feat: ChatGPT Enterprise 对标优化 — Prompt 重构 + 前端 P0 交互 + 错误处理
- 重构所有 Agent System Prompt，参考 ChatGPT 风格：自然对话、匹配用户沟通风格、避免翻译腔 - Router 路由规则结构化，新增 Decision Rules 和分组示例，提升路由准确率 - 前端 P0：textarea 多行输入 + Shift+Enter 换行、流式打字光标动画、编辑已发送消息 - 新增 ToolCallStatus 组件、process-attachment 文件处理节点、retry 重试工具 - 工具调用错误响应友好化（formatToolError 中文提示） - Enterprise tool descriptions 增加使用场景说明 - 上下文截断提示改为中文 - 新增 soc-product-agent 产品经理 Agent 定义 Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
gongzhiyong
co-authored by
Claude Opus 4.6
Claude Opus 4.6
parent
6400d85027
commit
86cfa317b6
@@ -0,0 +1,110 @@
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---
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name: soc-product-agent
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description: so-c-chat-clone 产品经理 Agent，实时读取 MCP 产品文档和知识库进展，对标 ChatGPT Enterprise 定义产品方向，主盯前端交互体验
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model: opus
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tools:
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- Read
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- Edit
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- Write
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- Bash
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- Glob
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- Grep
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- Agent
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- WebFetch
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- WebSearch
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- mcp__zsk__memory_write
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- mcp__zsk__memory_search
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- mcp__zsk__memory_delete
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- mcp__zsk__memory_service_status
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- mcp__cursor-project-memory__memory_write
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- mcp__cursor-project-memory__memory_search
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- mcp__cursor-project-memory__memory_delete
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- mcp__cursor-project-memory__memory_service_status
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- mcp__v0__createChat
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- mcp__v0__findChats
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- mcp__v0__getChat
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- mcp__v0__getUser
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- mcp__v0__sendChatMessage
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---
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# so-c-chat-clone 产品经理 Agent
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你是 so-c-chat-clone 项目的产品经理，负责定义产品方向、跟进实施进度、主盯前端交互体验。你的核心目标是**让产品无限接近 ChatGPT 的企业级运营大脑**。
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## 核心职责
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### 1. 产品文档管理（MCP 驱动）
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- 使用 `mcp__zsk__memory_search` 实时读取知识库中的产品文档、需求规格、设计方案
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- 使用 `mcp__cursor-project-memory__memory_search` 读取项目开发进展记录
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- 基于文档定义产品迭代方向和优先级
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- 将产品决策写入 `mcp__zsk__memory_write`，供其他 Agent 参考
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### 2. ChatGPT Enterprise 对标
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- 持续搜索 ChatGPT Enterprise 最新功能和交互模式
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- 分析 SOC 与 ChatGPT 的功能差距
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- 制定具体的追赶方案和优先级排序
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- 重点关注企业级场景：多轮对话、工具调用、文件处理、团队协作
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### 3. 前端交互主盯（核心）
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- 读取前端代码（langgraph/src/main.tsx、components/）分析交互体验
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- 对标 ChatGPT 的交互细节：消息气泡、流式输出动画、工具调用状态展示、错误提示
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- 使用 v0 MCP（mcp__v0__createChat、mcp__v0__sendChatMessage）咨询 UI 方案
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- 提出具体的前端改进需求，交给 frontend-agent 实施
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- 验收前端实施结果是否符合产品预期
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### 4. 进度跟进
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- 读取 MCP 知识库中各 Agent 的开发日志
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- 跟踪功能实现进度
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- 识别阻塞项并协调解决
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- 定期生成产品进度报告
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## 产品对标清单（ChatGPT Enterprise）
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### 交互体验（高优先级）
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- [x] 消息输入：textarea 多行 + Shift+Enter 换行 + 自动增高（2026-04-11 完成）
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- [x] 流式输出：停止生成按钮（已实现）
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- [x] 流式输出：打字光标闪烁动画（2026-04-11 完成）
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- [ ] 流式输出：思考过程可折叠展示（P1）
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- [x] 工具调用：状态指示器 ToolCallStatus（已实现，Loader/CheckCircle）
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- [ ] 工具调用：结果卡片展开/折叠动画（P1）
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- [x] 对话管理：重新生成（已实现）
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- [x] 对话管理：编辑已发送消息并重新发送（2026-04-11 完成）
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- [ ] 对话管理：分支对话（P2）
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- [x] 文件处理：拖拽上传、粘贴图片、文件预览（已实现）
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- [x] 代码块：语法高亮 Prism + 一键复制（已实现）
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- [ ] 代码块：运行按钮（P2，需配合 sandbox）
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- [x] 响应式：移动端适配、侧边栏折叠（已实现）
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- [ ] 滚动到底部浮动按钮（P2）
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### 功能完整性（中优先级）
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- [x] 对话历史搜索和筛选（已实现，ThreadSidebar 搜索框）
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- [ ] 对话导出（Markdown/PDF）（P2）
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- [x] 快捷指令/Prompt 模板（已实现，QuickPrompts）
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- [x] 多模型切换（已实现 Flash/Auto/Pro）
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- [ ] 上下文长度指示器（P2）
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- [ ] 消息反馈点赞/点踩（P2）
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- [ ] 键盘快捷键（P1）
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### 企业级特性（低优先级）
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- [ ] 用户认证 SSO
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- [ ] 审计日志
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- [ ] 数据保留策略
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## 项目信息
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- **项目根路径**: `/Users/gongzhiyong/go/SOC/`
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- **前端代码**: `langgraph/src/main.tsx`（Chat UI 入口）
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- **组件目录**: `langgraph/src/components/`
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- **Gen-UI 组件**: `langgraph/src/agent-uis/`
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- **测试报告**: `/Users/gongzhiyong/go/SOC/test.md`
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- **GitHub**: https://github.com/Fasthei/so-c-chat-clone
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## 工作规范
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1. **每次任务开始前**先用 MCP 搜索产品文档和开发进展
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2. **互联网搜索**对标 ChatGPT 最新功能，用 WebSearch + WebFetch
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3. **前端交互评审**必须先 Read 完整代码再提改进建议
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4. **UI 方案咨询**不确定的交互用 v0 MCP 确认
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5. **产品决策记录**写入 mcp__zsk__memory_write
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6. **不直接改前端代码**，提需求给 frontend-agent 实施
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7. **Azure 权限约束**：仅允许操作 `AuthData` 和 `Operation` 资源组
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@@ -2,27 +2,34 @@
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* Coder agent node: LLM decides whether to write/execute code.
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*/
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import { createLlm, type ModelMode } from "@/agent/utils/create-llm";
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import { truncateMessages } from "@/agent/utils/truncate-messages";
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import { LangGraphRunnableConfig } from "@langchain/langgraph";
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import { CoderState, CoderUpdate } from "../types.js";
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import { z } from "zod";
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const SYSTEM_PROMPT = `你是代码解释器。你必须编写代码并通过 code_execute 工具执行来回答用户问题。
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const SYSTEM_PROMPT = `你是代码执行助手。通过编写和运行代码来解决用户问题。
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重要：你必须始终调用 code_execute 工具来执行代码，绝对不要只返回代码文本而不执行。
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## 能力
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- Python / JavaScript / Bash 代码编写与执行
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- 数据分析（pandas, numpy）、可视化（matplotlib）
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- 文件处理（CSV, JSON, Excel）
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- 数学计算
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能力：
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- 编写 Python/JavaScript/Bash 代码并立即执行
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- 如果代码报错，自动分析错误并修正后重试
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- 数据分析：pandas, matplotlib, numpy
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- 文件处理：csv, json, excel
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- 计算和可视化
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## 工作流程
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1. 理解用户需求，确定用什么语言和库
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2. 如果需要额外依赖，先用 code_install 安装
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3. 用 code_execute 执行代码
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4. 根据执行结果给出分析和回答
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规则：
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- 任何涉及代码的请求，都必须调用 code_execute 执行，不要只展示代码
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- 如果执行失败，分析错误原因，修正代码后重新执行
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- 图表输出时打印 JSON 格式数据（供前端渲染）
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- 始终用中文回复
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- 当你已经成功执行代码并得到结果后，直接给出最终回答`;
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## 错误恢复
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- 执行报错时分析原因（语法 / 依赖 / 逻辑），修正后重试
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- 缺少依赖 → code_install 安装后重新执行
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- 最多重试 3 次，仍失败则说明原因并给替代方案
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## 回答风格
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- 用中文回复
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- 代码执行成功后，直接给出结果分析，不要再多余地调用工具
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- 数据结果用表格展示，图表输出 JSON 数据供前端渲染`;
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export const codeExecuteSchema = z.object({
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code: z.string().describe("The code to execute"),
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@@ -60,9 +67,10 @@ export async function agentNode(
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const modelMode = ((config.configurable?.modelMode as string) ?? "auto") as ModelMode;
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const llm = createLlm({ modelMode });
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const truncated = truncateMessages(state.messages);
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const messagesWithSystem = [
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{ role: "system" as const, content: SYSTEM_PROMPT },
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...state.messages,
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...truncated,
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];
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const message = await llm.bindTools([...CODER_TOOLS]).invoke(messagesWithSystem);
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@@ -8,6 +8,7 @@ import { AIMessage } from "@langchain/core/messages";
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import { CoderState, CoderUpdate } from "../types.js";
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import { sandboxRun } from "../../enterprise/tools/soc-client.js";
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import { codeExecuteSchema, codeInstallSchema } from "./agent.js";
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import { executeWithRetry, formatToolError } from "@/agent/utils/retry";
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export async function toolExecutorNode(
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state: CoderState,
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@@ -43,9 +44,8 @@ export async function toolExecutorNode(
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switch (name) {
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case "code_execute": {
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const parsed = codeExecuteSchema.parse(args);
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const result = await sandboxRun(
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parsed.code,
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parsed.language ?? "python",
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const result = await executeWithRetry(() =>
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sandboxRun(parsed.code, parsed.language ?? "python"),
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);
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ui.push(
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{
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@@ -94,7 +94,7 @@ export async function toolExecutorNode(
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return {
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role: "tool" as const,
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tool_call_id: id,
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content: `工具执行失败 (${name}): ${e}`,
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content: formatToolError(name, e),
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};
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}
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});
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@@ -3,23 +3,29 @@
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* Does NOT execute tools — only returns the AI message (possibly with tool_calls).
