||
vi.mock('electron-log', () => ({
|
||||
default: { info: vi.fn(), warn: vi.fn(), error: vi.fn(), debug: vi.fn() },
|
||||
}));
|
||||
|
||||
import { MCPManager } from '../mcp-manager.service';
|
||||
import type { ToolRegistry } from '../../harness/tools/registry';
|
||||
|
||||
function makeManager(): MCPManager {
|
||||
const fakeDB = {
|
||||
prepare: () => ({ run: () => ({ changes: 0 }) }),
|
||||
} as unknown as ConstructorParameters<typeof MCPManager>[0] extends () => infer D ? D : never;
|
||||
const registry = {
|
||||
registerMCP: vi.fn(() => true),
|
||||
unregisterMCPTools: vi.fn(),
|
||||
} as unknown as ToolRegistry;
|
||||
return new MCPManager(() => fakeDB, registry);
|
||||
}
|
||||
|
||||
/** 反射注入受控 server 状态（mock client） */
|
||||
function injectServer(manager: MCPManager, client: Record<string, ReturnType<typeof vi.fn>>): void {
|
||||
const states = (
|
||||
manager as unknown as {
|
||||
servers: Map<
|
||||
string,
|
||||
{
|
||||
config: { name: string };
|
||||
status: string;
|
||||
client: unknown;
|
||||
tools: unknown[];
|
||||
resources: unknown[];
|
||||
prompts: unknown[];
|
||||
}
|
||||
>;
|
||||
}
|
||||
).servers;
|
||||
states.set('test-server', {
|
||||
config: { name: 'test-server' } as never,
|
||||
status: 'connected',
|
||||
client: client as never,
|
||||
tools: [],
|
||||
resources: [],
|
||||
prompts: [],
|
||||
});
|
||||
}
|
||||
|
||||
describe('MCPManager — getPrompt（v0.8.1 P1-4）', () => {
|
||||
it('messages 展平为文本，多消息以空行连接', async () => {
|
||||
const manager = makeManager();
|
||||
injectServer(manager, {
|
||||
getPrompt: vi.fn().mockResolvedValue({
|
||||
description: 'demo prompt',
|
||||
messages: [
|
||||
{ role: 'user', content: { type: 'text', text: 'Line A' } },
|
||||
{ role: 'user', content: { type: 'text', text: 'Line B' } },
|
||||
],
|
||||
}),
|
||||
});
|
||||
const result = await manager.getPrompt('test-server', 'demo');
|
||||
expect(result).not.toBeNull();
|
||||
expect(result!.text).toBe('Line A\n\nLine B');
|
||||
expect(result!.description).toBe('demo prompt');
|
||||
});
|
||||
|
||||
it('server 未连接 → 显式抛错（调用方以 error 返回）', async () => {
|
||||
const manager = makeManager();
|
||||
await expect(manager.getPrompt('nope', 'demo')).rejects.toThrow(/not connected/);
|
||||
});
|
||||
});
|
||||
|
||||
describe('MCPManager — readResource（v0.8.1 P1-4）', () => {
|
||||
it('text 内容正常返回', async () => {
|
||||
const manager = makeManager();
|
||||
injectServer(manager, {
|
||||
readResource: vi.fn().mockResolvedValue({
|
||||
contents: [{ uri: 'file:///a.txt', text: 'hello resource', mimeType: 'text/plain' }],
|
||||
}),
|
||||
});
|
||||
const result = await manager.readResource('test-server', 'file:///a.txt');
|
||||
expect(result).toEqual({ text: 'hello resource', mimeType: 'text/plain' });
|
||||
});
|
||||
|
||||
it('二进制（blob）内容显式拒绝', async () => {
|
||||
const manager = makeManager();
|
||||
injectServer(manager, {
|
||||
readResource: vi.fn().mockResolvedValue({
|
||||
contents: [{ uri: 'file:///a.png', blob: 'aGVsbG8=' }],
|
||||
}),
|
||||
});
|
||||
await expect(manager.readResource('test-server', 'file:///a.png')).rejects.toThrow(/binary/);
|
||||
});
|
||||
|
||||
it('空 contents → null', async () => {
|
||||
const manager = makeManager();
|
||||
injectServer(manager, {
|
||||
readResource: vi.fn().mockResolvedValue({ contents: [] }),
|
||||
});
|
||||
expect(await manager.readResource('test-server', 'file:///none')).toBeNull();
|
||||
});
|
||||
});
|
||||
@@ -0,0 +1,114 @@
|
||||
/**
|
||||
* SessionService 游标分页测试（v0.8.1 P2-3）
|
||||
*
|
||||
* 契约：无 limit 全量；limit 无游标 = 尾部窗口（最后 N 条，升序返回）；
|
||||
* limit + beforeRowid = 游标之前的更早 N 条（升序返回）；完整性判定
|
||||
* （返回条数 < limit 即到会话开头）由调用方依据长度比较。
|
||||
*/
|
||||
|
||||
import { describe, it, expect, beforeAll, afterAll, vi } from 'vitest';
|
||||
|
||||
vi.mock('electron-log', () => ({
|
||||
default: { info: vi.fn(), warn: vi.fn(), error: vi.fn(), debug: vi.fn() },
|
||||
}));
|
||||
|
||||
let dbAvailable = true;
|
||||
let Database: typeof import('better-sqlite3');
|
||||
try {
|
||||
// eslint-disable-next-line @typescript-eslint/no-require-imports
|
||||
Database = require('better-sqlite3');
|
||||
const probe = new Database(':memory:');
|
||||
probe.close();
|
||||
} catch {
|
||||
dbAvailable = false;
|
||||
}
|
||||
|
||||
import { SessionService } from '../session.service';
|
||||
|
||||
describe.skipIf(!dbAvailable)('SessionService — 游标分页（v0.8.1 P2-3）', () => {
|
||||
let db: InstanceType<typeof Database>;
|
||||
let svc: SessionService;
|
||||
const sessionId = 's_page';
|
||||
|
||||
beforeAll(() => {
|
||||
if (!dbAvailable) return;
|
