thzxx
26169b7be4
P1 修复面收口: /clear 全链路根治(前端清空联动 DB messages+摘要游标+TRACE 快照, IPC 语义改"操作完成"; 流式中拒绝); web_browser open 补 SSRF 校验(Chromium 旁路关闭, 与 web_fetch/http_request 同源 validateSSRF); MCP 工具结果纳入注入扫描(mcp_* 前缀 按网络来源同级 full 模式, 收敛 resolveScanMode 单点); Trace 落库/入 store 双重瘦身 (tool_result base64/超长字段剥离, metadata 防 MB 级膨胀); 文本附件 512KB 闸门 (file.slice 首段读取+truncated 标志随消息持久化+主进程附件提示感知截断); 单实例锁(requestSingleInstanceLock + second-instance 聚焦已有窗口) P2 安全纵深: ConfirmationHook 多窗口化(确认请求/超时提示改全窗口广播, getAllWindows 空时回退 mainWindow, fail-closed 判定升级双通道); mcp_servers.headers 全链路接线(safeParseHeaders 容错解析+SSE/StreamableHTTP requestInit 注入+IPC 逐项 校验+设置页 JSON 输入, 远程 MCP 鉴权头可用) P3 断链接线: llm:listModels IPC(六家 adapter 动态模型发现首次接线, 配置完整性 前置校验); Ollama pullModel IPC+设置页下载卡片(进度/取消/能力徽标, v0.7.0 死代码 激活); 后台会话运行指示(sessionRunStates 图+Sidebar 状态点, 多会话并发可见); IR 卫生(移除 THINKING_START/END 死枚举, constraints 标注预留) P4 质量与文档: i18n 第二阶段(确认弹框/侧栏/状态栏/AgentMonitor/终止原因出层, 外观设置 zh-CN/en-US 切换, ui.locale 持久化, 渲染时求值规避异步注册); README/D1 文档对齐(http_request 风险等级/用例数/实现状态注记); 版本号 0.7.2 测试: 507 → 737 用例(+230, 11 个新文件)。覆盖补齐: context-builder/consolidator/ orchestrator/workspace.service/session-recorder/config-layering/secure-config/ network-proxy + IPC mcp/tasks/memory/app/data 域 + 渲染层 store 与流事件管线纯函数。 测试驱动修复: workspace.appendMemory 中文分区 \b 词边界失效(JS \b 不含 CJK), 固化条目恒追加文件末尾产生重复分区头 → (?=\n|$) 前瞻断言根治 回归: typecheck 双端 0 错误; ESLint 0/0; 系统 Node 687 通过 50 跳过; Electron ABI 全量 737/737 零跳过
1076 lines
41 KiB
TypeScript
1076 lines
41 KiB
TypeScript
/**
|
||
* IPC Agent Handlers — Agent 交互域（P2-9 从 handlers.ts 拆分）
|
||
*
|
||
* 职责：
|
||
* 1. agent:sendMessage — 消息发送编排（历史加载/记忆注入/注入检测/runStream/持久化）
|
||
* 2. agent:abortSession — 中断会话（联动 SubAgent）
|
||
* 3. 常驻引擎事件管道 — 流式转发（按会话节流）、状态广播、TRACE 录制（P1-6 补全 9 种事件）、
|
||
* 压缩/死循环/Provider 切换通知
|
||
*
|
||
* P2-10: 事件监听从"每消息 attach/detach"改为常驻管道（按 sessionId 隔离状态），
|
||
* 支持多会话并发流式与多窗口广播。
|
||
*/
|
||
|
||
import { ipcMain } from 'electron';
|
||
import type { IPCContext } from './context';
|
||
import { broadcast } from './context';
|
||
import type { MetonaMessage, MetonaStreamEvent, MetonaError } from '../harness/types';
|
||
import { MetonaErrorCode, MetonaStreamEventType } from '../harness/types';
|
||
import { estimateMessagesTokens } from '../harness/utils/token-estimator';
|
||
import { DeepSeekAdapter } from '../harness/adapters/deepseek.adapter';
|
||
import { OllamaAdapter } from '../harness/adapters/ollama.adapter';
|
||
import log from 'electron-log';
|
||
|
||
/** 单会话的 text_delta 节流状态 */
|
||
interface ThrottleState {
|
||
buffer: string;
|
||
lastEventMeta: Pick<
|
||
MetonaStreamEvent,
|
||
'requestId' | 'sessionId' | 'iteration' | 'seq' | 'timestamp' | 'runId'
|
||
> | null;
|
||
flushTimer: ReturnType<typeof setTimeout> | null;
|
||
}
|
||
|
||
/** 单会话的迭代录制状态（TRACE 层） */
|
||
interface IterationTrace {
|
||
iteration: number;
|
||
startedAt: number;
|
||
text: string;
|
||
usage?: { input: number; output: number; total: number };
|
||
responded: boolean; // 本轮 llm_response 是否已记录（PARSING 与下一轮 THINKING 去重）
|
||
}
|
||
|
||
export function registerAgentHandlers(ctx: IPCContext): void {
|
||
const {
|
||
agentEngineManager,
|
||
sessionRecorder,
|
||
configService,
|
||
sessionService,
|
||
workspaceService,
|
||
contextBuilder,
|
||
auditService,
|
||
memoryManager,
|
||
promptInjectionDefender,
|
||
outputValidator,
|
||
memoryConsolidator,
|
||
sessionSummaryService,
|
||
orchestrator,
|
||
confirmationHook,
|
||
} = ctx;
|
||
|
||
// ===== 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>();
|
||
|
||
const flushThrottle = (sessionId: string): void => {
|
||
const st = throttleStates.get(sessionId);
|
||
if (!st) return;
|
||
st.flushTimer = null;
|
||
if (!st.buffer || !st.lastEventMeta) {
|
||
st.buffer = '';
|
||
st.lastEventMeta = null;
|
||
return;
|
||
}
|
||
// 构建合并的 text_delta 事件，保留最后一个 delta 的元数据
|
||
const mergedEvent: MetonaStreamEvent = {
|
||
...st.lastEventMeta,
|
||
type: MetonaStreamEventType.TEXT_DELTA,
|
||
delta: st.buffer,
|
||
seq: st.lastEventMeta.seq,
|
||
timestamp: Date.now(),
|
||
};
|
||
broadcast('agent:streamEvent', mergedEvent);
|
||
st.buffer = '';
|
||
st.lastEventMeta = null;
|
||
};
|
||
|
||
const cleanupSessionState = (sessionId: string): void => {
|
||
const st = throttleStates.get(sessionId);
|
||
if (st?.flushTimer) {
|
||
clearTimeout(st.flushTimer);
|
||
flushThrottle(sessionId);
|
||
}
|
||
throttleStates.delete(sessionId);
|
||
iterationTraces.delete(sessionId);