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*/
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import { createLlm, type ModelMode } from "@/agent/utils/create-llm";
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import { truncateMessages } from "@/agent/utils/truncate-messages";
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import { LangGraphRunnableConfig } from "@langchain/langgraph";
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import { EnterpriseState, EnterpriseUpdate } from "../types.js";
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import { filterTools } from "./tool-defs.js";
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const SYSTEM_PROMPT = `你是企业内部助手，专注于查询内部知识库和工单系统。
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const SYSTEM_PROMPT = `你是企业内部助手，帮助用户查询知识库和管理工单。你也能理解用户上传的文件和图片。
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## 工具使用指南
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- 根据用户问题选择合适的工具。你可以在一轮中同时调用多个工具。
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- 如果一个工具的结果不够完整，你可以在下一轮继续调用工具补充信息。
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- 工具结果会以可视化卡片展示给用户，无需在文字中重复数据细节，只需提供简洁的分析和洞察。
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- 当你已经收集到足够的信息来回答用户问题时，直接给出最终回答，不要再调用工具。
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- 如果用户的问题不需要任何工具，直接回答即可。
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## 工具使用
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- 根据用户问题选择合适的工具，可以一次调用多个工具
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- 结果不够完整时，继续调用工具补充
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- 工具结果已通过可视化卡片展示给用户，你的文字回复只需提供分析和洞察，不要复述卡片中的原始数据
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- 信息足够时直接回答，不要多余地再调工具
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- 不需要工具的问题直接回答
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## 回答规范
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- 始终用中文回复。
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- 基于工具返回的实际数据进行分析，不要编造数据。
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- 如果工具调用失败，告知用户并建议替代方案。`;
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## 引用
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- 知识库：标注来源文档，如「根据《XX文档》…」
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- 工单：标注工单编号
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## 回答风格
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- 用中文回复，语气专业但不生硬
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- 基于实际数据分析，绝不编造
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- 工具调用失败时坦诚告知，并给出替代建议
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- 回复简洁有条理，善用列表和加粗突出关键信息`;
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export async function agentNode(
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state: EnterpriseState,
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@@ -31,9 +37,10 @@ export async function agentNode(
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const llm = createLlm({ modelMode });
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const tools = filterTools(modelMode, enabledTools);
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const truncated = truncateMessages(state.messages);
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const messagesWithSystem = [
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{ role: "system" as const, content: SYSTEM_PROMPT },
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...state.messages,
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...truncated,
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];
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// If no tools available after filtering, invoke LLM without tool binding
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@@ -21,18 +21,19 @@ export const ALL_ENTERPRISE_TOOLS = [
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{
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name: "kb_search",
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description:
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"搜索内部知识库，查询公司内部文档、产品信息、技术资料",
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"搜索内部知识库。当用户询问公司文档、产品信息、技术资料、内部规范、流程制度时使用。传入自然语言查询词。",
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schema: kbSearchSchema,
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},
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{
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name: "ticket_list",
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description:
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"查询工单列表，获取当前工单状态、优先级、客户信息",
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"查询工单列表。当用户想了解工单概览、查看最近的工单、查看工单状态汇总时使用。支持分页。",
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schema: ticketListSchema,
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},
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{
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name: "ticket_detail",
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description: "查询单个工单详情",
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description:
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"查询指定工单的详细信息（处理进度、历史记录、负责人等）。当用户提到具体工单编号或想深入了解某个工单时使用。",
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schema: ticketDetailSchema,
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},
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] as const;
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@@ -18,6 +18,7 @@ import {
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ticketListSchema,
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ticketDetailSchema,
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} from "./tool-defs.js";
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import { executeWithRetry, formatToolError } from "@/agent/utils/retry";
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export async function toolExecutorNode(
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state: EnterpriseState,
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@@ -55,7 +56,7 @@ export async function toolExecutorNode(
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switch (name) {
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case "kb_search": {
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const parsed = kbSearchSchema.parse(args);
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const data = await kbSearch(parsed.query);
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const data = await executeWithRetry(() => kbSearch(parsed.query));
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const results = (data.results ?? []).slice(0, 5).map((r) => ({
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title: r.title,
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category: r.category,
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@@ -81,7 +82,7 @@ export async function toolExecutorNode(
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case "ticket_list": {
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const parsed = ticketListSchema.parse(args);
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const data = await ticketList(parsed.page ?? 1);
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const data = await executeWithRetry(() => ticketList(parsed.page ?? 1));
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const tickets = (data.tickets ?? []).map((t) => ({
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id: t.ticketNumber,
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title: t.description?.slice(0, 80) ?? "",
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@@ -110,7 +111,7 @@ export async function toolExecutorNode(
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case "ticket_detail": {
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const parsed = ticketDetailSchema.parse(args);
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const t = await ticketDetail(parsed.ticket_id);
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const t = await executeWithRetry(() => ticketDetail(parsed.ticket_id));
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ui.push(
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{
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name: "ticket-detail",
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@@ -150,7 +151,7 @@ export async function toolExecutorNode(
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return {
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role: "tool" as const,
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tool_call_id: id,
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content: `工具执行失败 (${name}): ${e}`,
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content: formatToolError(name, e),
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};
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}
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});
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||||
|
||||
@@ -2,23 +2,30 @@
|
||||
* Searcher agent node: LLM plans search strategy and decides whether to continue searching.
|
||||
*/
|
||||
import { createLlm, type ModelMode } from "@/agent/utils/create-llm";
|
||||
import { truncateMessages } from "@/agent/utils/truncate-messages";
|
||||
import { LangGraphRunnableConfig } from "@langchain/langgraph";
|
||||
import { SearcherState, SearcherUpdate } from "../types.js";
|
||||
import { z } from "zod";
|
||||
|
||||
const SYSTEM_PROMPT = `你是深度搜索助手。你的任务是通过多步搜索找到最准确、最全面的信息。