||||
db = new Database(':memory:');
|
||||
db.exec(`
|
||||
CREATE TABLE sessions (
|
||||
id TEXT PRIMARY KEY,
|
||||
title TEXT NOT NULL DEFAULT '',
|
||||
created_at INTEGER,
|
||||
updated_at INTEGER,
|
||||
message_count INTEGER DEFAULT 0,
|
||||
total_tokens INTEGER DEFAULT 0,
|
||||
pinned INTEGER DEFAULT 0,
|
||||
archived INTEGER DEFAULT 0,
|
||||
deleted_at INTEGER,
|
||||
metadata TEXT DEFAULT '{}'
|
||||
);
|
||||
CREATE TABLE messages (
|
||||
id TEXT PRIMARY KEY,
|
||||
session_id TEXT NOT NULL,
|
||||
role TEXT,
|
||||
content TEXT,
|
||||
reasoning_content TEXT,
|
||||
tool_calls TEXT,
|
||||
tool_result TEXT,
|
||||
attachments TEXT,
|
||||
iteration INTEGER,
|
||||
created_at INTEGER
|
||||
);
|
||||
CREATE TABLE session_summaries (
|
||||
session_id TEXT PRIMARY KEY,
|
||||
summary TEXT,
|
||||
summarized_until_rowid INTEGER,
|
||||
updated_at INTEGER
|
||||
);
|
||||
CREATE TABLE tasks (
|
||||
id TEXT PRIMARY KEY,
|
||||
session_id TEXT,
|
||||
title TEXT,
|
||||
description TEXT,
|
||||
status TEXT,
|
||||
priority TEXT,
|
||||
parent_id TEXT,
|
||||
assigned_to TEXT,
|
||||
order_idx INTEGER,
|
||||
created_at INTEGER,
|
||||
updated_at INTEGER,
|
||||
completed_at INTEGER
|
||||
);
|
||||
`);
|
||||
db.prepare('INSERT INTO sessions (id) VALUES (?)').run(sessionId);
|
||||
svc = new SessionService(() => db);
|
||||
for (let i = 1; i <= 10; i++) {
|
||||
svc.saveMessage({ sessionId, role: 'user', content: `msg-${i}` });
|
||||
}
|
||||
});
|
||||
afterAll(() => {
|
||||
if (db) db.close();
|
||||
});
|
||||
|
||||
it('无 limit → 全量（向后兼容）', () => {
|
||||
expect(svc.getMessages(sessionId)).toHaveLength(10);
|
||||
});
|
||||
|
||||
it('limit 无游标 → 尾部窗口（最后 N 条，升序返回）', () => {
|
||||
const tail = svc.getMessages(sessionId, { limit: 3 });
|
||||
expect(tail.map((m) => m.content)).toEqual(['msg-8', 'msg-9', 'msg-10']);
|
||||
});
|
||||
|
||||
it('limit + beforeRowid → 游标之前 N 条（升序返回）', () => {
|
||||
const full = svc.getMessages(sessionId);
|
||||
const cursorRowId = full[6].rowId!; // msg-7 的 rowid
|
||||
const older = svc.getMessages(sessionId, { limit: 3, beforeRowid: cursorRowId });
|
||||
expect(older.map((m) => m.content)).toEqual(['msg-4', 'msg-5', 'msg-6']);
|
||||
});
|
||||
|
||||
it('游标翻到开头：返回条数 < limit 表示已完整', () => {
|
||||
const full = svc.getMessages(sessionId);
|
||||
const first = full[0].rowId!;
|
||||
const older = svc.getMessages(sessionId, { limit: 3, beforeRowid: first });
|
||||
expect(older).toHaveLength(0);
|
||||
});
|
||||
});
|
||||
@@ -21,6 +21,19 @@ function toErrorMessage(error: unknown): string {
|
||||
return error instanceof Error ? error.message : String(error);
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.8.1: 已废除的配置键（单一来源 —— 迁移 12 与全局配置层清理共用）。
|
||||
* 分 Provider contextWindow 与 ollama.numCtx 由全局 llm.contextWindow 取代。
|
||||
*/
|
||||
export const DEPRECATED_CONFIG_KEYS = [
|
||||
'ollama.numCtx',
|
||||
'deepseek.contextWindow',
|
||||
'agnes.contextWindow',
|
||||
'mimo.contextWindow',
|
||||
'openai.contextWindow',
|
||||
'anthropic.contextWindow',
|
||||
] as const;
|
||||
|
||||
/** 配置默认值条目（P1-13: 单一来源，global-config.service.ts 的 SEED_DEFAULTS 由此派生） */
|
||||
export interface ConfigDefaultEntry {
|
||||
key: string;
|
||||
@@ -42,7 +55,13 @@ export const CONFIG_DEFAULTS: ConfigDefaultEntry[] = [
|
||||
{ key: 'llm.baseURL', value: '', category: 'llm' },
|
||||
// F-8 接通: temperature/maxTokens 此前为死配置（引擎硬编码），现由 main.ts 注入引擎
|
||||
{ key: 'llm.temperature', value: 0, category: 'llm' },
|
||||
// v0.8.0 FEAT-1 / v0.8.1: 最大输出上限 —— 唯一合法的输出上限配置（全局，跨 Provider）
|
||||
{ key: 'llm.maxTokens', value: 63488, category: 'llm' },
|
||||
// v0.8.1: 上下文长度 —— 唯一合法的上下文窗口配置（全局，跨 Provider/模型；
|
||||
// 驱动引擎压缩预算、前端占用指示，Ollama 场景同时作为 num_ctx 下发）。
|
||||
// 分 Provider 的 deepseek/agnes/mimo/openai/anthropic.contextWindow 与 ollama.numCtx
|
||||
// 已废除（迁移 12 清理遗留键）。
|
||||
{ key: 'llm.contextWindow', value: 131072, category: 'llm' },
|
||||
// v0.5.4: 多模态总开关 — 即使模型支持多模态，未开启也不能上传图片（默认关闭，
|
||||
// 用户在设置/引导向导显式开启；上传入口 = 开关 × 模型能力双重判断）
|
||||
{ key: 'llm.multimodalEnabled', value: false, category: 'llm' },
|
||||
@@ -74,6 +93,9 @@ export const CONFIG_DEFAULTS: ConfigDefaultEntry[] = [
|
||||
{ key: 'memory.consolidationEnabled', value: true, category: 'memory' },
|
||||
{ key: 'memory.consolidationMinChars', value: 200, category: 'memory' },
|
||||
{ key: 'memory.consolidationIntervalMs', value: 600000, category: 'memory' },
|
||||
// v0.8.1 P1-1: 本地向量记忆嵌入模型（Ollama embedding 模型名，空 = 向量检索关闭，
|
||||
// 仅 TF-IDF）。唯一合法来源是设置面板 —— 代码中不存在默认模型名。
|
||||
{ key: 'memory.embeddingModel', value: '', category: 'memory' },
|
||||
|
||||