|
||
};
|
||
|
||
// ===== 常驻监听：流式事件（节流转发 + TRACE 录制） =====
|
||
agentEngineManager.on('streamEvent', (event: MetonaStreamEvent) => {
|
||
if (!event.sessionId) return;
|
||
const sessionId = event.sessionId;
|
||
const trace = iterationTraces.get(sessionId);
|
||
|
||
// F8: text_delta 聚合，其他事件立即转发（先 flush 保证顺序）
|
||
if (event.type === MetonaStreamEventType.TEXT_DELTA && event.delta) {
|
||
const st = throttleStates.get(sessionId) ?? {
|
||
buffer: '',
|
||
lastEventMeta: null,
|
||
flushTimer: null,
|
||
};
|
||
throttleStates.set(sessionId, st);
|
||
if (st.buffer === '') {
|
||
st.lastEventMeta = {
|
||
requestId: event.requestId,
|
||
sessionId: event.sessionId,
|
||
iteration: event.iteration,
|
||
seq: event.seq,
|
||
timestamp: event.timestamp,
|
||
runId: event.runId,
|
||
};
|
||
} else if (st.lastEventMeta) {
|
||
st.lastEventMeta = {
|
||
...st.lastEventMeta,
|
||
seq: event.seq,
|
||
timestamp: event.timestamp,
|
||
runId: event.runId,
|
||
};
|
||
}
|
||
st.buffer += event.delta;
|
||
if (st.flushTimer === null) {
|
||
st.flushTimer = setTimeout(() => flushThrottle(sessionId), 32);
|
||
}
|
||
// TRACE: 累积本轮 LLM 文本（供 llm_response 记录）
|
||
if (trace) trace.text += event.delta;
|
||
return;
|
||
}
|
||
|
||
// 非 text_delta 事件：先 flush 缓冲区，再立即转发（保证事件顺序）
|
||
const st = throttleStates.get(sessionId);
|
||
if (st?.flushTimer) {
|
||
clearTimeout(st.flushTimer);
|
||
flushThrottle(sessionId);
|
||
}
|
||
|
||
// TRACE: 工具调用/结果/usage 录制（P1-6 补全）
|
||
switch (event.type) {
|
||
case MetonaStreamEventType.TOOL_CALL_COMPLETE:
|
||
if (event.toolCall) {
|
||
sessionRecorder.recordToolCall({
|
||
sessionId,
|
||
iteration: event.iteration,
|
||
toolName: event.toolCall.name,
|
||
args: event.toolCall.args,
|
||
});
|
||
}
|
||
break;
|
||
case MetonaStreamEventType.TOOL_RESULT:
|
||
if (event.toolResult) {
|
||
const resultPreview =
|
||
typeof event.toolResult.result === 'string'
|
||
? event.toolResult.result
|
||
: JSON.stringify(event.toolResult.result);
|
||
sessionRecorder.recordToolResult({
|
||
sessionId,
|
||
iteration: event.iteration,
|
||
toolName: event.toolResult.toolName,
|
||
success: event.toolResult.success,
|
||
durationMs: event.toolResult.durationMs ?? 0,
|
||
resultPreview,
|
||
error: event.toolResult.error,
|
||
});
|
||
}
|
||
break;
|
||
case MetonaStreamEventType.USAGE:
|
||
if (trace && event.usage) {
|
||
trace.usage = {
|
||
input: event.usage.inputTokens ?? 0,
|
||
output: event.usage.outputTokens ?? 0,
|
||
total: event.usage.totalTokens ?? 0,
|
||
};
|
||
}
|
||
break;
|
||
case MetonaStreamEventType.DONE:
|
||
cleanupSessionState(sessionId);
|
||
break;
|
||
default:
|
||
break;
|
||
}
|
||
|
||
broadcast('agent:streamEvent', event);
|
||
});
|
||
|
||
// ===== 常驻监听：状态变化（广播 + TRACE 迭代录制） =====
|
||
agentEngineManager.on(
|
||
'stateChange',
|
||
(data: {
|
||
previous?: string;
|
||
current?: string;
|
||
state?: string;
|
||
sessionId?: string;
|
||
iteration?: number;
|
||
runId?: string;
|
||
}) => {
|
||
if (data.previous) log.info(`[AGENT] State: ${data.previous} → ${data.current}`);
|
||
broadcast('agent:stateChange', data);
|
||
|
||
const sessionId = data.sessionId;
|
||
if (!sessionId || data.iteration == null) return;
|
||
const stateValue = data.state ?? data.current ?? '';
|
||
const trace = iterationTraces.get(sessionId);
|
||
|
||
// THINKING 且迭代号变化 → 新迭代开始（关闭上一迭代）
|
||
if (stateValue === 'THINKING' && (!trace || trace.iteration !== data.iteration)) {
|
||
if (trace && !trace.responded) {
|
||
sessionRecorder.recordLLMResponse({
|
||
sessionId,
|
||
iteration: trace.iteration,
|
||
content: trace.text,
|
||
finishReason: 'stop',
|
||
tokenUsage: trace.usage ?? { input: 0, output: 0, total: 0 },
|
||
});
|
||
}
|
||
if (trace) {
|
||
sessionRecorder.recordIterationEnd(sessionId, {
|
||
iteration: trace.iteration,
|
||
durationMs: Date.now() - trace.startedAt,
|
||
});
|
||
}
|
||
iterationTraces.set(sessionId, {
|
||
iteration: data.iteration,
|
||
startedAt: Date.now(),
|
||
text: '',
|
||
responded: false,
|
||
});
|
||
sessionRecorder.recordIterationStart(sessionId, data.iteration);
|
||
const provider = configService.get<string>('llm.provider') ?? '';
|
||
const model = configService.get<string>('llm.model') ?? '';
|
||
sessionRecorder.recordLLMRequest({
|
||
sessionId,
|
||
iteration: data.iteration,
|
||
provider,
|
||
model,
|
||
messageCount: data.iteration + 1,
|
||
});
|
||
}
|
||
|
||
// PARSING → 本轮流式结束，记录 llm_response
|
||
if (