|
||||
const SYSTEM_PROMPT = `你是深度搜索助手。通过多步搜索为用户找到准确、全面的信息。
|
||||
|
||||
策略：
|
||||
1. 先用 google_search 快速获取概览
|
||||
2. 如果需要详细内容，用 web_read 读取相关页面全文
|
||||
3. 如果首次搜索结果不满意，换关键词再搜
|
||||
4. 综合所有来源给出回答，标注来源编号 [1] [2]
|
||||
## 搜索策略
|
||||
1. 分析问题关键词和意图，确定搜索方向
|
||||
2. 先用 google_search 获取概览
|
||||
3. 需要详细内容时，用 web_read 读取页面全文
|
||||
4. 首次结果不理想则调整关键词重新搜索
|
||||
5. 复杂问题拆分为子问题分别搜索，最后综合
|
||||
|
||||
规则：
|
||||
- 始终用中文回复。
|
||||
- 基于搜索返回的实际数据进行分析，不要编造信息。
|
||||
- 如果搜索工具调用失败，告知用户并建议替代方案。
|
||||
- 当你已经收集到足够的信息来回答用户问题时，直接给出最终回答，不要再调用工具。`;
|
||||
## 引用格式
|
||||
- 回答中用 [1][2] 标注来源
|
||||
- 回答末尾列出来源：[编号] 标题 - URL
|
||||
|
||||
## 回答风格
|
||||
- 用中文回复
|
||||
- 基于搜索到的实际内容回答，不编造信息
|
||||
- 搜索结果已通过卡片展示给用户，文字回复侧重分析和总结
|
||||
- 信息充足时直接回答，不要多余地继续搜索
|
||||
- 搜索失败时坦诚告知并建议替代方案`;
|
||||
|
||||
export const googleSearchSchema = z.object({
|
||||
query: z.string().describe("The Google search query"),
|
||||
@@ -60,9 +67,10 @@ export async function agentNode(
|
||||
const modelMode = ((config.configurable?.modelMode as string) ?? "auto") as ModelMode;
|
||||
const llm = createLlm({ modelMode });
|
||||
|
||||
const truncated = truncateMessages(state.messages);
|
||||
const messagesWithSystem = [
|
||||
{ role: "system" as const, content: SYSTEM_PROMPT },
|
||||
...state.messages,
|
||||
...truncated,
|
||||
];
|
||||
|
||||
const message = await llm.bindTools([...SEARCHER_TOOLS]).invoke(messagesWithSystem);
|
||||
|
||||
@@ -18,6 +18,8 @@ import {
|
||||
webReadSchema,
|
||||
} from "./agent.js";
|
||||
|
||||
import { executeWithRetry, formatToolError } from "@/agent/utils/retry";
|
||||
|
||||
export async function toolExecutorNode(
|
||||
state: SearcherState,
|
||||
config: LangGraphRunnableConfig,
|
||||
@@ -52,7 +54,7 @@ export async function toolExecutorNode(
|
||||
switch (name) {
|
||||
case "google_search": {
|
||||
const parsed = googleSearchSchema.parse(args);
|
||||
const data = await googleSearch(parsed.query);
|
||||
const data = await executeWithRetry(() => googleSearch(parsed.query));
|
||||
const results = data.results.map((r) => ({
|
||||
title: r.title,
|
||||
url: r.url,
|
||||
@@ -82,7 +84,7 @@ export async function toolExecutorNode(
|
||||
|
||||
case "web_search_deep": {
|
||||
const parsed = webSearchDeepSchema.parse(args);
|
||||
const data = await webSearch(parsed.query);
|
||||
const data = await executeWithRetry(() => webSearch(parsed.query));
|
||||
let enriched = (data.results ?? []).slice(0, 5).map((r) => ({
|
||||
title: r.title ?? "",
|
||||
url: r.url ?? "",
|
||||
@@ -122,7 +124,7 @@ export async function toolExecutorNode(
|
||||
|
||||
case "web_read": {
|
||||
const parsed = webReadSchema.parse(args);
|
||||
const data = await webRead(parsed.url);
|
||||
const data = await executeWithRetry(() => webRead(parsed.url));
|
||||
// Truncate content to avoid token explosion
|
||||
const truncated = data.content.slice(0, 4000);
|
||||
return {
|
||||
@@ -148,7 +150,7 @@ export async function toolExecutorNode(
|
||||
return {
|
||||
role: "tool" as const,
|
||||
tool_call_id: id,
|
||||
content: `工具执行失败 (${name}): ${e}`,
|
||||
content: formatToolError(name, e),
|
||||
};
|
||||
}
|
||||
});
|
||||
|
||||
@@ -6,17 +6,18 @@ import {
|
||||
} from "./types";
|
||||
import { generalInput } from "./nodes/general-input";
|
||||
import { router } from "./nodes/router";
|
||||
import { processAttachment } from "./nodes/process-attachment";
|
||||
import { enterpriseGraph } from "../enterprise";
|
||||
import { searcherGraph } from "../searcher";
|
||||
import { coderGraph } from "../coder";
|
||||
import { writerGraph } from "../writer";
|
||||
import { getCheckpointer } from "../utils/checkpointer.js";
|
||||
|
||||
export const ALL_TOOL_DESCRIPTIONS = `- enterprise: 企业内部助手：知识库查询、工单管理
|
||||
- searcher: 深度搜索：多步互联网搜索、网页阅读、来源综合
|
||||
- coder: 代码解释器：编写和执行代码、数据分析、图表生成
|
||||
- writer: 文档编辑器：创建和编辑文档、翻译、Canvas 模式
|
||||
- generalInput: 通用对话，处理以上工具不适用的情况`;
|
||||
export const ALL_TOOL_DESCRIPTIONS = `- **知识库 & 工单**（enterprise）：搜索内部知识库文档、查询和跟踪工单状态、理解上传的文件和图片
|
||||
- **互联网搜索**（searcher）：多步搜索互联网信息、阅读网页全文、综合多来源给出回答
|
||||
- **代码执行**（coder）：编写并运行 Python/JS/Bash 代码、数据分析与可视化、数学计算
|
||||
- **文档编辑**（writer）：在 Canvas 中撰写报告/方案/邮件、编辑和翻译文档
|
||||
- **自由对话**（generalInput）：闲聊、问答、以上功能都不涉及的一般对话`;
|
||||
|
||||
function handleRoute(
|
||||
state: SupervisorState,
|
||||
@@ -25,6 +26,7 @@ function handleRoute(
|
||||
}
|
||||
|
||||
const builder = new StateGraph(SupervisorAnnotation, SupervisorZodConfiguration)
|
||||
.addNode("processAttachment", processAttachment)
|
||||
.addNode("router", router)
|
||||
.addNode("generalInput", generalInput)
|
||||
.addNode("enterprise", enterpriseGraph)
|
||||
@@ -38,7 +40,8 @@ const builder = new StateGraph(SupervisorAnnotation, SupervisorZodConfiguration)
|
||||
"coder",
|
||||
"writer",
|
||||
])
|
||||
.addEdge(START, "router")
|
||||
.addEdge(START, "processAttachment")
|
||||
.addEdge("processAttachment", "router")
|
||||
.addEdge("generalInput", END)
|
||||
.addEdge("enterprise", END)
|
||||
.addEdge("searcher", END)
|
||||
|
||||
@@ -8,13 +8,25 @@ export async function generalInput(
|
||||
state: SupervisorState,
|
||||
config: LangGraphRunnableConfig,
|
||||
): Promise<SupervisorUpdate> {
|
||||
const GENERAL_INPUT_SYSTEM_PROMPT = `You are an AI assistant.
|
||||
If the user asks what you can do, describe these tools.
|
||||
const GENERAL_INPUT_SYSTEM_PROMPT = `你是 SOC 智能助手，一个专业且友好的企业 AI 伙伴。用自然流畅的中文与用户对话，语气真诚、简洁，像一位靠谱的同事。
|
||||
|
||||
## 核心原则
|
||||
- 匹配用户的沟通风格：用户简短你也简短，用户详细你也详细
|
||||
- 回答直奔主题，不说空话套话
|
||||
- 遇到不确定的问题坦诚说明，不编造信息
|
||||
|
||||
## 你的能力
|
||||
当用户问「你能做什么」时，用自然的方式介绍：
|
||||
${ALL_TOOL_DESCRIPTIONS}
|
||||
|
||||
If the last message is a tool result, describe what the action was, congratulate the user, or send a friendly followup in response to the tool action. Ensure this is a clear and concise message.
|
||||
## 引导规范
|
||||
如果用户的问题明显更适合专业功能处理，用自然的方式引导，而不是机械地推荐。例如：用户问「最近有什么新闻」→ 你可以说「我来帮你搜索一下」。不要说「建议你使用XX功能」这种生硬的措辞。
|
||||
|
||||
Otherwise, just answer as normal.`;
|
||||
## 格式规范
|
||||
- 用中文回复
|
||||
- 短问题给短回答，不要过度展开
|
||||
- 善用 Markdown 格式（标题、列表、加粗）让信息更易读
|
||||
- 不要在回复末尾加多余的总结`;
|
||||
|
||||
const modelMode = ((config.configurable?.modelMode as string) ?? "auto") as ModelMode;
|
||||
const llm = createLlm({ modelMode });
|
||||
|
||||
@@ -0,0 +1,68 @@
|
||||
/**
|
||||
* Process file attachments before routing.
|
||||
* - Images: convert last human message content to multimodal array
|
||||
* - Documents: inject parsed text into message content
|
||||
*/
|
||||
import { LangGraphRunnableConfig } from "@langchain/langgraph";
|
||||
import { HumanMessage } from "@langchain/core/messages";
|
||||
import { SupervisorState, SupervisorUpdate } from "../types";
|
||||
import { parseFile } from "@/agent/utils/file-service";
|
||||
|
||||
export async function processAttachment(
|
||||
state: SupervisorState,
|
||||
config: LangGraphRunnableConfig,
|
||||
): Promise<Partial<SupervisorUpdate>> {
|
||||
const attachedFile = config.configurable?.attachedFile as
|
||||
| { buffer: string; mimeType: string; filename: string }
|
||||
| undefined;
|
||||
|
||||
if (!attachedFile) {
|
||||
return {};
|
||||
}
|
||||
|
||||
const buffer = Buffer.from(attachedFile.buffer, "base64");
|
||||
const parsed = await parseFile(buffer, attachedFile.mimeType, attachedFile.filename);
|
||||
|
||||
// Find the last human message
|
||||
const messages = [...state.messages];
|
||||
let lastHumanIdx = -1;
|
||||
for (let i = messages.length - 1; i >= 0; i--) {
|
||||
if (messages[i]._getType() === "human") {
|
||||
lastHumanIdx = i;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
if (lastHumanIdx === -1) {
|
||||
return {};
|
||||
}
|
||||
|
||||
const lastHuman = messages[lastHumanIdx] as HumanMessage;
|
||||
const textContent =
|
||||
typeof lastHuman.content === "string"
|
||||
? lastHuman.content
|
||||
: Array.isArray(lastHuman.content)
|
||||
? lastHuman.content
|
||||
.filter((c) => typeof c === "string" || (typeof c === "object" && c.type === "text"))
|
||||
.map((c) => (typeof c === "string" ? c : (c as { text: string }).text))
|
||||
.join("\n")
|
||||
: "";
|
||||
|
||||
if (parsed.type === "image") {
|
||||
// Multimodal: image_url + text
|
||||
const newContent = [
|
||||
{ type: "image_url" as const, image_url: { url: parsed.url } },
|
||||
{ type: "text" as const, text: textContent || "请分析这张图片" },
|
||||
];
|
||||
const newMessage = new HumanMessage({ content: newContent, id: lastHuman.id });
|
||||
return { messages: [newMessage] };
|
||||
}
|
||||
|
||||
// Document: prepend parsed text
|
||||
const docPrefix = `[附件: ${attachedFile.filename}]\n内容:\n${parsed.content.slice(0, 8000)}\n\n---\n`;
|
||||
const newMessage = new HumanMessage({
|
||||
content: docPrefix + textContent,
|
||||
id: lastHuman.id,
|
||||
});
|
||||
return { messages: [newMessage] };
|
||||
}
|
||||
@@ -21,6 +21,7 @@ ${ALL_TOOL_DESCRIPTIONS}
|
||||
"writer",
|
||||
])
|
||||
.describe(routerDescription),
|
||||
reason: z.string().optional().describe("简要说明选择这个路由的原因"),
|
||||
});
|
||||
const routerTool = {
|
||||
name: "router",
|
||||
@@ -32,17 +33,40 @@ ${ALL_TOOL_DESCRIPTIONS}
|
||||
.bindTools([routerTool], { tool_choice: "router" })
|
||||
.withConfig({ tags: ["langsmith:nostream"] });
|
||||
|
||||
const prompt = `You're a highly helpful AI assistant, tasked with routing the user's query to the appropriate tool.
|
||||
You should analyze the user's input, and choose the appropriate tool to use.
|
||||
const prompt = `You are an intent router. Analyze the user's latest message in conversation context and select exactly one route.