// v0.7.3 P4-2: MCP 自动重连开关（mcp-manager.service 消费；断连后指数退避重试）
|
||||
{ key: 'mcp.autoReconnect', value: true, category: 'mcp' },
|
||||
@@ -88,16 +110,8 @@ export const CONFIG_DEFAULTS: ConfigDefaultEntry[] = [
|
||||
{ key: 'logging.auditEnabled', value: true, category: 'logging' },
|
||||
{ key: 'logging.traceEnabled', value: true, category: 'logging' },
|
||||
|
||||
// Ollama 配置
|
||||
{ key: 'ollama.numCtx', value: null, category: 'ollama' },
|
||||
|
||||
// Provider 上下文窗口配置（用于 Engine 压缩判断和 UI 显示）
|
||||
{ key: 'deepseek.contextWindow', value: 1000000, category: 'deepseek' },
|
||||
{ key: 'agnes.contextWindow', value: 1000000, category: 'agnes' },
|
||||
{ key: 'mimo.contextWindow', value: 1000000, category: 'mimo' },
|
||||
// P3: OpenAI / Anthropic Provider
|
||||
{ key: 'openai.contextWindow', value: 128000, category: 'openai' },
|
||||
{ key: 'anthropic.contextWindow', value: 200000, category: 'anthropic' },
|
||||
// v0.8.1: Ollama numCtx 独立配置已废除（与「上下文长度」合并为 llm.contextWindow）；
|
||||
// Provider 上下文窗口分键配置已废除 —— 遗留键由迁移 12 从存量库清理。
|
||||
|
||||
// Onboarding
|
||||
{ key: 'onboarding.completed', value: false, category: 'general' },
|
||||
@@ -114,8 +128,12 @@ export class DatabaseService {
|
||||
* v0.7.4 P4-4: 1 → 2 —— 迁移 9（messages_fts trigram）纳入版本化，
|
||||
* 失败中断批次不盖章 → 下次启动重试（根治"失败被吞 + 永久跳过"）。
|
||||
* v0.8.0 P2-1: 2 → 3 —— 迁移 10（sessions.deleted_at 回收站软删除列）。
|
||||
* v0.8.1: 3 → 4 —— 迁移 11（记忆表 embedding 列，本地向量混合检索）+
|
||||
* 迁移 12（清理已废除的 llm.contextWindow 分 Provider 键与 ollama.numCtx）。
|
||||
* v0.8.1 review: 4 → 5 —— 迁移 13（working_memories 补 sessions 外键 CASCADE，
|
||||
* 孤儿行清理；根治历史 schema 缺失级联导致的孤儿数据）。
|
||||
*/
|
||||
static readonly SCHEMA_VERSION = 3;
|
||||
static readonly SCHEMA_VERSION = 5;
|
||||
|
||||
constructor(workspacePath?: string) {
|
||||
const baseDir = workspacePath ?? join(app.getPath('userData'), 'MetonaWorkspaces', 'default');
|
||||
@@ -280,6 +298,7 @@ export class DatabaseService {
|
||||
);
|
||||
|
||||
-- ===== 工作记忆表 =====
|
||||
-- v0.8.1 review: 补 sessions 外键（CASCADE）—— 历史建表缺失级联
|
||||
CREATE TABLE IF NOT EXISTS working_memories (
|
||||
id TEXT PRIMARY KEY,
|
||||
session_id TEXT NOT NULL,
|
||||
@@ -287,7 +306,9 @@ export class DatabaseService {
|
||||
key TEXT NOT NULL,
|
||||
value TEXT NOT NULL,
|
||||
updated_at INTEGER NOT NULL DEFAULT (unixepoch() * 1000),
|
||||
UNIQUE(session_id, task_id, key)
|
||||
tf_cache TEXT,
|
||||
UNIQUE(session_id, task_id, key),
|
||||
FOREIGN KEY (session_id) REFERENCES sessions(id) ON DELETE CASCADE
|
||||
);
|
||||
|
||||
-- ===== v0.2.0: 任务表 =====
|
||||
@@ -657,6 +678,79 @@ export class DatabaseService {
|
||||
}
|
||||
}
|
||||
|
||||
// v0.8.1 P1-1 迁移 11: 记忆表 embedding 列（本地向量混合检索）。
|
||||
// 存量记忆行为 NULL = 未向量化（检索时回退 TF-IDF 路径），新写入由
|
||||
// MemoryEmbedder 异步回填；向量维度由用户配置的 embedding 模型决定，
|
||||
// 故用 BLOB 存 Float32Array 而非固定宽度 F32Blob 列。
|
||||
tryAddColumn('episodic_memories', 'embedding', 'BLOB');
|
||||
tryAddColumn('semantic_memories', 'embedding', 'BLOB');
|
||||
|
||||
// v0.8.1 迁移 12: 清理已废除的上下文窗口配置键（全局 llm.contextWindow 取代）。
|
||||
// 旧键残留会使设置面板与引擎出现双源语义，启动迁移一次性移除（幂等）。
|
||||
{
|
||||
const placeholders = DEPRECATED_CONFIG_KEYS.map(() => '?').join(', ');
|
||||
const result = db
|
||||
.prepare(`DELETE FROM app_config WHERE key IN (${placeholders})`)
|
||||
.run(...DEPRECATED_CONFIG_KEYS);
|
||||
if (result.changes > 0) {
|
||||
log.info(
|
||||
`[DB] Migration: removed ${result.changes} deprecated context-window config key(s) (replaced by llm.contextWindow)`,
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// v0.8.1 review 迁移 13: working_memories 补 sessions 外键（ON DELETE CASCADE）
|
||||
// 历史缺陷：该表自 v0.2.0 建表起就没有 sessions 外键 —— 会话删除/清空后
|
||||
// working 行成为孤儿（行为面已由 0.8.1 终态清理接线覆盖，此处根治 schema）。
|
||||
// 先清理孤儿行（早于 0.8.1 的历史删除遗留），再以 FK 重建。
|
||||
{
|
||||
const hasWorking = db
|
||||
.prepare("SELECT name FROM sqlite_master WHERE type='table' AND name='working_memories'")
|
||||
.get();
|
||||
// hasSessions 由迁移 10 块内查询（块作用域），此处独立复查
|
||||
const hasSessions = !!db
|
||||
.prepare("SELECT name FROM sqlite_master WHERE type='table' AND name='sessions'")
|
||||
.get();
|
||||
const fkRows = hasWorking
|
||||
? (db.prepare('PRAGMA foreign_key_list(working_memories)').all() as Array<{
|
||||
table: string;
|
||||
}>)
|
||||
: [];
|
||||
const hasSessionsFk = fkRows.some((fk) => fk.table === 'sessions');
|
||||
if (hasWorking && hasSessions && !hasSessionsFk) {
|
||||
log.info('[DB] Migration: rebuilding working_memories with sessions FK (CASCADE)');
|
||||
const rebuildWorking = db.transaction(() => {
|
||||
db.exec(`
|
||||
DELETE FROM working_memories
|
||||
