From 1396d652f70e38f4b7db46dc1411b8f59f6c47fa Mon Sep 17 00:00:00 2001 From: minorQAQ <202200120184@mail.sdu.edu.cn> Date: Wed, 17 Jun 2026 12:58:18 +0800 Subject: [PATCH] 20260617 --- .env | 38 - docs/feishu_development_guide.md | 373 ++++ pyproject.toml | 1 - requirements.txt | 1 - src/__pycache__/__init__.cpython-312.pyc | Bin 0 -> 162 bytes src/__pycache__/__init__.cpython-313.pyc | Bin 0 -> 162 bytes .../__pycache__/__init__.cpython-313.pyc | Bin 0 -> 168 bytes .../__pycache__/chatbot_agent.cpython-312.pyc | Bin 9712 -> 1839 bytes .../__pycache__/chatbot_agent.cpython-313.pyc | Bin 0 -> 1681 bytes src/agent/__pycache__/llm.cpython-312.pyc | Bin 25815 -> 264 bytes src/agent/__pycache__/llm.cpython-313.pyc | Bin 0 -> 264 bytes src/agent/agents/__init__.py | 31 + .../__pycache__/__init__.cpython-312.pyc | Bin 0 -> 1086 bytes .../__pycache__/__init__.cpython-313.pyc | Bin 0 -> 272 bytes .../__pycache__/browser_agent.cpython-312.pyc | Bin 0 -> 259 bytes 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src/web/__pycache__/__init__.cpython-313.pyc create mode 100644 src/web/__pycache__/server.cpython-313.pyc create mode 100644 src/web/__pycache__/ui_session.cpython-313.pyc diff --git a/.env b/.env deleted file mode 100644 index 3fbfb93..0000000 --- a/.env +++ /dev/null @@ -1,38 +0,0 @@ -LANGSMITH_TRACING=True -LANGSMITH_ENDPOINT=https://api.smith.langchain.com -LANGSMITH_API_KEY=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 -LANGSMITH_PROJECT="agent_learning" -DATABASE_URI=sqlite:///./langgraph.db -REDIS_URI=redis://localhost:6379/0 -#spark： -LLM_API_KEY='semi-ai.tech' -# LLM_BASE_URL="http://192.168.137.96:8000/v1" -LLM_BASE_URL="http://semi-ai.tech:50400/v1" -LLM_MODEL = "Qwen/Qwen3.6-35B-A3B-FP8" -# ASR_TTS_BASE_URL = "http://192.168.137.96:8100" -# ASR_BASE_URL = "http://192.168.137.96:8901/v1" -# TTS_BASE_URL = "http://192.168.137.96:8902" -# RAG_BASE_URL = "http://192.168.137.96:8903" - -#6000pro： -# LLM_API_KEY='semi-ai.tech' -# LLM_BASE_URL="http://semi-ai.tech:40056/v1" -# LLM_MODEL = "Intel/Qwen3.6-35B-A3B-int4-mixed-AutoRound" - -#A100： -# LLM_API_KEY = 'sk-kGhWAQTqLk9ECsmIDb758a456bD34e92B9771dF2E36432Fb' -# LLM_BASE_URL = "https://app.semi-ai.tech:60004/v1" -# LLM_MODEL = "Qwen3.5-122B-A10B" -# LLM_TIMEOUT = 300 -# LLM_REQUESTS_PER_SECOND = 10.0 -# LLM_MAX_BUCKET_SIZE = 10 -# LLM_CHECK_EVERY_N_SECONDS = 0.1 - -#context: -LLM_CONTEXT_WINDOW=32768 -MINI_COMPRESS_RATE=0.4 -HARD_COMPRESS_RATE=0.6 -CURRENT_TURNS=4 -IMG_SIZE=768 -RAG_CHUNK_SIZE=500 -RAG_CHUNK_OVERLAP=50 \ No newline at end of file diff --git a/docs/feishu_development_guide.md b/docs/feishu_development_guide.md new file mode 100644 index 0000000..d7185a4 --- /dev/null +++ b/docs/feishu_development_guide.md @@ -0,0 +1,373 @@ +# Minor Agent 系统架构、部署与开发指南（飞书版） + +## 1. 文档目标 + +本文面向 Minor Agent 的研发、部署和维护人员，说明当前系统的整体架构、核心运行链路、Plan TodoList 机制、工具扩展方式、Web 前端交互、部署步骤与常见排障方法。 + +## 2. 系统概览 + +Minor Agent 是一个基于 LangGraph 的多模态本地 Agent 系统，提供 Web 对话界面，支持文本、图片、音频和文件输入，并通过 LangChain Tool 接入外部能力。 + +### 2.1 核心能力 + +- 多模态对话：文本、图片、音频、普通文件。 +- Agent 模式：普通 agent、plan、team 预留。 +- ReAct 工具调用：浏览器控制、屏幕截图、目录查看、人工交互、本机软件控制、Todo 状态更新等。 +- 历史持久化：SQLite 保存对话历史与压缩元信息。 +- 上下文压缩：按 token 阈值触发轻量/全量摘要。 +- Plan TodoList：Plan 模式下生成 `.todo`，前端浮窗展示，执行过程中动态更新。 + +## 3. 目录结构 + +```text +Agent/ +├─ requirements.txt +├─ pyproject.toml +├─ README.md +├─ docs/ +│ └─ feishu_development_guide.md +└─ src/ + ├─ web/ + │ ├─ server.py # FastAPI 后端 API 与静态资源服务 + │ ├─ app.js # Web 前端主逻辑 + │ ├─ app.css # Web 前端样式 + │ └─ index.html # Web 页面入口 + └─ agent/ + ├─ agents/ + │ └─ main_agent.py # 主 Agent 装配入口 + ├─ core/ + │ ├─ runtime.py # Agent 执行、Plan 确认、人工交互续跑 + │ ├─ graph_builder.py # LangGraph ReAct 图构建 + │ ├─ nodes.py # LLM 节点与工具产物处理 + │ ├─ routing.py # 工具调用路由/审批识别 + │ ├─ plan.py # Plan 模式路径选择 pending action + │ ├─ todo_manager.py # `.todo` 读写、解析、状态更新、上下文注入 + │ └─ state.py # LangGraph 状态定义 + ├─ memory/ + │ └─ system_prompt.py # 系统提示词、Plan 提示词 + ├─ tools/ + │ ├─ tool.py # 通用工具集合 + │ ├─ tool_software_control.py # Windows 本机软件控制工具 + │ └─ __init__.py # 工具注册 + ├─ utils/ + │ └─ agent_utils.py # 历史、压缩、多模态文件处理 + ├─ history/ # 聊天历史、会话附件、数据库 + └─ workspace/ + └─ .todo # Plan TodoList 文件 +``` + +## 4. 后端架构 + +### 4.1 Web API 层 + +入口文件：`src/web/server.py` + +主要职责： + +- 提供静态页面：`/`、`/app.js`、`/app.css`、`/image/{asset}`。 +- 会话管理：新建、切换、清空、删除。 +- 多模态上传：将上传文件临时落盘，再同步到会话目录。 +- Agent 执行：调用 `chatbot_agent` 间接进入 `AgentRuntime`。 +- 人工交互回调：处理工具审批、Plan 路径确认等 pending action。 +- Todo API：`GET /api/todo` 返回当前 `.todo` 内容和结构化任务列表。 + +### 4.2 Runtime 层 + +入口文件：`src/agent/core/runtime.py` + +核心接口： + +- `execute_agent(...)`：处理用户新消息，执行普通 agent / plan / team 流程。 +- `continue_after_human_action(...)`：处理人工确认、Plan 路径选择、工具审批后的续跑。 + +运行流程： + +1. Web UI 提交用户消息。 +2. Runtime 调用 `summarizer(...)` 组装模型上下文。 +3. 普通模式直接执行 LangGraph。 +4. Plan 模式先进入研究阶段，只输出可选路径，不执行任务。 +5. 用户确认路径后，Runtime 调用模型生成 TodoList，并覆盖写入 `agent/workspace/.todo`。 +6. 后续 Plan 上下文会在系统提示词后注入 `.todo` 内容，确保压缩不影响 TodoList。 + +### 4.3 LangGraph 层 + +入口文件：`src/agent/core/graph_builder.py` + +图结构： + +```text +START -> agent -> (tools | END) +tools -> process_tool_artifact -> agent +``` + +说明： + +- `agent` 节点调用已绑定工具的 LLM。 +- `tools` 节点执行 LangChain Tool。 +- `process_tool_artifact` 处理截图等 artifact，将图片转为模型可见的多模态消息。 + +## 5. Plan TodoList 机制 + +### 5.1 设计目标 + +Plan 模式应实现： + +- 根据用户选择的路径生成动态 TodoList。 +- TodoList 写入 `src/agent/workspace/.todo`。 +- 每次 Plan 模式制定 TodoList 时覆盖旧 `.todo`。 +- 前端以浮窗展示，可收起为悬浮球。 +- Plan 模式上下文开头携带 `.todo`，紧跟系统提示词。 +- 上下文压缩不应压缩或丢失 `.todo`。 +- Agent 执行过程中可更新任务状态。 + +### 5.2 当前实现 + +核心文件：`src/agent/core/todo_manager.py` + +能力： + +- `read_todo_text()`：读取 `.todo`。 +- `write_todo_text()`：覆盖写入 `.todo`。 +- `overwrite_todo_from_plan()`：Plan 路径确认后覆盖旧 TodoList。 +- `todo_context_block()`：生成上下文注入块。 +- `system_prompt_with_todo()`：将 `.todo` 紧贴系统提示词之后注入。 +- `parse_todo_items()`：解析 Markdown checkbox 和状态前缀。 +- `update_todo_status()`：按任务描述更新状态，不匹配时追加新任务。 +- `todo_payload()`：提供给前端的结构化数据。 + +### 5.3 前端展示 + +核心文件：`src/web/app.js`、`src/web/app.css` + +- 前端启动时调用 `/api/todo`。 +- 每 2.5 秒自动刷新一次。 +- TodoList 显示为右侧浮窗。 +- 点击收起后变成悬浮球。 +- 展示任务状态、完成数量、任务文本。 + +### 5.4 Agent 更新 Todo 状态 + +工具：`todo_status_update` + +注册位置：`src/agent/tools/__init__.py` + +适用场景： + +- 任务开始：更新为 `in_progress`。 +- 任务完成：更新为 `completed`。 +- 任务取消：更新为 `cancelled`。 +- 计划变化：追加或修改任务状态。 + +## 6. 工具系统 + +### 6.1 工具注册 + +入口文件：`src/agent/tools/__init__.py` + +当前主 Agent 工具包含： + +- `web_search_class`：网络搜索。 +- `date_query_class`：当前日期时间。 +- `screen_capture`：屏幕截图。 +- `browser_control`：浏览器打开/关闭/刷新。 +- `human_interaction`：人工补充、选择、审批。 +- `software_control`：Windows 本机软件打开/关闭/强制关闭。 +- `todo_status_update`：更新 `.todo` 中任务状态。 +- `directory_listing`：查看目录。 + +### 6.2 本机软件控制工具 + +文件：`src/agent/tools/tool_software_control.py` + +能力： + +- 扫描 Windows 开始菜单、桌面快捷方式和 App Paths。 +- 建立软件索引：`src/agent/history/data_base/software_index.json`。 +- 根据软件名做模糊匹配。 +- 支持打开、正常关闭、强制关闭。 + +安全策略： + +- 仅当用户明确要求操作本机软件时调用。 +- `close` 只发送正常关闭请求。 +- `force_close` 或 `force=True` 才允许强制结束进程。 +- 非 Windows 平台返回明确不支持提示。 + +## 7. 历史与上下文压缩 + +核心文件：`src/agent/utils/agent_utils.py` + +### 7.1 历史存储 + +- SQLite 数据库：`src/agent/history/data_base/chat_history.db` +- 表： + - `message_store`：聊天消息。 + - `session_meta`：压缩游标、摘要、上次 token 用量。 + +### 7.2 压缩策略 + +- 当 token 超过轻量阈值，触发轻量压缩，保留最近若干轮对话。 +- 当 token 超过硬阈值，触发全量压缩。 +- `.todo` 不进入历史压缩摘要，而是每次 Plan 模式构造系统提示词时重新从文件读取并注入，因此不会被压缩丢失。 + +## 8. 前端架构 + +文件：`src/web/index.html`、`src/web/app.js`、`src/web/app.css` + +主要模块： + +- 会话侧栏：新建、切换、删除会话。 +- 消息区：Markdown 渲染、代码块复制、图片/音频展示。 +- 输入区：文本、文件、麦克风、自动语音输入。 +- Pending Action 卡片：工具审批、人工交互、Plan 路径选择。 +- Todo 浮窗：Plan TodoList 展示和自动刷新。 + +## 9. 部署指南 + +### 9.1 环境准备 + +推荐 Python 3.10+。 + +```bash +cd Agent +python -m venv myenv +``` + +Windows PowerShell： + +```powershell +.\myenv\Scripts\Activate.ps1 +``` + +macOS / Linux： + +```bash +source myenv/bin/activate +``` + +安装依赖： + +```bash +pip install --upgrade pip +pip install -r requirements.txt +pip install -e . +``` + +### 9.2 环境变量 + +在 `Agent/.env` 中配置： + +```text +LLM_BASE_URL=... +LLM_API_KEY=... +LLM_MODEL=... +TTS_BASE_URL=... +ASR_BASE_URL=... +RAG_BASE_URL=... +``` + +其中 TTS、ASR、RAG 可按实际功能启用情况配置。 + +### 9.3 启动 Web 服务 + +Windows PowerShell： + +```powershell +cd Agent +$env:PYTHONPATH="src;$env:PYTHONPATH" +uvicorn web.server:app --host 127.0.0.1 --port 8765 +``` + +macOS / Linux： + +```bash +cd Agent +export PYTHONPATH=src:$PYTHONPATH +uvicorn web.server:app --host 127.0.0.1 --port 8765 +``` + +访问： + +```text +http://127.0.0.1:8765 +``` + +### 9.4 公网访问（可选） + +```bash +cloudflared tunnel --url http://localhost:8765 +``` + +## 10. 开发指南 + +### 10.1 新增工具 + +1. 在 `src/agent/tools/tool.py` 或独立工具文件中实现 `BaseTool`。 +2. 定义 `args_schema`、`name`、`description`、`_run()`、可选 `_arun()`。 +3. 在 `src/agent/tools/__init__.py` 的 `_all_tools` 中注册。 +4. 重启服务，让 `llm.bind_tools(...)` 重新绑定工具。 + +### 10.2 修改 Plan 流程 + +相关文件： + +- `src/agent/memory/system_prompt.py` +- `src/agent/core/plan.py` +- `src/agent/core/runtime.py` +- `src/agent/core/todo_manager.py` + +注意事项： + +- 研究阶段不能执行实际任务或调用工具。 +- 用户确认路径后才能生成 TodoList。 +- 生成 TodoList 后必须覆盖 `.todo`。 +- 执行阶段应根据任务进度调用 `todo_status_update`。 + +### 10.3 修改前端 Todo 浮窗 + +相关文件： + +- `src/web/app.js`：渲染、刷新、收起逻辑。 +- `src/web/app.css`：浮窗视觉样式。 +- `src/web/server.py`：`/api/todo` 数据接口。 + +## 11. 常见问题排查 + +### 11.1 前端看不到 Todo 浮窗 + +检查： + +1. 后端 `/api/todo` 是否返回内容。 +2. `src/agent/workspace/.todo` 是否存在且非空。 +3. 浏览器控制台是否有 JS 错误。 +4. 是否被收起为右侧悬浮球。 + +### 11.2 Plan 模式没有写入 `.todo` + +检查： + +1. 是否完成了路径选择确认。 +2. `continue_after_human_action` 中 pending type 是否为 `plan_choice`。 +3. 模型是否返回了 TodoList 文本。 +4. 进程是否有写入 `src/agent/workspace/.todo` 的权限。 + +### 11.3 软件控制工具找不到软件 + +处理： + +1. 使用更具体的软件名称。 +2. 调用时设置 `rescan=True` 重建索引。 +3. 确认软件有开始菜单或桌面快捷方式，或注册了 App Paths。 +4. 检查索引文件：`src/agent/history/data_base/software_index.json`。 + +### 11.4 上下文过长 + +系统会基于 token 阈值自动压缩历史。如果超过硬限制，会提示新建会话。Plan TodoList 不依赖历史摘要保存，而是从 `.todo` 文件注入，因此不会因压缩丢失。 + +## 12. 维护建议 + +- `.todo` 属于运行态工作区文件，不建议作为长期知识库。 +- 对高风险工具应保持人工审批或明确调用条件。 +- 工具描述应写清楚“何时调用”和“何时严禁调用”，降低误调用概率。 +- 修改工具注册后必须重启服务。 +- 修改前端静态文件后刷新浏览器缓存。 diff --git a/pyproject.toml b/pyproject.toml index df5644b..0d68180 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -10,7 +10,6 @@ license = { text = "MIT" } requires-python = ">=3.10" dependencies = [ "langgraph>=1.0.0", - 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z+Bo>|H}^@!3x*yYd{lB&NR>-(Tl5tWn&=@kF;R~C*)qzO7v3u6FNo&XM)GUJk3n|G ze%#*6NS3^T^*1aPlC^lMlD&fl==+iiSi+4G*PISYbCkBqJZ>zzW2CB`(C*A=cVc@r zw3p8sDUHqp)3xHO{u10o(&Q-t~e3?COA*VdZ7Kf~asqOF-Zo(w`QIdm|Sj@_Zzy>Z+x|pi#hRQLx|$ zgKCJfCk8UWQf-QpL9+nbp!XeOqIv znIEetx@MZH`wz+yqc;CpqoXx*6eK@dDS8duPiQQE))dl3vlJwAS}4wv;xf`uOg}}> vQkbeuq+&I->b_asQ#7rY3QK0SnEAeNg;E0?^$X?^M96-i&h7^V%L^I0tlMWuNqnYpP7PdBtZowMS_{+Tb^d!DYF^<-z~mas`KB1; RlMl>{jEuJ!9Ew^M96-iYhG2#whIB?vrcx^)_vNCN=Tm3C-!tp^ zf+^2eZhN|Rf%L^I0tlMWuNqnYpP7PdBtZowMS_{+Tb^d!DYF^<-z~mas`KB1; RlMl>{jEuJ!9Ew dict: + """重新加载配置并重建主 Agent 运行时，返回当前状态供调试。""" + global main_agent_runtime + import agent.chatbot_agent as ca + main_agent_runtime = _build_main_agent_runtime() + # 更新 chatbot_agent 中缓存的引用 + ca.main_agent_runtime = main_agent_runtime + ca.agent_graph = main_agent_runtime.graph + ca.tools = main_agent_runtime.tools + ca.TOOLS_BY_NAME = main_agent_runtime.tools_by_name + ca.llm = main_agent_runtime.llm + return { + "name": main_agent_runtime.name, + "tools_count": len(main_agent_runtime.tools), + "max_iterations": main_agent_runtime.max_iterations, + } diff --git a/src/agent/agents/multi_agent_runtime.py b/src/agent/agents/multi_agent_runtime.py new file mode 100644 index 0000000..ef9b7e5 --- /dev/null +++ b/src/agent/agents/multi_agent_runtime.py @@ -0,0 +1,252 @@ +"""多 Agent 运行时：一主多子 Agent 体系的初始化与管理。 + +系统定位: + 提供 ``MultiAgentRuntime`` 将主 Agent 与多个子 Agent 组装为 + 完整的 SupervisorGraph 运行时，供 Web 层调用。 + +与单 Agent 的区别: + - 使用 MultiAgentState 替代 AgentState + - 使用 build_multi_agent_graph 替代 build_agent_graph + - 额外维护 graph_events / agent_status / tool_calls_history 供前端 graph 面板 + +可扩展性: + - 支持运行时动态添加/移除子 Agent + - 支持并行子 Agent 执行策略 +""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Any + +from agent.core.multi_agent_graph import build_multi_agent_graph +from agent.core.multi_agent_state import ( + AgentStatusInfo, + GraphEvent, + ToolCallEvent, + init_multi_agent_state, +) +from agent.tools.tool_agent_call import AgentCallTool + + +@dataclass +class SubAgentConfig: + """子 Agent 配置容器。 + + 字段: + name: 子 Agent 名称。 + graph: 编译后的 LangGraph 图。 + llm: 原始 LLM 实例。 + tools: 工具列表。 + tools_by_name: 工具名 -> 工具实例映射。 + system_prompt: 系统提示词。 + icon: 前端展示图标。 + """ + name: str + graph: Any + llm: Any + tools: list[Any] = field(default_factory=list) + tools_by_name: dict[str, Any] = field(default_factory=dict) + system_prompt: str = "" + icon: str = "" + + +@dataclass +class MultiAgentRuntime: + """多 Agent 运行时配置容器。 + + 字段: + name: 运行时标识（如 "multi_agent_v1"）。 + graph: 编译后的 SupervisorGraph。 + llm: 主 Agent 的 LLM 实例。 + main_agent_name: 主 Agent 名称。 + agents: {name: SubAgentConfig} 所有 agent 配置（含主 agent 信息）。 + graph_events: 最近一次执行的图事件列表（浅拷贝）。 + agent_status: 最近一次执行的 agent 状态。 + tool_calls_history: 最近一次执行的工具调用历史。 + """ + name: str + graph: Any + llm: Any + main_agent_name: str = "main" + agents: dict[str, SubAgentConfig] = field(default_factory=dict) + graph_events: list[GraphEvent] = field(default_factory=list) + agent_status: dict[str, AgentStatusInfo] = field(default_factory=dict) + tool_calls_history: dict[str, list[ToolCallEvent]] = field(default_factory=dict) + + +def build_multi_agent_runtime( + main_runtime: Any, + sub_runtimes: list[dict[str, Any]] | None = None, +) -> MultiAgentRuntime: + """根据主 Agent 和子 Agent 运行时列表构建多 Agent 运行时。 + + 输入: + main_runtime: 主 Agent 的 AgentRuntime 实例（name/graph/llm/tools/tools_by_name/system_prompt）。 + sub_runtimes: 子 Agent 配置列表，每项为 dict，含: + - runtime: AgentRuntime 实例（必填） + - icon: 前端图标（可选，默认 🔧） + sub_runtimes 为 None 时构建纯主 Agent 的单 Agent 运行时。 + + 输出: + MultiAgentRuntime 实例，其 graph 为编译后的 SupervisorGraph。 + + 示例:: + + from agent.agents.main_agent import main_agent_runtime + runtime = build_multi_agent_runtime( + main_runtime=main_agent_runtime, + sub_runtimes=[ + {"runtime": code_agent_runtime, "icon": "💻"}, + {"runtime": research_agent_runtime, "icon": "🔍"}, + ], + ) + """ + sub_runtimes = sub_runtimes or [] + + # 组装主 agent 工具（含 agent_call） + main_tools = list(main_runtime.tools) + [AgentCallTool()] + main_tools_by_name = dict(main_runtime.tools_by_name) + main_tools_by_name["agent_call"] = AgentCallTool() + main_model_with_tools = main_runtime.llm.bind_tools(main_tools, tool_choice="auto") + + # 子 agent 配置列表 + sub_agent_configs: list[dict[str, Any]] = [] + agent_configs: dict[str, SubAgentConfig] = {} + + main_agent_name = main_runtime.name + + agent_configs[main_agent_name] = SubAgentConfig( + name=main_agent_name, + graph=main_runtime.graph, + llm=main_runtime.llm, + tools=main_tools, + tools_by_name=main_tools_by_name, + system_prompt=main_runtime.system_prompt, + icon="🧠", + ) + + for sr in sub_runtimes: + sr_runtime = sr["runtime"] + sr_icon = sr.get("icon", "🔧") + sa_config = SubAgentConfig( + name=sr_runtime.name, + graph=sr_runtime.graph, + llm=sr_runtime.llm, + tools=sr_runtime.tools, + tools_by_name=sr_runtime.tools_by_name, + system_prompt=sr_runtime.system_prompt, + icon=sr_icon, + ) + agent_configs[sr_runtime.name] = sa_config + sub_agent_configs.append({ + "name": sr_runtime.name, + "graph": sr_runtime.graph, + "tools_by_name": sr_runtime.tools_by_name, + }) + + # 构建 SupervisorGraph + graph = build_multi_agent_graph( + main_model_with_tools=main_model_with_tools, + main_tools=main_tools, + main_tools_by_name=main_tools_by_name, + sub_agents=sub_agent_configs, + main_agent_name=main_agent_name, + ) + + return MultiAgentRuntime( + name=f"multi_{main_agent_name}", + graph=graph, + llm=main_runtime.llm, + main_agent_name=main_agent_name, + agents=agent_configs, + ) + + +def get_agent_graph_snapshot(runtime: MultiAgentRuntime) -> dict: + """获取当前多 Agent 运行时状态快照，供前端 graph 面板渲染。 + + 输入: + runtime: MultiAgentRuntime 实例。 + + 输出: + { + "agents": [{"name": str, "role": "main"|"sub", "icon": str, "status": str, "error_message": str}, ...], + "edges": [{"source": str, "target": str, "highlighted": bool}, ...], + "events": [...], + "tool_calls": {agent_name: [ToolCallEvent, ...]}, + } + """ + agents_list = [] + for name, cfg in runtime.agents.items(): + status_info = runtime.agent_status.get(name) + status = status_info.status if status_info and isinstance(status_info, AgentStatusInfo) else "idle" + error_msg = status_info.error_message if status_info and isinstance(status_info, AgentStatusInfo) else "" + agents_list.append({ + "name": name, + "role": "main" if name == runtime.main_agent_name else "sub", + "icon": cfg.icon, + "status": status, + "error_message": error_msg, + }) + + # 构建边：主 agent 与每个子 agent 之间两条单向线 + edges = [] + main_name = runtime.main_agent_name + for name in runtime.agents: + if name == main_name: + continue + # 检查是否有消息传递事件 + highlighted_to_sub = any( + e.event_type == "message_pass" and e.source_agent == main_name and e.target_agent == name + for e in runtime.graph_events + ) + highlighted_to_main = any( + e.event_type == "message_pass" and e.source_agent == name and e.target_agent == main_name + for e in runtime.graph_events + ) + edges.append({ + "source": main_name, + "target": name, + "highlighted": highlighted_to_sub, + }) + edges.append({ + "source": name, + "target": main_name, + "highlighted": highlighted_to_main, + }) + + # 序列化事件 + events_list = [] + for e in runtime.graph_events: + events_list.append({ + "event_type": e.event_type, + "source_agent": e.source_agent, + "target_agent": e.target_agent, + "agent_name": e.agent_name, + "tool_name": e.tool_name, + "tool_args": e.tool_args, + "tool_result_preview": e.tool_result_preview, + "timestamp": e.timestamp, + "message_preview": e.message_preview, + }) + + # 序列化工具调用历史 + tool_calls_serialized = {} + for agent_name, calls in runtime.tool_calls_history.items(): + tool_calls_serialized[agent_name] = [ + { + "tool_name": c.tool_name, + "tool_args": c.tool_args, + "result_preview": c.result_preview, + "status": c.status, + "timestamp": c.timestamp, + } + for c in calls + ] + + return { + "agents": agents_list, + "edges": edges, + "events": events_list, + "tool_calls": tool_calls_serialized, + } diff --git a/src/agent/agents/research_agent.py b/src/agent/agents/research_agent.py new file mode 100644 index 0000000..76a7d8a --- /dev/null +++ b/src/agent/agents/research_agent.py @@ -0,0 +1,12 @@ +"""研究子 Agent（预留）。 + +系统定位: + 计划用于深度检索、文献综述等专用任务，与 main Agent 通过 team 模式 + 或子图委派协作。 + +可扩展性: + 实现步骤建议: + 1. 定义 RESEARCH_SYSTEM_PROMPT 与精简工具集（web_search、RAG 等） + 2. build_agent_graph(tools, model) 或自定义图（含检索节点） + 3. 导出 research_agent_runtime = AgentRuntime(...) +""" diff --git a/src/agent/chatbot_agent.py b/src/agent/chatbot_agent.py index 53bd378..5579c56 100644 --- a/src/agent/chatbot_agent.py +++ b/src/agent/chatbot_agent.py @@ -1,280 +1,51 @@ -from typing import Any, Literal, Sequence, Annotated, TypedDict -import json +from __future__ import annotations -from langgraph.graph import StateGraph, START, END -from langgraph.prebuilt import ToolNode -from langgraph.graph.message import add_messages -from langchain_core.messages import BaseMessage, HumanMessage, AIMessage, ToolMessage +from typing import Any, Literal -from agent.utils.image_utils import image_bytes_to_openai_image_url_part -from agent.llm import llm -from agent.tools import get_tools -from agent.utils.agent_utils import ( - assistant_text, - get_history, - summarizer, - get_session_meta, - update_session_meta, -) -from web.ui_session import ( - rewrite_user_message_files_to_session_dir, - session_dir, - sync_manifest_from_chat_history, -) -from agent.memory.system_prompt import MAIN_SYSTEM_PROMPT +from agent.agents.main_agent import main_agent_runtime +from agent.core.runtime import continue_after_human_action as continue_runtime_after_human_action +from agent.core.runtime import execute_agent as execute_runtime_agent -# ---------- 1. 工具与模型 ---------- -tools = get_tools() -model_with_tools = llm.bind_tools(tools) +AgentMode = Literal["agent", "team"] -# ---------- 2. 定义状态 ---------- -class AgentState(TypedDict): - messages: Annotated[Sequence[BaseMessage], add_messages] -# ---------- 3. 节点定义 ---------- -def call_model(state: AgentState) -> dict: - """调用 LLM 生成回复或工具调用""" - response = model_with_tools.invoke(state["messages"]) - - # ----- 打印 token 使用情况 ----- - usage = response.usage_metadata if hasattr(response, 'usage_metadata') else {} - if usage: - input_tokens = usage.get('input_tokens', '?') - output_tokens = usage.get('output_tokens', '?') - total_tokens = usage.get('total_tokens', '?') - print(f"[LLM] Token使用: input={input_tokens}, output={output_tokens}, total={total_tokens}") - - # ----- 打印工具调用信息 ----- - if hasattr(response, 'tool_calls') and response.tool_calls: - for tc in response.tool_calls: - # tc 是 ToolCall 对象，可直接访问 .name 和 .args - tool_name = tc.get('name') if isinstance(tc, dict) else tc.name - args = tc.get('args') if isinstance(tc, dict) else tc.args - if isinstance(args, str): - try: - args = json.loads(args) - except Exception: - pass - print(f"[LLM] 工具调用: {tool_name}, 参数: {args}") - - return {"messages": [response]} - -# 工具执行节点（LangGraph 内置，自动解包 content_and_artifact 到 ToolMessage.artifact） -tool_node = ToolNode(tools) - -def process_tool_artifact(state: AgentState) -> dict: - last_msg = state["messages"][-1] - - # 日志打印 - if isinstance(last_msg, ToolMessage): - content_preview = str(last_msg.content) - if len(content_preview) > 200: - content_preview = content_preview[:200] + '...' - print(f"[Tool] 工具返回 (id={last_msg.tool_call_id}): {content_preview}") - - # 只处理 ToolMessage 且有 artifact 的情况 - if not isinstance(last_msg, ToolMessage) or not last_msg.artifact: - return {} - - artifact = last_msg.artifact - - # ---------- 统一提取所有 image_bytes ---------- - img_bytes_list: list[bytes] = [] - - if isinstance(artifact, dict) and "image_bytes" in artifact: - # 单张图片：artifact = {"image_bytes": b"..."} - img_bytes_list.append(artifact["image_bytes"]) - - elif isinstance(artifact, list): - for item in artifact: - if isinstance(item, dict) and "image_bytes" in item: - img_bytes_list.append(item["image_bytes"]) - elif isinstance(item, bytes): - img_bytes_list.append(item) - - # 兼容 artifact 直接就是 bytes 的情况 - elif isinstance(artifact, bytes): - img_bytes_list.append(artifact) - - if not img_bytes_list: - return {} - - # ---------- 将图片转换为多模态 content ---------- - content_parts = [] - failed_count = 0 - - for img_bytes in img_bytes_list: - data_url = image_bytes_to_openai_image_url_part(img_bytes) - if data_url is None: - failed_count += 1 - continue - content_parts.append(data_url) - - # 所有图片都处理失败时的降级方案 - if not content_parts: - human_msg = HumanMessage( - content="工具返回了截图，但图片处理失败，请向用户说明。", - additional_kwargs={"synthetic": True} - ) - return {"messages": [human_msg]} - - # 追加引导文本（放在最后，也可以放在最前，取决于你的需求） - content_parts.append({ - "type": "text", - "text": "上面是工具返回的截图。" - }) - - # 如果有部分图片失败，可以在 text 中提示（可选） - if failed_count > 0: - content_parts[-1]["text"] += f"（其中 {failed_count} 张图片处理失败，请忽略）" - - human_msg = HumanMessage( - content=content_parts, - additional_kwargs={"synthetic": True} - ) - return {"messages": [human_msg]} - -# ---------- 4. 条件路由 ---------- -def should_continue(state: AgentState) -> Literal["tools", END]: - last_msg = state["messages"][-1] - if hasattr(last_msg, "tool_calls") and last_msg.tool_calls: - return "tools" - return END - -# ---------- 5. 构建 Graph ---------- -workflow = StateGraph(AgentState) - -workflow.add_node("agent", call_model) -workflow.add_node("tools", tool_node) -workflow.add_node("process_tool_artifact", process_tool_artifact) - -workflow.add_edge(START, "agent") -workflow.add_conditional_edges("agent", should_continue, { - "tools": "tools", - END: END, -}) -workflow.add_edge("tools", "process_tool_artifact") -workflow.add_edge("process_tool_artifact", "agent") - -agent_graph = workflow.compile() - -# ---------- 6. 辅助函数：保存消息（过滤合成消息） ---------- -def save_messages_to_history(session_id: str, user_msg: HumanMessage, assistant_msg: AIMessage) -> None: - if not session_id: - return - history = get_history(session_id) - # 只保存非合成消息 - if not user_msg.additional_kwargs.get("synthetic"): - history.add_message(user_msg) - history.add_message(assistant_msg) - -# ---------- 7. 核心执行函数 ---------- def execute_agent( chat_history: list[dict[str, Any]], session_id: str = "test", output_type: str = "text", + agent_mode: AgentMode = "agent", ) -> list[dict[str, Any]]: - chat_history = chat_history or [] - - if not chat_history or chat_history[-1].get("role") != "user": - return chat_history - - # 上传文件处理 - if session_id: - rewrite_user_message_files_to_session_dir(chat_history[-1], session_id) - - user_content = chat_history[-1]["content"] - - # 上下文感知构建 - messages, context_exceeded = summarizer( + """Web/API 层入口：使用 main_agent 运行时处理用户新一轮输入。""" + return execute_runtime_agent( + runtime=main_agent_runtime, + chat_history=chat_history, session_id=session_id, - agent_name="main", - system_prompt=MAIN_SYSTEM_PROMPT, - user_content=user_content, - llm=llm, + output_type=output_type, + agent_mode=agent_mode, ) - if context_exceeded: - chat_history.append({ - "role": "assistant", - "content": "上下文已满，请新建会话继续对话。", - }) - if session_id: - sync_manifest_from_chat_history(session_id, chat_history, "") - return chat_history - graph_config = { - "configurable": {"session_id": session_id}, - "metadata": { - "output_type": output_type, - "audio_output_dir": session_dir(session_id) if session_id else None, - }, - } +def continue_after_human_action( + chat_history: list[dict[str, Any]], + session_id: str, + approval_id: str, + decision: Literal["approve", "reject", "edit"], + instruction: str | None = None, + output_type: str = "text", +) -> list[dict[str, Any]]: + """Web/API 层入口：用户对待确认项决策后继续执行 main_agent。""" + return continue_runtime_after_human_action( + runtime=main_agent_runtime, + chat_history=chat_history, + session_id=session_id, + approval_id=approval_id, + decision=decision, + instruction=instruction, + output_type=output_type, + ) - try: - # 调用自定义 Graph - result = agent_graph.invoke({"messages": messages}, config=graph_config) - # 提取最后一条 AI 消息 - all_messages = result["messages"] - assistant_msg = all_messages[-1] if all_messages else AIMessage(content="") - text_content = assistant_text(assistant_msg.content) - - # token 统计更新 - usage = assistant_msg.usage_metadata if hasattr(assistant_msg, 'usage_metadata') else {} - total_tokens = usage.get('total_tokens', 0) if isinstance(usage, dict) else 0 - if total_tokens > 0: - meta = get_session_meta(session_id, "main") - update_session_meta( - session_id=session_id, - agent_name="main", - cursor_index=meta["cursor_index"], - compressed_content=meta["compressed_content"], - last_total_tokens=total_tokens, - ) - - # TTS 处理 - output_type_from_config = graph_config.get("metadata", {}).get("output_type", "text") - audio_data = llm.last_audio_kwargs or {} - if audio_data.get("output_type") != output_type_from_config: - audio_data = {} - - assistant_meta = { - "output_type": output_type_from_config, - "autoplay": False, - "audio": audio_data.get("audio"), - } - - if output_type_from_config == "audio" and assistant_meta.get("audio"): - assistant_content = [] - elif output_type_from_config == "both" and assistant_meta.get("audio"): - assistant_content = text_content - else: - assistant_content = text_content if text_content else "（空回复）" - - chat_history.append({ - "role": "assistant", - "content": assistant_content, - "meta": assistant_meta, - }) - - # 持久化历史消息（注意过滤掉合成消息） - save_messages_to_history( - session_id, - HumanMessage(content=user_content), - AIMessage(content=text_content), - ) - - llm.last_audio_kwargs = None - - except Exception as e: - import traceback - print("[ERROR] 调用模型时出错:") - traceback.print_exc() - err_text = "抱歉，我遇到了一些问题，请稍后再试。" - chat_history.append({"role": "assistant", "content": err_text}) - - if session_id: - sync_manifest_from_chat_history(session_id, chat_history, "") - - return chat_history \ No newline at end of file +agent_graph = main_agent_runtime.graph +tools = main_agent_runtime.tools +TOOLS_BY_NAME = main_agent_runtime.tools_by_name +llm = main_agent_runtime.llm diff --git a/src/agent/core/__init__.py b/src/agent/core/__init__.py new file mode 100644 index 0000000..c2a4525 --- /dev/null +++ b/src/agent/core/__init__.py @@ -0,0 +1,16 @@ +"""Agent 核心运行时包。 + +包含: + - state / graph_builder: LangGraph 状态与图拓扑 + - nodes / routing: 图节点与条件路由 + - runtime: 对外执行入口 execute_agent / continue_after_human_action + - llm: ChatQwen 多模态网关 + - approvals / human_interaction / tool_policy: HITL 与安全策略 + - plan: Plan 模式 UI 构建 + +系统定位: + Agent 业务逻辑中枢，``agents`` 包在此之上组装具体 Agent 实例。 + +可扩展性: + 子 Agent 可复用本包组件，仅替换 tools、system_prompt 与 graph 变体。 +""" diff --git a/src/agent/core/__pycache__/__init__.cpython-312.pyc b/src/agent/core/__pycache__/__init__.cpython-312.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ef548deafaecf5522bddbe4f0202629d4ee0276b GIT binary patch literal 762 zcmXw1O;6N77;cd$*@XBP=E^~d7ov?ZF_7nT=CK& 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"https://dashscope.aliyuncs.com/compatible-mode/v1", + "timeout": 60.0, + "max_retries": 0 + }, + "llm_model_id": "a44b226e", + "tools": [ + "web_search_class", + "date_query_class", + "screen_capture", + "browser_control", + "human_interaction", + "software_control", + "directory_listing" + ], + "enabled": true, + "role": "main" + } +] diff --git a/src/agent/core/approvals.py b/src/agent/core/approvals.py new file mode 100644 index 0000000..e94d348 --- /dev/null +++ b/src/agent/core/approvals.py @@ -0,0 +1,166 @@ +"""待确认操作（HITL）内存注册与 UI 元数据构建。 + +系统定位: + 维护进程内 ``PENDING_TOOL_APPROVALS`` 字典，在图因需确认工具而暂停时 + 保存上下文；``runtime.continue_after_human_action`` 消费并清理。 + +注意: + 当前为内存存储，进程重启后待确认项丢失；生产环境可改为 Redis/DB。 + +可扩展性: + - 增加 TTL、session 级清理、持久化后端。 + - ``build_*_meta`` 可与前端组件协议版本化。 +""" +from __future__ import annotations + +import json +import uuid +from typing import Any, Sequence + +from langchain_core.messages import BaseMessage +from langchain_core.messages import messages_to_dict + +from agent.core.human_interaction import normalize_human_interaction_args +from agent.utils.tool_call_utils import json_safe + +PENDING_TOOL_APPROVALS: dict[str, dict[str, Any]] = {} + + +def build_approval_meta(session_id: str, tool_call: dict[str, Any], messages: Sequence[BaseMessage]) -> dict[str, Any]: + """为需确认的普通工具调用构建 UI 元数据并注册 pending。 + + 输入: + session_id: 会话 ID。 + tool_call: normalize 后的工具调用 dict。 + messages: 当前图内消息序列（序列化后存入 pending）。 + + 输出: + ``{"pending_action": {...}}``，含 id、type=tool_call、args、options 等。 + + 系统定位: + ``routing.first_pending_tool_meta`` 对 confirm 类工具调用。 + + 可扩展性: + 可按工具名定制 title、风险提示、参数展示模板。 + """ + approval_id = uuid.uuid4().hex + tool_name = tool_call.get("name") or "unknown_tool" + args = json_safe(tool_call.get("args") or {}) + display_call = {"name": tool_name, "args": args} + instruction = json.dumps(display_call, ensure_ascii=False, indent=2) + PENDING_TOOL_APPROVALS[approval_id] = { + "session_id": session_id, + "type": "tool_call", + "tool_call": {"id": tool_call.get("id"), "name": tool_name, "args": args}, + "messages": messages_to_dict(list(messages)), + } + return { + "pending_action": { + "id": approval_id, + "type": "tool_call", + "title": f"待确认执行工具：{tool_name}", + "tool_name": tool_name, + "args": display_call, + "instruction": instruction, + "options": ["approve", "reject"], + "resolved": False, + } + } + + +def build_human_interaction_meta(session_id: str, tool_call: dict[str, Any], messages: Sequence[BaseMessage]) -> dict[str, Any]: + """为 human_interaction 工具构建 UI 元数据并注册 pending。 + + 输入: + session_id: 会话 ID。 + tool_call: 含 name=args 的工具调用。 + messages: 当前消息序列。 + + 输出: + ``{"pending_action": {...}}``，type=human_interaction，含 interaction_type 等。 + + 系统定位: + 模型主动请求用户补充信息/选择/审批时使用。 + + 可扩展性: + 可扩展表单 schema、多步向导式交互。 + """ + approval_id = uuid.uuid4().hex + args = normalize_human_interaction_args(tool_call.get("args") or {}) + interaction_type = args.get("interaction_type") or "information" + default_titles = { + "information": "请补充信息", + "selection": "请选择一个选项", + "approval": "需要人工审批", + } + PENDING_TOOL_APPROVALS[approval_id] = { + "session_id": session_id, + "type": "human_interaction", + "tool_call": {"id": tool_call.get("id"), "name": "human_interaction", "args": args}, + "messages": messages_to_dict(list(messages)), + } + return { + "pending_action": { + "id": approval_id, + "type": "human_interaction", + "interaction_type": interaction_type, + "title": args.get("title") or default_titles.get(str(interaction_type), "需要人工处理"), + "prompt": args.get("prompt") or "请处理后继续。", + "tool_name": "human_interaction", + "args": { + "交互类型": interaction_type, + "说明": args.get("prompt") or "请处理后继续。", + "可选项": args.get("options") or [], + }, + "instruction": args.get("suggested_response") or "", + "options": args.get("options") or [], + "resolved": False, + } + } + + +def pending_assistant_message(meta: dict[str, Any], content: str = "需要你确认后继续执行。") -> dict[str, Any]: + """构造带 pending_action 的 assistant 聊天消息 dict。 + + 输入: + meta: 通常含 pending_action 的字典。 + content: 展示给用户的提示文案。 + + 输出: + Web UI 使用的 ``{"role": "assistant", "content": ..., "meta": ...}``。 + + 系统定位: + ``runtime.execute_agent`` 检测到待确认时追加到 chat_history。 + + 可扩展性: + 可统一 output_type、国际化 content 模板。 + """ + return { + "role": "assistant", + "content": content, + "meta": {"output_type": "text", "autoplay": False, **meta}, + } + + +def remove_pending_ui_message(chat_history: list[dict[str, Any]], approval_id: str) -> None: + """在 chat_history 中将指定 approval_id 的 pending_action 标记为已解决。 + + 输入: + chat_history: 可变聊天历史列表（原地修改）。 + approval_id: 待确认项 ID。 + + 输出: + 无（副作用：匹配消息的 meta.pending_action.resolved = True）。 + + 系统定位: + ``runtime.continue_after_human_action`` 用户操作后更新 UI 状态。 + + 可扩展性: + 可改为从 history 中移除而非仅标记 resolved。 + """ + for msg in reversed(chat_history): + pending = ((msg.get("meta") or {}).get("pending_action") or {}) if isinstance(msg, dict) else {} + if pending.get("id") == approval_id: + pending["resolved"] = True + msg.setdefault("meta", {})["pending_action"] = pending + return diff --git a/src/agent/core/config_manager.py b/src/agent/core/config_manager.py new file mode 100644 index 0000000..7815c66 --- /dev/null +++ b/src/agent/core/config_manager.py @@ -0,0 +1,339 @@ +"""Agent 配置管理：从 JSON 文件加载/保存 agent、tool、env、model 配置。 + +系统定位: + 提供统一接口读写 ``agent/core/`` 下的 JSON 配置文件， + 使前端手动配置的 JSON 可以替代 core 底下的相应 .py 硬编码。 + +配置文件: + agent_config.json - Agent 运行时配置（名称、模型引用、工具列表、system_prompt） + tool_config.json - 工具权限与参数配置 + env_config.json - 环境变量与 LLM 模型列表 + +可扩展性: + 可增加版本号、校验 schema、多环境配置切换。 +""" +from __future__ import annotations + +import json +import os +import uuid +from dataclasses import dataclass, field, asdict +from pathlib import Path +from typing import Any + +# ---------- 配置文件路径 ---------- +_CORE_DIR = Path(__file__).resolve().parent +AGENT_CONFIG_PATH = _CORE_DIR / "agent_config.json" +TOOL_CONFIG_PATH = _CORE_DIR / "tool_config.json" +ENV_CONFIG_PATH = _CORE_DIR / "env_config.json" + + +# ---------- 模型配置 ---------- +@dataclass +class ModelConfig: + id: str = "" # 唯一标识 + name: str = "" # 显示名称 + api_key: str = "" + base_url: str = "" + model: str = "" # API 模型名 + timeout: float = 60.0 + max_retries: int = 0 + + +# ---------- LLM 配置结构（保留兼容） ---------- +@dataclass +class LLMConfig: + model: str = "" + api_key: str = "" + base_url: str = "" + timeout: float = 60.0 + max_retries: int = 0 + + +@dataclass +class AgentConfig: + name: str = "" + system_prompt: str = "" + max_iterations: int = 200 + llm: LLMConfig = field(default_factory=LLMConfig) # 保留兼容 + llm_model_id: str = "" # 新：引用 models 列表中的模型 ID + tools: list[str] = field(default_factory=list) + enabled: bool = True + role: str = "sub" # "main" | "sub" + + +@dataclass +class ToolConfig: + name: str = "" + display_name: str = "" + description: str = "" + permission: str = "confirm" # "direct" | "confirm" + auto_execute_rules: list[dict] = field(default_factory=list) # [{"parameter":"action","operator":"equals","value":"open"}, ...] + enabled: bool = True + + +# ---------- 通用 JSON 读写 ---------- +def _read_json(path: Path) -> dict | list | None: + """读取 JSON 文件，不存在或格式错误返回 None。""" + if not path.is_file(): + return None + try: + with open(path, "r", encoding="utf-8") as f: + return json.load(f) + except (json.JSONDecodeError, OSError): + return None + + +def _write_json(path: Path, data: Any) -> bool: + """写入 JSON 文件，成功返回 True。""" + try: + with open(path, "w", encoding="utf-8") as f: + json.dump(data, f, ensure_ascii=False, indent=2) + return True + except OSError: + return False + + +# ---------- Agent 配置 ---------- +def _default_agent_config() -> list[dict]: + """生成默认 agent 配置（单 agent: main）。""" + from agent.memory.system_prompt import MAIN_SYSTEM_PROMPT + from agent.tools import get_all_available_tool_names + models = load_models() + default_model_id = models[0]["id"] if models else "" + return [ + { + "name": "main", + "system_prompt": MAIN_SYSTEM_PROMPT, + "max_iterations": 200, + "llm_model_id": default_model_id, + "tools": get_all_available_tool_names(), + "enabled": True, + "role": "main", + } + ] + + +def load_agent_configs() -> list[AgentConfig]: + """从 agent_config.json 加载 agent 配置列表，无文件时返回默认。""" + raw = _read_json(AGENT_CONFIG_PATH) + if not raw or not isinstance(raw, list): + raw = _default_agent_config() + _write_json(AGENT_CONFIG_PATH, raw) + + configs = [] + for item in raw: + llm_raw = item.get("llm", {}) + configs.append(AgentConfig( + name=item.get("name", ""), + system_prompt=item.get("system_prompt", ""), + max_iterations=item.get("max_iterations", 200), + llm=LLMConfig( + model=llm_raw.get("model", ""), + api_key=llm_raw.get("api_key", ""), + base_url=llm_raw.get("base_url", ""), + timeout=llm_raw.get("timeout", 60.0), + max_retries=llm_raw.get("max_retries", 0), + ), + llm_model_id=item.get("llm_model_id", ""), + tools=item.get("tools", []), + enabled=item.get("enabled", True), + role=item.get("role", "sub"), + )) + return configs + + +def save_agent_configs(configs: list[dict]) -> bool: + """将 agent 配置列表写入 agent_config.json。""" + return _write_json(AGENT_CONFIG_PATH, configs) + + +# ---------- Tool 配置 ---------- + +# 各工具的默认 auto_execute_rules —— 与 tool_policy.py 的硬编码规则对应 +_TOOL_DEFAULT_AUTO_RULES: dict[str, list[dict]] = { + "browser_control": [ + {"parameter": "action", "operator": "equals", "value": "open"}, + ], + "rag_tool": [ + {"parameter": "action_type", "operator": "equals", "value": "query"}, + ], +} + + +def _get_tool_parameters() -> dict[str, list[dict]]: + """获取每个工具的参数名和类型信息，供前端生成 auto_execute_rules 提示。 + + 返回: + {tool_name: [{"name": "action", "type": "Literal[open,close]"}, ...], ...} + """ + from agent.tools import _ALL_AVAILABLE_TOOL_CLASSES + import typing + result: dict[str, list[dict]] = {} + for name, cls in _ALL_AVAILABLE_TOOL_CLASSES.items(): + try: + inst = cls() + schema = getattr(inst, "args_schema", None) + if schema is None: + result[name] = [] + continue + fields_info = getattr(schema, "model_fields", {}) + params = [] + for fname, finfo in fields_info.items(): + annotation = finfo.annotation + type_str = str(annotation) if annotation is not None else "Any" + # 简化 Literal 显示 + import re + m = re.search(r"Literal\[(.+?)\]", type_str) + if m: + type_str = "Literal[" + m.group(1) + "]" + params.append({"name": fname, "type": type_str}) + result[name] = params + except Exception: + result[name] = [] + return result + + +def _default_tool_config() -> list[dict]: + """根据所有可用工具生成默认 tool 配置。""" + from agent.tools import get_all_available_tool_names, get_all_available_tool_descriptions + from agent.core.tool_policy import classify_tool_execution + all_names = get_all_available_tool_names() + all_descriptions = get_all_available_tool_descriptions() + result = [] + for tname in all_names: + display_name, desc = all_descriptions.get(tname, (tname, "")) + perm = classify_tool_execution(tname, {}) + default_rules = _TOOL_DEFAULT_AUTO_RULES.get(tname, []) + result.append({ + "name": tname, + "display_name": display_name, + "description": desc, + "permission": perm, + "auto_execute_rules": default_rules, + "enabled": True, + }) + return result + + +def load_tool_configs() -> list[ToolConfig]: + """从 tool_config.json 加载工具配置，无文件时返回默认。""" + raw = _read_json(TOOL_CONFIG_PATH) + if not raw or not isinstance(raw, list): + raw = _default_tool_config() + _write_json(TOOL_CONFIG_PATH, raw) + + configs = [] + for item in raw: + configs.append(ToolConfig( + name=item.get("name", ""), + display_name=item.get("display_name", ""), + description=item.get("description", ""), + permission=item.get("permission", "confirm"), + auto_execute_rules=item.get("auto_execute_rules", []), + enabled=item.get("enabled", True), + )) + return configs + + +def save_tool_configs(configs: list[dict]) -> bool: + """将 tool 配置列表写入 tool_config.json。""" + return _write_json(TOOL_CONFIG_PATH, configs) + + +# ---------- 强制权限工具（前端锁死，不可修改） ---------- +# 仅必须 confirm 的工具锁死；safe_tools 默认 direct 但用户可改为 confirm +_FORCED_CONFIRM_TOOLS = {"human_interaction"} + + +def get_tool_forced_permissions() -> dict[str, str]: + """返回强制权限的工具映射 {tool_name: "confirm"}， + 前端应对这些工具禁用权限下拉框和 auto_execute_rules 编辑。""" + return {name: "confirm" for name in _FORCED_CONFIRM_TOOLS} + + +# ---------- 模型管理 ---------- +def _default_models() -> list[dict]: + """生成默认模型列表，首次启动时从 .env 继承已有 LLM 配置。""" + import os + api_key = os.environ.get("LLM_API_KEY", "") + base_url = os.environ.get("LLM_BASE_URL", "") + model = os.environ.get("LLM_MODEL", "") + timeout = os.environ.get("LLM_TIMEOUT", "60") + if api_key and base_url and model: + return [ + { + "id": str(uuid.uuid4())[:8], + "name": "默认模型", + "api_key": api_key, + "base_url": base_url, + "model": model, + "timeout": float(timeout) if timeout else 60.0, + "max_retries": 0, + } + ] + return [] + + +def load_models() -> list[dict]: + """从 env_config.json 的 models 字段加载模型列表。""" + raw = _read_json(ENV_CONFIG_PATH) + if isinstance(raw, dict) and "models" in raw and isinstance(raw["models"], list) and raw["models"]: + return raw["models"] + return _default_models() + + +def save_models(models: list[dict]) -> bool: + """将模型列表写入 env_config.json（合并其他字段）。""" + current = load_env_config() + current["models"] = models + return _write_json(ENV_CONFIG_PATH, current) + + +# ---------- Env 配置 ---------- +def _default_env_config() -> dict: + """生成默认 env 配置（不含 models，models 由 load_models 独立处理）。""" + return { + "TTS_BASE_URL": "", + "ASR_BASE_URL": "", + "RAG_BASE_URL": "", + "LLM_CONTEXT_WINDOW": "131072", + "MINI_COMPRESS_RATE": "0.5", + "HARD_COMPRESS_RATE": "0.75", + "CURRENT_TURNS": "4", + "IMG_SIZE": "768", + "RAG_CHUNK_SIZE": "500", + "RAG_CHUNK_OVERLAP": "50", + "GROUNDING_WIDTH": "1000", + "GROUNDING_HEIGHT": "1000", + } + + +def load_env_config() -> dict: + """从 env_config.json 加载环境变量配置，无文件时返回默认。""" + raw = _read_json(ENV_CONFIG_PATH) + if not raw or not isinstance(raw, dict): + raw = _default_env_config() + raw["models"] = _default_models() + _write_json(ENV_CONFIG_PATH, raw) + return raw + # 确保有 models 字段（向后兼容旧配置文件） + if "models" not in raw or not isinstance(raw.get("models"), list): + raw["models"] = _default_models() + return raw + + +def save_env_config(config: dict) -> bool: + """将 env 配置写入 env_config.json（保留已有 models）。""" + # 保留已有的 models 列表（由 models API 单独管理） + existing = _read_json(ENV_CONFIG_PATH) + if isinstance(existing, dict) and "models" in existing: + config["models"] = existing["models"] + return _write_json(ENV_CONFIG_PATH, config) + + +# ---------- 获取所有已注册工具的基础信息 ---------- +def get_registered_tool_names() -> list[str]: + """返回所有可用工具名称（含数据库工具）。""" + from agent.tools import get_all_available_tool_names + return get_all_available_tool_names() diff --git a/src/agent/core/env_config.json b/src/agent/core/env_config.json new file mode 100644 index 0000000..bdb3a46 --- /dev/null +++ b/src/agent/core/env_config.json @@ -0,0 +1,25 @@ +{ + "TTS_BASE_URL": "", + "ASR_BASE_URL": "", + "RAG_BASE_URL": "", + "LLM_CONTEXT_WINDOW": "262144", + "MINI_COMPRESS_RATE": "0.4", + "HARD_COMPRESS_RATE": "0.6", + "CURRENT_TURNS": "4", + "IMG_SIZE": "768", + "RAG_CHUNK_SIZE": "500", + "RAG_CHUNK_OVERLAP": "50", + "GROUNDING_WIDTH": "1000", + "GROUNDING_HEIGHT": "1000", + "models": [ + { + "id": "a44b226e", + "name": "阿里云Qwen", + "api_key": "sk-a44b226ec79a40fd8fcdbbaa74ba133e", + "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", + "model": "qwen3.6-flash", + "timeout": 60, + "max_retries": 0 + } + ] +} \ No newline at end of file diff --git a/src/agent/core/graph_builder.py b/src/agent/core/graph_builder.py new file mode 100644 index 0000000..1168926 --- /dev/null +++ b/src/agent/core/graph_builder.py @@ -0,0 +1,53 @@ +"""LangGraph ReAct 工作流图构建。 + +系统定位: + 将 LLM（已 bind_tools）、工具列表组装为可编译的 StateGraph， + 供 ``agents/main_agent`` 及各子 Agent 的 ``AgentRuntime`` 使用。 + +图结构: + START -> agent -> (tools | END) + tools -> process_tool_artifact -> agent + +可扩展性: + - 可增加 checkpoint、interrupt 节点、子图路由。 + - 可为不同 Agent 注入不同的 conditional_edges 或额外节点（如 RAG 检索）。 +""" +from __future__ import annotations + +from typing import Any + +from langgraph.graph import END, START, StateGraph +from langgraph.prebuilt import ToolNode + +from agent.core.nodes import make_call_model_node, process_tool_artifact +from agent.core.routing import should_continue +from agent.core.state import AgentState + + +def build_agent_graph(tools: list[Any], model_with_tools: Any): + """构建并编译标准 ReAct Agent 图。 + + 输入: + tools: LangChain BaseTool 列表，供 ToolNode 执行。 + model_with_tools: 已 ``bind_tools`` 的聊天模型，供 agent 节点调用。 + + 输出: + 编译后的 LangGraph CompiledGraph，支持 ``invoke({"messages": ...})``。 + + 系统定位: + ``agents/main_agent`` 入口；子 Agent 可复用同一拓扑并替换 tools/model。 + + 可扩展性: + 返回前可 ``workflow.compile(checkpointer=...)`` 启用持久化； + 可参数化节点名以支持多 Agent 图合并。 + """ + workflow = StateGraph(AgentState) + workflow.add_node("agent", make_call_model_node(model_with_tools)) + workflow.add_node("tools", ToolNode(tools)) + workflow.add_node("process_tool_artifact", process_tool_artifact) + + workflow.add_edge(START, "agent") + workflow.add_conditional_edges("agent", should_continue, {"tools": "tools", END: END}) + workflow.add_edge("tools", "process_tool_artifact") + workflow.add_edge("process_tool_artifact", "agent") + return workflow.compile() diff --git a/src/agent/core/human_interaction.py b/src/agent/core/human_interaction.py new file mode 100644 index 0000000..f0bf1ba --- /dev/null +++ b/src/agent/core/human_interaction.py @@ -0,0 +1,75 @@ +"""人工交互（HITL）参数规范化与结果文案生成。 + +系统定位: + 连接 ``human_interaction`` 工具与 ``core/approvals`` / ``core/runtime``， + 将模型发起的人工交互请求转为 UI 可展示结构，并在用户操作后生成 + ToolMessage 内容供图继续执行。 + +可扩展性: + - 新增 interaction_type 时同步扩展 ``normalize_human_interaction_args`` + 与 ``human_interaction_result`` 的分支。 + - 可与前端约定更丰富的 payload（表单字段、文件上传等）。 +""" +from __future__ import annotations + +from typing import Any + +from agent.tools.tool import HumanInteraction + + +def normalize_human_interaction_args(args: dict[str, Any]) -> dict[str, Any]: + """规范化 human_interaction 工具参数为固定 schema。 + + 输入: + args: 模型传入的原始参数字典。 + + 输出: + 含 interaction_type、title、prompt、options、suggested_response 的字典； + interaction_type 限定为 information | selection | approval。 + + 系统定位: + ``approvals.build_human_interaction_meta`` 构建待确认 UI 前调用。 + + 可扩展性: + 可增加 locale、超时、默认值校验；options 可支持 label/value 结构。 + """ + normalized = dict(args or {}) + normalized["interaction_type"] = str(normalized.get("interaction_type") or "information") + if normalized["interaction_type"] not in {"information", "selection", "approval"}: + normalized["interaction_type"] = "information" + normalized["title"] = str(normalized.get("title") or "需要人工处理") + normalized["prompt"] = str(normalized.get("prompt") or "请处理后继续。") + normalized["options"] = HumanInteraction.normalize_options(normalized.get("options")) + normalized["suggested_response"] = str(normalized.get("suggested_response") or "") + return normalized + + +def human_interaction_result(pending: dict[str, Any], decision: str, instruction: str | None) -> str: + """根据用户决策生成写入 ToolMessage 的文本结果。 + + 输入: + pending: PENDING_TOOL_APPROVALS 中 type=human_interaction 的条目。 + decision: approve | reject | edit。 + instruction: 用户输入文本（补充信息、选项、修改说明等）。 + + 输出: + 供 LangGraph 继续推理的 ToolMessage 字符串内容。 + + 系统定位: + ``runtime.continue_after_human_action`` 在人工交互分支调用。 + + 可扩展性: + 可返回结构化 JSON 字符串，供下游节点解析；支持多语言模板。 + """ + tool_call = pending.get("tool_call") or {} + args = normalize_human_interaction_args(tool_call.get("args") or {}) + interaction_type = args.get("interaction_type") or "information" + text = (instruction or "").strip() + if interaction_type == "approval": + if decision == "reject": + return "用户拒绝" + return "用户同意" + if interaction_type == "selection": + selected = text or args.get("suggested_response") or "" + return f"用户选择：{selected}" + return f"用户补充信息如下：{text}" diff --git a/src/agent/core/llm.py b/src/agent/core/llm.py new file mode 100644 index 0000000..37c1db3 --- /dev/null +++ b/src/agent/core/llm.py @@ -0,0 +1,670 @@ +"""ChatQwen：基于 OpenAI 兼容 API 的多模态 LangChain 聊天模型。 + +系统定位: + 项目默认 LLM 单例 ``llm``，被 agents 绑定工具，并承担： + - 用户消息预处理（附件、ASR、图片 data URL） + - TTS 输出与音频元数据 + - 非标准 tool_call XML/JSON 解析 + - Vision 描述接口 + +依赖: + env_utils 中的 LLM_* / TTS_BASE_URL / ASR_BASE_URL + +可扩展性: + - 可子类化覆盖 _preprocess_messages、_create_chat_result + - 流式、重试、限流可在 _generate 层扩展 +""" +import io +import os +import tempfile +import wave +import pyaudio +from contextlib import closing +from typing import Any, List, Dict, Optional +from pydantic import Field +import re, json +from datetime import datetime as dt +import requests +from concurrent.futures import ThreadPoolExecutor, as_completed + +from langchain_core.messages import HumanMessage, AIMessage, BaseMessage, SystemMessage, ToolCall +from langchain_core.outputs import ChatResult, ChatGeneration +from langchain_openai import ChatOpenAI + +from agent.utils.image_utils import image_bytes_to_openai_image_url_part +from agent.utils.env_utils import LLM_API_KEY, LLM_BASE_URL, LLM_MODEL, LLM_TIMEOUT +import agent.utils.env_utils as env_utils +from agent.utils.agent_utils import assistant_text, Documents_process, split_text, merge_wavs +from agent.utils.image_utils import image_path_to_openai_image_url_part, image_bytes_to_openai_image_url_part, IMAGE_FILE_EXTENSIONS +from agent.memory.system_prompt import VISION_SYSTEM_PROMPT + +class ChatQwen(ChatOpenAI): + """Qwen 系列模型的 LangChain 封装，扩展多模态与 TTS 能力。 + + 属性: + output_type: 默认输出模式 text | audio | both。 + stream_play: TTS 后是否本地播放。 + last_audio_kwargs: 最近一次 TTS 的文件路径与时长等元数据。 + + 系统定位: + agents/main_agent 的 llm 与 bind_tools 基底。 + + 可扩展性: + 新增厂商响应字段时在 _create_chat_result 中适配。 + """ + + output_type: str = Field(default="text") + stream_play: bool = Field(default=False) + last_audio_kwargs: Optional[dict] = Field(default=None) + + def __init__(self, **kwargs: Any) -> None: + """从环境变量或 kwargs 初始化 OpenAI 兼容客户端。 + + 输入: + kwargs: 可覆盖 model、api_key、base_url、timeout、max_retries。 + + 输出: + 无。 + + 系统定位: + 模块级 ``llm = ChatQwen()`` 单例构造。 + + 可扩展性: + 可增加 default_headers、proxy 等 ChatOpenAI 参数。 + """ + super().__init__( + model=kwargs.pop("model", os.environ.get("LLM_MODEL", "")), + api_key=kwargs.pop("api_key", os.environ.get("LLM_API_KEY", "")), + base_url=kwargs.pop("base_url", os.environ.get("LLM_BASE_URL", "")), + timeout=kwargs.pop("timeout", float(os.environ.get("LLM_TIMEOUT", 60))), + max_retries=kwargs.pop("max_retries", 0), + extra_body={ + "chat_template_kwargs": {"enable_thinking": False} + }, + **kwargs + ) + + @staticmethod + def play_audio_bytes(audio_bytes: bytes, chunk_size: int = 1024) -> None: + """通过 pyaudio 播放 WAV 字节流。 + + 输入: audio_bytes WAV 二进制；chunk_size 播放块大小。 + 输出: 无。 + 系统定位: _apply_tts 在 stream_play=True 时调用。 + 可扩展性: 可换用 sounddevice 或仅写文件不播放。 + """ + if not audio_bytes: + return + wf = wave.open(io.BytesIO(audio_bytes), 'rb') + p = pyaudio.PyAudio() + stream = p.open( + format=p.get_format_from_width(wf.getsampwidth()), + channels=wf.getnchannels(), + rate=wf.getframerate(), + output=True, + ) + data = wf.readframes(chunk_size) + while data: + stream.write(data) + data = wf.readframes(chunk_size) + stream.stop_stream() + stream.close() + p.terminate() + wf.close() + + @staticmethod + def _process_image_content(part: Dict[str, Any]) -> Dict[str, Any]: + """将 image_url 块中的本地路径或字节转为 data URL。 + + 输入: OpenAI 风格 content part dict。 + 输出: 处理后的 part 或失败时 text 占位。 + 系统定位: _preprocess_messages 多模态分支。 + 可扩展性: 委托 image_utils，保持单一图片处理逻辑。 + """ + image_type = part.get("type") + if image_type != "image_url": + return part + image_url = part.get("image_url", {}) + url = image_url.get("url", "") + if url.startswith(("http://", "https://", "data:")): + return part + if isinstance(url, str) and os.path.exists(url): + ext = os.path.splitext(url)[1].lower() + if ext in IMAGE_FILE_EXTENSIONS: + result = image_path_to_openai_image_url_part(url) + return result if result else {"type": "text", "text": "[图片处理失败]"} + if isinstance(url, bytes): + result = image_bytes_to_openai_image_url_part(url) + return result if result else {"type": "text", "text": "[图片处理失败]"} + return part + + @staticmethod + def _convert_audio_to_wav(audio_path: str) -> str: + """非 WAV 音频经 pydub 转为临时 WAV 文件路径。 + + 输入: audio_path 本地音频路径。 + 输出: WAV 文件路径（可能为临时文件，调用方负责清理）。 + 系统定位: _audio_to_text 前置格式统一。 + 可扩展性: 需系统安装 ffmpeg；可缓存转换结果。 + """ + ext = os.path.splitext(audio_path)[1].lower() + if ext == ".wav": + return audio_path + try: + from pydub import AudioSegment + except ImportError as exc: + raise RuntimeError("当前 ASR 仅支持 WAV，但未安装 pydub/ffmpeg") from exc + try: + audio = AudioSegment.from_file(audio_path) + except Exception as exc: + raise RuntimeError(f"无法读取音频文件: {exc}") from exc + fd, wav_path = tempfile.mkstemp(suffix=".wav", prefix="asr_") + os.close(fd) + try: + audio.export(wav_path, format="wav") + return wav_path + except Exception: + if os.path.exists(wav_path): + os.remove(wav_path) + raise + + def _audio_to_text(self, audio_path: str) -> str: + """调用 ASR 服务将音频转为文本。 + + 输入: audio_path 本地音频文件。 + 输出: 识别文本字符串。 + 系统定位: 用户消息含 audio part 时替换为 text。 + 可扩展性: ASR_BASE_URL 需配置；可支持流式识别。 + """ + if not os.path.exists(audio_path): + raise FileNotFoundError(f"音频文件不存在: {audio_path}") + + wav_path = self._convert_audio_to_wav(audio_path) + cleanup_wav = (wav_path != audio_path) + + try: + # 读取音频文件二进制内容 + with open(wav_path, "rb") as f: + audio_bytes = f.read() + + # 构建 Data URL + import base64 + b64 = base64.b64encode(audio_bytes).decode("ascii") + mime_type = "audio/wav" # 我们已经转换为 wav + data_url = f"data:{mime_type};base64,{b64}" + + payload = { + "messages": [ + { + "role": "user", + "content": [ + {"type": "audio_url", "audio_url": {"url": data_url}} + ] + } + ] + } + endpoint = f"{env_utils.ASR_BASE_URL.rstrip('/')}/chat/completions" + resp = requests.post( + endpoint, + headers={"Content-Type": "application/json"}, + json=payload, + timeout=120 + ) + resp.raise_for_status() + result = resp.json() + content = result["choices"][0]["message"]["content"] + return content.strip() + finally: + if cleanup_wav and os.path.exists(wav_path): + os.remove(wav_path) + + def _preprocess_messages(self, messages: List[BaseMessage]) -> List[BaseMessage]: + """入模前统一处理：JSON 列表、附件、ASR、图片、文档解析。 + + 输入: LangChain BaseMessage 列表。 + 输出: 规范化后的新消息列表（不修改原列表）。 + 系统定位: _generate 调用 super()._generate 之前的适配层。 + 可扩展性: 新增 part.type 时在此增加分支。 + """ + new_messages = [] + for msg in messages: + if isinstance(msg, HumanMessage) and isinstance(msg.content, str): + text = msg.content.strip() + if text.startswith("[") and text.endswith("]"): + try: + possible_list = json.loads(text) + if isinstance(possible_list, list): + msg = HumanMessage(content=possible_list, additional_kwargs=msg.additional_kwargs) + except (json.JSONDecodeError, ValueError): + pass + if isinstance(msg, HumanMessage) and isinstance(msg.content, dict): + # 尝试从 dict 中提取文本，否则序列化为字符串 + text = msg.content.get("text") or msg.content.get("content") or "" + if text: + msg = HumanMessage(content=str(text), additional_kwargs=msg.additional_kwargs) + else: + msg = HumanMessage(content=json.dumps(msg.content, ensure_ascii=False), additional_kwargs=msg.additional_kwargs) + elif isinstance(msg, HumanMessage) and isinstance(msg.content, list): + new_content: List[Dict[str, Any] | str] = [] + for part in msg.content: + if isinstance(part, dict): + if part.get("type") == "audio": + audio_path = part.get("audio_path") + if audio_path: + try: + text = self._audio_to_text(audio_path) + new_content.append({"type": "text", "text": text}) + except Exception as e: + new_content.append({"type": "text", "text": f"[语音识别失败: {e}]"}) + elif part.get("type") == "image_url": + new_content.append(self._process_image_content(part)) + elif part.get("type") == "text": + text = (part.get("text") or "").strip() + if text: + new_content.append({"type": "text", "text": text}) + elif part.get("path") or (part.get("file") or {}).get("path"): + file_path = part.get("path") or (part.get("file") or {}).get("path") + if isinstance(file_path, str) and os.path.isfile(file_path): + ext = os.path.splitext(file_path)[1].lower() + normalized_path = file_path.replace("\\", "/") + if ext in IMAGE_FILE_EXTENSIONS: + new_content.append(self._process_image_content({"type": "image_url", "image_url": {"url": file_path}})) + else: + try: + file_text, file_images = Documents_process.process(file_path) + new_content.append({"type": "text", "text": f"用户上传了一个名为\"{normalized_path}\"的文件。"}) + if file_text.strip(): + new_content.append({ + "type": "text", + "text": f"以下是文件 {normalized_path} 的内容：\n{file_text.strip()}" + }) + for image_bytes in file_images or []: + image_part = image_bytes_to_openai_image_url_part(image_bytes) + if image_part is not None: + new_content.append(image_part) + if not file_text.strip() and not file_images: + new_content.append({"type": "text", "text": f"[用户上传了附件文件: {normalized_path}]"}) + except Exception as e: + new_content.append({"type": "text", "text": f"[附件处理失败: {normalized_path}: {e}]"}) + else: + text = assistant_text(part) + if text: + new_content.append({"type": "text", "text": text}) + elif isinstance(part, str): + text = part.strip() + if text: + new_content.append(text) + if len(new_content) == 1 and isinstance(new_content[0], str): + msg = HumanMessage(content=new_content[0], additional_kwargs=msg.additional_kwargs) + else: + msg = HumanMessage(content=new_content, additional_kwargs=msg.additional_kwargs) + new_messages.append(msg) + return new_messages + + def _text_to_audio(self, text: str) -> bytes: + """调用 TTS 服务合成语音，长文本分块并行后 merge_wavs。 + + 输入: text 待朗读文本。 + 输出: WAV 二进制。 + 系统定位: _apply_tts 核心。 + 可扩展性: TTS_BASE_URL、并发 max_workers 可配置化。 + """ + chunks = split_text(text, min_effective_chars=20) + if len(chunks) == 1: + resp = requests.get(f"{env_utils.TTS_BASE_URL}/tts", params={"text": chunks[0]}) + resp.raise_for_status() + return resp.content + + # 多块并行请求 + audio_parts = [None] * len(chunks) + with ThreadPoolExecutor(max_workers=min(len(chunks), 4)) as pool: + future_to_idx = { + pool.submit( + lambda t: requests.get( + f"{env_utils.TTS_BASE_URL}/tts", params={"text": t} + ).content, + chunk + ): idx for idx, chunk in enumerate(chunks) + } + for future in as_completed(future_to_idx): + idx = future_to_idx[future] + try: + audio_parts[idx] = future.result() + except Exception as e: + raise RuntimeError(f"TTS request failed for chunk {idx}: {e}") + return merge_wavs(audio_parts) + + @staticmethod + def _get_wav_duration_seconds(audio_bytes: bytes) -> float | None: + """从 WAV 字节计算时长（秒）。 + + 输入: audio_bytes WAV 数据。 + 输出: 秒数或 None（解析失败）。 + 系统定位: _apply_tts 写入 last_audio_kwargs.duration_seconds。 + 可扩展性: 可支持 mp3 等格式。 + """ + if not audio_bytes: + return None + try: + with closing(wave.open(io.BytesIO(audio_bytes), "rb")) as wf: + frame_rate = wf.getframerate() + frame_count = wf.getnframes() + if frame_rate <= 0: + return None + return round(frame_count / frame_rate, 2) + except wave.Error: + return None + + def _apply_tts(self, result: ChatResult, output_type: str, audio_output_dir: str | None = None) -> ChatResult: + """根据 output_type 为 AI 回复合成语音并写入会话目录。 + + 输入: + result: ChatResult 含 AIMessage。 + output_type: audio | both。 + audio_output_dir: WAV 保存目录。 + + 输出: + 可能清空 audio 模式下 content 的 ChatResult；设置 last_audio_kwargs。 + + 系统定位: + _generate 在模型返回后的后处理。 + + 可扩展性: + 可上传对象存储而非本地路径。 + """ + if output_type not in ("audio", "both") or not audio_output_dir: + return result + os.makedirs(audio_output_dir, exist_ok=True) + for idx, gen in enumerate(result.generations): + if not isinstance(gen, ChatGeneration): + continue + msg = gen.message + if not isinstance(msg, AIMessage): + continue + text = assistant_text(msg.content) + if not text.strip(): + continue + try: + audio_bytes = self._text_to_audio(text) + timestamp = dt.now().strftime("%H%M%S") + audio_path = os.path.join(audio_output_dir, f"assistant_tts_{timestamp}_{idx}.wav") + with open(audio_path, "wb") as f: + f.write(audio_bytes) + duration_seconds = self._get_wav_duration_seconds(audio_bytes) + # 仅保存到实例属性，不再写入 additional_kwargs + if idx == 0: + self.last_audio_kwargs = { + "audio": { + "sample_rate": 24000, + "format": "wav", + "path": audio_path, + "duration_seconds": duration_seconds, + }, + "output_type": output_type, + } + if self.stream_play: + self.play_audio_bytes(audio_bytes) + if output_type == "audio": + msg.content = "" + except Exception as e: + print(f"[TTS ERROR] 合成语音失败: {e}") + # 发生错误时也清空 last_audio_kwargs，避免污染 + self.last_audio_kwargs = None + return result + + def _generate( + self, + messages: List[BaseMessage], + stop: Optional[List[str]] = None, + run_manager: Optional[Any] = None, + **kwargs: Any, + ) -> ChatResult: + """LangChain 生成入口：预处理 -> API 调用 -> 可选 TTS。 + + 输入: + messages: 对话消息。 + stop: 停止词。 + run_manager: 含 metadata.output_type、audio_output_dir。 + + 输出: + ChatResult。 + + 系统定位: + 所有 invoke/stream 最终经过此方法。 + + 可扩展性: + 可在此注入 token 统计回调、链路追踪 span。 + """ + metadata = {} + if run_manager: + metadata = getattr(run_manager, 'metadata', {}) + output_type = metadata.get("output_type", self.output_type) + audio_output_dir = metadata.get("audio_output_dir") + + # 移除自定义参数，防止它们传入 OpenAI API + kwargs.pop("output_type", None) + kwargs.pop("audio_output_dir", None) + + messages = self._preprocess_messages(messages) + + result = super()._generate(messages, stop=stop, run_manager=run_manager, **kwargs) + + if output_type in ("audio", "both"): + result = self._apply_tts(result, output_type, audio_output_dir=audio_output_dir) + if output_type == "audio": + for gen in result.generations: + if isinstance(gen, ChatGeneration) and isinstance(gen.message, AIMessage): + gen.message.content = "" + return result + + def _create_chat_result(self, response, generation_info=None) -> ChatResult: + """解析 API 响应：reasoning 合并、XML/JSON tool_call 提取。 + + 输入: + response: OpenAI 兼容 API 原始响应。 + generation_info: LangChain 生成元信息。 + + 输出: + ChatResult，AIMessage 可能含 tool_calls。 + + 系统定位: + 兼容非 OpenAI 标准 tool_calls 格式的关键适配点。 + + 可扩展性: + 新增模型格式时在 regex/JSON 分支扩展。 + """ + # ---------- 打印原始 tool_calls ---------- + try: + raw = response if isinstance(response, dict) else response.model_dump() + print("[RAW_RESPONSE]", json.dumps(raw, indent=2, ensure_ascii=False)) + choices = raw.get("choices", []) + if choices: + msg = choices[0].get("message", {}) + tool_calls = msg.get("tool_calls") + if tool_calls: + print("[RAW_TOOLCALLS]", json.dumps(tool_calls, indent=2, ensure_ascii=False)) + except Exception: + pass + + result = super()._create_chat_result(response, generation_info) + try: + dumped = response if isinstance(response, dict) else response.model_dump(exclude={"choices": {"__all__": {"message": {"parsed"}}}}) + for i, choice in enumerate(dumped.get("choices") or []): + if i >= len(result.generations): + break + lc_msg = result.generations[i].message + msg = choice.get("message") or {} + ak = lc_msg.additional_kwargs + # 优先处理 reasoning 内容 + merged = "" + for key in ("reasoning_content", "reasoning_details", "reasoning"): + merged = assistant_text(msg.get(key)) + if merged: + break + if merged: + ak.setdefault("reasoning_content", merged) + raw_content = assistant_text(msg.get("content")) + if not raw_content and merged: + lc_msg.content = merged + elif raw_content: + lc_msg.content = raw_content + + content_text = lc_msg.content if isinstance(lc_msg.content, str) else "" + + # 匹配 ... 块 + pattern = re.compile(r'\s*(.*?)\s*', re.DOTALL) + matches = pattern.findall(content_text) + if matches: + tool_calls = [] + for idx, match in enumerate(matches): + # 先尝试 JSON 解析（兼容旧格式） + try: + call_data = json.loads(match) + tool_calls.append({ + "id": f"call_{idx}", + "type": "function", + "function": { + "name": call_data.get("name", ""), + "arguments": json.dumps(call_data.get("arguments", {})) + } + }) + continue # 解析成功，跳过 XML 尝试 + except json.JSONDecodeError: + pass + + # 再尝试 XML 解析 + func_match = re.search(r'', match) + if not func_match: + continue + func_name = func_match.group(1) + + params = {} + for param_match in re.finditer(r'\s*(.*?)\s*', match, re.DOTALL): + key = param_match.group(1) + value = param_match.group(2).strip() + # 可选：智能类型转换，先保持简单 + params[key] = value + + tool_calls.append({ + "id": f"call_{idx}", + "type": "function", + "function": { + "name": func_name, + "arguments": json.dumps(params) + } + }) + + if tool_calls: + ak["tool_calls"] = tool_calls + # 构造 LangChain 的 ToolCall 对象列表 + lc_tool_calls = [] + for idx, tc in enumerate(tool_calls): + lc_tool_calls.append( + ToolCall( + id=tc["id"], + name=tc["function"]["name"], + args=json.loads(tc["function"]["arguments"]) + ) + ) + lc_msg.tool_calls = lc_tool_calls + # 清理内容中的 XML/JSON 工具调用痕迹 + cleaned = pattern.sub("", content_text).strip() + lc_msg.content = cleaned if cleaned else "" + except Exception: + pass + return result + + def describe_image(self, image_path_or_url: str, prompt: str = "请详细描述这张图片的内容。") -> str: + """Vision 便捷接口：单张图片 + 提示词 -> 文本描述。 + + 输入: + image_path_or_url: 路径或 http(s)/data URL。 + prompt: 用户侧提示。 + + 输出: + 模型描述文本。 + + 系统定位: + 工具或测试脚本直接调用，不经 LangGraph。 + + 可扩展性: + 可返回结构化 JSON（物体列表等）。 + """ + messages = [ + SystemMessage(content=[{"type": "text", "text": VISION_SYSTEM_PROMPT}]), + HumanMessage(content=[ + {"type": "image_url", "image_url": {"url": image_path_or_url}}, + {"type": "text", "text": prompt} + ]) + ] + result = self.invoke(messages) + return result.content + + def describe_image_bytes(self, image_bytes: bytes, prompt: str = "请详细描述这张图片的内容。") -> str: + """Vision 便捷接口：内存图片字节 -> 文本描述。 + + 输入: + image_bytes: 图片二进制。 + prompt: 提示词。 + + 输出: + 描述文本；处理失败返回固定错误文案。 + + 系统定位: + 截屏、工具 artifact 等无路径场景。 + + 可扩展性: + 与 describe_image 共用 VISION_SYSTEM_PROMPT。 + """ + img_part = image_bytes_to_openai_image_url_part(image_bytes) + if img_part is None: + return "图片处理失败" + messages = [ + SystemMessage(content=[{"type": "text", "text": VISION_SYSTEM_PROMPT}]), + HumanMessage(content=[img_part, {"type": "text", "text": prompt}]) + ] + result = self.invoke(messages) + return result.content + +llm = ChatQwen() + + +def reload_llm() -> None: + """热重载：重新从环境变量创建 LLM 实例。""" + global llm + llm = ChatQwen() + + +if __name__ == "__main__": + import sys + + # 使用原始字符串避免转义问题 + TEST_IMAGE_PATH = r"C:\Users\86166\Desktop\2026-05-22\20260522-DSC01513.jpg" + + if not os.path.exists(TEST_IMAGE_PATH): + print(f"❌ 图片不存在: {TEST_IMAGE_PATH}") + sys.exit(1) + + # 测试1: 纯图片识别 + print("=" * 50) + print("测试1: 纯图片识别（无文本）") + print("=" * 50) + result1 = llm.invoke([ + HumanMessage(content=[{"type": "image_url", "image_url": {"url": TEST_IMAGE_PATH}}]) + ]) + print(f"返回类型: {type(result1)}") + print(f"返回内容: {result1.content}") + print(f"所有属性: {[attr for attr in dir(result1) if not attr.startswith('_')]}") + + # 测试2: 图片 + 文本 + print("\n" + "=" * 50) + print("测试2: 图文混合输入") + print("=" * 50) + result2 = llm.invoke([ + HumanMessage(content=[ + {"type": "image_url", "image_url": {"url": TEST_IMAGE_PATH}}, + {"type": "text", "text": "这张图片里有什么？请详细描述"} + ]) + ]) + print(f"回答: {result2.content}") + diff --git a/src/agent/core/multi_agent_graph.py b/src/agent/core/multi_agent_graph.py new file mode 100644 index 0000000..a5f6ccf --- /dev/null +++ b/src/agent/core/multi_agent_graph.py @@ -0,0 +1,533 @@ +"""多 Agent 体系 SupervisorGraph：一主多子 / 主从工作流编排。 + +系统定位: + 为 "一主多子" 和 "主从" 架构提供统一的 LangGraph 图构建， + 支持主 Agent 向子 Agent 委派任务，子 Agent 完成后回传结果。 + +图结构:: + + START -> supervisor + supervisor -> main_agent | END + main_agent -> (根据 tool_calls 中的 agent_call) 路由到子 agent | supervisor + sub_agent_N -> supervisor (子 agent 完成后回到 supervisor) + +Agent 节点签名: + 每个 agent 节点接收 MultiAgentState，返回 {"messages": [...], "agent_status": {...}, ...} + +可扩展性: + - 子 Agent 可拥有独立图（自定义 build_graph），也可复用 ReAct。 + - 可增加并行子 Agent 执行、超时、重试等策略。 +""" +from __future__ import annotations + +import json +import time +from typing import Any, Callable + +from langgraph.graph import END, START, StateGraph +from langgraph.prebuilt import ToolNode + +from agent.core.multi_agent_state import ( + AgentStatusInfo, + GraphEvent, + MultiAgentState, + ToolCallEvent, +) +from agent.core.nodes import make_call_model_node, process_tool_artifact +from agent.core.routing import should_continue + + +# ---------- 事件发射 ---------- +def _emit_event(state: dict, event: GraphEvent) -> None: + """向 state 中追加一个图事件。""" + events: list[GraphEvent] = state.setdefault("graph_events", []) + # 从 dict 还原为 GraphEvent 对象 + if isinstance(events, list): + events.append(event) + + +def _update_agent_status(state: dict, name: str, status: str, error_msg: str = "") -> None: + """更新 state 中指定 agent 的运行状态。""" + agent_status: dict = state.setdefault("agent_status", {}) + if name in agent_status: + info = agent_status[name] + if isinstance(info, dict): + info["status"] = status + if error_msg: + info["error_message"] = error_msg + elif isinstance(info, AgentStatusInfo): + info.status = status # type: ignore[misc] + if error_msg: + info.error_message = error_msg + + +def _record_tool_call(state: dict, agent_name: str, tool_name: str, args: dict, result_preview: str = "", status: str = "success") -> None: + """记录工具调用到指定 agent 的工具调用历史。""" + history: dict = state.setdefault("tool_calls_history", {}) + if agent_name not in history: + history[agent_name] = [] + history[agent_name].append(ToolCallEvent( + tool_name=tool_name, + tool_args=args, + result_preview=result_preview[:200] if result_preview else "", + status=status, # type: ignore[arg-type] + timestamp=time.time(), + )) + + +# ---------- Supervisor 节点（调度器） ---------- +def make_supervisor_node( + agent_names: list[str], + main_agent_name: str = "main", +) -> Callable[[dict], dict]: + """创建 supervisor 调度节点。 + + Supervisor 职责: + 1. 首次进入：设定 active_agent 为 main，标记 main 为 running。 + 2. 子 agent 完成返回：若还有待处理，标记下一个 agent；否则标记 main 为 done。 + 3. 错误恢复：若子 agent 出错，回主 agent。 + + 输入: + agent_names: 所有 agent 名称列表（含 main）。 + main_agent_name: 主 agent 名称。 + + 输出: + 符合 LangGraph 节点签名的 supervisor 函数。 + """ + sub_agent_names = [n for n in agent_names if n != main_agent_name] + + def supervisor(state: dict) -> dict: + now = time.time() + agent_status: dict = state.get("agent_status", {}) + messages: list = state.get("messages", []) + + # 首次进入 + current_active: str | None = state.get("active_agent") + if current_active is None: + # 启动主 agent + _update_agent_status(state, main_agent_name, "running") + _emit_event(state, GraphEvent( + event_type="agent_start", + agent_name=main_agent_name, + timestamp=now, + )) + return { + "active_agent": main_agent_name, + "agent_status": agent_status, + "graph_events": state.get("graph_events", []), + } + + # 主 agent 执行完毕回来 + if current_active == main_agent_name: + # 检查最后一条消息是否含 agent_call（委派子 agent 的标记） + last_msg = messages[-1] if messages else None + delegated_agent = _extract_delegation(last_msg) + if delegated_agent and delegated_agent in sub_agent_names: + # 主 agent 委派子 agent + _update_agent_status(state, main_agent_name, "done") + _emit_event(state, GraphEvent( + event_type="agent_end", + agent_name=main_agent_name, + timestamp=now, + )) + _update_agent_status(state, delegated_agent, "running") + _emit_event(state, GraphEvent( + event_type="agent_start", + agent_name=delegated_agent, + timestamp=now, + )) + _emit_event(state, GraphEvent( + event_type="message_pass", + source_agent=main_agent_name, + target_agent=delegated_agent, + message_preview=f"主 Agent 委派任务给 {delegated_agent}", + timestamp=now, + )) + return { + "active_agent": delegated_agent, + "agent_status": agent_status, + "graph_events": state.get("graph_events", []), + } + else: + # 主 agent 结束，整体完成 + _update_agent_status(state, main_agent_name, "done") + _emit_event(state, GraphEvent( + event_type="agent_end", + agent_name=main_agent_name, + timestamp=now, + )) + return { + "active_agent": "__end__", + "agent_status": agent_status, + "graph_events": state.get("graph_events", []), + } + + # 子 agent 执行完毕回来 + if current_active in sub_agent_names: + _update_agent_status(state, current_active, "done") + _emit_event(state, GraphEvent( + event_type="agent_end", + agent_name=current_active, + timestamp=now, + )) + _emit_event(state, GraphEvent( + event_type="message_pass", + source_agent=current_active, + target_agent=main_agent_name, + message_preview=f"{current_active} 回传结果给主 Agent", + timestamp=now, + )) + # 回到主 agent + _update_agent_status(state, main_agent_name, "running") + _emit_event(state, GraphEvent( + event_type="agent_start", + agent_name=main_agent_name, + timestamp=now, + )) + return { + "active_agent": main_agent_name, + "agent_status": agent_status, + "graph_events": state.get("graph_events", []), + } + + return { + "active_agent": "__end__", + "agent_status": agent_status, + "graph_events": state.get("graph_events", []), + } + + return supervisor + + +def _extract_delegation(last_msg: Any) -> str | None: + """从最后一条 AI 消息中提取 agent_call 委派信息。 + + 委派格式： + - 在消息 content 中包含 ``[DELEGATE:agent_name]`` + - 或在 tool_calls 中有一条名为 ``agent_call`` 的工具调用 + """ + if last_msg is None: + return None + # 方式1: tool_calls 中的 agent_call + if hasattr(last_msg, "tool_calls") and last_msg.tool_calls: + for tc in last_msg.tool_calls: + tc_name = tc.get("name") if isinstance(tc, dict) else getattr(tc, "name", "") + if tc_name == "agent_call": + args = tc.get("args") if isinstance(tc, dict) else getattr(tc, "args", {}) + return args.get("agent_name") or args.get("agent") + # 方式2: 文本标记 + content = getattr(last_msg, "content", "") + if isinstance(content, str) and "[DELEGATE:" in content: + import re + m = re.search(r"\[DELEGATE:(\w+)\]", content) + if m: + return m.group(1) + return None + + +# ---------- 路由函数 ---------- +def supervisor_route(state: dict) -> str: + """Supervisor 之后的路由：根据 active_agent 决定进入哪个 agent 节点或 END。""" + active = state.get("active_agent", "") + if active == "__end__": + return END + if active: + return active + return END + + +# ---------- 子 Agent 节点工厂 ---------- +def make_sub_agent_node( + agent_graph: Any, + agent_name: str, + tools_by_name: dict[str, Any] | None = None, +) -> Callable[[dict], dict]: + """创建一个子 Agent 的图节点。 + + 子 Agent 节点： + 1. 用自己的图执行推理 + 2. 发出 agent_start / tool_call / agent_end 事件 + 3. 更新自己的工具调用历史 + + 输入: + agent_graph: 编译后的子 Agent LangGraph 图。 + agent_name: 子 Agent 名称。 + tools_by_name: 工具名 -> 工具实例映射（用于记录 tool_call）。 + + 输出: + LangGraph 节点函数。 + """ + tools_by_name = tools_by_name or {} + + def sub_agent_node(state: dict) -> dict: + now = time.time() + messages: list = state.get("messages", []) + + # 标记自己为 running + _update_agent_status(state, agent_name, "running") + _emit_event(state, GraphEvent( + event_type="agent_start", + agent_name=agent_name, + timestamp=now, + )) + + # 用子 Agent 图执行 + try: + result = agent_graph.invoke({"messages": messages}) + result_messages: list = result.get("messages", []) + except Exception as e: + _update_agent_status(state, agent_name, "error", str(e)) + _emit_event(state, GraphEvent( + event_type="error", + agent_name=agent_name, + message_preview=str(e)[:200], + timestamp=time.time(), + )) + return { + "messages": messages, + "agent_status": state.get("agent_status", {}), + "graph_events": state.get("graph_events", []), + "tool_calls_history": state.get("tool_calls_history", {}), + } + + # 记录工具调用 + for msg in result_messages: + if hasattr(msg, "tool_calls") and msg.tool_calls: + for tc in msg.tool_calls: + tc_name = tc.get("name") if isinstance(tc, dict) else getattr(tc, "name", "") + tc_args = tc.get("args") if isinstance(tc, dict) else getattr(tc, "args", {}) + if isinstance(tc_args, str): + try: + tc_args = json.loads(tc_args) + except Exception: + pass + _record_tool_call(state, agent_name, tc_name, tc_args or {}) + _emit_event(state, GraphEvent( + event_type="tool_call", + agent_name=agent_name, + tool_name=tc_name, + tool_args=tc_args or {}, + timestamp=time.time(), + )) + + _update_agent_status(state, agent_name, "done") + _emit_event(state, GraphEvent( + event_type="agent_end", + agent_name=agent_name, + timestamp=time.time(), + )) + + # 追加 agent 标记信息到消息 + from langchain_core.messages import AIMessage + final_text = "" + for msg in reversed(result_messages): + if isinstance(msg, AIMessage) and not getattr(msg, "tool_calls", None): + content = msg.content + if isinstance(content, str): + final_text = content + break + + tag_msg = AIMessage( + content=f"[AGENT:{agent_name}] {final_text}" if final_text else f"[AGENT:{agent_name}] 任务完成", + additional_kwargs={"agent_name": agent_name}, + ) + new_messages = list(messages) + list(result_messages) + [tag_msg] + + return { + "messages": new_messages, + "agent_status": state.get("agent_status", {}), + "graph_events": state.get("graph_events", []), + "tool_calls_history": state.get("tool_calls_history", {}), + "agent_messages": state.get("agent_messages", {}), + } + + return sub_agent_node + + +# ---------- 主 Agent 节点工厂（多 Agent 版） ---------- +def make_main_agent_node( + model_with_tools: Any, + tools: list[Any], + agent_name: str = "main", + tools_by_name: dict[str, Any] | None = None, +) -> Callable[[dict], dict]: + """创建主 Agent 的图节点（含完整的 ReAct 循环 + 委派能力）。 + + 与单 agent 的 call_model 不同，主 agent 节点内含完整的 + agent -> tools -> process_tool_artifact -> agent 循环， + 并检测 agent_call 工具调用来委派子 agent。 + + 输入: + model_with_tools: bind_tools 后的 LLM。 + tools: 工具列表（含 agent_call 工具）。 + agent_name: 主 agent 名称。 + tools_by_name: 工具名映射。 + + 输出: + LangGraph 节点函数。 + """ + tools_by_name = tools_by_name or {} + + def main_agent_node(state: dict) -> dict: + now = time.time() + messages: list = list(state.get("messages", [])) + + _update_agent_status(state, agent_name, "running") + + # 执行完整的 ReAct 循环（限制步数防止无限循环） + max_steps = 50 + for step in range(max_steps): + # 调用 LLM + response = model_with_tools.invoke(messages) + messages.append(response) + + # 记录工具调用事件 + if hasattr(response, "tool_calls") and response.tool_calls: + for tc in response.tool_calls: + tc_name = tc.get("name") if isinstance(tc, dict) else getattr(tc, "name", "") + tc_args = tc.get("args") if isinstance(tc, dict) else getattr(tc, "args", {}) + if isinstance(tc_args, str): + try: + tc_args = json.loads(tc_args) + except Exception: + pass + + # 检测 agent_call（委派） + if tc_name == "agent_call": + # 不在 ToolNode 中执行，由 supervisor 处理 + _record_tool_call(state, agent_name, tc_name, tc_args or {}, "委派子 agent", "pending") + _emit_event(state, GraphEvent( + event_type="tool_call", + agent_name=agent_name, + tool_name=tc_name, + tool_args=tc_args or {}, + timestamp=time.time(), + )) + _update_agent_status(state, agent_name, "done") + return { + "messages": messages, + "agent_status": state.get("agent_status", {}), + "graph_events": state.get("graph_events", []), + "tool_calls_history": state.get("tool_calls_history", {}), + } + + _record_tool_call(state, agent_name, tc_name, tc_args or {}) + _emit_event(state, GraphEvent( + event_type="tool_call", + agent_name=agent_name, + tool_name=tc_name, + tool_args=tc_args or {}, + timestamp=time.time(), + )) + + # 执行工具（非 agent_call） + for tc in response.tool_calls: + tc_name = tc.get("name") if isinstance(tc, dict) else getattr(tc, "name", "") + if tc_name == "agent_call": + continue + tc_args = tc.get("args") if isinstance(tc, dict) else getattr(tc, "args", {}) + tc_id = tc.get("id") if isinstance(tc, dict) else getattr(tc, "id", "") + tool = tools_by_name.get(tc_name) + if tool: + try: + result = tool.invoke(tc_args) + result_content = result[0] if isinstance(result, tuple) else str(result) + except Exception as e: + result_content = f"工具执行出错: {e}" + from langchain_core.messages import ToolMessage + messages.append(ToolMessage( + content=str(result_content), + tool_call_id=tc_id or f"tc_{step}", + name=tc_name, + )) + _record_tool_call(state, agent_name, tc_name, tc_args or {}, str(result_content)[:200]) + _emit_event(state, GraphEvent( + event_type="tool_call", + agent_name=agent_name, + tool_name=tc_name, + tool_args=tc_args or {}, + tool_result_preview=str(result_content)[:200], + timestamp=time.time(), + )) + else: + # 没有工具调用，主 agent 完成 + break + + _update_agent_status(state, agent_name, "done") + return { + "messages": messages, + "agent_status": state.get("agent_status", {}), + "graph_events": state.get("graph_events", []), + "tool_calls_history": state.get("tool_calls_history", {}), + } + + return main_agent_node + + +# ---------- 多 Agent 图构建 ---------- +def build_multi_agent_graph( + main_model_with_tools: Any, + main_tools: list[Any], + main_tools_by_name: dict[str, Any], + sub_agents: list[dict[str, Any]], + main_agent_name: str = "main", +) -> Any: + """构建一主多子 SupervisorGraph。 + + 输入: + main_model_with_tools: 主 Agent 的已 bind_tools LLM。 + main_tools: 主 Agent 工具列表（含 agent_call）。 + main_tools_by_name: 主 Agent 工具名映射。 + sub_agents: 子 Agent 配置列表，每项含: + - name: str + - graph: 编译后的子 Agent LangGraph 图 + - tools_by_name: dict（可选） + main_agent_name: 主 agent 名称。 + + 输出: + 编译后的 LangGraph CompiledGraph。 + + 图结构:: + + START -> supervisor + supervisor --路由--> main | sub_1 | sub_2 | ... | END + main -> supervisor + sub_N -> supervisor + """ + workflow = StateGraph(MultiAgentState) + + # 所有 agent 名称 + all_agent_names = [main_agent_name] + [sa["name"] for sa in sub_agents] + + # Supervisor 节点 + workflow.add_node("supervisor", make_supervisor_node(all_agent_names, main_agent_name)) + + # 主 Agent 节点 + workflow.add_node( + main_agent_name, + make_main_agent_node(main_model_with_tools, main_tools, main_agent_name, main_tools_by_name), + ) + + # 子 Agent 节点 + for sa in sub_agents: + sa_name = sa["name"] + sa_graph = sa["graph"] + sa_tools_by_name = sa.get("tools_by_name", {}) + workflow.add_node( + sa_name, + make_sub_agent_node(sa_graph, sa_name, sa_tools_by_name), + ) + + # 路由 + workflow.add_edge(START, "supervisor") + + route_map = {name: name for name in all_agent_names} + route_map[END] = END + workflow.add_conditional_edges("supervisor", supervisor_route, route_map) + + # 所有 agent 节点完成后回到 supervisor + workflow.add_edge(main_agent_name, "supervisor") + for sa in sub_agents: + workflow.add_edge(sa["name"], "supervisor") + + return workflow.compile() diff --git a/src/agent/core/multi_agent_state.py b/src/agent/core/multi_agent_state.py new file mode 100644 index 0000000..c236458 --- /dev/null +++ b/src/agent/core/multi_agent_state.py @@ -0,0 +1,172 @@ +"""多 Agent 体系共享状态：Agent 运行态、图事件、工具调用记录。 + +系统定位: + 为一主多子 / 主从 Agent 体系提供统一的运行时状态类型， + 被 ``multi_agent_graph`` 的 SupervisorGraph 使用。 + +状态字段: + - messages: 全局对话消息（所有 agent 共享）。 + - agent_status: 各 agent 当前运行状态（idle/running/done/error）。 + - active_agent: 当前正在执行的 agent 名称。 + - graph_events: 图执行事件时间线，供前端 graph 面板渲染。 + - tool_calls_history: 按 agent 分组的工具调用记录。 + - agent_messages: 按 agent 分组的主-子消息传递记录。 + +可扩展性: + - 可增加 token 用量统计、执行耗时、错误堆栈等字段。 +""" +from __future__ import annotations + +from dataclasses import dataclass, field +from typing import Annotated, Any, Literal, Sequence, TypedDict + +from langchain_core.messages import BaseMessage +from langgraph.graph.message import add_messages + + +# ---------- Agent 运行状态 ---------- +AgentStatus = Literal["idle", "running", "done", "error"] + + +@dataclass +class AgentStatusInfo: + """单个 Agent 的运行时状态信息。 + + 字段: + name: Agent 名称。 + status: 当前状态：idle（等待）/ running（执行中）/ done（已完成）/ error（出错）。 + role: main | sub。 + icon: 前端展示图标（默认 🧠）。 + error_message: 出错时的错误信息。 + """ + name: str = "" + status: AgentStatus = "idle" + role: Literal["main", "sub"] = "sub" + icon: str = "🧠" + error_message: str = "" + + +# ---------- 图事件 ---------- +@dataclass +class GraphEvent: + """图执行过程中的单个事件，供前端 graph 面板实时更新。 + + 字段: + event_type: 事件类型（agent_start / agent_end / tool_call / message_pass / error）。 + source_agent: 源 agent 名称（message_pass 时为主 agent）。 + target_agent: 目标 agent 名称（message_pass 时为子 agent）。 + agent_name: 当前 agent 名称（非 message_pass 事件）。 + tool_name: 工具名称（tool_call 事件）。 + tool_args: 工具参数（tool_call 事件）。 + tool_result_preview: 工具返回摘要（tool_call 事件）。 + timestamp: 事件发生时间戳。 + message_preview: 消息传递内容摘要。 + """ + event_type: Literal["agent_start", "agent_end", "tool_call", "message_pass", "error"] = "agent_start" + source_agent: str = "" + target_agent: str = "" + agent_name: str = "" + tool_name: str = "" + tool_args: dict[str, Any] = field(default_factory=dict) + tool_result_preview: str = "" + timestamp: float = 0.0 + message_preview: str = "" + + +@dataclass +class ToolCallEvent: + """工具调用记录（持久化到历史）。""" + tool_name: str = "" + tool_args: dict[str, Any] = field(default_factory=dict) + result_preview: str = "" + status: Literal["success", "error", "pending"] = "success" + timestamp: float = 0.0 + + +# ---------- 多 Agent 状态 ---------- +class MultiAgentState(TypedDict, total=False): + """多 Agent 体系共享运行时状态。 + + 字段: + messages: 全局对话消息序列（add_messages 合并）。 + agent_status: {agent_name: AgentStatusInfo} 各 agent 运行状态。 + active_agent: 当前正在执行的 agent 名称。 + graph_events: 图执行事件时间线（追加）。 + tool_calls_history: {agent_name: [ToolCallEvent]} 工具调用记录。 + agent_messages: {agent_name: [dict]} 各 agent 收发的消息摘要。 + user_query: 本轮用户原始查询。 + final_response: 最终响应的文本内容。 + """ + messages: Annotated[Sequence[BaseMessage], add_messages] + agent_status: dict[str, AgentStatusInfo] + active_agent: str | None + graph_events: list[GraphEvent] + tool_calls_history: dict[str, list[ToolCallEvent]] + agent_messages: dict[str, list[dict[str, Any]]] + user_query: str + final_response: str + + +# ---------- 状态初始化辅助 ---------- +def init_agent_status( + agents: list[dict[str, str]], +) -> dict[str, AgentStatusInfo]: + """根据 agent 配置列表初始化 agent_status。 + + 输入: + agents: [{"name": "main", "role": "main", "icon": "🧠"}, ...] + + 输出: + {agent_name: AgentStatusInfo}，所有 agent 初始为 idle。 + """ + result: dict[str, AgentStatusInfo] = {} + for a in agents: + name = a.get("name", "") + result[name] = AgentStatusInfo( + name=name, + status="idle", + role=a.get("role", "sub"), # type: ignore[arg-type] + icon=a.get("icon", "🧠"), + ) + return result + + +def init_multi_agent_state( + agents: list[dict[str, str]], + messages: list[BaseMessage] | None = None, + user_query: str = "", +) -> dict: + """创建 MultiAgentState 的初始输入字典。 + + 输入: + agents: agent 配置列表。 + messages: 初始消息列表。 + user_query: 用户查询文本。 + + 输出: + 可直接传入 graph.invoke() 的初始状态字典。 + """ + return { + "messages": messages or [], + "agent_status": init_agent_status(agents), + "active_agent": None, + "graph_events": [], + "tool_calls_history": {}, + "agent_messages": {}, + "user_query": user_query, + "final_response": "", + } + + +def create_empty_multi_agent_state() -> MultiAgentState: + """创建空的 MultiAgentState，用于类型标注时的默认值。""" + return MultiAgentState( + messages=[], + agent_status={}, + active_agent=None, + graph_events=[], + tool_calls_history={}, + agent_messages={}, + user_query="", + final_response="", + ) diff --git a/src/agent/core/nodes.py b/src/agent/core/nodes.py new file mode 100644 index 0000000..72e57eb --- /dev/null +++ b/src/agent/core/nodes.py @@ -0,0 +1,143 @@ +"""LangGraph 图节点：模型调用与工具产物后处理。 + +系统定位: + 实现 ReAct 循环中的 ``agent`` 节点工厂与 ``process_tool_artifact`` 节点， + 被 ``graph_builder`` 注册到 StateGraph。 + +可扩展性: + - ``make_call_model_node`` 可注入 streaming、重试、用量统计回调。 + - ``process_tool_artifact`` 可扩展 audio、文件、结构化 JSON 等 artifact 类型。 +""" +from __future__ import annotations + +import json +import time +from typing import Any, Callable + +from langchain_core.messages import HumanMessage, ToolMessage + +from agent.core.state import AgentState +from agent.utils.image_utils import image_bytes_to_openai_image_url_part +from agent.utils.tool_call_utils import normalize_tool_call + + +def make_call_model_node(model_with_tools: Any) -> Callable[[AgentState], dict]: + """创建调用已绑定工具的 LLM 的图节点闭包。 + + 输入: + model_with_tools: bind_tools 后的 LangChain 聊天模型。 + + 输出: + 符合 LangGraph 节点签名的 ``call_model(state) -> {"messages": [AIMessage]}``。 + + 系统定位: + 图中 ``agent`` 节点：读取 state.messages，调用模型，追加 AI 响应。 + + 可扩展性: + 可包装为 async 节点；可在此统一记录 token 用量到 session_meta。 + """ + + def call_model(state: AgentState) -> dict: + response = model_with_tools.invoke(state["messages"]) + + usage = response.usage_metadata if hasattr(response, "usage_metadata") else {} + if usage: + input_tokens = usage.get("input_tokens", "?") + output_tokens = usage.get("output_tokens", "?") + total_tokens = usage.get("total_tokens", "?") + print(f"[LLM] Token使用: input={input_tokens}, output={output_tokens}, total={total_tokens}") + + if hasattr(response, "tool_calls") and response.tool_calls: + for tc in response.tool_calls: + tc_normalized = normalize_tool_call(tc) + tool_name = tc_normalized.get("name", "unknown") + tool_args = tc_normalized.get("args", {}) + print(f"[LLM] 工具调用: {tool_name}, 参数: {tool_args}") + # 实时记录工具调用 + try: + from agent.history.tool_call_recorder import record_tool_call_live, get_current_session + sid = get_current_session() + if sid: + record_tool_call_live(sid, tool_name, tool_args) + except Exception: + pass + + return {"messages": [response]} + + return call_model + + +def process_tool_artifact(state: AgentState) -> dict: + last_msg = state["messages"][-1] + + if isinstance(last_msg, ToolMessage): + content_preview = str(last_msg.content) + if len(content_preview) > 200: + content_preview = content_preview[:200] + "..." + print(f"[Tool] 工具返回 (id={last_msg.tool_call_id}): {content_preview}") + + # 实时记录工具返回结果 + try: + from agent.history.tool_call_recorder import record_tool_result_live, get_current_session + sid = get_current_session() + if sid: + img_bytes = None + if last_msg.artifact: + if isinstance(last_msg.artifact, dict) and "image_bytes" in last_msg.artifact: + img_bytes = last_msg.artifact["image_bytes"] + elif isinstance(last_msg.artifact, list): + for item in last_msg.artifact: + if isinstance(item, dict) and "image_bytes" in item: + img_bytes = item["image_bytes"] + break + elif isinstance(item, bytes): + img_bytes = item + break + elif isinstance(last_msg.artifact, bytes): + img_bytes = last_msg.artifact + record_tool_result_live(sid, last_msg.name or "unknown", str(last_msg.content) if last_msg.content else "", img_bytes) + except Exception: + pass + + if not isinstance(last_msg, ToolMessage) or not last_msg.artifact: + return {} + + artifact = last_msg.artifact + img_bytes_list: list[bytes] = [] + + if isinstance(artifact, dict) and "image_bytes" in artifact: + img_bytes_list.append(artifact["image_bytes"]) + elif isinstance(artifact, list): + for item in artifact: + if isinstance(item, dict) and "image_bytes" in item: + img_bytes_list.append(item["image_bytes"]) + elif isinstance(item, bytes): + img_bytes_list.append(item) + elif isinstance(artifact, bytes): + img_bytes_list.append(artifact) + + if not img_bytes_list: + return {} + + content_parts = [] + failed_count = 0 + for img_bytes in img_bytes_list: + data_url = image_bytes_to_openai_image_url_part(img_bytes) + if data_url is None: + failed_count += 1 + continue + content_parts.append(data_url) + + if not content_parts: + human_msg = HumanMessage( + content="工具返回了截图，但图片处理失败，请向用户说明。", + additional_kwargs={"synthetic": True}, + ) + return {"messages": [human_msg]} + + content_parts.append({"type": "text", "text": "上面是工具返回的截图。"}) + if failed_count > 0: + content_parts[-1]["text"] += f"（其中 {failed_count} 张图片处理失败，请忽略）" + + human_msg = HumanMessage(content=content_parts, additional_kwargs={"synthetic": True}) + return {"messages": [human_msg]} diff --git a/src/agent/core/routing.py b/src/agent/core/routing.py new file mode 100644 index 0000000..8aa88fd --- /dev/null +++ b/src/agent/core/routing.py @@ -0,0 +1,77 @@ +"""LangGraph 条件路由：工具循环 vs 结束 vs 待确认暂停。 + +系统定位: + 连接 ``graph_builder`` 的条件边与 ``runtime.execute_agent`` 的 + 后处理逻辑，在模型输出 tool_calls 时决定图走向或提取 UI 待确认元数据。 + +可扩展性: + - ``should_continue`` 可扩展为多目标路由（如专用 RAG 节点）。 + - ``first_pending_tool_meta`` 可支持多个并行待确认项的优先级队列。 +""" +from __future__ import annotations + +from typing import Any, Literal, Sequence + +from langchain_core.messages import BaseMessage +from langgraph.graph import END + +from agent.core.approvals import build_approval_meta, build_human_interaction_meta +from agent.core.state import AgentState +from agent.core.tool_policy import classify_tool_execution +from agent.utils.tool_call_utils import normalize_tool_call + + +def should_continue(state: AgentState) -> Literal["tools", "__end__"]: + """根据最后一条 AI 消息中的 tool_calls 决定下一节点。 + + 输入: + state: 含 messages 的 AgentState。 + + 输出: + ``"tools"`` 进入 ToolNode；``"__end__"`` 结束图（含需确认工具场景）。 + + 系统定位: + ``graph_builder`` 中 agent 节点的 conditional_edges 路由函数。 + 若任一 tool_call 策略为 confirm，则直接 END，由 runtime 展示确认 UI。 + + 可扩展性: + 可先执行 direct 类工具、仅对 confirm 类暂停（需拆分 tool_calls 处理）。 + """ + last_msg = state["messages"][-1] + if hasattr(last_msg, "tool_calls") and last_msg.tool_calls: + for raw_tool_call in last_msg.tool_calls: + tool_call = normalize_tool_call(raw_tool_call) + if classify_tool_execution(tool_call.get("name"), tool_call.get("args") or {}) == "confirm": + return END + return "tools" + return END + + +def first_pending_tool_meta(messages: Sequence[BaseMessage], session_id: str) -> dict[str, Any] | None: + """从图执行结果中提取第一个需 UI 处理的待确认项元数据。 + + 输入: + messages: 图 invoke 返回的完整消息列表。 + session_id: 当前会话 ID，写入 PENDING_TOOL_APPROVALS。 + + 输出: + 含 ``pending_action`` 的 meta 字典；无待确认项时返回 None。 + + 系统定位: + ``runtime.execute_agent`` 在图结束后调用，决定是否追加确认型 assistant 消息。 + + 可扩展性: + 可返回列表以支持批量确认；可按工具类型定制 meta 结构。 + """ + if not messages: + return None + last_msg = messages[-1] + if not hasattr(last_msg, "tool_calls") or not last_msg.tool_calls: + return None + for raw_tool_call in last_msg.tool_calls: + tool_call = normalize_tool_call(raw_tool_call) + if tool_call.get("name") == "human_interaction": + return build_human_interaction_meta(session_id, tool_call, messages) + if classify_tool_execution(tool_call.get("name"), tool_call.get("args") or {}) == "confirm": + return build_approval_meta(session_id, tool_call, messages) + return None diff --git a/src/agent/core/runtime.py b/src/agent/core/runtime.py new file mode 100644 index 0000000..8a605e8 --- /dev/null +++ b/src/agent/core/runtime.py @@ -0,0 +1,476 @@ +"""Agent 运行时：对话执行、历史持久化与人工确认续跑。 + +系统定位: + Web 层（如 ``web.ui_session``）与 LangGraph 之间的编排层。 + ``agents/*`` 组装 ``AgentRuntime`` 实例，本模块提供统一执行 API。 + +主要流程: + execute_agent: 用户消息 -> summarizer 压缩 -> graph.invoke -> 响应/待确认 + continue_after_human_action: 用户确认/拒绝 -> 工具执行或图续跑 + +可扩展性: + - ``AgentRuntime`` 可增加 checkpointer、tracing、多模态配置。 + - 可将 team 模式拆为独立 executor 类。 +""" +from __future__ import annotations + +from dataclasses import dataclass +from typing import Any, Literal + +from langchain_core.messages import AIMessage, HumanMessage, ToolMessage +from langchain_core.messages import messages_from_dict +from langgraph.errors import GraphRecursionError + +from agent.core.approvals import PENDING_TOOL_APPROVALS, pending_assistant_message, remove_pending_ui_message +from agent.core.human_interaction import human_interaction_result +from agent.history.tool_call_recorder import save_turn, start_live_turn, end_live_turn, set_current_turn, set_current_session +from agent.memory.system_prompt import TEAM_MODE_PROMPT +from agent.core.routing import first_pending_tool_meta +from agent.core.tool_policy import classify_tool_execution +from agent.utils.agent_utils import assistant_text, get_history, get_session_meta, summarizer, update_session_meta +from agent.utils.tool_call_utils import normalize_tool_call +from web.ui_session import rewrite_user_message_files_to_session_dir, session_dir, sync_manifest_from_chat_history + +AgentMode = Literal["agent", "team"] + + +@dataclass +class AgentRuntime: + """单个 Agent 的运行时配置容器。 + + 字段: + name: Agent 标识，用于 session_meta 与日志。 + graph: 编译后的 LangGraph。 + llm: 原始 LLM 实例（压缩、TTS 等）。 + tools: 工具列表。 + tools_by_name: 工具名 -> 工具实例，供确认后手动 invoke。 + system_prompt: 系统提示词，传入 summarizer。 + max_iterations: 最大迭代次数，超过则输出 OOM（默认 200）。 + + 系统定位: + ``agents/main_agent`` 等模块的装配结果，传给 execute_agent。 + + 可扩展性: + 可增加 default_mode、专属 checkpointer 等字段。 + """ + + name: str + graph: Any + llm: Any + tools: list[Any] + tools_by_name: dict[str, Any] + system_prompt: str + max_iterations: int = 200 + + +_OOM_MESSAGE = "OOM: 已达到最大迭代次数限制，任务未能完成。请简化任务或增加 max_iterations 配置后重试。" + + +def _invoke_with_recursion_limit( + runtime: AgentRuntime, + graph: Any, + input_data: dict, + config: dict | None = None, +) -> dict: + """包装 graph.invoke，注入 recursion_limit 并捕获超限错误返回 OOM 消息。""" + cfg = dict(config or {}) + cfg["recursion_limit"] = runtime.max_iterations + try: + return graph.invoke(input_data, config=cfg) + except GraphRecursionError: + print(f"[OOM] Agent '{runtime.name}' 达到最大迭代次数 {runtime.max_iterations}，输出 OOM。") + return {"messages": [AIMessage(content=_OOM_MESSAGE)], "_oom": True} + + +def save_messages_to_history(session_id: str, user_msg: HumanMessage, assistant_msg: AIMessage) -> None: + """将本轮用户与助手消息写入 SQLite 聊天历史。 + + 输入: + session_id: 会话 ID；空则跳过。 + user_msg: 用户 HumanMessage；含 synthetic 标记时不写入。 + assistant_msg: 助手 AIMessage。 + + 输出: + 无。 + + 系统定位: + execute_agent 正常回复结束后持久化对话。 + + 可扩展性: + 可改为异步写入、双写向量库、按 agent_name 分表。 + """ + if not session_id: + return + history = get_history(session_id) + if not user_msg.additional_kwargs.get("synthetic"): + history.add_message(user_msg) + history.add_message(assistant_msg) + + +def _message_content_equals(left: Any, right: Any) -> bool: + """宽松比较消息 content，兼容纯文本与多模态列表。""" + if left == right: + return True + return assistant_text(left) == assistant_text(right) + + +def _pending_action_id(meta: dict[str, Any] | None) -> str | None: + """从 assistant meta 中提取 pending_action.id。""" + pending_action = (meta or {}).get("pending_action") or {} + return pending_action.get("id") + + +def _remember_pending_user_content(pending_meta: dict[str, Any], user_content: Any) -> None: + """将本轮用户输入缓存到 PENDING_TOOL_APPROVALS，供确认续跑时对齐历史。""" + approval_id = _pending_action_id(pending_meta) + if approval_id and approval_id in PENDING_TOOL_APPROVALS: + PENDING_TOOL_APPROVALS[approval_id]["user_content"] = user_content + + +def _persist_messages_from_pending_turn( + session_id: str, + messages: list[Any], + pending: dict[str, Any], +) -> None: + if not session_id or not messages: + return + # 找到本轮用户消息 + user_content = pending.get("user_content") + start_index = 0 + if user_content is not None: + for idx in range(len(messages) - 1, -1, -1): + msg = messages[idx] + if isinstance(msg, HumanMessage) and not msg.additional_kwargs.get("synthetic"): + if _message_content_equals(msg.content, user_content): + start_index = idx + break + # 只提取： + # - 用户消息（跳过 synthetic） + # - 最终一条不包含 tool_calls 的助手消息 + user_msg = None + final_assistant = None + + for msg in messages[start_index:]: + if isinstance(msg, HumanMessage) and not msg.additional_kwargs.get("synthetic"): + user_msg = msg + if isinstance(msg, AIMessage) and not msg.tool_calls: + final_assistant = msg + + history = get_history(session_id) + if user_msg: + history.add_message(user_msg) + if final_assistant: + history.add_message(final_assistant) + + +def _persist_pending_rejection(session_id: str, pending: dict[str, Any], content: str) -> None: + """将用户拒绝待确认操作这一轮写入历史，避免下一轮上下文断层。""" + prior_messages = messages_from_dict(pending.get("messages") or []) + prior_messages.append(AIMessage(content=content)) + _persist_messages_from_pending_turn(session_id, prior_messages, pending) + + +def _invoke_after_manual_tool_result( + runtime: AgentRuntime, + messages: list[Any], + session_id: str, + *, + max_steps: int = 8, +) -> tuple[list[Any], dict[str, Any] | None]: + """人工确认后继续推进图，并在遇到新的待确认项时暂停。""" + current_messages = messages + for _ in range(max_steps): + result = _invoke_with_recursion_limit(runtime, runtime.graph, {"messages": current_messages}, {"configurable": {"session_id": session_id}}) + all_messages = result["messages"] + pending_meta = first_pending_tool_meta(all_messages, session_id) + if pending_meta: + return all_messages, pending_meta + + last_msg = all_messages[-1] if all_messages else None + if not isinstance(last_msg, AIMessage) or not last_msg.tool_calls: + return all_messages, None + + next_messages = [*all_messages] + progressed = False + for raw_tool_call in last_msg.tool_calls: + tool_call = normalize_tool_call(raw_tool_call) + tool_name = tool_call.get("name") + tool_args = tool_call.get("args") or {} + if classify_tool_execution(tool_name, tool_args) != "direct": + continue + tool = runtime.tools_by_name.get(tool_name or "") + if not tool: + continue + result = tool.invoke(tool_args) + tool_content = result[0] if isinstance(result, tuple) else result + call_id = tool_call.get("id") or f"manual_{session_id}_{len(next_messages)}" + next_messages.append(ToolMessage(content=str(tool_content), tool_call_id=call_id, name=tool_name or "unknown_tool")) + progressed = True + if not progressed: + return all_messages, None + current_messages = next_messages + return current_messages, None + + +def execute_agent( + runtime: AgentRuntime, + chat_history: list[dict[str, Any]], + session_id: str = "test", + output_type: str = "text", + agent_mode: AgentMode = "agent", +) -> list[dict[str, Any]]: + """处理用户新一轮输入，驱动 Agent 图执行并更新 chat_history。 + + 输入: + runtime: Agent 运行时配置。 + chat_history: Web UI 聊天历史（dict 列表）；末条须为 user。 + session_id: 会话 ID，用于历史、文件目录、manifest 同步。 + output_type: text | audio | both，传入图 metadata。 + agent_mode: agent（默认）| team（占位提示）。 + + 输出: + 更新后的 chat_history；可能追加 assistant 消息或 pending_action。 + + 系统定位: + 对外主入口，由 Web/API 层在每轮用户发送后调用。 + + 可扩展性: + 可返回 generator 支持流式；可注入 middleware 做鉴权、限流。 + """ + chat_history = chat_history or [] + + if not chat_history or chat_history[-1].get("role") != "user": + return chat_history + + if session_id: + rewrite_user_message_files_to_session_dir(chat_history[-1], session_id) + + user_content = chat_history[-1]["content"] + + if agent_mode == "team": + chat_history.append({ + "role": "assistant", + "content": TEAM_MODE_PROMPT, + "meta": {"output_type": "text", "autoplay": False, "agent_mode": "team"}, + }) + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + + messages, context_exceeded = summarizer( + session_id=session_id, + agent_name=runtime.name, + system_prompt=runtime.system_prompt, + user_content=user_content, + llm=runtime.llm, + ) + + if context_exceeded: + chat_history.append({"role": "assistant", "content": "上下文已满，请新建会话继续对话。"}) + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + + graph_config = { + "configurable": {"session_id": session_id}, + "metadata": { + "output_type": output_type, + "audio_output_dir": session_dir(session_id) if session_id else None, + }, + } + + try: + # 开始实时记录（新 turn 隔离旧数据） + turn_id = "" + if session_id: + turn_id = start_live_turn(session_id) + set_current_session(session_id) + result = _invoke_with_recursion_limit(runtime, runtime.graph, {"messages": messages}, graph_config) + set_current_turn("") + set_current_session("") + all_messages = result["messages"] + pending_meta = first_pending_tool_meta(all_messages, session_id) + if pending_meta: + end_live_turn(session_id) + _remember_pending_user_content(pending_meta, user_content) + chat_history.append(pending_assistant_message(pending_meta)) + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + + end_live_turn(session_id) + + assistant_msg = all_messages[-1] if all_messages else AIMessage(content="") + text_content = assistant_text(assistant_msg.content) + + usage = assistant_msg.usage_metadata if hasattr(assistant_msg, "usage_metadata") else {} + total_tokens = usage.get("total_tokens", 0) if isinstance(usage, dict) else 0 + if total_tokens > 0: + meta = get_session_meta(session_id, runtime.name) + update_session_meta( + session_id=session_id, + agent_name=runtime.name, + cursor_index=meta["cursor_index"], + compressed_content=meta["compressed_content"], + last_total_tokens=total_tokens, + ) + + output_type_from_config = graph_config.get("metadata", {}).get("output_type", "text") + audio_data = runtime.llm.last_audio_kwargs or {} + if audio_data.get("output_type") != output_type_from_config: + audio_data = {} + + assistant_meta = { + "output_type": output_type_from_config, + "autoplay": False, + "audio": audio_data.get("audio"), + } + + if output_type_from_config == "audio" and assistant_meta.get("audio"): + assistant_content = [] + elif output_type_from_config == "both" and assistant_meta.get("audio"): + assistant_content = text_content + else: + assistant_content = text_content if text_content else "（空回复）" + + chat_history.append({"role": "assistant", "content": assistant_content, "meta": assistant_meta}) + save_messages_to_history(session_id, HumanMessage(content=user_content), AIMessage(content=text_content)) + runtime.llm.last_audio_kwargs = None + + # 保存工具调用记录 + if session_id and turn_id: + save_turn(session_id, turn_id, {"role": "user", "content": user_content}, all_messages) + + except Exception: + import traceback + set_current_turn("") + set_current_session("") + end_live_turn(session_id) + print("[ERROR] 调用模型时出错:") + traceback.print_exc() + chat_history.append({"role": "assistant", "content": "抱歉，我遇到了一些问题，请稍后再试。"}) + + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + + +def continue_after_human_action( + runtime: AgentRuntime, + chat_history: list[dict[str, Any]], + session_id: str, + approval_id: str, + decision: Literal["approve", "reject", "edit"], + instruction: str | None = None, + output_type: str = "text", +) -> list[dict[str, Any]]: + """用户对待确认项做出决策后，继续执行或结束该轮交互。 + + 输入: + runtime: Agent 运行时。 + chat_history: 当前聊天历史。 + session_id: 会话 ID。 + approval_id: pending_action.id。 + decision: approve | reject | edit（edit 用于 human_interaction selection 提交文本）。 + instruction: selection 时为用户输入文本。 + output_type: 续跑后的回复输出类型。 + + 输出: + 更新后的 chat_history。 + + 系统定位: + UI 确认框回调入口；按 pending.type 分支： + human_interaction / tool_call。 + + 可扩展性: + 可增加 timeout 自动 reject；支持批量 approve 多个 tool_call。 + """ + chat_history = chat_history or [] + pending = PENDING_TOOL_APPROVALS.pop(approval_id, None) + if not pending: + chat_history.append({"role": "assistant", "content": "该待确认项已处理或已过期。", "meta": {"output_type": "text"}}) + return chat_history + + remove_pending_ui_message(chat_history, approval_id) + + if decision == "reject" and pending.get("type") != "human_interaction": + content = "已拒绝，本次待确认操作不会执行。" + chat_history.append({"role": "assistant", "content": content, "meta": {"output_type": "text"}}) + _persist_pending_rejection(session_id, pending, content) + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + + if pending.get("type") == "human_interaction": + try: + tool_call = dict(pending.get("tool_call") or {}) + prior_messages = messages_from_dict(pending.get("messages") or []) + call_id = tool_call.get("id") or f"human_{approval_id}" + tool_content = human_interaction_result(pending, decision, instruction) + prior_messages.append(ToolMessage(content=tool_content, tool_call_id=call_id, name="human_interaction")) + all_messages, pending_meta = _invoke_after_manual_tool_result(runtime, prior_messages, session_id) + if pending_meta: + _remember_pending_user_content(pending_meta, pending.get("user_content")) + chat_history.append(pending_assistant_message(pending_meta)) + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + assistant_msg = all_messages[-1] if all_messages else AIMessage(content="") + content = assistant_text(assistant_msg.content) or "（空回复）" + persisted_messages = all_messages + except Exception: + import traceback + traceback.print_exc() + content = "处理人工交互结果时遇到问题。" + persisted_messages = messages_from_dict(pending.get("messages") or []) + chat_history.append({"role": "assistant", "content": content, "meta": {"output_type": output_type, "autoplay": False}}) + _persist_messages_from_pending_turn(session_id, persisted_messages, pending) + + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + + tool_call = dict(pending.get("tool_call") or {}) + tool_name = tool_call.get("name") + tool_args = tool_call.get("args") or {} + tool = runtime.tools_by_name.get(tool_name) + if not tool: + chat_history.append({"role": "assistant", "content": f"找不到工具：{tool_name}", "meta": {"output_type": "text"}}) + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + + try: + result = tool.invoke(tool_args) + tool_content = result[0] if isinstance(result, tuple) else result + prior_messages = messages_from_dict(pending.get("messages") or []) + call_id = tool_call.get("id") or f"manual_{approval_id}" + prior_messages.append(ToolMessage(content=str(tool_content), tool_call_id=call_id, name=tool_name)) + all_messages, pending_meta = _invoke_after_manual_tool_result(runtime, prior_messages, session_id) + if pending_meta: + _remember_pending_user_content(pending_meta, pending.get("user_content")) + chat_history.append(pending_assistant_message(pending_meta)) + if session_id: + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history + assistant_msg = all_messages[-1] if all_messages else AIMessage(content="") + content = assistant_text(assistant_msg.content) or "（空回复）" + persisted_messages = all_messages + except Exception: + import traceback + traceback.print_exc() + content = "执行确认后的工具调用时遇到问题。" + prior_messages = messages_from_dict(pending.get("messages") or []) + call_id = tool_call.get("id") or f"manual_{approval_id}" + prior_messages.extend([ + ToolMessage(content=content, tool_call_id=call_id, name=tool_name or "unknown_tool"), + AIMessage(content=content), + ]) + persisted_messages = prior_messages + + chat_history.append({"role": "assistant", "content": content, "meta": {"output_type": output_type, "autoplay": False}}) + _persist_messages_from_pending_turn(session_id, persisted_messages, pending) + if session_id: + save_turn(session_id, pending.get("user_content") or {}, persisted_messages) + sync_manifest_from_chat_history(session_id, chat_history, "") + return chat_history diff --git a/src/agent/core/state.py b/src/agent/core/state.py new file mode 100644 index 0000000..aaf9c01 --- /dev/null +++ b/src/agent/core/state.py @@ -0,0 +1,32 @@ +"""LangGraph Agent 状态类型定义。 + +系统定位: + 定义图运行时共享状态 ``AgentState``，被 ``graph_builder``、``nodes``、 + ``routing`` 作为 StateGraph 的 schema 使用。 + +可扩展性: + - 可在此增加 ``session_id``、``agent_mode``、``pending_approval`` 等字段， + 需配合 reducer（如 add_messages）声明合并策略。 +""" +from __future__ import annotations + +from typing import Annotated, Sequence, TypedDict + +from langchain_core.messages import BaseMessage +from langgraph.graph.message import add_messages + + +class AgentState(TypedDict): + """LangGraph 图状态：消息列表为唯一状态字段。 + + 字段: + messages: 对话消息序列，通过 ``add_messages`` 追加合并。 + + 系统定位: + ReAct 循环（agent -> tools -> process_tool_artifact -> agent）的载体。 + + 可扩展性: + 扩展字段时保持 TypedDict 与 reducer 注解一致，避免并发更新冲突。 + """ + + messages: Annotated[Sequence[BaseMessage], add_messages] diff --git a/src/agent/core/tool_config.json b/src/agent/core/tool_config.json new file mode 100644 index 0000000..2d14630 --- /dev/null +++ b/src/agent/core/tool_config.json @@ -0,0 +1,58 @@ +[ + { + "name": "web_search_class", + "display_name": "WebSearch", + "description": "一个网络搜索工具，可以根据用户的查询返回相关的搜索结果。仅当需要查询近期知识或已有知识无法解答需要联网查询时调用，其余任何情况严禁调用。", + "permission": "direct", + "auto_execute_rules": [], + "enabled": true + }, + { + "name": "date_query_class", + "display_name": "DateQuery", + "description": "一个日期查询工具，可以返回当前的日期信息。仅当需要查询当前时间时调用，其余任何情况严禁调用。", + "permission": "direct", + "auto_execute_rules": [], + "enabled": true + }, + { + "name": "screen_capture", + "display_name": "ScreenCapture", + "description": "截取当前主屏幕画面。除了返回文字描述外，也会提供原始截图给模型进行视觉分析。注意：这个工具仅用于获取当前屏幕的实时画面，其余任何情况严禁调用。", + "permission": "direct", + "auto_execute_rules": [], + "enabled": true + }, + { + "name": "browser_control", + "display_name": "BrowserControl", + "description": "统一控制浏览器：打开/关闭浏览器、刷新/关闭标签页、打开指定 URL。", + "permission": "direct", + "auto_execute_rules": [], + "enabled": true + }, + { + "name": "human_interaction", + "display_name": "HumanInteraction", + "description": "当任务缺少关键信息、需要人类选择路径、或需要人类审批后才能继续时调用。三种类型分别是：information=请求人类补充缺失信息；selection=让人类在多个选项中选择且可编辑；approval=请求人类审批高影响决策或继续执行。工具返回必须被视为用户原始意图的纯字符串反馈。其余情况严禁调用。", + "permission": "confirm", + "auto_execute_rules": [], + "enabled": true + }, + { + "name": "software_control", + "display_name": "SoftwareControl", + "description": "操作本机已安装的软件。工具会自动扫描 Windows 开始菜单快捷方式、桌面快捷方式和 App Paths，建立软件索引后根据用户输入做模糊匹配。action=open 时打开软件；action=close 时仅做正常关闭；action=force_close 时才允许在正常关闭失败后强制结束进程。仅当用户明确要求打开、关闭或强制关闭某个本机软件时调用。", + "permission": "direct", + "auto_execute_rules": [], + "enabled": true + }, + { + "name": "directory_listing", + "display_name": "DirectoryListing", + "description": "列出指定目录下的所有文件和子目录。可用于探索文件系统、查找文件位置。仅当需要获取目录内容或确认文件结构时调用，其余情况严禁调用。", + "permission": "direct", + "auto_execute_rules": [], + "enabled": true + } +] \ No newline at end of file diff --git a/src/agent/core/tool_policy.py b/src/agent/core/tool_policy.py new file mode 100644 index 0000000..af8dc3e --- /dev/null +++ b/src/agent/core/tool_policy.py @@ -0,0 +1,144 @@ +"""工具执行安全策略：直接执行 vs 需人工确认。 + +系统定位: + 在 ``core/routing.should_continue`` 中决定图是否进入 ToolNode， + 或在遇到需确认工具时提前结束图执行，交由 UI 与 ``runtime`` 处理。 + +自动执行规则 (auto_execute_rules): + 对于权限为 ``confirm`` 的工具，可通过 tool_config.json 中的 + ``auto_execute_rules`` 定义参数条件，匹配时自动升级为 ``direct``。 + 每条规则格式: ``{"parameter": "action", "operator": "equals", "value": "query"}`` + 含义: 当工具调用的 ``action`` 参数等于 ``"query"`` 时，跳过确认直接执行。 + +可扩展性: + - 可增加 operator 类型（contains、in、regex）。 + - 可返回 ``Literal["direct", "confirm", "deny"]`` 支持完全禁止类工具。 +""" +from __future__ import annotations + +from contextlib import contextmanager +from contextvars import ContextVar +from typing import Literal + +_FORCE_CONFIRM_TOOLS: ContextVar[bool] = ContextVar("force_confirm_tools", default=False) + +# 从 tool_config.json 加载的自动执行规则缓存 +_AUTO_EXECUTE_RULES_CACHE: dict[str, list[dict]] | None = None +# 从 tool_config.json 加载的工具权限缓存 +_TOOL_PERMISSIONS_CACHE: dict[str, str] | None = None + + +def _get_tool_permissions() -> dict[str, str]: + """懒加载 tool_config.json 中各工具的 permission 字段。 + + 返回: + {tool_name: "direct"|"confirm"} 映射；加载失败返回空字典。 + """ + global _TOOL_PERMISSIONS_CACHE + if _TOOL_PERMISSIONS_CACHE is not None: + return _TOOL_PERMISSIONS_CACHE + try: + from agent.core.config_manager import load_tool_configs + configs = load_tool_configs() + _TOOL_PERMISSIONS_CACHE = {} + for c in configs: + if c.enabled: + _TOOL_PERMISSIONS_CACHE[c.name] = c.permission + except Exception: + _TOOL_PERMISSIONS_CACHE = {} + return _TOOL_PERMISSIONS_CACHE + + +def _get_auto_execute_rules() -> dict[str, list[dict]]: + """懒加载 tool_config.json 中的 auto_execute_rules。 + + 返回: + {tool_name: [rule, ...]} 映射；加载失败返回空字典。 + """ + global _AUTO_EXECUTE_RULES_CACHE + if _AUTO_EXECUTE_RULES_CACHE is not None: + return _AUTO_EXECUTE_RULES_CACHE + try: + from agent.core.config_manager import load_tool_configs + configs = load_tool_configs() + _AUTO_EXECUTE_RULES_CACHE = {} + for c in configs: + if c.enabled and c.auto_execute_rules: + _AUTO_EXECUTE_RULES_CACHE[c.name] = c.auto_execute_rules + except Exception: + _AUTO_EXECUTE_RULES_CACHE = {} + return _AUTO_EXECUTE_RULES_CACHE + + +def reload_auto_execute_rules() -> None: + """清除缓存，下次 classify 时重新从 JSON 加载。""" + global _AUTO_EXECUTE_RULES_CACHE, _TOOL_PERMISSIONS_CACHE + _AUTO_EXECUTE_RULES_CACHE = None + _TOOL_PERMISSIONS_CACHE = None + + +@contextmanager +def force_confirm_tools(): + token = _FORCE_CONFIRM_TOOLS.set(True) + try: + yield + finally: + _FORCE_CONFIRM_TOOLS.reset(token) + + +def _match_auto_execute_rule(rule: dict, args: dict) -> bool: + """检查工具调用的 args 是否匹配单条自动执行规则。 + + 输入: + rule: {"parameter": str, "operator": str, "value": str} + args: 工具调用的参数 dict。 + + 输出: + 匹配则返回 True。 + + 支持的 operator: + - "equals": 参数值（转为字符串后）完全等于 rule value。 + """ + param = rule.get("parameter", "") + op = rule.get("operator", "equals") + expected = str(rule.get("value", "")) + actual = str(args.get(param, "")) + if op == "equals": + return actual == expected + return False + + +def classify_tool_execution(tool_name: str | None, args: dict) -> Literal["direct", "confirm"]: + """判定工具调用是否可在图内自动执行。 + + 判定优先级: + 1. 空名称 / force_confirm / human_interaction → 强制 confirm + 2. tool_config.json 中 permission == "direct" → direct + 3. tool_config.json 中 auto_execute_rules 匹配 → direct + 4. 其余 → confirm + + 注意: + 参数提取时使用安全默认值，避免 KeyError 或 None 导致的误判。 + """ + if not tool_name: + return "confirm" + if _FORCE_CONFIRM_TOOLS.get(): + return "confirm" + # human_interaction 必须人工确认，不可绕过 + if tool_name == "human_interaction": + return "confirm" + + # 读取 tool_config.json 中的权限（含 safe_tools 默认 direct，用户可改） + permissions = _get_tool_permissions() + if permissions.get(tool_name) == "direct": + return "direct" + + # 检查 JSON 配置的 auto_execute_rules + rules = _get_auto_execute_rules().get(tool_name) + if rules: + for rule in rules: + if _match_auto_execute_rule(rule, args): + return "direct" + + # 未匹配默认需确认 + return "confirm" diff --git a/src/agent/history/data_base/chat_history.db b/src/agent/history/data_base/chat_history.db deleted file mode 100644 index 597207190745435f4bf94b6f5c87d3a498017d21..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 57344 zcmeHwU94PJmX;kmaemtO_RyfTkb%PuJ#lVqr~b~L)jfkd$t0dQKb<(;w5Fo#Q&s2q z*0^rf?W($Q+}%M+I-{N*Bp%Q_%nRaynU)Ypyo}}v#0yAB1L9?P0P%!)KxhUD9^ioo z_`db+bIz`+TQ^ReHr-HGQZCmyd+)W^-(Gv|wbp*;TQ`RDJSZm9gKQqOPks5+>C>nF zdJvpCb?R08zlHzHKab$c+43Lw`?U9c-Pc!7U3vd+o!|Nor_P>#?bLR(^`Fkaw)OYd 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"relpath": "20260527_215121/后端建议_20260527_215121_1.png", - "name": "后端建议.png" - }, - { - "type": "text", - "text": "帮我总结一下这张图片的内容" - } - ] - }, - "assistant": { - "text": "这张图片是一份关于**后端编程语言推荐**的个人建议清单，内容如下：\n\n**标题**：后端编程语言推荐 \n**副标题**：以下仅代表我个人建议\n\n**具体建议**：\n\n1. **Java**：如果你打算未来深耕 Web 后端开发，推荐选择 Java。理由是 Java 在国内 Web 开发领域的地位非常稳固，难以撼动。\n2. **Node.js**：如果你是前端程序员，想转行做后端，推荐选择 Node.js（因为前后端技术栈接近，上手快）。\n3. **Python**：除上述两种情况外，其他场景都推荐 Python。其最大优点是**简单、效率高**。\n4. **PHP**：明确建议“千万不要学 PHP”，表达了一种强烈的否定态度。\n\n✅ 总结： \n这是一份带有强烈个人倾向的后端语言选择指南，核心逻辑是：\n- 想稳扎稳打做企业级 Web 后端 → 选 Java；\n- 前端转后端 → 选 Node.js；\n- 其他通用或快速开发场景 → 选 Python；\n- PHP → 不推荐。\n\n⚠️ 注意：图中明确标注“仅代表我个人建议”，说明这不是权威结论，而是作者基于自身经验或市场观察给出的主观推荐。\n\n（注：虽然图中未提及 Go、C#、Rust 等现代后端语言，但这是“个人建议”，并非全面技术选型指南。）", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260527_215121/后端建议_20260527_215121_1.png 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z8@Rn^_-z14`pdxY9mH+u?R4okREy#d=~A~(U`T~f-`lEt8~)Fj_;>gg c&EMdEMo2AHG{oux0N996AVS!i=zf0tKkb~Kv;Y7A diff --git a/src/agent/history/multi_inputs/20260527_215307/ui_messages.json b/src/agent/history/multi_inputs/20260527_215307/ui_messages.json deleted file mode 100644 index 9da7e4e..0000000 --- a/src/agent/history/multi_inputs/20260527_215307/ui_messages.json +++ /dev/null @@ -1,27 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "file", - "relpath": "20260527_215307/22级-25年优秀学生干部_20260527_215307_1.xlsx", - "name": "22级-25年优秀学生干部.xlsx" - }, - { - "type": "text", - "text": "帮我提取内容并加以说明" - } - ] - }, - "assistant": { - "text": "这份文件是一份关于**22级学生评选2025年优秀学生干部**的名单汇总表。\n\n### 📋 内容提取与整理\n\n该表格共包含9个班级/项目，每个单位评选出了一名**校级**优秀学生干部和一名**院级**优秀学生干部。具体名单如下：\n\n| 班级/项目 | 校级优秀学生干部 | 院级优秀学生干部 |\n| :--- | :--- | :--- |\n| **通信一班** | 刘和宝 | 郭思宇 |\n| **通信二班** | 董卓然 | 王雨萌 |\n| **通信三班** | 朱洪亿 | 李爽爽 |\n| **电科一班** | 李皓威 | 邱浩原 |\n| **电科二班** | 赵永顺 | 王子瑶 |\n| **光信** | 纪天泽 | 温致涵 |\n| **光创** | 王志成 | 曹晶晶 |\n| **微纳** | 王隆昊 | 张加祥 |\n| **崇新** | 庞开升 | 刘希言 |\n\n---\n\n### 💡 说明与分析\n\n1. **评选层级**：\n * **校级**：代表该班级/项目中最优秀的学生干部，将代表学院参加学校层面的评选或表彰。\n * **院级**：代表学院内部评选出的优秀学生干部，是校级评选的后备或并列荣誉。\n\n2. **涉及专业/班级**：\n * 名单涵盖了**通信工程**（分三个班）、**电子信息科学与技术**（分两个班）以及**光电信息**相关方向（光信、光创、微纳、崇新等创新班或项目班）。这表明该评选主要面向电子信息类相关专业的大二学生（22级）。\n\n3. **文件命名含义**：\n * `22级-25年优秀学生干部`：表明这是针对2022级入学学生，在2025年进行的优秀学生干部评选（通常评选周期可能跨越学年，或指2025年度的荣誉）。\n * `20260527_215307`：文件生成的时间戳显示为 **2026年5月27日 21:53:07**。这暗示该文件可能是基于未来时间设定的模拟数据，或者是系统时间设置在未来/测试环境下的产物。\n\n4. **数据用途**：\n * 该表格通常用于公示评选结果、录入教务系统、制作荣誉证书或作为档案留存。\n\n如果您需要将这些数据转换为特定的格式（如CSV、JSON）或进行统计分析，请随时告诉我！", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_121405/ui_messages.json b/src/agent/history/multi_inputs/20260603_121405/ui_messages.json deleted file mode 100644 index 808aa20..0000000 --- a/src/agent/history/multi_inputs/20260603_121405/ui_messages.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_121521/ui_messages.json b/src/agent/history/multi_inputs/20260603_121521/ui_messages.json deleted file mode 100644 index 808aa20..0000000 --- a/src/agent/history/multi_inputs/20260603_121521/ui_messages.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_121652/ui_messages.json b/src/agent/history/multi_inputs/20260603_121652/ui_messages.json deleted file mode 100644 index 6441e65..0000000 --- a/src/agent/history/multi_inputs/20260603_121652/ui_messages.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - }, - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_121729/ui_messages.json b/src/agent/history/multi_inputs/20260603_121729/ui_messages.json deleted file mode 100644 index 808aa20..0000000 --- a/src/agent/history/multi_inputs/20260603_121729/ui_messages.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_122026/ui_messages.json b/src/agent/history/multi_inputs/20260603_122026/ui_messages.json deleted file mode 100644 index 808aa20..0000000 --- a/src/agent/history/multi_inputs/20260603_122026/ui_messages.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_122112/ui_messages.json b/src/agent/history/multi_inputs/20260603_122112/ui_messages.json deleted file mode 100644 index 808aa20..0000000 --- a/src/agent/history/multi_inputs/20260603_122112/ui_messages.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_122357/ui_messages.json b/src/agent/history/multi_inputs/20260603_122357/ui_messages.json deleted file mode 100644 index 808aa20..0000000 --- a/src/agent/history/multi_inputs/20260603_122357/ui_messages.json +++ /dev/null @@ -1,22 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "你好" - } - ] - }, - "assistant": { - "text": "抱歉，我遇到了一些问题，请稍后再试。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_172211/ui_messages.json b/src/agent/history/multi_inputs/20260603_172211/ui_messages.json deleted file mode 100644 index c995483..0000000 --- a/src/agent/history/multi_inputs/20260603_172211/ui_messages.