|
||
stateValue === 'PARSING' &&
|
||
trace &&
|
||
trace.iteration === data.iteration &&
|
||
!trace.responded
|
||
) {
|
||
trace.responded = true;
|
||
sessionRecorder.recordLLMResponse({
|
||
sessionId,
|
||
iteration: trace.iteration,
|
||
content: trace.text,
|
||
finishReason: 'stop',
|
||
tokenUsage: trace.usage ?? { input: 0, output: 0, total: 0 },
|
||
});
|
||
}
|
||
|
||
// TERMINATED → 补记最终迭代的 iteration_end（正常流程只在下一轮 THINKING 补记，
|
||
// 最终轮无后续迭代，需在此补齐 TRACE 完整性）+ 兜底清理会话管道状态
|
||
if (stateValue === 'TERMINATED') {
|
||
if (trace) {
|
||
sessionRecorder.recordIterationEnd(sessionId, {
|
||
iteration: trace.iteration,
|
||
durationMs: Date.now() - trace.startedAt,
|
||
});
|
||
}
|
||
cleanupSessionState(sessionId);
|
||
}
|
||
},
|
||
);
|
||
|
||
// ===== 常驻监听：上下文压缩（toast + streamEvent 通知） =====
|
||
agentEngineManager.on(
|
||
'compressed',
|
||
(data: {
|
||
sessionId?: string;
|
||
iteration?: number;
|
||
originalTokens?: number;
|
||
compressedTokens?: number;
|
||
}) => {
|
||
const savedTokens = Math.max(0, (data.originalTokens ?? 0) - (data.compressedTokens ?? 0));
|
||
// 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}）`,
|
||
});
|
||
// 通过 streamEvent 转发，前端 useAgentStream 监听 'compressed' 类型后更新 store
|
||
broadcast('agent:streamEvent', {
|
||
type: 'compressed',
|
||
sessionId: data.sessionId ?? '',
|
||
iteration: data.iteration ?? 0,
|
||
originalTokens: data.originalTokens,
|
||
compressedTokens: data.compressedTokens,
|
||
savedTokens,
|
||
timestamp: Date.now(),
|
||
});
|
||
},
|
||
);
|
||
|
||
// ===== 常驻监听：死循环检测（toast 警告） =====
|
||
agentEngineManager.on('deadLoop', (data: { iteration?: number; sessionId?: string }) => {
|
||
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`,
|
||
});
|
||
});
|
||
|
||
// ===== 常驻监听：Provider 故障转移（P1，通知前端 + toast） =====
|
||
agentEngineManager.on(
|
||
'providerSwitched',
|
||
(data: { from?: string; to?: string; reason?: string; sessionId?: string }) => {
|
||
broadcast('agent:providerSwitched', {
|
||
from: data.from,
|
||
to: data.to,
|
||
reason: data.reason ?? 'failover',
|
||
sessionId: data.sessionId ?? '',
|
||
});
|
||
broadcast('toast:show', {
|
||
type: 'warning',
|
||
message: `Provider 故障转移: ${data.from ?? '?'} → ${data.to ?? '?'}（主 Provider 请求失败）`,
|
||
});
|
||
},
|
||
);
|
||
|
||
// ===== Agent 消息发送 =====
|
||
|
||
ipcMain.handle(
|
||
'agent:sendMessage',
|
||
async (_event, userMessage: MetonaMessage, sessionId: string) => {
|
||
// M-33 修复: 参数校验，防止 undefined/非字符串导致下游异常
|
||
// P1-5 修复: 校验失败时也发 ERROR+DONE 流事件，防止 isStreaming 永久卡死
|
||
const sendErrorEvent = (message: string, sid: string): void => {
|
||
const errorEvent: MetonaStreamEvent = {
|
||
type: MetonaStreamEventType.ERROR,
|
||
requestId: '',
|
||
sessionId: sid,
|
||
iteration: 0,
|
||
seq: 0,
|
||
timestamp: Date.now(),
|
||
error: { code: MetonaErrorCode.UNKNOWN, message, retryable: false },
|
||
};
|
||
broadcast('agent:streamEvent', errorEvent);
|
||
broadcast('agent:streamEvent', { ...errorEvent, type: MetonaStreamEventType.DONE });
|
||
};
|
||
|
||
if (!sessionId || typeof sessionId !== 'string') {
|
||
log.warn('[AGENT] sendMessage rejected: invalid sessionId');
|
||
sendErrorEvent('无效的会话 ID', sessionId ?? '');
|
||
return { success: false, error: 'Invalid sessionId' };
|
||
}
|
||
if (
|
||
!userMessage ||
|
||
typeof userMessage !== 'object' ||
|
||
typeof userMessage.content !== 'string'
|
||
) {
|
||
log.warn('[AGENT] sendMessage rejected: invalid userMessage');
|
||
sendErrorEvent('无效的消息格式', sessionId);
|
||
return { success: false, error: 'Invalid message format' };
|
||
}
|
||
log.info('[AGENT] sendMessage:', sessionId, (userMessage.content ?? '').slice(0, 80));
|
||
|
||
// 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）';
|
||
log.error('[AGENT]', errorMsg);
|
||
sendErrorEvent(errorMsg, sessionId);
|
||
sessionRecorder.stopRecording(sessionId, {
|
||
totalIterations: 0,
|
||
totalTokens: 0,
|
||
durationMs: 0,
|
||
terminationReason: 'error',
|
||
});
|
||
return { success: false, error: errorMsg };
|
||
}
|
||
|
||
// TRACE 层：开始录制 / TOOL 层：记录会话开始
|
||
sessionRecorder.startRecording(sessionId);
|
||
auditService.logSessionStart(sessionId);
|
||
|
||
// 保存用户消息到数据库
|
||
sessionService.saveMessage({
|
||
sessionId,
|
||
role: 'user',
|
||
content: userMessage.content,
|
||
attachments: (userMessage as MetonaMessage & { attachments?: unknown[] }).attachments,
|
||
});
|
||
|
||
// P2-11: 分层加载历史——存在滚动摘要时只加载 [摘要 + 近期原文]