WHERE session_id NOT IN (SELECT id FROM sessions);
|
||||
|
||||
CREATE TABLE working_memories_new (
|
||||
id TEXT PRIMARY KEY,
|
||||
session_id TEXT NOT NULL,
|
||||
task_id TEXT NOT NULL,
|
||||
key TEXT NOT NULL,
|
||||
value TEXT NOT NULL,
|
||||
updated_at INTEGER NOT NULL DEFAULT (unixepoch() * 1000),
|
||||
tf_cache TEXT,
|
||||
UNIQUE(session_id, task_id, key),
|
||||
FOREIGN KEY (session_id) REFERENCES sessions(id) ON DELETE CASCADE
|
||||
);
|
||||
|
||||
INSERT INTO working_memories_new (id, session_id, task_id, key, value, updated_at, tf_cache)
|
||||
SELECT id, session_id, task_id, key, value, updated_at, tf_cache
|
||||
FROM working_memories;
|
||||
|
||||
DROP TABLE working_memories;
|
||||
ALTER TABLE working_memories_new RENAME TO working_memories;
|
||||
|
||||
CREATE INDEX IF NOT EXISTS idx_working_session_task ON working_memories(session_id, task_id);
|
||||
`);
|
||||
});
|
||||
rebuildWorking();
|
||||
log.info('[DB] Migration: working_memories rebuilt with sessions FK');
|
||||
}
|
||||
}
|
||||
|
||||
// v0.7.4 P4-4 迁移 9: messages_fts 升级 trigram tokenizer
|
||||
// 存量库的 messages_fts 建表语句不含 trigram —— 直接 DROP + 重建 + rebuild，
|
||||
// 使中文非连续子串搜索（trigram ≥3 字符）可用。检测方式：读 sqlite_master 的
|
||||
|
||||
@@ -11,8 +11,10 @@
|
||||
* 工作空间 DB 仍保留会话/消息/记忆/审计等"工作空间级数据"。
|
||||
*
|
||||
* 全局 key 规则：
|
||||
* - llm.* / agent.* / ollama.* / {provider}.contextWindow / security.* / ui.* / logging.*
|
||||
* - llm.* / agent.* / security.* / ui.* / logging.* / memory.*
|
||||
* - onboarding.completed
|
||||
* （v0.8.1: 分 Provider contextWindow 键与 ollama.numCtx 废除 —— 相应前缀从
|
||||
* 全局判定清单移除，存量废键由 initialize 清理，与工作空间 DB 迁移 12 对齐）
|
||||
* 写入时同时写工作空间 DB（兼容旧逻辑）和全局 JSON；
|
||||
* 读取时优先工作空间 DB，miss 时回退到全局 JSON。
|
||||
*
|
||||
@@ -23,7 +25,7 @@ import { app } from 'electron';
|
||||
import { join } from 'path';
|
||||
import { existsSync, readFileSync, writeFileSync, mkdirSync } from 'fs';
|
||||
import log from 'electron-log';
|
||||
import { CONFIG_DEFAULTS } from './database.service';
|
||||
import { CONFIG_DEFAULTS, DEPRECATED_CONFIG_KEYS } from './database.service';
|
||||
import {
|
||||
decryptConfigValue,
|
||||
encryptConfigValue,
|
||||
@@ -37,15 +39,9 @@ const GLOBAL_CONFIG_FILE = join(app.getPath('userData'), 'global-config.json');
|
||||
const GLOBAL_KEY_PREFIXES = [
|
||||
'llm.',
|
||||
'agent.',
|
||||
'ollama.',
|
||||
'security.',
|
||||
'ui.',
|
||||
'logging.',
|
||||
'deepseek.',
|
||||
'agnes.',
|
||||
'mimo.',
|
||||
'openai.',
|
||||
'anthropic.',
|
||||
'onboarding.',
|
||||
// v0.7.3 P1-5: 记忆固化节流（机器级策略，跨工作空间一致）
|
||||
'memory.',
|
||||
@@ -110,6 +106,19 @@ export class GlobalConfigService {
|
||||
if (existsSync(GLOBAL_CONFIG_FILE)) {
|
||||
const raw = readFileSync(GLOBAL_CONFIG_FILE, 'utf-8');
|
||||
this.data = JSON.parse(raw) as GlobalConfigData;
|
||||
// v0.8.1 review: 清除已废除的配置键（分 Provider contextWindow / ollama.numCtx），
|
||||
// 与工作空间 DB 迁移 12 对齐 —— 全局层残留会使双源语义复活
|
||||
let purged = 0;
|
||||
for (const key of DEPRECATED_CONFIG_KEYS) {
|
||||
if (key in this.data) {
|
||||
delete this.data[key];
|
||||
purged++;
|
||||
}
|
||||
}
|
||||
if (purged > 0) {
|
||||
this.flush();
|
||||
log.info(`[GlobalConfig] Purged ${purged} deprecated config key(s)`);
|
||||
}
|
||||
log.info(
|
||||
`[GlobalConfig] Loaded ${Object.keys(this.data).length} keys from ${GLOBAL_CONFIG_FILE}`,
|
||||
);
|
||||
|
||||
@@ -871,6 +871,59 @@ export class MCPManager {
|
||||
return { resources: state.resources ?? [], prompts: state.prompts ?? [] };
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.8.1 P1-4: 获取 MCP Prompt 渲染结果（prompts/get）—— 供 ChatInput 斜杠
|
||||
* 菜单填充输入框。messages 展平为文本（text 块拼接；role 前缀保留多消息语义）。
|
||||
*/
|
||||
async getPrompt(
|
||||
serverName: string,
|
||||
promptName: string,
|
||||
args?: Record<string, string>,
|
||||
): Promise<{ text: string; description?: string } | null> {
|
||||
const state = this.servers.get(serverName);
|
||||
if (!state || state.status !== 'connected' || !state.client) {
|
||||
throw new Error(`MCP server '${serverName}' is not connected`);
|
||||
}
|
||||
const res = await state.client.getPrompt({
|
||||
name: promptName,
|
||||
arguments: args,
|
||||
});
|
||||
const parts: string[] = [];
|
||||
for (const m of res.messages ?? []) {
|
||||
// MCP PromptMessage.content 是单个 ContentBlock（text/image/audio/resource_link
|
||||
// 联合）—— 仅提取文本块；经 unknown 中转以匹配 SDK 联合类型
|
||||
const content = m.content as unknown as { type?: string; text?: string } | undefined;
|
||||
const text = content?.type === 'text' ? (content.text ?? '') : '';
|
||||
parts.push(text);
|
||||
}
|
||||
return { text: parts.filter((t) => t.length > 0).join('\n\n'), description: res.description };