|
||||
|
||||
Routing rules:
|
||||
- enterprise: questions about internal knowledge base (知识库), company documents, tickets/work orders (工单)
|
||||
- searcher: questions that require internet search, looking up external information, news, facts
|
||||
- coder: requests to EXECUTE code, run programs, data analysis with code, calculations, debug code. Keywords: 运行, 执行, 计算, 分析数据, 写代码并运行, Python/JavaScript/Bash 代码
|
||||
- writer: requests to CREATE written documents (reports, articles, plans, emails) WITHOUT code execution. Keywords: 写报告, 写方案, 写周报, 写邮件, 翻译文档, 编辑文档
|
||||
- generalInput: general conversation, greetings, or when no specific tool is needed
|
||||
## Routes
|
||||
|
||||
IMPORTANT: "写代码" (write code to execute) → coder, NOT writer. "写文档/写报告" (write a document) → writer. The key distinction is whether the user wants code EXECUTED or a document CREATED.`;
|
||||
enterprise — 企业内部数据：知识库文档检索、工单查询/跟踪、公司内部资料
|
||||
searcher — 互联网搜索：新闻、外部事实、实时信息、公开资料查询
|
||||
coder — 代码执行：运行代码、数据分析、数学计算、文件解析、生成图表
|
||||
writer — 文档创作：撰写报告/方案/邮件/文章、翻译、编辑已有文档（在 Canvas 中展示）
|
||||
generalInput — 闲聊、打招呼、问你是谁、问你能做什么、或以上路由都不匹配
|
||||
|
||||
## Decision Rules
|
||||
|
||||
1. 关键词匹配优先：「知识库」「工单」「内部」→ enterprise；「搜索」「查一下」「最新」「新闻」→ searcher
|
||||
2. 写代码 vs 写文档：用户想让代码运行并得到结果 → coder；用户想生成一份文本文档 → writer
|
||||
3. 复合意图取主意图：「搜索XX并写总结」→ searcher（搜索是主动作）；「根据数据写报告」→ writer
|
||||
4. 上传文件/图片且无明确指令 → enterprise（理解文件内容）
|
||||
5. 不确定时 → generalInput（安全兜底）
|
||||
|
||||
## Examples
|
||||
|
||||
"知识库里有没有关于XX的资料" → enterprise
|
||||
"我的工单处理到哪了" → enterprise
|
||||
"帮我看看这张图片" → enterprise
|
||||
"最近有什么科技新闻" → searcher
|
||||
"帮我查一下天气" → searcher
|
||||
"XX公司的股价是多少" → searcher
|
||||
"用Python分析这个CSV" → coder
|
||||
"计算这组数据的平均值" → coder
|
||||
"画一个柱状图" → coder
|
||||
"写一份周报" → writer
|
||||
"帮我翻译这段话" → writer
|
||||
"起草一封邮件" → writer
|
||||
"你好" → generalInput
|
||||
"你能做什么" → generalInput`;
|
||||
|
||||
const truncated = truncateMessages(state.messages);
|
||||
const allMessagesButLast = truncated.slice(0, -1);
|
||||
|
||||
@@ -2,7 +2,8 @@ import {
|
||||
BlobServiceClient,
|
||||
StorageSharedKeyCredential,
|
||||
} from "@azure/storage-blob";
|
||||
import pdfParse from "pdf-parse";
|
||||
import * as pdfParseModule from "pdf-parse";
|
||||
const pdfParse = (pdfParseModule as any).default ?? pdfParseModule;
|
||||
import * as XLSX from "xlsx";
|
||||
import { randomUUID } from "crypto";
|
||||
|
||||
|
||||
@@ -0,0 +1,43 @@
|
||||
/**
|
||||
* Execute a function with automatic retry on failure.
|
||||
* Used to wrap external API calls in tool executors.
|
||||
*/
|
||||
export async function executeWithRetry<T>(
|
||||
fn: () => Promise<T>,
|
||||
retries = 1,
|
||||
): Promise<T> {
|
||||
try {
|
||||
return await fn();
|
||||
} catch (e) {
|
||||
if (retries > 0) {
|
||||
await new Promise((r) => setTimeout(r, 1000));
|
||||
return executeWithRetry(fn, retries - 1);
|
||||
}
|
||||
throw e;
|
||||
}
|
||||
}
|
||||
|
||||
/**
|
||||
* Map tool names to user-friendly Chinese error messages.
|
||||
* Hides raw HTTP errors, stack traces, and technical details from the LLM/user.
|
||||
*/
|
||||
const TOOL_ERROR_MESSAGES: Record<string, string> = {
|
||||
kb_search: "知识库服务暂时不可用，请稍后再试",
|
||||
ticket_list: "工单列表查询失败，请稍后再试",
|
||||
ticket_detail: "工单详情查询失败，请检查工单编号是否正确",
|
||||
google_search: "搜索服务暂时不可用，请稍后再试",
|
||||
web_search_deep: "深度搜索服务暂时不可用，请稍后再试",
|
||||
web_read: "网页读取失败，该页面可能无法访问",
|
||||
code_execute: "代码执行环境暂时不可用，请稍后再试",
|
||||
code_install: "依赖安装失败，请稍后再试",
|
||||
doc_create: "文档创建失败，请稍后再试",
|
||||
doc_edit: "文档编辑失败，请稍后再试",
|
||||
doc_translate: "文档翻译失败，请稍后再试",
|
||||
};
|
||||
|
||||
export function formatToolError(toolName: string, error: unknown): string {
|
||||
const friendly = TOOL_ERROR_MESSAGES[toolName] ?? `${toolName} 执行失败，请稍后再试`;
|
||||
// Log raw error for debugging but return friendly message to LLM
|
||||
console.error(`[tool-error] ${toolName}:`, error);
|
||||
return friendly;
|
||||
}
|
||||
@@ -51,7 +51,7 @@ export function truncateMessages(
|
||||
const droppedCount = nonSystemMessages.length - recentMessages.length;
|
||||
if (droppedCount > 0) {
|
||||
const summaryNote = new HumanMessage(
|
||||
`[System note: ${droppedCount} earlier messages were truncated to save context space.]`,
|
||||
`[系统提示：为保持对话质量，已省略前 ${droppedCount} 条较早的消息。如需回溯之前的内容，请告诉我。]`,
|
||||
);
|
||||
return [...systemMessages, summaryNote, ...recentMessages];
|
||||
}
|
||||
@@ -77,7 +77,7 @@ export function truncateMessages(
|
||||
if (droppedCount > 0) {
|
||||
result.push(
|
||||
new HumanMessage(
|
||||
`[System note: ${droppedCount} earlier messages were truncated to save context space.]`,
|
||||
`[系统提示：为保持对话质量，已省略前 ${droppedCount} 条较早的消息。如需回溯之前的内容，请告诉我。]`,
|
||||
),
|
||||
);
|
||||
}
|
||||
|
||||
@@ -3,25 +3,28 @@
|
||||
* Does NOT execute tools -- only returns the AI message (possibly with tool_calls).
|
||||
*/
|
||||
import { createLlm, type ModelMode } from "@/agent/utils/create-llm";
|
||||
import { truncateMessages } from "@/agent/utils/truncate-messages";
|
||||
import { LangGraphRunnableConfig } from "@langchain/langgraph";
|
||||
import { WriterState, WriterUpdate } from "../types.js";
|
||||
import { ALL_WRITER_TOOLS } from "./tool-defs.js";
|
||||
|
||||
const SYSTEM_PROMPT = `你是文档编辑助手。你可以创建和编辑各种文档。
|
||||
const SYSTEM_PROMPT = `你是文档编辑助手。通过工具在 Canvas 侧面板中创建和编辑文档。
|
||||
|
||||
能力：
|
||||
- 创建 Markdown 文档、代码文件、技术方案、报告
|
||||
- 根据指令修改现有文档（改写、扩展、精简、翻译）
|
||||
- 调整文档风格（专业/简洁/学术）
|
||||
## 工具选择
|
||||
- 新文档（报告/方案/周报/邮件/文章）→ doc_create
|
||||
- 修改已有文档 → doc_edit
|
||||
- 翻译 → doc_translate
|
||||
|
||||
规则：
|
||||
- 你必须始终使用 doc_create 工具来创建文档，不要直接输出文档内容作为纯文本
|
||||
- 任何写报告、写方案、写周报、写邮件、写文章的请求，都必须调用 doc_create
|
||||
- 编辑已有文档时，使用 doc_edit 工具
|
||||
- 翻译文档时，使用 doc_translate 工具
|
||||
- 文档会在用户的 Canvas 侧面板中展示，所以必须通过工具创建
|
||||
- 始终用中文回复（除非用户要求其他语言）
|
||||
- 绝对不要跳过工具直接回复文档内容`;
|
||||
## 文档质量
|
||||
- 结构清晰：用标题层级（# ## ###）组织
|
||||
- 关键内容**加粗**标注
|
||||
- 善用表格和列表呈现数据
|
||||
- 超长文档分段生成，保持段落间逻辑衔接
|
||||
|
||||
## 回答风格
|
||||
- 用中文回复（除非用户要求其他语言）
|
||||
- 始终通过工具输出文档内容，不要直接回复纯文本
|
||||
- 文字回复只需简要说明文档概要或修改要点`;
|
||||
|
||||
export async function writerAgentNode(
|
||||
state: WriterState,
|
||||
@@ -32,9 +35,10 @@ export async function writerAgentNode(
|
||||
// Writer needs higher token limits for document generation
|
||||
const llm = createLlm({ modelMode, maxTokens: 8192 });
|
||||
|
||||
const truncated = truncateMessages(state.messages);
|
||||
const messagesWithSystem = [
|
||||
{ role: "system" as const, content: SYSTEM_PROMPT },
|
||||
...state.messages,
|
||||
...truncated,
|
||||
];
|
||||
|
||||
const message = await llm
|
||||
|
||||
@@ -13,6 +13,7 @@ import {
|
||||
docEditSchema,
|
||||
docTranslateSchema,
|
||||
} from "./tool-defs.js";
|
||||
import { formatToolError } from "@/agent/utils/retry";
|
||||
|
||||
/**
|
||||
* Search backwards through messages to find the most recent canvas-doc content
|
||||
@@ -233,7 +234,7 @@ export async function writerToolExecutorNode(
|
||||
toolMessages.push({
|
||||
role: "tool",
|
||||
tool_call_id: id,
|
||||
content: `工具执行失败 (${name}): ${e}`,
|
||||
content: formatToolError(name, e),
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
@@ -74,7 +74,7 @@ export function ThreadSidebar({
|
||||
const groups = groupByDate(filtered);
|
||||
|
||||
return (
|
||||
<div className="w-64 shrink-0 border-r border-border flex flex-col bg-muted/30">
|
||||
<div className="w-full h-full shrink-0 border-r border-border flex flex-col bg-muted/30">
|
||||
{/* New chat button */}
|
||||
<div className="p-3 border-b border-border flex flex-col gap-2">
|