|
||
const history = sessionSummaryService.buildHistoryMessages(sessionId).slice(0, -1);
|
||
|
||
// 从工作空间文件构建 System Prompt
|
||
const workspaceFiles = workspaceService.getFiles();
|
||
const systemPrompt = contextBuilder.buildSystemPrompt(
|
||
workspaceFiles,
|
||
workspaceService.getPath(),
|
||
);
|
||
|
||
// v0.3.18 修复: SOUL.md 为空或不存在时降级到默认身份，向前端发 toast 提示用户
|
||
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',
|
||
});
|
||
}
|
||
|
||
// 检索与用户消息相关的记忆，注入到 System Prompt 动态区
|
||
try {
|
||
const memories = memoryManager.search(userMessage.content, { topK: 5, minImportance: 0.3 });
|
||
if (memories.length > 0) {
|
||
const memorySection = memories
|
||
.map(
|
||
(m, i) =>
|
||
`[${i + 1}] (${m.type}, 重要度: ${m.importance.toFixed(1)}) ${m.content.slice(0, 200)}`,
|
||
)
|
||
.join('\n');
|
||
const memoryBlock = `## Relevant Memories (Retrieved)\n${memorySection}`;
|
||
systemPrompt.dynamicReminders = systemPrompt.dynamicReminders
|
||
? `${systemPrompt.dynamicReminders}\n\n---\n\n${memoryBlock}`
|
||
: memoryBlock;
|
||
log.debug(`[AGENT] Injected ${memories.length} memories into system prompt`);
|
||
}
|
||
} catch (err) {
|
||
log.warn('[AGENT] Memory retrieval failed, proceeding without memories:', err);
|
||
}
|
||
|
||
// 附件提示注入：用户直接上传的文件/图片，避免 LLM 误以为需要在工作空间查找
|
||
const attachments = (
|
||
userMessage as MetonaMessage & { attachments?: Array<{ name: string; type: string }> }
|
||
).attachments;
|
||
if (Array.isArray(attachments) && attachments.length > 0) {
|
||
const attachmentList = attachments
|
||
.map((att, i) => {
|
||
const typeLabel =
|
||
att.type === 'image' ? 'image' : att.type === 'text' ? 'text file' : 'file';
|
||
// v0.7.2 A5: 文本附件被上传入口截断（512KB 上限）时，明确告知 LLM 内容不完整，
|
||
// 防止模型把残缺内容当作完整文件事实
|
||
const truncatedNote =
|
||
(att as { truncated?: boolean }).truncated === true
|
||
? ' (TRUNCATED — only the first 512KB is included; the full content is NOT available)'
|
||
: '';
|
||
const note =
|
||
att.type === 'image'
|
||
? 'already provided to you via vision capability — you can SEE it directly, do NOT call view_image or any tool to read it again'
|
||
: att.type === 'text'
|
||
? `content${truncatedNote} already inlined in the user message, do NOT search in workspace or read it again`
|
||
: 'uploaded directly by user, do NOT search in workspace';
|
||
return `${i + 1}. [${typeLabel}] ${att.name} — ${note}`;
|
||
})
|
||
.join('\n');
|
||
|
||
const attachmentBlock = `## User Attachments (Direct Upload)\nThe following files were uploaded directly by the user to this conversation. They are inline attachments, NOT workspace files:\n${attachmentList}\n\n**IMPORTANT**: Images listed above are already visible to you in this conversation. Do NOT call \`view_image\`, \`read_file\`, or any file tool to read them — doing so wastes a tool call and may fail (they are not workspace files).`;
|
||
|
||
systemPrompt.dynamicReminders = systemPrompt.dynamicReminders
|
||
? `${systemPrompt.dynamicReminders}\n\n---\n\n${attachmentBlock}`
|
||
: attachmentBlock;
|
||
|
||
log.debug(`[AGENT] Injected ${attachments.length} attachment hints into system prompt`);
|
||
}
|
||
|
||
try {
|
||
// 提示注入检测（安全模块）
|
||
// F-8 接通: security.promptInjectionDefense=false 时跳过用户消息检测
|
||
// （工具结果侧的 SecurityScanHook 由 main.ts 按同一配置决定是否挂载）
|
||
// fail-secure: 仅显式 false 才关闭 —— 配置值异常（空串/null/类型错误）时保持防护开启
|
||
const injectionEnabled =
|
||
configService.get<boolean>('security.promptInjectionDefense') !== false;
|
||
if (injectionEnabled) {
|
||
const injectionResult = promptInjectionDefender.detect(userMessage.content);
|
||
if (injectionResult.riskScore >= 7) {
|
||
log.warn('[PromptInjectionDefender] Blocked message:', injectionResult.findings);
|
||
sendErrorEvent(
|
||
`Message blocked by prompt injection defense: ${injectionResult.recommendation}`,
|
||
sessionId,
|
||
);
|
||
sessionRecorder.stopRecording(sessionId, {
|
||
totalIterations: 0,
|
||
totalTokens: 0,
|
||
durationMs: 0,
|
||
terminationReason: 'error',
|
||
});
|
||
return { success: false, error: 'Message blocked by prompt injection defense' };
|
||
}
|
||
if (injectionResult.riskScore >= 4) {
|
||
log.warn(
|
||
'[PromptInjectionDefender] Suspicious patterns detected:',
|
||
injectionResult.findings,
|
||
);
|
||
}
|
||
}
|
||
|
||
// TRACE 层：记录上下文构建
|
||
sessionRecorder.recordContextBuilt(sessionId, {
|
||
tokenCount: estimateMessagesTokens(history),
|
||
usageRatio: 0,
|
||
});
|
||
|
||
// 启动 Agent Loop（P2-10: 每会话独立引擎）