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.8.1 P1-4: 读取 MCP Resource 内容（resources/read）—— 供 @mcp 提及注入。
|
||||
* 文本类内容展平返回；二进制（blob）内容拒绝（与附件管线"二进制拒绝"口径一致）。
|
||||
*/
|
||||
async readResource(
|
||||
serverName: string,
|
||||
uri: string,
|
||||
): Promise<{ text: string; mimeType?: string } | null> {
|
||||
const state = this.servers.get(serverName);
|
||||
if (!state || state.status !== 'connected' || !state.client) {
|
||||
throw new Error(`MCP server '${serverName}' is not connected`);
|
||||
}
|
||||
const res = await state.client.readResource({ uri });
|
||||
const contents = res.contents ?? [];
|
||||
const first = contents[0];
|
||||
if (!first) return null;
|
||||
if ('blob' in first && typeof first.blob === 'string') {
|
||||
throw new Error(`Resource '${uri}' is binary content — inline injection is not supported`);
|
||||
}
|
||||
return {
|
||||
text: ('text' in first ? first.text : '') ?? '',
|
||||
mimeType: first.mimeType,
|
||||
};
|
||||
}
|
||||
|
||||
/**
|
||||
* 关闭所有连接
|
||||
*/
|
||||
|
||||
@@ -283,12 +283,40 @@ export class SessionService {
|
||||
* P2-11: 新增 afterRowid 参数——仅返回 rowid 大于该值的消息（分层上下文加载游标）；
|
||||
* 排序改用 rowid（插入序号），与截断/摘要游标语义一致
|
||||
*/
|
||||
/**
|
||||
* 分页语义（v0.8.1 P2-3 游标分页）：
|
||||
* - 无 limit：全量（向后兼容 —— 导出/右键菜单等仍取完整历史）；
|
||||
* - limit + 无 beforeRowid：取**最后 N 条**（尾部窗口，会话打开首屏）；
|
||||
* - limit + beforeRowid：取该游标之前（更早）的 N 条（向上翻页）。
|
||||
* 返回统一按 rowid 升序排列。
|
||||
*/
|
||||
getMessages(
|
||||
sessionId: string,
|
||||
options: { limit?: number; offset?: number; afterRowid?: number } = {},
|
||||
options: { limit?: number; offset?: number; afterRowid?: number; beforeRowid?: number } = {},
|
||||
): MessageInfo[] {
|
||||
const db = this.getDBFn();
|
||||
const { limit = 0, offset = 0, afterRowid = 0 } = options;
|
||||
const { limit = 0, offset = 0, afterRowid = 0, beforeRowid = 0 } = options;
|
||||
|
||||
// 游标向上翻页：rowid < beforeRowid，DESC 取 N 条后反转为升序
|
||||
if (limit > 0 && beforeRowid > 0) {
|
||||
const rows = db
|
||||
.prepare(
|
||||
`SELECT rowid AS row_id, * FROM messages WHERE session_id = ? AND rowid < ? ORDER BY rowid DESC LIMIT ?`,
|
||||
)
|
||||
.all(sessionId, beforeRowid, limit) as MessageRow[];
|
||||
return rows.reverse().map((row) => this.toMessageInfo(row));
|
||||
}
|
||||
|
||||
// 尾部窗口：无游标且无 offset 时指定 limit → 最后 N 条（DESC 取后反转为升序）。
|
||||
// offset>0 保留旧版 ASC limit/offset 分页语义（向后兼容既有调用方）。
|
||||
if (limit > 0 && afterRowid === 0 && offset === 0) {
|
||||
const rows = db
|
||||
.prepare(
|
||||
`SELECT rowid AS row_id, * FROM messages WHERE session_id = ? ORDER BY rowid DESC LIMIT ? OFFSET ?`,
|
||||
)
|
||||
.all(sessionId, limit, offset) as MessageRow[];
|
||||
return rows.reverse().map((row) => this.toMessageInfo(row));
|
||||
}
|
||||
|
||||
let sql = 'SELECT rowid AS row_id, * FROM messages WHERE session_id = ?';
|
||||
const params: unknown[] = [sessionId];
|
||||
|
||||
@@ -13,7 +13,16 @@
|
||||
*/
|
||||
|
||||
import { join, resolve, sep } from 'path';
|
||||
import { existsSync, mkdirSync, readFileSync, readdirSync, writeFileSync, statSync } from 'fs';
|
||||
import {
|
||||
existsSync,
|
||||
mkdirSync,
|
||||
readFileSync,
|
||||
readdirSync,
|
||||
writeFileSync,
|
||||
statSync,
|
||||
renameSync,
|
||||
unlinkSync,
|
||||
} from 'fs';
|
||||
import { app } from 'electron';
|
||||
import log from 'electron-log';
|
||||
// v0.8.0 P2-4: @ 提及文本片段的编码检测（与 read_file 同源）
|
||||
@@ -306,6 +315,35 @@ export class WorkspaceService {
|
||||
log.info(`MEMORY.md: appended to section "${section}"`);
|
||||
}
|
||||
|
||||
/**
|
||||
* v0.8.1 P1-2: 整体改写 MEMORY.md（记忆整理闭环的唯一写入口）。
|
||||
*
|
||||
* 供 MemoryMaintainer 应用"删除/合并"动作后重写文件 —— 工具层（file-guard）
|
||||
* 对工作空间根目录 MEMORY.md 的写保护不受影响：本方法只由主进程维护链路调用，
|
||||
* 不经过 Agent 工具执行管道，Agent 仍无法绕过保护直接改写根目录记忆。
|
||||
* 原子性：tmp + rename，与 write_file 工具同口径；写后同步 files.memory 缓存
|
||||
* 并更新时间戳。
|
||||
*/
|
||||
rewriteMemory(content: string): void {
|
||||
const memoryPath = join(this.workspacePath, 'MEMORY.md');
|
||||
// 时间戳随写随更
|
||||
const stamped = content.replace(/> 最后更新: .*/, `> 最后更新: ${new Date().toISOString()}`);
|
||||
const tmpPath = `${memoryPath}.tmp_${Date.now()}_${Math.random().toString(36).slice(2, 8)}`;
|
||||
writeFileSync(tmpPath, stamped, 'utf-8');
|
||||
try {
|
||||
renameSync(tmpPath, memoryPath);
|
||||
} catch (err) {
|
||||
try {
|
||||
unlinkSync(tmpPath);
|
||||
} catch {
|
||||
/* 忽略清理失败 */
|
||||
}
|
||||
throw err;
|
||||
}
|
||||
this.files.memory = stamped;
|
||||
log.info('MEMORY.md rewritten (memory maintenance)');
|
||||
}
|
||||
|
||||
/**
|
||||
* 校验 MEMORY.md 格式
|