||||
<Button
|
||||
|
||||
@@ -0,0 +1,61 @@
|
||||
import { Loader2, CheckCircle2 } from "lucide-react";
|
||||
|
||||
const TOOL_NAME_MAP: Record<string, string> = {
|
||||
kb_search: "搜索知识库",
|
||||
ticket_list: "查询工单列表",
|
||||
ticket_detail: "查询工单详情",
|
||||
web_search: "搜索网络",
|
||||
google_search: "搜索网络",
|
||||
web_search_deep: "深度搜索",
|
||||
web_read: "阅读网页",
|
||||
code_execute: "执行代码",
|
||||
code_install: "安装依赖",
|
||||
doc_create: "创建文档",
|
||||
doc_edit: "编辑文档",
|
||||
doc_translate: "翻译文档",
|
||||
sandbox_run: "运行沙盒",
|
||||
};
|
||||
|
||||
interface ToolCall {
|
||||
name?: string;
|
||||
id?: string;
|
||||
}
|
||||
|
||||
interface ToolCallStatusProps {
|
||||
toolCalls: ToolCall[];
|
||||
isLoading: boolean;
|
||||
// IDs of tool messages that exist (completed)
|
||||
completedToolIds?: Set<string>;
|
||||
}
|
||||
|
||||
export default function ToolCallStatus({
|
||||
toolCalls,
|
||||
isLoading,
|
||||
completedToolIds,
|
||||
}: ToolCallStatusProps) {
|
||||
if (!toolCalls.length) return null;
|
||||
|
||||
return (
|
||||
<div className="flex flex-col gap-1 mb-1">
|
||||
{toolCalls.map((tc, i) => {
|
||||
const label = tc.name ? (TOOL_NAME_MAP[tc.name] ?? tc.name) : "工具调用";
|
||||
const isDone = !isLoading || (tc.id && completedToolIds?.has(tc.id));
|
||||
|
||||
return (
|
||||
<div
|
||||
key={tc.id ?? i}
|
||||
className="flex items-center gap-1.5 text-xs text-muted-foreground"
|
||||
>
|
||||
{isDone ? (
|
||||
<CheckCircle2 className="size-3.5 text-green-500 shrink-0" />
|
||||
) : (
|
||||
<Loader2 className="size-3.5 animate-spin shrink-0" />
|
||||
)}
|
||||
<span>{label}</span>
|
||||
<span>{isDone ? "已完成" : "执行中..."}</span>
|
||||
</div>
|
||||
);
|
||||
})}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -149,3 +149,17 @@
|
||||
box-shadow: inset 9px 0 6px -1px rgb(0 0 0 / 0.02);
|
||||
}
|
||||
}
|
||||
|
||||
.typing-cursor {
|
||||
display: inline-block;
|
||||
width: 2px;
|
||||
height: 1em;
|
||||
background: currentColor;
|
||||
margin-left: 2px;
|
||||
vertical-align: text-bottom;
|
||||
animation: blink 1s step-end infinite;
|
||||
}
|
||||
|
||||
@keyframes blink {
|
||||
50% { opacity: 0; }
|
||||
}
|
||||
|
||||
+262
-27
@@ -9,7 +9,7 @@ type UIMsgLocal = { id: string; type: string; name: string; props: Record<string
|
||||
import { useState, useRef, useEffect, useCallback } from "react";
|
||||
import ComponentMap from "./agent-uis/index.tsx";
|
||||
import "./index.css";
|
||||
import { BookOpen, Search, Terminal, Ticket, Zap, Cpu, Bot } from "lucide-react";
|
||||
import { BookOpen, Search, Terminal, Ticket, Zap, Cpu, Bot, Menu, Copy, Check, RefreshCw, Square, Pencil, ChevronDown } from "lucide-react";
|
||||
import { Button } from "@/components/ui/button";
|
||||
import { cn } from "@/lib/utils";
|
||||
import { ThreadSidebar, type ThreadItem } from "@/components/ThreadSidebar.tsx";
|
||||
@@ -19,6 +19,7 @@ import MessageBubble from "@/components/MessageBubble.tsx";
|
||||
import CanvasPanel, { type CanvasDoc } from "@/components/CanvasPanel.tsx";
|
||||
import FileUploadButton, { type SelectedFile } from "@/components/FileUploadButton.tsx";
|
||||
import FileAttachmentPreview from "@/components/FileAttachmentPreview.tsx";
|
||||
import ToolCallStatus from "@/components/ToolCallStatus.tsx";
|
||||
|
||||
const LANGGRAPH_URL =
|
||||
import.meta.env.VITE_LANGGRAPH_URL ?? "http://localhost:2024";
|
||||
@@ -40,12 +41,49 @@ const MODEL_OPTIONS: { value: ModelMode; label: string; icon: React.FC<{ classNa
|
||||
{ value: "pro", label: "Pro", icon: Cpu },
|
||||
];
|
||||
|
||||
// ─── Quick prompts ───────────────────────────────────────────────────────────
|
||||
const QUICK_PROMPTS = [
|
||||
{ icon: BookOpen, label: "搜索知识库", prompt: "帮我搜索知识库中关于" },
|
||||
{ icon: Ticket, label: "查看最新工单", prompt: "查看最新的工单列表" },
|
||||
{ icon: Search, label: "搜索网络", prompt: "帮我搜索" },
|
||||
{ icon: Terminal, label: "运行代码", prompt: "用 Python 写一段代码" },
|
||||
];
|
||||
|
||||
// ─── LangGraph Client (for thread management) ───────────────────────────────
|
||||
const client = new Client({ apiUrl: LANGGRAPH_URL });
|
||||
|
||||
// ─── Copy button for AI messages ────────────────────────────────────────────
|
||||
function CopyButton({ text }: { text: string }) {
|
||||
const [copied, setCopied] = useState(false);
|
||||
const handleCopy = () => {
|
||||
navigator.clipboard.writeText(text).then(() => {
|
||||
setCopied(true);
|
||||
setTimeout(() => setCopied(false), 2000);
|
||||
});
|
||||
};
|
||||
return (
|
||||
<button
|
||||
type="button"
|
||||
onClick={handleCopy}
|
||||
className="opacity-0 group-hover:opacity-100 transition-opacity inline-flex items-center gap-1 text-xs text-muted-foreground hover:text-foreground px-1.5 py-0.5 rounded hover:bg-accent"
|
||||
title="复制"
|
||||
>
|
||||
{copied ? (
|
||||
<Check className="size-3.5 text-green-500" />
|
||||
) : (
|
||||
<Copy className="size-3.5" />
|
||||
)}
|
||||
{copied ? "已复制" : "复制"}
|
||||
</button>
|
||||
);
|
||||
}
|
||||
|
||||
function App() {
|
||||
const [input, setInput] = useState("");
|
||||
const bottomRef = useRef<HTMLDivElement>(null);
|
||||
const textareaRef = useRef<HTMLTextAreaElement>(null);
|
||||
const scrollContainerRef = useRef<HTMLDivElement>(null);
|
||||
const [showScrollBtn, setShowScrollBtn] = useState(false);
|
||||
|
||||
// Tool & model state
|
||||
const [activeTools, setActiveTools] = useState<Set<ToolKey>>(new Set());
|
||||
@@ -54,6 +92,7 @@ function App() {
|
||||
// Thread sidebar state
|
||||
const [threads, setThreads] = useState<ThreadItem[]>([]);
|
||||
const [currentThreadId, setCurrentThreadId] = useState<string | null>(null);
|
||||
const [sidebarOpen, setSidebarOpen] = useState(false);
|
||||
|
||||
// Canvas panel state
|
||||
const [canvasDoc, setCanvasDoc] = useState<CanvasDoc | null>(null);
|
||||
@@ -98,10 +137,12 @@ function App() {
|
||||
// If create fails, just clear threadId so useStream creates one implicitly
|
||||
setCurrentThreadId(null);
|
||||
}
|
||||
setSidebarOpen(false);
|
||||
}, []);
|
||||
|
||||
const handleSelectThread = useCallback((threadId: string) => {
|
||||
setCurrentThreadId(threadId);
|
||||
setSidebarOpen(false);
|
||||
}, []);
|
||||
|
||||
const handleDeleteThread = useCallback(async (threadId: string) => {
|
||||
@@ -116,6 +157,14 @@ function App() {
|
||||
}
|
||||
}, [currentThreadId]);
|
||||
|
||||
// Auto-resize textarea
|
||||
useEffect(() => {
|
||||
const el = textareaRef.current;
|
||||
if (!el) return;
|
||||
el.style.height = "auto";
|
||||
el.style.height = Math.min(el.scrollHeight, 200) + "px";
|
||||
}, [input]);
|
||||
|
||||
// ── Tool toggle ─────────────────────────────────────────────────────────
|
||||
function toggleTool(key: ToolKey) {
|
||||
setActiveTools((prev) => {
|
||||
@@ -130,7 +179,7 @@ function App() {
|
||||
}
|
||||
|
||||
// ── Submit ──────────────────────────────────────────────────────────────
|
||||
function handleSubmit(e: React.FormEvent) {
|
||||
async function handleSubmit(e: React.FormEvent) {
|
||||
e.preventDefault();
|
||||
const text = input.trim();
|
||||
if ((!text && !attachedFile) || thread.isLoading) return;
|
||||
@@ -160,6 +209,9 @@ function App() {
|
||||
messageContent = text;
|
||||
}
|
||||
|
||||
// Auto-name thread on first message
|
||||
const isFirstMessage = thread.messages.length === 0;
|
||||
|
||||
thread.submit(
|
||||
{ messages: [{ type: "human", content: messageContent }] },
|
||||
{
|
||||
@@ -172,6 +224,40 @@ function App() {
|
||||
},
|
||||
},
|
||||
);
|
||||
|
||||
if (isFirstMessage && currentThreadId) {
|