|
||
const engine = agentEngineManager.getEngine(sessionId);
|
||
const output = await engine.runStream(userMessage, sessionId, history, systemPrompt);
|
||
|
||
// 输出验证（不阻塞响应，仅记录警告）
|
||
// v0.3.0 修复: 传入 toolResults 和 context，启用事实一致性检查和幻觉检测
|
||
// v0.4.1: warning 及以上级别的 issue 通过 VALIDATION 流事件推送前端展示（此前仅写日志，用户不可感知）
|
||
try {
|
||
const toolResults = output.iterations
|
||
.flatMap((step) => step.toolResults ?? [])
|
||
.map((r) => (typeof r.result === 'string' ? r.result : JSON.stringify(r.result)));
|
||
const context = [...history, { role: 'user', content: userMessage.content }]
|
||
.map((m) => `${m.role}: ${m.content}`)
|
||
.join('\n');
|
||
const validation = await outputValidator.validate(output.finalAnswer, {
|
||
toolResults: toolResults.length > 0 ? toolResults : undefined,
|
||
context,
|
||
});
|
||
if (!validation.valid || validation.issues.length > 0) {
|
||
log.warn('[OutputValidator] Validation issues:', validation.issues);
|
||
}
|
||
log.debug(`[OutputValidator] Score: ${validation.score}, Valid: ${validation.valid}`);
|
||
|
||
// v0.4.1: 推送验证结果到前端 — 只推送 warning/error 级（info 级为噪声）
|
||
// 类型谓词收窄 severity，确保与 MetonaValidationPayload.issues 的类型一致
|
||
const visibleIssues = validation.issues
|
||
.filter(
|
||
(i): i is typeof i & { severity: 'warning' | 'error' } =>
|
||
i.severity === 'warning' || i.severity === 'error',
|
||
)
|
||
.slice(0, 5);
|
||
if (visibleIssues.length > 0) {
|
||
broadcast('agent:streamEvent', {
|
||
type: MetonaStreamEventType.VALIDATION,
|
||
requestId: '',
|
||
sessionId,
|
||
iteration: output.iterations.length,
|
||
seq: 0,
|
||
timestamp: Date.now(),
|
||
validation: {
|
||
score: validation.score,
|
||
issues: visibleIssues.map((i) => ({
|
||
severity: i.severity,
|
||
type: i.type,
|
||
message: i.message,
|
||
})),
|
||
},
|
||
} satisfies MetonaStreamEvent);
|
||
}
|
||
} catch (err) {
|
||
log.error('[OutputValidator] Validation failed:', err);
|
||
}
|
||
|
||
// 保存每轮迭代的 assistant 消息到数据库（含思考内容和工具调用）
|
||
// 崩溃修复（会话停止根因的持久化侧）: 原 `if (!step.thought) continue;` 把
|
||
// 纯工具调用轮（零文本零思考）整个跳过 — assistant 与 tool 结果都不落库。
|
||
// 后果：① 重启后历史缺失工具上下文（模型"忘记"做过什么，重复调用 →
|
||
// 表现为工具调用不稳定）；② 与 engine 运行时缺陷同源。现改为：
|
||
// 有 thought 或有 toolCalls 的步骤都保存。
|
||
for (const step of output.iterations) {
|
||
if (!step.thought && !(step.toolCalls && step.toolCalls.length > 0)) continue;
|
||
|
||
const toolCallsWithResults = step.toolCalls?.map((tc) => {
|
||
const result = step.toolResults?.find((r) => r.toolCallId === tc.id);
|
||
return {
|
||
id: tc.id,
|
||
name: tc.name,
|
||
args: tc.args,
|
||
status: result?.success ? ('success' as const) : ('error' as const),
|
||
result: result?.result,
|
||
durationMs: result?.durationMs,
|
||
error: result?.error,
|
||
};
|
||
});
|
||
|
||
// 只有当有内容、思考内容或工具调用时才保存
|
||
if (
|
||
step.thought?.content ||
|
||
step.thought?.reasoningContent ||
|
||
toolCallsWithResults?.length
|
||
) {
|
||
// C-6 修复: assistant 消息仅有 tool_calls 时 content 必须为 null（而非空字符串）
|
||
const assistantContent =
|
||
toolCallsWithResults?.length && !step.thought?.content
|
||
? null
|
||
: (step.thought?.content ?? null);
|
||
sessionService.saveMessage({
|
||
sessionId,
|
||
role: 'assistant',
|
||
content: assistantContent,
|
||
reasoningContent: step.thought?.reasoningContent || undefined,
|
||
toolCalls: toolCallsWithResults,
|
||
iteration: step.iteration,
|
||
});
|
||
}
|
||
|
||
// v0.3.0 修复: 保存 tool 结果消息到数据库
|
||
// OpenAI 兼容 API 要求 assistant 消息有 tool_calls 时，后续必须有对应的 tool 结果消息
|
||
if (step.toolResults) {
|
||
for (const result of step.toolResults) {
|
||
const resultContent =
|
||
typeof result.result === 'string' ? result.result : JSON.stringify(result.result);
|
||
sessionService.saveMessage({
|
||
sessionId,
|
||
role: 'tool',
|
||
content: result.error ?? resultContent,
|
||
toolResult: result,
|
||
iteration: step.iteration,
|
||
});
|
||
}
|
||
}
|
||
}
|
||
|
||
// 更新 Token 统计
|
||
if (output.totalTokenUsage.totalTokens > 0) {
|
||
sessionService.updateTokenUsage(sessionId, output.totalTokenUsage.totalTokens);
|
||
}
|
||
|
||
// 更新 MEMORY.md 时间戳
|
||
workspaceService.updateMemoryTimestamp();
|
||
|
||
// 会话结束：AI 判断本次对话有哪些重要内容需要持久化到 MEMORY.md
|
||
// 异步执行，不阻塞主流程返回；失败仅记录日志
|
||
memoryConsolidator
|
||
.consolidate(userMessage.content, output.finalAnswer, output.iterations)
|
||
.then((result) => {
|
||
if (result.appended > 0) {
|
||
log.info(
|
||
`[AGENT] Memory consolidated: ${result.appended} entries appended to MEMORY.md`,