||||
*
|
||||
|
||||
@@ -59,6 +59,14 @@ vi.mock('undici', () => ({
|
||||
undiciMocks.proxyAgentCalls.push(opts.uri);
|
||||
}
|
||||
},
|
||||
// v0.8.1 P2-5: 组合 dispatcher 基类（回环直连 + 其余走代理）
|
||||
Dispatcher: class {
|
||||
dispatch(): boolean {
|
||||
return true;
|
||||
}
|
||||
async close(): Promise<void> {}
|
||||
async destroy(): Promise<void> {}
|
||||
},
|
||||
setGlobalDispatcher: (...args: unknown[]) => undiciMocks.setGlobalDispatcher(...args),
|
||||
}));
|
||||
|
||||
@@ -89,6 +97,7 @@ describe('applySessionProxy — 双通道应用', () => {
|
||||
expect(sessionMocks.defaultSetProxy).toHaveBeenCalledWith(expected);
|
||||
expect(sessionMocks.partitionSetProxy).toHaveBeenCalledWith(expected);
|
||||
expect(undiciMocks.proxyAgentCalls).toEqual(['http://127.0.0.1:7890']);
|
||||
expect(undiciMocks.agentCalls).toBeGreaterThanOrEqual(1); // 回环直连 Agent 同步构建
|
||||
expect(undiciMocks.setGlobalDispatcher).toHaveBeenCalledTimes(1);
|
||||
});
|
||||
|
||||
|
||||
@@ -40,9 +40,11 @@ import {
|
||||
isEncryptedValue,
|
||||
encryptConfigValue,
|
||||
decryptConfigValue,
|
||||
resetEncryptionUsableForTests,
|
||||
} from '../secure-config';
|
||||
|
||||
beforeEach(() => {
|
||||
resetEncryptionUsableForTests();
|
||||
mockState.encryptionAvailable = true;
|
||||
mockState.failDecrypt = false;
|
||||
});
|
||||
@@ -113,13 +115,22 @@ describe('加密降级与失败语义', () => {
|
||||
expect(isEncryptedValue(value)).toBe(false);
|
||||
});
|
||||
|
||||
it('加密过程抛错 → 回退明文存储（不阻断配置保存）', () => {
|
||||
// decryptString 抛错不影响 encrypt；此处验证 decrypt 失败语义
|
||||
mockState.failDecrypt = true;
|
||||
it('解密失败（跨机器/重装）→ 返回空串（不阻断，引导重录）', () => {
|
||||
// v0.8.1: roundtrip probe 需要一次可用加解密 —— 先完成加密，再注入解密失败
|
||||
const encrypted = encryptConfigValue('sk-x') as string;
|
||||
expect(isEncryptedValue(encrypted)).toBe(true);
|
||||
mockState.failDecrypt = true;
|
||||
expect(decryptConfigValue(encrypted)).toBe(''); // 失败 → 空串（createAdapter 判定未配置，引导重录）
|
||||
});
|
||||
|
||||
it('roundtrip 探测失败 → 会话级降级明文存储（v0.8.1 新增）', () => {
|
||||
// failDecrypt 令 probe 失败 → usable=false → 加密直接降级明文
|
||||
mockState.failDecrypt = true;
|
||||
const value = encryptConfigValue('sk-probe-fail') as string;
|
||||
expect(value).toBe('sk-probe-fail');
|
||||
expect(isEncryptedValue(value)).toBe(false);
|
||||
});
|
||||
|
||||
it('safeStorage 不可用时已加密值仍可识别且不被二次"加密"', () => {
|
||||
const once = encryptConfigValue('sk-again') as string;
|
||||
mockState.encryptionAvailable = false;
|
||||
|
||||
@@ -0,0 +1,125 @@
|
||||
/**
|
||||
* Main-process Locale — 主进程侧文案双语（v0.8.1 P0-4 i18n 收口第二期）
|
||||
*
|
||||
* 背景：v0.7.2 建立了渲染层 i18next 集中文案字典，但主进程直接 broadcast 的
|
||||
* toast / 系统通知（压缩、死循环、故障转移、记忆固化、sendMessage 错误路径等）
|
||||
* 全部硬编码中文 —— en-US 用户看到的系统提示仍是中文，i18n 收口未完成。
|
||||
*
|
||||
* 契约：
|
||||
* - 语言偏好唯一来源是设置面板 `ui.locale`（zh-CN / en-US）；
|
||||
* - main.ts 启动时注入一次，config:set/setBatch 变更 ui.locale 时经
|
||||
* applyConfigSideEffects 热切换（无需重启）；
|
||||
* - mt(key, params) 为唯一取词入口，缺 key 时回退 zh 表，再缺失时回退 key 本身
|
||||
* （与渲染层 t() 的回退语义一致）；
|
||||
* - 文案表只覆盖"主进程主动产生"的文案；渲染层文案仍归 i18n-strings.ts。
|
||||
*/
|
||||
|
||||
export type MainLocale = 'zh-CN' | 'en-US';
|
||||
|
||||
let currentLocale: MainLocale = 'zh-CN';
|
||||
|
||||
/** 设置主进程语言（非法值保持不变；null/undefined 视为未配置 → zh-CN 默认） */
|
||||
export function setMainLocale(locale: string | null | undefined): void {
|
||||
if (locale === 'en-US' || locale === 'zh-CN') {
|
||||
currentLocale = locale;
|
||||
}
|
||||
}
|
||||
|
||||
export function getMainLocale(): MainLocale {
|
||||
return currentLocale;
|
||||
}
|
||||
|
||||
const zh: Record<string, string> = {
|
||||
// ===== sendMessage 错误路径 =====
|
||||
'agent.error.invalidSessionId': '无效的会话 ID',
|
||||
'agent.error.invalidMessage': '无效的消息格式',
|
||||
'agent.error.sessionBusy': '该会话正在执行任务，请等待完成或先中断后再发送',
|
||||
'agent.error.sessionMissing': '会话不存在或已被删除，请刷新后重试',
|
||||
'agent.error.adapterLoadFailed':
|
||||
'Adapter 加载失败，请检查 LLM 配置（Provider、API Key、Base URL、Model 是否完整）',
|
||||
// ===== SOUL.md 降级提示 =====
|
||||
'agent.soul.fallbackToast':
|
||||
'未找到 SOUL.md 或内容为空，已使用默认 Metona 身份。可在工作空间根目录创建 SOUL.md 自定义 Agent 人格',
|
||||
// ===== 引擎事件 toast =====
|
||||
'agent.toast.compressed': '上下文压缩: {{original}} → {{compressed}} tokens（节省 {{saved}}）',
|
||||
'agent.toast.deadLoop':
|
||||
'检测到死循环（第 {{iteration}} 轮）：连续3轮重复相同工具调用，已自动终止',
|
||||
'agent.toast.providerSwitched': 'Provider 故障转移: {{from}} → {{to}}（主 Provider 请求失败）',
|
||||
'agent.toast.consolidated': 'AI 已将 {{count}} 条重要记忆写入 MEMORY.md',
|
||||
'agent.toast.memoryOversize': 'MEMORY.md 已超过 50KB，建议在「记忆」面板整理记忆',
|
||||
// ===== LLM 运行时查询 =====
|
||||
'llm.balance.queryFailed': '余额查询失败（API Key 无效或网络错误）',