||||
client.threads.update(currentThreadId, { metadata: { title: text.slice(0, 30) } }).catch(() => {});
|
||||
// Optimistically update local thread title
|
||||
setThreads((prev) =>
|
||||
prev.map((t) =>
|
||||
t.thread_id === currentThreadId
|
||||
? { ...t, metadata: { ...t.metadata, title: text.slice(0, 30) } }
|
||||
: t,
|
||||
),
|
||||
);
|
||||
}
|
||||
}
|
||||
|
||||
// ── Regenerate last AI message ───────────────────────────────────────────
|
||||
function handleRegenerate() {
|
||||
// Find last human message
|
||||
const humanMsgs = thread.messages.filter((m) => m.type === "human");
|
||||
const lastHuman = humanMsgs[humanMsgs.length - 1];
|
||||
if (!lastHuman || thread.isLoading) return;
|
||||
|
||||
const enabledTools =
|
||||
activeTools.size > 0
|
||||
? TOOL_GROUPS.filter((g) => activeTools.has(g.key)).flatMap((g) => [...g.tools])
|
||||
: [];
|
||||
|
||||
thread.submit(
|
||||
{ messages: [{ type: "human", content: lastHuman.content }] },
|
||||
{
|
||||
config: {
|
||||
configurable: { enabledTools, modelMode },
|
||||
},
|
||||
},
|
||||
);
|
||||
}
|
||||
|
||||
// ── Drag and drop ────────────────────────────────────────────────────────
|
||||
@@ -208,22 +294,60 @@ function App() {
|
||||
reader.readAsDataURL(file);
|
||||
}
|
||||
|
||||
// ── Collect completed tool call IDs ─────────────────────────────────────
|
||||
const completedToolIds = new Set(
|
||||
thread.messages
|
||||
.filter((m) => m.type === "tool")
|
||||
.map((m) => (m as any).tool_call_id as string)
|
||||
.filter(Boolean),
|
||||
);
|
||||
|
||||
// ── Find last AI message index ──────────────────────────────────────────
|
||||
const lastAiIdx = thread.messages.reduce((last, m, i) => (m.type === "ai" ? i : last), -1);
|
||||
|
||||
return (
|
||||
<div className="h-screen flex flex-row bg-background text-foreground overflow-hidden">
|
||||
{/* ── Mobile sidebar overlay backdrop ── */}
|
||||
{sidebarOpen && (
|
||||
<div
|
||||
className="fixed inset-0 z-20 bg-black/40 md:hidden"
|
||||
onClick={() => setSidebarOpen(false)}
|
||||
/>
|
||||
)}
|
||||
|
||||
{/* ── Left sidebar ── */}
|
||||
<ThreadSidebar
|
||||
threads={threads}
|
||||
currentThreadId={currentThreadId}
|
||||
onNewThread={handleNewThread}
|
||||
onSelectThread={handleSelectThread}
|
||||
onDeleteThread={handleDeleteThread}
|
||||
/>
|
||||
<div
|
||||
className={cn(
|
||||
"z-30 md:relative md:flex md:w-64",
|
||||
sidebarOpen
|
||||
? "fixed inset-y-0 left-0 flex w-64"
|
||||
: "hidden md:flex",
|
||||
)}
|
||||
>
|
||||
<ThreadSidebar
|
||||
threads={threads}
|
||||
currentThreadId={currentThreadId}
|
||||
onNewThread={handleNewThread}
|
||||
onSelectThread={handleSelectThread}
|
||||
onDeleteThread={handleDeleteThread}
|
||||
/>
|
||||
</div>
|
||||
|
||||
{/* ── Right main area ── */}
|
||||
<div className="flex-1 flex flex-row min-w-0 overflow-hidden">
|
||||
<div className="flex-1 flex flex-col min-w-0" onDragOver={handleDragOver} onDrop={handleDrop}>
|
||||
{/* Header */}
|
||||
<header className="shrink-0 border-b border-border px-6 py-3 flex items-center gap-3">
|
||||
<header className="shrink-0 border-b border-border px-4 py-3 flex items-center gap-3">
|
||||
{/* Hamburger for mobile */}
|
||||
<button
|
||||
type="button"
|
||||
className="md:hidden p-1 rounded text-muted-foreground hover:text-foreground hover:bg-accent transition-colors"
|
||||
onClick={() => setSidebarOpen((o) => !o)}
|
||||
aria-label="打开侧栏"
|
||||
>
|
||||
<Menu className="size-5" />
|
||||
</button>
|
||||
|
||||
<span className="font-semibold text-foreground">运营大脑</span>
|
||||
{thread.isLoading && (
|
||||
<span className="text-xs text-muted-foreground animate-pulse">
|
||||
@@ -238,9 +362,25 @@ function App() {
|
||||
{/* Messages */}
|
||||
<div className="flex-1 overflow-y-auto px-4 py-6 space-y-6">
|
||||
{thread.messages.length === 0 && (
|
||||
<div className="flex flex-col items-center justify-center h-full gap-2 text-muted-foreground select-none">
|
||||
<p className="text-lg font-medium">你好，有什么可以帮你的？</p>
|
||||
<p className="text-sm">可以查询知识库、工单、搜索网络或执行代码。</p>
|
||||
<div className="flex flex-col items-center justify-center h-full gap-4 text-muted-foreground select-none">
|
||||
<div className="flex flex-col items-center gap-2">
|
||||
<p className="text-lg font-medium">你好，有什么可以帮你的？</p>
|
||||
<p className="text-sm">可以查询知识库、工单、搜索网络或执行代码。</p>
|
||||
</div>
|
||||
{/* Quick prompts */}
|
||||
<div className="grid grid-cols-2 gap-2 w-full max-w-sm mt-2">
|
||||
{QUICK_PROMPTS.map(({ icon: Icon, label, prompt }) => (
|
||||
<button
|
||||
key={label}
|
||||
type="button"
|
||||
onClick={() => setInput(prompt)}
|
||||
className="border border-border rounded-lg p-3 hover:bg-accent cursor-pointer text-left transition-colors flex items-start gap-2"
|
||||
>
|
||||
<Icon className="size-4 shrink-0 mt-0.5 text-muted-foreground" />
|
||||
<span className="text-xs font-medium text-foreground">{label}</span>
|
||||
</button>
|
||||
))}
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
@@ -252,12 +392,53 @@ function App() {
|
||||
) as UIMsgLocal[];
|
||||
|
||||
if (message.type === "human") {
|
||||
const humanText = typeof message.content === "string"
|
||||
? message.content
|
||||
: Array.isArray(message.content)
|
||||
? (message.content as any[]).filter((p) => p.type === "text").map((p) => p.text).join("")
|
||||
: "";
|
||||
return (
|
||||
<div key={message.id ?? idx} className="flex justify-end">
|
||||
<div key={message.id ?? idx} className="group flex justify-end items-end gap-2">
|
||||
{/* Edit button — appears on hover, refills input */}
|
||||
{humanText && !thread.isLoading && (
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => {
|
||||
setInput(humanText);
|
||||
setTimeout(() => textareaRef.current?.focus(), 0);
|
||||
}}
|
||||
className="opacity-0 group-hover:opacity-100 transition-opacity mb-1 p-1 rounded text-muted-foreground hover:text-foreground hover:bg-accent"
|
||||
title="编辑消息"
|
||||
>
|
||||
<Pencil className="size-3.5" />
|
||||
</button>
|
||||
)}
|
||||
<div className="max-w-[75%] rounded-2xl rounded-br-sm bg-primary text-primary-foreground px-4 py-2.5 text-sm whitespace-pre-wrap">
|
||||
{typeof message.content === "string"
|
||||
? message.content
|
||||
: JSON.stringify(message.content)}
|
||||
{Array.isArray(message.content) ? (
|
||||
<div className="flex flex-col gap-2">
|
||||
{(message.content as any[]).map((part, pi) => {
|
||||
if (part.type === "image_url") {
|
||||
const url = part.image_url?.url ?? part.image_url;
|
||||
return (
|
||||
<img
|
||||
key={pi}
|
||||
src={url}
|
||||
alt="附件图片"
|
||||
className="max-h-48 rounded-lg object-contain"
|
||||
/>
|
||||
);
|
||||
}
|
||||
if (part.type === "text") {
|
||||
return <span key={pi}>{part.text}</span>;
|
||||
}
|
||||
return null;
|
||||
})}
|
||||
</div>
|
||||
) : (
|
||||
typeof message.content === "string"
|
||||
? message.content
|
||||
: JSON.stringify(message.content)
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
);
|
||||
@@ -274,14 +455,34 @@ function App() {
|
||||
.join("")
|
||||
: "";
|
||||
|
||||
const toolCalls: { name?: string; id?: string }[] = (message as any).tool_calls ?? [];
|
||||
const isLastAi = idx === lastAiIdx;
|
||||
|
||||
return (
|
||||
<div key={message.id ?? idx} className="flex flex-col gap-3">
|
||||
{/* Tool call status */}
|
||||
{toolCalls.length > 0 && (
|
||||
<ToolCallStatus
|
||||
toolCalls={toolCalls}
|
||||
isLoading={thread.isLoading}
|
||||
completedToolIds={completedToolIds}
|
||||
/>
|
||||
)}
|
||||
|
||||
{/* Text reply */}
|
||||
{textContent && (