|
||
);
|
||
broadcast('toast:show', {
|
||
type: 'info',
|
||
message: `AI 已将 ${result.appended} 条重要记忆写入 MEMORY.md`,
|
||
});
|
||
}
|
||
})
|
||
.catch((err) => {
|
||
log.warn('[AGENT] Memory consolidation failed:', err);
|
||
});
|
||
|
||
// TOOL 层：记录会话结束 / TRACE 层：停止录制
|
||
auditService.logSessionEnd({
|
||
sessionId,
|
||
totalIterations: output.iterations.length,
|
||
totalTokens: output.totalTokenUsage.totalTokens,
|
||
durationMs: output.durationMs,
|
||
terminationReason: output.terminationReason,
|
||
});
|
||
sessionRecorder.stopRecording(sessionId, {
|
||
totalIterations: output.iterations.length,
|
||
totalTokens: output.totalTokenUsage.totalTokens,
|
||
durationMs: output.durationMs,
|
||
terminationReason: output.terminationReason,
|
||
});
|
||
|
||
// P2-11: 会话结束后评估滚动摘要（fire-and-forget，失败仅记录）
|
||
sessionSummaryService.maybeSummarize(sessionId).catch((err) => {
|
||
log.warn('[AGENT] Session summary generation failed:', err);
|
||
});
|
||
|
||
log.info(
|
||
`[AGENT] Completed: ${output.terminationReason}, ${output.iterations.length} iterations, ${output.durationMs}ms`,
|
||
);
|
||
|
||
return { success: true };
|
||
} catch (error) {
|
||
log.error('[AGENT] Error:', error);
|
||
|
||
// TOOL 层：记录错误
|
||
auditService.log({
|
||
sessionId,
|
||
eventType: 'error',
|
||
actor: 'agent',
|
||
target: 'agent_loop',
|
||
details: { error: (error as Error).message },
|
||
outcome: 'error',
|
||
});
|
||
|
||
// TRACE 层：停止录制
|
||
sessionRecorder.stopRecording(sessionId, {
|
||
totalIterations: 0,
|
||
totalTokens: 0,
|
||
durationMs: 0,
|
||
terminationReason: 'error',
|
||
});
|
||
|
||
// 发送错误事件到 UI
|
||
const metonaError: MetonaError = {
|
||
code: MetonaErrorCode.UNKNOWN,
|
||
message: (error as Error).message,
|
||
retryable: false,
|
||
};
|
||
broadcast('agent:streamEvent', {
|
||
type: MetonaStreamEventType.ERROR,
|
||
requestId: '',
|
||
sessionId,
|
||
iteration: 0,
|
||
seq: 0,
|
||
timestamp: Date.now(),
|
||
error: metonaError,
|
||
} satisfies MetonaStreamEvent);
|
||
|
||
return { success: false, error: (error as Error).message };
|
||
}
|
||
},
|
||
);
|
||
|
||
// ===== v0.5.0: DeepSeek 余额查询（复用 DeepSeekAdapter.getBalance，原为死代码） =====
|
||
ipcMain.handle('llm:getBalance', async () => {
|
||
try {
|
||
const adapter = agentEngineManager.getAdapter();
|
||
if (!(adapter instanceof DeepSeekAdapter)) {
|
||
return {
|
||
success: false,
|
||
error: 'Balance query is only supported for the DeepSeek provider',
|
||
};
|
||
}
|
||
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: true, data: balance };
|
||
} catch (error) {
|
||
log.warn('[AGENT] getBalance failed:', (error as Error).message);
|
||
return { success: false, error: (error as Error).message };
|
||
}
|
||
});
|
||
|
||
// ===== v0.7.2 P3-9: 动态模型列表（六家 adapter 的 listModels 首次获得 IPC 消费者）=====
|
||
// 此前 DeepSeek/OpenAI/Ollama 均实现了实时模型发现（/models、/api/tags），
|
||
// 但 preload/IPC 层无任何通道 —— 设置页模型输入只能靠用户手填。
|
||
// 契约：列表基于"已保存"的 LLM 配置（与引擎实际使用的 adapter 同源）；
|
||
// 配置不完整时显式失败，绝不返回兜底 adapter 的误导性静态列表。
|
||
ipcMain.handle('llm:listModels', async () => {
|
||
try {
|
||
const provider = configService.get<string>('llm.provider') ?? '';
|
||
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' };
|
||
}
|
||
if (!apiKey && provider !== 'ollama') {
|
||
return { success: false, error: 'API Key 未配置，无法获取模型列表' };
|
||
}
|
||
// 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' };
|
||
}
|
||
const adapter = agentEngineManager.getAdapter();
|
||
if (!adapter.listModels) {
|
||
return { success: false, error: '当前 Provider 不支持模型列表查询' };
|
||
}
|
||
const models = await adapter.listModels();
|
||
return { success: true, data: models };
|
||
} catch (error) {
|
||
log.warn('[AGENT] listModels failed:', (error as Error).message);
|
||
return { success: false, error: (error as Error).message };
|
||
}
|
||
});
|
||
|
||
// ===== v0.7.2 P3-10: Ollama 模型下载（adapter.pullModel 首次接线 IPC/UI）=====
|
||
// v0.6.4 P4-1 已实现 pull 的进度回调与外部取消信号，但主进程侧零消费者。
|
||
// 契约：同一时刻仅允许一个下载任务（全局 AbortController）；
|
||
// 进度经 llm:ollamaPullProgress 广播（{ model, status, completed?, total? }），
|
||
// 结束（成功/失败/取消）统一广播 llm:ollamaPullEnded 供前端复位 UI。
|
||
let ollamaPullController: AbortController | null = null;
|
||
|
||
ipcMain.handle('llm:ollamaPull', async (_event, modelName: unknown) => {
|
||
if (typeof modelName !== 'string' || !modelName.trim()) {
|
||
return { success: false, error: 'Invalid model name' };
|
||
}
|