|
||||
'llm.listModels.notConfigured': 'LLM 未配置（Provider/Model 为空），无法获取模型列表',
|
||||
'llm.listModels.noApiKey': 'API Key 未配置，无法获取模型列表',
|
||||
'llm.listModels.configInvalid': 'LLM 配置校验失败，请先在设置中修正配置',
|
||||
'llm.listModels.unsupported': '当前 Provider 不支持模型列表查询',
|
||||
'llm.pull.ollamaOnly': '仅 Ollama Provider 支持模型下载',
|
||||
'llm.pull.inProgress': '已有模型下载任务进行中，请先取消',
|
||||
'llm.pull.none': '没有进行中的下载任务',
|
||||
// ===== main.ts 适配器/Provider =====
|
||||
'config.toast.configIncomplete':
|
||||
'LLM 配置不完整，请在设置中补全 Provider、API Key、Base URL 和 Model',
|
||||
'config.toast.providerSwitched': 'Provider 已切换: {{from}} → {{to}}',
|
||||
'config.toast.providerSwitchFailed': 'Provider 切换失败: {{message}}',
|
||||
// ===== 系统通知 =====
|
||||
'notify.updateAvailable.title': 'MetonaAI 更新可用',
|
||||
'notify.updateAvailable.body': '新版本 {{version}} 已发布，可在 设置 → 日志与数据 中下载安装',
|
||||
'notify.taskCompleted.title': 'MetonaAI — 任务完成',
|
||||
'notify.taskCompleted.body': 'Agent 已完成任务 ({{seconds}}s)',
|
||||
// ===== shared.ts 配置副作用 =====
|
||||
'config.error.configIncomplete':
|
||||
'LLM 配置不完整，请检查 Provider、API Key、Base URL 和 Model 是否都已填写',
|
||||
'config.error.workspaceSaveFailed': '工作空间路径保存失败：{{message}}',
|
||||
// ===== 记忆维护（v0.8.1 P1-2） =====
|
||||
'memory.maintain.auditTitle': 'MEMORY.md 记忆整理',
|
||||
};
|
||||
|
||||
const en: Record<string, string> = {
|
||||
'agent.error.invalidSessionId': 'Invalid session ID',
|
||||
'agent.error.invalidMessage': 'Invalid message format',
|
||||
'agent.error.sessionBusy':
|
||||
'This session is already running. Please wait for it to finish or abort it first.',
|
||||
'agent.error.sessionMissing': 'Session does not exist or has been deleted. Please refresh.',
|
||||
'agent.error.adapterLoadFailed':
|
||||
'Adapter load failed. Check your LLM config (Provider, API Key, Base URL, Model).',
|
||||
'agent.soul.fallbackToast':
|
||||
'SOUL.md not found or empty — using the default Metona identity. Create SOUL.md in the workspace root to customize the agent persona.',
|
||||
'agent.toast.compressed':
|
||||
'Context compressed: {{original}} → {{compressed}} tokens (saved {{saved}})',
|
||||
'agent.toast.deadLoop':
|
||||
'Dead loop detected (iteration {{iteration}}): the same tool call repeated 3 times — aborted automatically',
|
||||
'agent.toast.providerSwitched': 'Provider failover: {{from}} → {{to}} (primary provider failed)',
|
||||
'agent.toast.consolidated': '{{count}} important memories were written to MEMORY.md',
|
||||
'agent.toast.memoryOversize':
|
||||
'MEMORY.md exceeds 50KB — consider tidying it up in the Memory panel',
|
||||
'llm.balance.queryFailed': 'Balance query failed (invalid API key or network error)',
|
||||
'llm.listModels.notConfigured': 'LLM not configured (Provider/Model empty) — cannot list models',
|
||||
'llm.listModels.noApiKey': 'API Key not configured — cannot list models',
|
||||
'llm.listModels.configInvalid': 'LLM config validation failed — fix it in Settings first',
|
||||
'llm.listModels.unsupported': 'The current provider does not support model listing',
|
||||
'llm.pull.ollamaOnly': 'Only the Ollama provider supports model download',
|
||||
'llm.pull.inProgress': 'A model download is already in progress — cancel it first',
|
||||
'llm.pull.none': 'No download in progress',
|
||||
'config.toast.configIncomplete':
|
||||
'LLM config incomplete. Please set Provider, API Key, Base URL and Model in Settings.',
|
||||
'config.toast.providerSwitched': 'Provider switched: {{from}} → {{to}}',
|
||||
'config.toast.providerSwitchFailed': 'Provider switch failed: {{message}}',
|
||||
'notify.updateAvailable.title': 'MetonaAI update available',
|
||||
'notify.updateAvailable.body':
|
||||
'Version {{version}} has been released. Install it in Settings → Logs & Data.',
|
||||
'notify.taskCompleted.title': 'MetonaAI — task completed',
|
||||
'notify.taskCompleted.body': 'The agent finished the task ({{seconds}}s)',
|
||||
'config.error.configIncomplete':
|
||||
'LLM config incomplete. Check that Provider, API Key, Base URL and Model are all filled in.',
|
||||
'config.error.workspaceSaveFailed': 'Failed to save workspace path: {{message}}',
|
||||
'memory.maintain.auditTitle': 'MEMORY.md maintenance',
|
||||
};
|
||||
|
||||
const DICTS: Record<MainLocale, Record<string, string>> = { 'zh-CN': zh, 'en-US': en };
|
||||
|
||||
/** 主进程文案取词：缺 key 回退 zh 表；参数以 {{name}} 占位替换 */
|
||||
export function mt(key: string, params?: Record<string, string | number>): string {
|
||||
const raw = DICTS[currentLocale][key] ?? zh[key] ?? key;
|
||||
if (!params) return raw;
|
||||
return raw.replace(/\{\{(\w+)\}\}/g, (_, name: string) => String(params[name] ?? ''));