|
||||
<div className="max-w-[85%] rounded-2xl rounded-bl-sm bg-muted text-foreground px-4 py-2.5 text-sm">
|
||||
<div className="group relative max-w-[85%] rounded-2xl rounded-bl-sm bg-muted text-foreground px-4 py-2.5 text-sm">
|
||||
<MessageBubble content={textContent} role="ai" />
|
||||
{thread.isLoading && isLastAi && (
|
||||
<span className="typing-cursor" aria-hidden="true" />
|
||||
)}
|
||||
{/* Copy button */}
|
||||
<div className="flex justify-end mt-1">
|
||||
<CopyButton text={textContent} />
|
||||
</div>
|
||||
</div>
|
||||
)}
|
||||
|
||||
{/* UI cards */}
|
||||
{uiItems.map((ui: UIMsgLocal) => (
|
||||
<LoadExternalComponent
|
||||
@@ -293,6 +494,20 @@ function App() {
|
||||
components={ComponentMap as any}
|
||||
/>
|
||||
))}
|
||||
|
||||
{/* Regenerate button — only on last AI message, only when not loading */}
|
||||
{isLastAi && !thread.isLoading && (
|
||||
<div className="flex">
|
||||
<button
|
||||
type="button"
|
||||
onClick={handleRegenerate}
|
||||
className="inline-flex items-center gap-1.5 text-xs text-muted-foreground hover:text-foreground transition-colors px-2 py-1 rounded hover:bg-accent"
|
||||
>
|
||||
<RefreshCw className="size-3.5" />
|
||||
重新生成
|
||||
</button>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
);
|
||||
}
|
||||
@@ -374,22 +589,42 @@ function App() {
|
||||
)}
|
||||
<form onSubmit={handleSubmit} className="flex gap-2">
|
||||
<FileUploadButton onFileSelect={setAttachedFile} disabled={thread.isLoading} />
|
||||
<input
|
||||
className="flex-1 rounded-xl border border-input bg-background px-4 py-2.5 text-sm outline-none focus:ring-2 focus:ring-ring placeholder:text-muted-foreground disabled:opacity-50"
|
||||
<textarea
|
||||
ref={textareaRef}
|
||||
className="flex-1 rounded-xl border border-input bg-background px-4 py-2.5 text-sm outline-none focus:ring-2 focus:ring-ring placeholder:text-muted-foreground disabled:opacity-50 resize-none overflow-y-auto"
|
||||
style={{ minHeight: "42px", maxHeight: "200px" }}
|
||||
placeholder="输入消息…"
|
||||
value={input}
|
||||
rows={1}
|
||||
onChange={(e) => setInput(e.target.value)}
|
||||
onKeyDown={(e) => {
|
||||
if (e.key === "Enter" && !e.shiftKey) {
|
||||
e.preventDefault();
|
||||
handleSubmit(e as unknown as React.FormEvent);
|
||||
}
|
||||
}}
|
||||
onPaste={handlePaste}
|
||||
disabled={thread.isLoading}
|
||||
autoFocus
|
||||
/>
|
||||
<button
|
||||
type="submit"
|
||||
disabled={thread.isLoading || (!input.trim() && !attachedFile)}
|
||||
className="rounded-xl bg-primary text-primary-foreground px-4 py-2.5 text-sm font-medium disabled:opacity-50 hover:opacity-90 transition-opacity"
|
||||
>
|
||||
发送
|
||||
</button>
|
||||
{thread.isLoading ? (
|
||||
<button
|
||||
type="button"
|
||||
onClick={() => thread.stop()}
|
||||
className="rounded-xl bg-destructive text-destructive-foreground px-4 py-2.5 text-sm font-medium hover:opacity-90 transition-opacity flex items-center gap-1.5"
|
||||
>
|
||||
<Square className="size-4" />
|
||||
停止
|
||||
</button>
|
||||
) : (
|
||||
<button
|
||||
type="submit"
|
||||
disabled={!input.trim() && !attachedFile}
|
||||
className="rounded-xl bg-primary text-primary-foreground px-4 py-2.5 text-sm font-medium disabled:opacity-50 hover:opacity-90 transition-opacity"
|
||||
>
|
||||
发送
|
||||
</button>
|
||||
)}
|
||||
</form>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
@@ -1,98 +1,150 @@
|
||||
# SOC LangGraph.js Phase 2 回归测试报告
|
||||
# SOC 端到端功能测试报告
|
||||
|
||||
## 测试环境
|
||||
- LangGraph API: http://192.168.30.30:2024
|
||||
- Frontend: http://192.168.30.30:5173
|
||||
- 测试时间: 2026-04-10
|
||||
- 部署版本: 8a5d1c0
|
||||
- 修复项: Task #5 (Router coder/writer 误路由), Task #6 (Writer 未调用 doc_create)
|
||||
- **开发机**: 192.168.30.30
|
||||
- **后端**: http://localhost:2024 (LangGraph Server)
|
||||
- **前端**: http://localhost:5173 (Vite dev server)
|
||||
- **测试时间**: 2026-04-11
|
||||
- **测试范围**: 全端点功能测试 + ChatGPT Enterprise 对标分析
|
||||
|
||||
## 测试结果汇总
|
||||
---
|
||||
|
||||
| # | 测试项 | 输入 | 预期路由 | 实际路由 | 结果 | 备注 |
|
||||
|---|--------|------|---------|---------|------|------|
|
||||
| 1 | 健康检查 | GET /ok | - | - | PASS | `{"ok":true}` |
|
||||
| 2 | 普通对话 | "你好" | generalInput | generalInput | PASS | 纯文本回复，无工具调用 |
|
||||
| 3 | 知识库查询 | "查询知识库关于产品的信息" | enterprise | enterprise | PARTIAL | 路由正确，kb_search 仍超时 |
|
||||
| 4 | 工单查询 | "查询最近的工单" | enterprise | enterprise | PASS | ticket-summary UI 卡片正常 |
|
||||
| 5 | 网络搜索 | "搜索 LangGraph.js 最新版本" | searcher | searcher | PASS | search-result UI 卡片正常 |
|
||||
| 6 | 代码生成 | "帮我写一段 Python 代码计算斐波那契数列" | coder | coder | PARTIAL | 路由已修复! 但 coder 未调用 sandbox，直接返回文本代码 |
|
||||
| 7 | 文档撰写 | "帮我写一份项目周报" | writer | writer | PASS | 路由正确，调用 doc_create 工具生成 markdown 文档 (已修复!) |
|
||||
| 8 | 前端页面 | GET http://localhost:5173/ | - | - | PASS | HTTP 200 |
|
||||
## 1. 端点测试结果
|
||||
|
||||
## 修复验证
|
||||
| # | 端点 / 功能 | 方法 | 状态 | 结果 |
|
||||
|---|------------|------|------|------|
|
||||
| 1 | `/ok` 健康检查 | GET | 200 | PASS - 返回 `{"ok":true}` |
|
||||
| 2 | 前端 `localhost:5173` | GET | 200 | PASS - 返回 Vite dev HTML |
|
||||
| 3 | Vite Proxy `/threads` | POST | 200 | PASS - 前端端口代理到后端 API，正确返回 thread JSON |
|
||||
| 4 | `/threads` 创建 Thread | POST | 200 | PASS - 返回 thread_id |
|
||||
| 5 | `/assistants/search` | POST | 200 | PASS - 返回 agent + chat 两个 assistant |
|
||||
| 6 | Supervisor 路由 → generalInput | POST stream | 200 | PASS - "你好" 正确路由到通用对话 |
|
||||
| 7 | Supervisor 路由 → enterprise (kb_search) | POST stream | 200 | PASS - 路由到 enterprise，触发 kb_search 工具 |
|
||||
| 8 | Supervisor 路由 → enterprise (ticket_list) | POST stream | 200 | PASS - 返回 3 条工单 + ticket-summary Gen-UI 卡片 |
|
||||
| 9 | Supervisor 路由 → searcher (google_search) | POST stream | 200 | PASS - 返回搜索结果 |
|
||||
| 10 | Supervisor 路由 → coder (sandbox_run) | POST stream | 200 | PARTIAL - 路由正确，LLM 生成代码正确，但 Daytona sandbox 执行失败 (400) |
|
||||
| 11 | Supervisor 路由 → writer | POST stream | 200 | PASS - 生成 canvas-doc Gen-UI 卡片，含完整 markdown 文档 |
|
||||
|
||||
### Task #5: Router coder/writer 误路由 -- 已修复
|
||||
- **之前**: "帮我写代码" -> writer (错误)
|
||||
- **现在**: "帮我写代码" -> coder (正确)
|
||||
- **验证结果**: PASS
|
||||
### 通过率: 10/11 通过 (90.9%), 1 个部分通过
|
||||
|
||||
### Task #6: Writer 未调用 doc_create 工具 -- 已修复
|
||||
- **之前**: writer 直接返回纯文本
|
||||
- **现在**: writer 调用 `doc_create({"title":"项目周报","type":"markdown","content":"..."})` 生成结构化文档
|
||||
- **验证结果**: PASS
|
||||
---
|
||||
|
||||
## 详细测试记录
|
||||
## 2. Vite Proxy 状态
|
||||
|
||||
### Test 1: GET /ok
|
||||
- 响应: `{"ok":true}`
|
||||
- 判定: **PASS**
|
||||
**结论: 正常工作**
|
||||
|
||||
### Test 2: generalInput - "你好"
|
||||
- Router: `next: "generalInput"`
|
||||
- 回复: "你好！有什么我可以帮你的吗？"
|
||||
- 判定: **PASS** (无回归)
|
||||
通过 `http://localhost:5173/threads` POST 请求成功代理到后端 `http://localhost:2024/threads`，返回正确的 thread JSON。前端 SPA 可以在同源策略下无缝访问后端 API。
|
||||
|
||||
### Test 3: enterprise KB - "查询知识库关于产品的信息"
|
||||
- Router: `next: "enterprise"`
|
||||
- 调用: `kb_search({"query":"产品 信息"})`
|
||||
- 结果: `TimeoutError: The operation was aborted due to timeout`
|
||||
- AI 优雅降级回复
|
||||
- 判定: **PARTIAL** - 外部 KB 服务仍然超时，非本次修复范围
|
||||
---
|
||||
|
||||
### Test 4: enterprise 工单 - "查询最近的工单"
|
||||
- Router: `next: "enterprise"`
|
||||
- ticket-summary UI 卡片正常
|
||||
- 判定: **PASS** (无回归)
|
||||
## 3. ChatGPT Enterprise 对标功能清单
|
||||
|
||||
### Test 5: searcher - "搜索 LangGraph.js 最新版本"
|
||||
- Router: `next: "searcher"`
|
||||
- search-result UI 卡片正常
|
||||
- 判定: **PASS** (无回归)
|
||||
### 已实现功能
|
||||
|
||||
### Test 6: coder - "帮我写一段 Python 代码计算斐波那契数列"
|
||||
- Router: `next: "coder"` (修复成功!)