||
// 模型名仅进入 JSON body（不经 shell），仍做字符白名单防转义边界
|
||
// 合法形态如 qwen3:8b / llama3.1:70b-instruct-q4_K_M / user/model
|
||
if (!/^[A-Za-z0-9._:/-]+$/.test(modelName.trim())) {
|
||
return { success: false, error: `Invalid model name: ${modelName.slice(0, 60)}` };
|
||
}
|
||
const adapter = agentEngineManager.getAdapter();
|
||
if (!(adapter instanceof OllamaAdapter)) {
|
||
return { success: false, error: '仅 Ollama Provider 支持模型下载' };
|
||
}
|
||
if (ollamaPullController) {
|
||
return { success: false, error: '已有模型下载任务进行中，请先取消' };
|
||
}
|
||
const controller = new AbortController();
|
||
ollamaPullController = controller;
|
||
const trimmed = modelName.trim();
|
||
try {
|
||
await adapter.pullModel(
|
||
trimmed,
|
||
(progress) => {
|
||
broadcast('llm:ollamaPullProgress', { model: trimmed, ...progress });
|
||
},
|
||
controller.signal,
|
||
);
|
||
return { success: true };
|
||
} catch (error) {
|
||
return {
|
||
success: false,
|
||
error: (error as Error).message,
|
||
aborted: controller.signal.aborted,
|
||
};
|
||
} finally {
|
||
ollamaPullController = null;
|
||
broadcast('llm:ollamaPullEnded', { model: trimmed });
|
||
}
|
||
});
|
||
|
||
ipcMain.handle('llm:ollamaPullCancel', async () => {
|
||
if (!ollamaPullController) {
|
||
return { success: false, error: '没有进行中的下载任务' };
|
||
}
|
||
ollamaPullController.abort();
|
||
return { success: true };
|
||
});
|
||
|
||
// ===== 中断会话 =====
|
||
|
||
ipcMain.handle('agent:abortSession', async (_event, sessionId) => {
|
||
log.info('[AGENT] Abort:', sessionId);
|
||
// P2-10: 联动中断该会话派生的所有 SubAgent（消除"会话停了子任务还在跑"）
|
||
// v0.5.1: 记录被中止的 taskId — SubAgent 的 pending 确认以 taskId 为 sessionId，
|
||
// 需一并清理，否则中止后孤儿工具在用户补批残留弹框时会真实执行副作用
|
||
const abortedTaskIds = orchestrator.abortByParent(sessionId);
|
||
agentEngineManager.abort(sessionId);
|
||
// MT-1 修复: 等待当前 run 完全结束再返回，防止用户立即重发时新消息卡在等待中
|
||
await agentEngineManager.waitForAbort(sessionId);
|
||
// v0.3.0 修复: 清理所有等待中的工具确认，避免定时器泄漏和超时 toast 在新会话中弹出
|
||
// v0.5.0: 按会话清理 — 只拒绝被中断会话的 pending，不影响其他并发会话等待中的确认
|
||
confirmationHook.clearPending(sessionId);
|
||
// v0.5.1: 被中止 SubAgent 的 pending 确认一并拒绝（含 SubAgent 递归派生的孙任务）
|
||
for (const taskId of abortedTaskIds) {
|
||
confirmationHook.clearPending(taskId);
|
||
}
|
||
|
||
// TOOL 层：记录中断
|
||
auditService.log({
|
||
sessionId,
|
||
eventType: 'session_end',
|
||
actor: 'user',
|
||
target: 'session',
|
||
details: { reason: 'user_abort' },
|
||
outcome: 'denied',
|
||
});
|
||
|
||
return { success: true };
|
||
});
|
||
|
||
// ===== v0.5.0: SubAgent 可观测性 =====
|
||
// 1) 生命周期事件广播给前端（AgentMonitor 的 SubAgent 状态区）
|
||
// 2) SubEngine 流事件录制到独立 TRACE 文件（sessionId = taskId），不污染父会话的流
|
||
// 此前 orchestrator 的 6 个事件全项目零消费者，SubAgent 执行过程对 UI 与 TRACE 完全不可见
|
||
|
||
/** SubAgent 元信息（description/depth，供完成/失败事件广播时补全载荷） */
|
||
const subMeta = new Map<string, { description: string; depth: number }>();
|
||
/** SubAgent 迭代录制状态（THINKING 驱动新迭代，text 累积供 llm_response） */
|
||
interface SubTraceState {
|
||
iteration: number;
|
||
startedAt: number;
|
||
text: string;
|
||
usage: { input: number; output: number; total: number };
|
||
responded: boolean;
|
||
}
|
||
const subTraces = new Map<string, SubTraceState>();
|
||
|
||
const finishSubTrace = (
|
||
taskId: string,
|
||
reason: string,
|
||
durationMs: number,
|
||
iterations: number,
|
||
): void => {
|
||
sessionRecorder.stopRecording(taskId, {
|
||
totalIterations: iterations,
|
||
totalTokens: 0,
|
||
durationMs,
|
||
terminationReason: reason,
|
||
});
|
||
subTraces.delete(taskId);
|
||
subMeta.delete(taskId);
|
||
};
|
||
|
||
orchestrator.on(
|
||
'taskDelegated',
|
||
(d: { taskId: string; description: string; parentSessionId: string; depth: number }) => {
|
||
broadcast('subagent:event', {
|
||
taskId: d.taskId,
|
||
parentSessionId: d.parentSessionId,
|
||
description: d.description,
|
||
status: 'delegated',
|
||
depth: d.depth,
|
||
});
|
||
subMeta.set(d.taskId, { description: d.description, depth: d.depth });
|
||
sessionRecorder.startRecording(d.taskId);
|
||
},
|
||
);
|
||
|
||
orchestrator.on(
|
||
'taskStarted',
|
||
(d: { taskId: string; description: string; parentSessionId: string; depth: number }) => {
|
||
broadcast('subagent:event', {
|
||
taskId: d.taskId,
|
||
parentSessionId: d.parentSessionId,
|
||
description: d.description,
|
||
status: 'running',
|
||
depth: d.depth,
|
||
});
|
||
},
|
||
);
|
||
|
||
orchestrator.on(
|
||
'taskCompleted',
|
||
(r: {
|
||
taskId: string;