|
||||
}
|
||||
@@ -80,12 +80,46 @@ export async function applySessionProxy(proxyUrl: string | null | undefined): Pr
|
||||
// ===== 通道二：主进程 Node fetch（undici 全局 dispatcher）=====
|
||||
// 动态 import：主进程所有 fetch 出口共享该调度器；setGlobalDispatcher 写入的
|
||||
// 全局符号对所有引用同一 undici registry 的 fetch 实例生效。失败不阻断主流程。
|
||||
//
|
||||
// v0.8.1 根治: 代理 dispatcher 必须放行回环目标（127.0.0.1 / ::1 / localhost）。
|
||||
// 此前全局 ProxyAgent 会把发往本机的请求（本地 Ollama / SearXNG / E2E mock LLM
|
||||
// / 指向 localhost 的任意 Provider）也交给系统代理 —— 代理不可用或拒绝回环时
|
||||
// 这类请求全部 "fetch failed"。现用组合 dispatcher：回环直连、其余走代理
|
||||
// （与 NO_PROXY=127.0.0.1,localhost 的通用语义一致）。
|
||||
await (async () => {
|
||||
try {
|
||||
const { Agent, ProxyAgent, setGlobalDispatcher } = await import('undici');
|
||||
const undici = await import('undici');
|
||||
const { Agent, ProxyAgent, Dispatcher, setGlobalDispatcher } = undici;
|
||||
if (rules !== '') {
|
||||
setGlobalDispatcher(new ProxyAgent({ uri: rules, connectTimeout: 15_000 }));
|
||||
log.info(`[Network] Node fetch dispatcher → ProxyAgent(${rules})`);
|
||||
const direct = new Agent({ connectTimeout: 15_000 });
|
||||
const proxy = new ProxyAgent({ uri: rules, connectTimeout: 15_000 });
|
||||
type DispatchArgs = Parameters<InstanceType<typeof Dispatcher>['dispatch']>;
|
||||
class LoopbackBypassDispatcher extends Dispatcher {
|
||||
override dispatch(...args: DispatchArgs): boolean {
|
||||
const opts = args[0];
|
||||
let host = '';
|
||||
if (typeof opts.origin === 'string') {
|
||||
host = new URL(opts.origin).hostname;
|
||||
} else if (opts.origin instanceof URL) {
|
||||
host = opts.origin.hostname;
|
||||
}
|
||||
const isLoopback =
|
||||
host === '127.0.0.1' ||
|
||||
host === '::1' ||
|
||||
host === '[::1]' ||
|
||||
host === 'localhost' ||
|
||||
host.endsWith('.localhost');
|
||||
return (isLoopback ? direct : proxy).dispatch(...args);
|
||||
}
|
||||
override async close(): Promise<void> {
|
||||
await Promise.all([direct.close(), proxy.close()]);
|
||||
}
|
||||
override async destroy(): Promise<void> {
|
||||
await Promise.all([direct.destroy(), proxy.destroy()]);
|
||||
}
|
||||
}
|
||||
setGlobalDispatcher(new LoopbackBypassDispatcher());
|
||||
log.info(`[Network] Node fetch dispatcher → ProxyAgent(${rules}) + loopback bypass`);
|
||||
} else {
|
||||
setGlobalDispatcher(new Agent());
|
||||
log.info('[Network] Node fetch dispatcher → direct Agent');
|
||||
|
||||
@@ -17,6 +17,49 @@ import log from 'electron-log';
|
||||
/** 加密值前缀标记（版本化，便于未来算法升级） */
|
||||
const ENCRYPTION_PREFIX = 'metona-enc:v1:';
|
||||
|
||||
/**
|
||||
* v0.8.1 根治: 加密可用性探测（roundtrip probe）。
|
||||
*
|
||||
* isEncryptionAvailable()=true 并不保证加解密可实际往返（部分桌面/服务会话下
|
||||
* DPAPI/keyring 返回的密文无法回解，写后读必失败 → API Key 被静默清空）。
|
||||
* 现在进程内首次使用时做一次 encrypt→decrypt 回环校验：
|
||||
* - 通过 → 正常加密存储；
|
||||
* - 失败 → 本次会话降级为明文存储（与 isEncryptionAvailable=false 同语义），
|
||||
* 保持功能可用并 WARN 留痕；读取侧对"本会话明文"无感知（无前缀原样返回）。
|
||||
*/
|
||||
let encryptionUsable: boolean | null = null;
|
||||
|
||||
function isEncryptionUsable(): boolean {
|
||||
if (encryptionUsable !== null) return encryptionUsable;
|
||||
try {
|
||||
if (!safeStorage.isEncryptionAvailable()) {
|
||||
encryptionUsable = false;
|
||||
return false;
|
||||
}
|
||||
const probe = 'metona-probe-0123456789abcdef';
|
||||
const cipher = safeStorage.encryptString(probe);
|
||||
const roundtrip = safeStorage.decryptString(cipher);
|
||||
encryptionUsable = roundtrip === probe;
|
||||
if (!encryptionUsable) {
|
||||
log.warn(
|
||||
'[SecureConfig] safeStorage roundtrip probe failed — falling back to plaintext storage for this session',
|
||||
);
|
||||
}
|
||||
} catch (err) {
|
||||
log.warn(
|
||||
'[SecureConfig] safeStorage probe threw — falling back to plaintext:',
|
||||
(err as Error).message,
|
||||
);
|
||||
encryptionUsable = false;
|
||||
}
|
||||
return encryptionUsable;
|
||||
}
|
||||
|
||||
/** 测试专用：清除 roundtrip 探测缓存（生产代码不得调用） */
|
||||
export function resetEncryptionUsableForTests(): void {
|
||||
encryptionUsable = null;
|
||||
}
|
||||
|
||||
/** 敏感配置 key 匹配模式（与 IPC 层审计脱敏规则保持一致） */
|
||||
const SENSITIVE_KEY_PATTERNS = [
|
||||
'apikey',
|
||||
@@ -57,7 +100,7 @@ export function encryptConfigValue(value: unknown): unknown {
|
||||
if (typeof value !== 'string' || value.length === 0) return value;
|
||||
if (isEncryptedValue(value)) return value; // 已加密，幂等
|
||||
try {
|
||||
if (!safeStorage.isEncryptionAvailable()) {
|
||||
if (!isEncryptionUsable()) {
|
||||
log.warn('[SecureConfig] safeStorage 不可用，敏感配置将以明文存储');
|
||||
return value;
|
||||
}
|
||||
|
||||
Reference in New Issue
Block a user