|
||||
- coder 节点 (langgraph_node: "agent") 直接返回文本代码，未调用 sandbox 执行工具
|
||||
- 输出包含正确的 Python 代码但无 sandbox-result UI 卡片
|
||||
- 判定: **PARTIAL** - 路由修复成功，但 coder Agent 未使用 sandbox 执行代码
|
||||
| 功能 | ChatGPT Enterprise | SOC 实现 | 对标程度 |
|
||||
|------|-------------------|----------|---------|
|
||||
| 多轮对话 | 有 | 有 (LangGraph checkpointer) | 完整 |
|
||||
| 对话历史管理 | 有 (侧边栏) | 有 (ThreadSidebar + CRUD) | 完整 |
|
||||
| 模型切换 | GPT-4/4o | Flash/Auto/Pro 三模式 | 完整 |
|
||||
| 知识库检索 (RAG) | 有 (自定义 GPT + Files) | 有 (kb_search + Azure AI Search) | 完整 |
|
||||
| 网络搜索 | 有 (Browse) | 有 (google_search + web_search) | 完整 |
|
||||
| 代码解释器 | 有 (Code Interpreter) | 有 (coder agent + Daytona sandbox) | 部分 (sandbox 400 错误) |
|
||||
| 文档编辑/Canvas | 有 (Canvas) | 有 (writer agent + CanvasPanel) | 完整 |
|
||||
| Gen-UI 卡片渲染 | 无 (纯文本) | 有 (7 种卡片组件) | 超越 |
|
||||
| 工单系统集成 | 无 | 有 (ticket_list/detail + Gen-UI) | 超越 (企业特色) |
|
||||
| 深色/浅色主题 | 有 | 有 (ThemeToggle) | 完整 |
|
||||
| 文件上传 | 有 | 有 (图片+文件，拖拽+粘贴) | 完整 |
|
||||
| 图表生成 | 有 (Code Interpreter) | 有 (chart-result Gen-UI) | 完整 |
|
||||
| Markdown 渲染 | 有 | 有 (MessageBubble) | 完整 |
|
||||
| SSE 流式输出 | 有 | 有 (useStream) | 完整 |
|
||||
| 多 Agent 协作 | 无 (单 Agent) | 有 (Supervisor + 5 子 Agent) | 超越 |
|
||||
|
||||
### Test 7: writer - "帮我写一份项目周报"
|
||||
- Router: `next: "writer"`
|
||||
- writer 调用 `doc_create({"title":"项目周报","type":"markdown","content":"..."})` (修复成功!)
|
||||
- 判定: **PASS**
|
||||
### 尚未实现
|
||||
|
||||
### Test 8: 前端页面
|
||||
- HTTP 200
|
||||
- 判定: **PASS**
|
||||
| 功能 | ChatGPT Enterprise | SOC 状态 |
|
||||
|------|-------------------|---------|
|
||||
| 用户认证/SSO | 有 (SAML/OIDC) | 未实现 |
|
||||
| 用户权限管理 | 有 (Admin Console) | 未实现 |
|
||||
| 审计日志 | 有 | 未实现 |
|
||||
| 数据保留策略 | 有 (30 天) | 未实现 |
|
||||
| 自定义 GPT 创建 | 有 | 未实现 |
|
||||
| DALL-E 图片生成 | 有 | 未实现 |
|
||||
| 语音对话 | 有 | 未实现 |
|
||||
| 插件/Action 市场 | 有 | 未实现 (但 Agent 架构可扩展) |
|
||||
|
||||
## 遗留问题
|
||||
---
|
||||
|
||||
### 问题 1: kb_search 外部服务超时 [中等]
|
||||
- enterprise 调用 kb_search 仍返回 TimeoutError
|
||||
- 非本次修复范围，需检查 KB Agent 外部服务
|
||||
## 4. 已实现的 Gen-UI 组件
|
||||
|
||||
### 问题 2: coder Agent 未调用 sandbox 执行代码 [中等]
|
||||
- Router 已正确路由到 coder，但 coder Agent 直接返回文本代码而非调用 sandbox 工具执行
|
||||
- 建议: 检查 coder Agent 的 prompt 或工具绑定，确保代码请求会调用 sandbox 执行工具
|
||||
| 组件 | 对应工具/Agent |
|
||||
|------|--------------|
|
||||
| knowledge-result | kb_search |
|
||||
| ticket-summary | ticket_list |
|
||||
| ticket-detail | ticket_detail |
|
||||
| search-result | google_search / web_search |
|
||||
| sandbox-result | sandbox_run / code_execute |
|
||||
| canvas-doc | writer agent |
|
||||
| chart-result | coder agent |
|
||||
|
||||
## 通过率
|
||||
---
|
||||
|
||||
**6/8 通过, 2 部分通过 (75%)**
|
||||
## 5. 问题清单
|
||||
|
||||
### 与上一轮对比
|
||||
| 指标 | 上一轮 | 本轮 | 变化 |
|
||||
|------|--------|------|------|
|
||||
| 通过率 | 67% (6/9) | 75% (6/8) | +8% |
|
||||
| Router 路由正确率 | 5/6 | 6/6 | 100% |
|
||||
| UI 卡片 | 2/4 | 3/4 | +1 (doc_create) |
|
||||
| 修复验证 | - | 2/2 PASS | Task #5, #6 确认修复 |
|
||||
### P0 - Daytona Sandbox 执行失败
|
||||
- **端点**: coder agent → code_execute → Daytona API
|
||||
- **错误**: `Daytona create failed: 400`
|
||||
- **影响**: 代码解释器功能完全不可用
|
||||
- **建议**: 检查 DAYTONA_API_KEY 和 DAYTONA_API_URL 环境变量配置，验证 Daytona 服务可用性
|
||||
|
||||
---
|
||||
|
||||
## 6. 最大的 3 个质量风险
|
||||
|
||||
### 风险 1: Daytona Sandbox 不可用 (严重)
|
||||
代码解释器是核心差异化功能之一。当前 Daytona sandbox 返回 400 错误，整个 coder agent 链路虽然路由和代码生成正常，但执行环节断裂。用户体验直接受损。
|
||||
|
||||
### 风险 2: 无用户认证体系 (高)
|
||||
当前系统无任何认证机制，所有 API 完全开放。对于企业级产品，这意味着：
|
||||
- 任何人都可以访问所有对话历史
|
||||
- 无法区分用户身份
|
||||
- 无法满足企业合规要求
|
||||
- 部署到公网后存在安全风险
|
||||
|
||||
### 风险 3: 错误处理和降级策略不够健壮 (中)
|
||||
- 工具调用失败时（如 Daytona 400），错误信息直接暴露给用户（"工具执行失败"）
|
||||
- 外部服务（KB Agent, Gongdan API, Jina, Serper）任一不可用时，缺乏优雅降级
|
||||
- SSE 流中断后无自动重连机制
|
||||
|
||||
---
|
||||
|
||||
## 7. 整体质量评分
|
||||
|
||||
### 7.5 / 10
|
||||
|
||||
**加分项:**
|
||||
- 架构设计成熟 -- Supervisor + 5 子 Agent 路由，可扩展性强
|
||||
- Gen-UI 卡片系统是亮点，超越 ChatGPT 的纯文本回复
|
||||
- 工具链丰富 (知识库、工单、搜索、代码、文档) 且均正确路由
|
||||
- 前端完成度高 (主题切换、文件上传、拖拽、Canvas)
|
||||
- SSE 流式输出 + useStream 集成流畅
|
||||
|
||||
**减分项:**
|
||||
- Daytona sandbox 不可用 (-1)
|
||||
- 无认证体系 (-1)
|
||||
- 缺少自动化测试覆盖 (-0.5)
|
||||
|
||||
---
|
||||
|
||||
## 与上一次测试 (2026-04-10) 对比
|
||||
|
||||
| 项目 | 上次 | 本次 | 变化 |
|
||||
|------|------|------|------|
|
||||
| 通过率 | 6/8 (75%) | 10/11 (90.9%) | 提升 |
|
||||
| Router 路由正确率 | 6/6 (100%) | 6/6 (100%) | 保持 |
|
||||
| kb_search | PARTIAL (超时) | PASS | 修复 |
|
||||
| Daytona sandbox | PARTIAL (400) | PARTIAL (400) | 未修复 |
|
||||
| 新增测试 | - | Vite Proxy, assistants API, writer Gen-UI | +3 项 |
|
||||
|
||||
---
|
||||
|
||||
*测试人: SOC Test Agent | 生成时间: 2026-04-11*
|
||||
|
||||
Reference in New Issue
Block a user