|
||
parentSessionId: string;
|
||
success: boolean;
|
||
durationMs: number;
|
||
iterations: number;
|
||
}) => {
|
||
const meta = subMeta.get(r.taskId);
|
||
broadcast('subagent:event', {
|
||
taskId: r.taskId,
|
||
parentSessionId: r.parentSessionId,
|
||
description: meta?.description ?? '',
|
||
status: r.success ? 'completed' : 'error',
|
||
depth: meta?.depth ?? 1,
|
||
durationMs: r.durationMs,
|
||
iterations: r.iterations,
|
||
});
|
||
finishSubTrace(r.taskId, r.success ? 'completed' : 'error', r.durationMs, r.iterations);
|
||
},
|
||
);
|
||
|
||
orchestrator.on(
|
||
'taskError',
|
||
(d: { taskId: string; parentSessionId: string; description: string; error: string }) => {
|
||
const meta = subMeta.get(d.taskId);
|
||
broadcast('subagent:event', {
|
||
taskId: d.taskId,
|
||
parentSessionId: d.parentSessionId,
|
||
description: d.description ?? meta?.description ?? '',
|
||
status: 'error',
|
||
depth: meta?.depth ?? 1,
|
||
error: d.error,
|
||
});
|
||
finishSubTrace(d.taskId, 'error', 0, 0);
|
||
},
|
||
);
|
||
|
||
// SubEngine 状态事件 → TRACE 迭代录制（与主管道相同的事件形状，sessionId = taskId）
|
||
orchestrator.on(
|
||
'subStateChange',
|
||
({
|
||
taskId,
|
||
data,
|
||
}: {
|
||
taskId: string;
|
||
data: { state?: string; current?: string; iteration?: number };
|
||
}) => {
|
||
const st = subTraces.get(taskId);
|
||
const stateValue = data.state ?? data.current ?? '';
|
||
|
||
// THINKING 且迭代号变化 → 新迭代开始（关闭上一迭代）
|
||
if (
|
||
stateValue === 'THINKING' &&
|
||
data.iteration != null &&
|
||
(!st || st.iteration !== data.iteration)
|
||
) {
|
||
if (st && !st.responded) {
|
||
sessionRecorder.recordLLMResponse({
|
||
sessionId: taskId,
|
||
iteration: st.iteration,
|
||
content: st.text,
|
||
finishReason: 'stop',
|
||
tokenUsage: st.usage,
|
||
});
|
||
sessionRecorder.recordIterationEnd(taskId, {
|
||
iteration: st.iteration,
|
||
durationMs: Date.now() - st.startedAt,
|
||
});
|
||
}
|
||
subTraces.set(taskId, {
|
||
iteration: data.iteration,
|
||
startedAt: Date.now(),
|
||
text: '',
|
||
usage: { input: 0, output: 0, total: 0 },
|
||
responded: false,
|
||
});
|
||
sessionRecorder.recordIterationStart(taskId, data.iteration);
|
||
const provider = configService.get<string>('llm.provider') ?? '';
|
||
const model = configService.get<string>('llm.model') ?? '';
|
||
sessionRecorder.recordLLMRequest({
|
||
sessionId: taskId,
|
||
iteration: data.iteration,
|
||
provider,
|
||
model,
|
||
messageCount: data.iteration + 1,
|
||
});
|
||
return;
|
||
}
|
||
|
||
// TERMINATED → 补记最终迭代（正常流程只在下一轮 THINKING 补记，最终轮无后续）
|
||
if (stateValue === 'TERMINATED' && st) {
|
||
if (!st.responded) {
|
||
sessionRecorder.recordLLMResponse({
|
||
sessionId: taskId,
|
||
iteration: st.iteration,
|
||
content: st.text,
|
||
finishReason: 'stop',
|
||
tokenUsage: st.usage,
|
||
});
|
||
}
|
||
sessionRecorder.recordIterationEnd(taskId, {
|
||
iteration: st.iteration,
|
||
durationMs: Date.now() - st.startedAt,
|
||
});
|
||
}
|
||
},
|
||
);
|
||
|
||
// SubEngine 流事件 → TRACE 内容录制（文本累积 + 工具调用/结果）
|
||
orchestrator.on(
|
||
'subStreamEvent',
|
||
({ taskId, event }: { taskId: string; event: MetonaStreamEvent }) => {
|
||
const st = subTraces.get(taskId);
|
||
if (!st) return;
|
||
|
||
switch (event.type) {
|
||
case MetonaStreamEventType.TEXT_DELTA:
|
||
if (event.delta) st.text += event.delta;
|
||
break;
|
||
case MetonaStreamEventType.USAGE:
|
||
if (event.usage) {
|
||
st.usage = {
|
||
input: event.usage.inputTokens ?? 0,
|
||
output: event.usage.outputTokens ?? 0,
|
||
total: event.usage.totalTokens ?? 0,
|
||
};
|
||
}
|
||
break;
|
||
case MetonaStreamEventType.TOOL_CALL_COMPLETE:
|
||
if (event.toolCall) {
|
||
sessionRecorder.recordToolCall({
|
||
sessionId: taskId,
|
||
iteration: event.iteration,
|
||
toolName: event.toolCall.name,
|
||
args: event.toolCall.args,
|
||
});
|
||
}
|
||
break;
|
||
case MetonaStreamEventType.TOOL_RESULT:
|
||
if (event.toolResult) {
|
||
const resultPreview =
|
||
typeof event.toolResult.result === 'string'
|
||
? event.toolResult.result
|
||
: JSON.stringify(event.toolResult.result);
|
||
sessionRecorder.recordToolResult({
|
||
sessionId: taskId,
|
||
iteration: event.iteration,
|
||
toolName: event.toolResult.toolName,
|
||
success: event.toolResult.success,
|
||
durationMs: event.toolResult.durationMs ?? 0,
|
||
resultPreview,
|
||
error: event.toolResult.error,
|
||
});
|
||
}
|
||
break;
|
||
default:
|
||
break;
|
||
}
|
||
},
|
||
);
|
||
}
|