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "帮我打开b站" - } - ] - }, - "assistant": { - "text": "\n\n\nopen\n\n\nhttps://www.bilibili.com\n\n\n", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - }, - { - "user": { - "content": [ - { - "type": "text", - "text": "现在几点了" - } - ] - }, - "assistant": { - "text": "\n\n\n", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/multi_inputs/20260603_175632/ui_messages.json b/src/agent/history/multi_inputs/20260603_175632/ui_messages.json deleted file mode 100644 index 999dab1..0000000 --- a/src/agent/history/multi_inputs/20260603_175632/ui_messages.json +++ /dev/null @@ -1,39 +0,0 @@ -{ - "version": 2, - "turns": [ - { - "user": { - "content": [ - { - "type": "text", - "text": "现在几点了" - } - ] - }, - "assistant": { - "text": "现在是 2026年6月3日 17:56:36。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - }, - { - "user": { - "content": [ - { - "type": "text", - "text": "帮我打开b站和虎牙" - } - ] - }, - "assistant": { - "text": "好的！从截图中可以看到：\n\n1. **B站已经打开了**：第5个标签页显示的是\"啊哈啊哈！ - bilibili\"\n2. **虎牙也打开了**：当前页面正在浏览虎牙直播网站，可以看到虎牙的Logo、导航栏（首页、直播、分类、视频、游戏等），以及正在播放的直播内容和右侧的推荐直播间\n\n两个网站都已经成功打开。当前显示的是虎牙直播页面，如果您想查看B站，可以点击第5个标签页切换到B站。", - "output_type": "text", - "autoplay": false, - "audio_duration_seconds": null, - "audio_relpath": null - } - } - ] -} \ No newline at end of file diff --git a/src/agent/history/tool_call_recorder.py b/src/agent/history/tool_call_recorder.py new file mode 100644 index 0000000..7101914 --- /dev/null +++ b/src/agent/history/tool_call_recorder.py @@ -0,0 +1,263 @@ +"""工具调用历史记录器：按 session/turn 存储及实时推送。 + +设计: + 每次用户输入 → 新建一个 turn，图节点执行期间通过 live 字典实时推送， + 前端轮询显示；turn 结束后 persist 到 JSON 并清除 live 记录。 + 不同 turn 间数据完全隔离，不会泄漏到新对话中。 +""" +from __future__ import annotations + +import json +import os +import base64 +import time +import threading +from contextvars import ContextVar +from datetime import datetime +from pathlib import Path +from typing import Any + +from langchain_core.messages import BaseMessage, AIMessage, HumanMessage, ToolMessage + +_HISTORY_ROOT = Path(__file__).resolve().parent +TOOL_CALLING_ROOT = _HISTORY_ROOT / "tool_calling" + +# ---------- 当前 turn 上下文 ---------- +_current_turn_id: ContextVar[str] = ContextVar("current_turn_id", default="") +_current_session_id: ContextVar[str] = ContextVar("current_session_id", default="") + +# ---------- 实时记录（线程安全） ---------- +_live_lock = threading.Lock() +_live_records: dict[str, list[dict[str, Any]]] = {} # key = session_id + + +def set_current_turn(turn_id: str) -> None: + _current_turn_id.set(turn_id) + + +def get_current_turn() -> str: + return _current_turn_id.get() + + +def set_current_session(session_id: str) -> None: + _current_session_id.set(session_id) + + +def get_current_session() -> str: + return _current_session_id.get() + + +def start_live_turn(session_id: str) -> str: + """开始一个新 turn 并返回 turn_id。清除该 session 旧 live 记录。""" + turn_id = datetime.now().strftime("%Y%m%d_%H%M%S") + set_current_turn(turn_id) + with _live_lock: + _live_records[session_id] = [] + return turn_id + + +def record_tool_call_live(session_id: str, name: str, args: dict[str, Any]) -> None: + """图节点中调用：记录工具调用开始。""" + with _live_lock: + if session_id not in _live_records: + _live_records[session_id] = [] + _live_records[session_id].append({ + "name": name, + "args_summary": _short_args(args), + "status": "running", + "result_text": "", + "timestamp": time.time(), + }) + + +def record_tool_result_live(session_id: str, name: str, result_text: str, + image_bytes: bytes | None = None) -> None: + """图节点中调用：记录工具返回结果。""" + with _live_lock: + records = _live_records.get(session_id, []) + for rec in reversed(records): + if rec["name"] == name and rec["status"] == "running": + rec["status"] = "done" + rec["result_text"] = (result_text or "")[:500] + if image_bytes: + rec["image_base64"] = base64.standard_b64encode(image_bytes).decode("ascii") + return + + +def end_live_turn(session_id: str) -> list[dict[str, Any]]: + """结束实时记录并返回该 turn 所有记录。""" + set_current_turn("") + with _live_lock: + return _live_records.pop(session_id, []) + + +def get_live_tool_calls(session_id: str) -> list[dict[str, Any]]: + """轮询接口：当前执行中 turn 的实时工具调用列表。""" + with _live_lock: + return list(_live_records.get(session_id, [])) + + +def _short_args(args: dict[str, Any], max_len: int = 40) -> str: + s = json.dumps(args, ensure_ascii=False) + if len(s) > max_len: + return s[:max_len] + "..." + return s + + +# ---------- 持久化 ---------- +def _ensure_dir(path: Path) -> None: + path.mkdir(parents=True, exist_ok=True) + + +def session_tool_dir(session_id: str) -> Path: + return TOOL_CALLING_ROOT / session_id + + +def extract_tool_calls_from_messages(messages: list[BaseMessage]) -> list[dict[str, Any]]: + """从图执行后的消息列表中提取完整的工具调用链。""" + records: list[dict[str, Any]] = [] + pending: dict[str, dict[str, Any]] = {} + for msg in messages: + if isinstance(msg, AIMessage) and msg.tool_calls: + for tc in msg.tool_calls: + d = tc if isinstance(tc, dict) else {"name": tc.name, "args": tc.args, "id": tc.id} + if d.get("id"): + pending[d["id"]] = { + "name": d.get("name", "unknown"), + "args": _json_safe(d.get("args", {})), + "result_text": "", + "result_files": [], + } + elif isinstance(msg, ToolMessage) and msg.tool_call_id: + cid = msg.tool_call_id + if cid in pending: + entry = pending.pop(cid) + entry["result_text"] = str(msg.content) if msg.content else "" + if msg.artifact: + entry["result_files"] = _save_artifact_files(msg.artifact, msg.tool_call_id) + records.append(entry) + for entry in pending.values(): + records.append(entry) + return records + + +def _json_safe(value: Any) -> Any: + try: + json.dumps(value, ensure_ascii=False) + return value + except TypeError: + if isinstance(value, dict): + return {str(k): _json_safe(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [_json_safe(v) for v in value] + return str(value) + + +def _save_artifact_files(artifact: Any, base_name: str) -> list[str]: + files: list[str] = [] + if isinstance(artifact, dict) and "image_bytes" in artifact: + files.append(f"_artifact_{base_name}_0.png") + elif isinstance(artifact, list): + for i, item in enumerate(artifact): + if isinstance(item, dict) and "image_bytes" in item: + files.append(f"_artifact_{base_name}_{i}.png") + elif isinstance(item, bytes): + files.append(f"_artifact_{base_name}_{i}.png") + elif isinstance(artifact, bytes): + files.append(f"_artifact_{base_name}.png") + return files + + +def _find_artifact_bytes(messages: list[BaseMessage], fname: str) -> bytes | None: + for msg in messages: + if isinstance(msg, ToolMessage) and msg.artifact: + a = msg.artifact + if isinstance(a, dict) and "image_bytes" in a: + return a["image_bytes"] + if isinstance(a, list): + for item in a: + if isinstance(item, dict) and "image_bytes" in item: + return item["image_bytes"] + if isinstance(item, bytes): + return item + if isinstance(a, bytes): + return a + return None + + +def save_turn(session_id: str, turn_id: str, user_message: dict[str, Any], + messages: list[BaseMessage]) -> str | None: + """持久化一轮完整的工具调用记录。""" + tool_calls = extract_tool_calls_from_messages(messages) + if not tool_calls: + return None + + assistant_response = "" + for msg in reversed(messages): + if isinstance(msg, AIMessage) and not msg.tool_calls: + assistant_response = msg.content if isinstance(msg.content, str) else str(msg.content) + break + + session_dir = session_tool_dir(session_id) + turn_dir = session_dir / turn_id + _ensure_dir(turn_dir) + + for tc in tool_calls: + for fname in list(tc.get("result_files", [])): + img_bytes = _find_artifact_bytes(messages, fname) + if img_bytes: + (turn_dir / fname).write_bytes(img_bytes) + else: + tc["result_files"].remove(fname) + + record = { + "session_id": session_id, + "turn_id": turn_id, + "user_message": _json_safe(user_message), + "tool_calls": tool_calls, + "assistant_response": assistant_response, + "timestamp": turn_id, + } + json_path = session_dir / f"turn_{turn_id}.json" + with open(json_path, "w", encoding="utf-8") as f: + json.dump(record, f, ensure_ascii=False, indent=2) + return str(json_path) + + +def list_turns(session_id: str) -> list[dict[str, Any]]: + """列出会话下所有 turn（按时间倒序，最近5轮）。""" + session_dir = session_tool_dir(session_id) + if not session_dir.is_dir(): + return [] + turns = [] + for f in sorted(session_dir.glob("turn_*.json"), reverse=True): + try: + with open(f, "r", encoding="utf-8") as fh: + turns.append(json.load(fh)) + except (json.JSONDecodeError, OSError): + pass + return turns[:5] + + +def get_latest_turn(session_id: str) -> dict[str, Any] | None: + """获取最近一个 turn 的完整记录。""" + turns = list_turns(session_id) + return turns[0] if turns else None + + +def get_tool_calls_for_latest_turn(session_id: str) -> list[dict[str, Any]]: + """获取最近一个 turn 的工具调用（供 UI 渲染）。""" + latest = get_latest_turn(session_id) + if not latest: + return [] + turn_id = latest.get("turn_id", "") + result = [] + for tc in latest.get("tool_calls", []): + entry = dict(tc) + full_files = [] + for fname in tc.get("result_files", []): + full_files.append(str(session_tool_dir(session_id) / turn_id / fname)) + entry["result_files_full"] = full_files + entry["turn_id"] = turn_id + result.append(entry) + return result diff --git a/src/agent/llm.py b/src/agent/llm.py index 6f29272..a88b66b 100644 --- a/src/agent/llm.py +++ b/src/agent/llm.py @@ -1,489 +1,3 @@ -# agent/llm.py -import io -import os -import tempfile -import wave -import pyaudio -from contextlib import closing -from typing import Any, List, Dict, Optional -from pydantic import Field -import re, json -from datetime import datetime as dt -import requests -from concurrent.futures import ThreadPoolExecutor, as_completed - -from langchain_core.messages import HumanMessage, AIMessage, BaseMessage, SystemMessage, ToolCall -from langchain_core.outputs import ChatResult, ChatGeneration -from langchain_openai import ChatOpenAI - -from agent.utils.image_utils import image_bytes_to_openai_image_url_part -from agent.utils.env_utils import LLM_API_KEY, LLM_BASE_URL, LLM_MODEL, LLM_TIMEOUT, TTS_BASE_URL, ASR_BASE_URL -from agent.utils.agent_utils import assistant_text, Documents_process, split_text, merge_wavs -from agent.utils.image_utils import image_path_to_openai_image_url_part, image_bytes_to_openai_image_url_part, IMAGE_FILE_EXTENSIONS -from agent.memory.system_prompt import VISION_SYSTEM_PROMPT - -class ChatQwen(ChatOpenAI): - output_type: str = Field(default="text") - stream_play: bool = Field(default=False) - last_audio_kwargs: Optional[dict] = Field(default=None) - def __init__(self, **kwargs: Any) -> None: - super().__init__( - model=kwargs.pop("model", LLM_MODEL), - api_key=kwargs.pop("api_key", LLM_API_KEY), - base_url=kwargs.pop("base_url", LLM_BASE_URL), - timeout=kwargs.pop("timeout", LLM_TIMEOUT), - max_retries=kwargs.pop("max_retries", 0), - **kwargs - ) - - @staticmethod - def play_audio_bytes(audio_bytes: bytes, chunk_size: int = 1024) -> None: - if not audio_bytes: - return - wf = wave.open(io.BytesIO(audio_bytes), 'rb') - p = pyaudio.PyAudio() - stream = p.open( - format=p.get_format_from_width(wf.getsampwidth()), - channels=wf.getnchannels(), - rate=wf.getframerate(), - output=True, - ) - data = wf.readframes(chunk_size) - while data: - stream.write(data) - data = wf.readframes(chunk_size) - stream.stop_stream() - stream.close() - p.terminate() - wf.close() - - @staticmethod - def _process_image_content(part: Dict[str, Any]) -> Dict[str, Any]: - image_type = part.get("type") - if image_type != "image_url": - return part - image_url = part.get("image_url", {}) - url = image_url.get("url", "") - if url.startswith(("http://", "https://", "data:")): - return part - if isinstance(url, str) and os.path.exists(url): - ext = os.path.splitext(url)[1].lower() - if ext in IMAGE_FILE_EXTENSIONS: - result = image_path_to_openai_image_url_part(url) - return result if result else {"type": "text", "text": "[图片处理失败]"} - if isinstance(url, bytes): - result = image_bytes_to_openai_image_url_part(url) - return result if result else {"type": "text", "text": "[图片处理失败]"} - return part - - @staticmethod - def _convert_audio_to_wav(audio_path: str) -> str: - ext = os.path.splitext(audio_path)[1].lower() - if ext == ".wav": - return audio_path - try: - from pydub import AudioSegment - except ImportError as exc: - raise RuntimeError("当前 ASR 仅支持 WAV，但未安装 pydub/ffmpeg") from exc - try: - audio = AudioSegment.from_file(audio_path) - except Exception as exc: - raise RuntimeError(f"无法读取音频文件: {exc}") from exc - fd, wav_path = tempfile.mkstemp(suffix=".wav", prefix="asr_") - os.close(fd) - try: - audio.export(wav_path, format="wav") - return wav_path - except Exception: - if os.path.exists(wav_path): - os.remove(wav_path) - raise - - def _audio_to_text(self, audio_path: str) -> str: - if not os.path.exists(audio_path): - raise FileNotFoundError(f"音频文件不存在: {audio_path}") - - wav_path = self._convert_audio_to_wav(audio_path) - cleanup_wav = (wav_path != audio_path) - - try: - # 读取音频文件二进制内容 - with open(wav_path, "rb") as f: - audio_bytes = f.read() - - # 构建 Data URL - import base64 - b64 = base64.b64encode(audio_bytes).decode("ascii") - mime_type = "audio/wav" # 我们已经转换为 wav - data_url = f"data:{mime_type};base64,{b64}" - - payload = { - "messages": [ - { - "role": "user", - "content": [ - {"type": "audio_url", "audio_url": {"url": data_url}} - ] - } - ] - } - endpoint = f"{ASR_BASE_URL.rstrip('/')}/chat/completions" - resp = requests.post( - endpoint, - headers={"Content-Type": "application/json"}, - json=payload, - timeout=120 - ) - resp.raise_for_status() - result = resp.json() - content = result["choices"][0]["message"]["content"] - return content.strip() - finally: - if cleanup_wav and os.path.exists(wav_path): - os.remove(wav_path) - - def _preprocess_messages(self, messages: List[BaseMessage]) -> List[BaseMessage]: - new_messages = [] - for msg in messages: - if isinstance(msg, HumanMessage) and isinstance(msg.content, str): - text = msg.content.strip() - if text.startswith("[") and text.endswith("]"): - try: - possible_list = json.loads(text) - if isinstance(possible_list, list): - msg = HumanMessage(content=possible_list, additional_kwargs=msg.additional_kwargs) - except (json.JSONDecodeError, ValueError): - pass - if isinstance(msg, HumanMessage) and isinstance(msg.content, dict): - # 尝试从 dict 中提取文本，否则序列化为字符串 - text = msg.content.get("text") or msg.content.get("content") or "" - if text: - msg = HumanMessage(content=str(text), additional_kwargs=msg.additional_kwargs) - else: - msg = HumanMessage(content=json.dumps(msg.content, ensure_ascii=False), additional_kwargs=msg.additional_kwargs) - elif isinstance(msg, HumanMessage) and isinstance(msg.content, list): - new_content: List[Dict[str, Any] | str] = [] - for part in msg.content: - if isinstance(part, dict): - if part.get("type") == "audio": - audio_path = part.get("audio_path") - if audio_path: - try: - text = self._audio_to_text(audio_path) - new_content.append({"type": "text", "text": text}) - except Exception as e: - new_content.append({"type": "text", "text": f"[语音识别失败: {e}]"}) - elif part.get("type") == "image_url": - new_content.append(self._process_image_content(part)) - elif part.get("type") == "text": - text = (part.get("text") or "").strip() - if text: - new_content.append({"type": "text", "text": text}) - elif part.get("path") or (part.get("file") or {}).get("path"): - file_path = part.get("path") or (part.get("file") or {}).get("path") - if isinstance(file_path, str) and os.path.isfile(file_path): - ext = os.path.splitext(file_path)[1].lower() - normalized_path = file_path.replace("\\", "/") - if ext in IMAGE_FILE_EXTENSIONS: - new_content.append(self._process_image_content({"type": "image_url", "image_url": {"url": file_path}})) - else: - try: - file_text, file_images = Documents_process.process(file_path) - new_content.append({"type": "text", "text": f"用户上传了一个名为\"{normalized_path}\"的文件。"}) - if file_text.strip(): - new_content.append({ - "type": "text", - "text": f"以下是文件 {normalized_path} 的内容：\n{file_text.strip()}" - }) - for image_bytes in file_images or []: - image_part = image_bytes_to_openai_image_url_part(image_bytes) - if image_part is not None: - new_content.append(image_part) - if not file_text.strip() and not file_images: - new_content.append({"type": "text", "text": f"[用户上传了附件文件: {normalized_path}]"}) - except Exception as e: - new_content.append({"type": "text", "text": f"[附件处理失败: {normalized_path}: {e}]"}) - else: - text = assistant_text(part) - if text: - new_content.append({"type": "text", "text": text}) - elif isinstance(part, str): - text = part.strip() - if text: - new_content.append(text) - if len(new_content) == 1 and isinstance(new_content[0], str): - msg = HumanMessage(content=new_content[0], additional_kwargs=msg.additional_kwargs) - else: - msg = HumanMessage(content=new_content, additional_kwargs=msg.additional_kwargs) - new_messages.append(msg) - return new_messages - - def _text_to_audio(self, text: str) -> bytes: - chunks = split_text(text, min_effective_chars=20) - if len(chunks) == 1: - resp = requests.get(f"{TTS_BASE_URL}/tts", params={"text": chunks[0]}) - resp.raise_for_status() - return resp.content - - # 多块并行请求 - audio_parts = [None] * len(chunks) - with ThreadPoolExecutor(max_workers=min(len(chunks), 4)) as pool: - future_to_idx = { - pool.submit( - lambda t: requests.get( - f"{TTS_BASE_URL}/tts", params={"text": t} - ).content, - chunk - ): idx for idx, chunk in enumerate(chunks) - } - for future in as_completed(future_to_idx): - idx = future_to_idx[future] - try: - audio_parts[idx] = future.result() - except Exception as e: - raise RuntimeError(f"TTS request failed for chunk {idx}: {e}") - return merge_wavs(audio_parts) - - @staticmethod - def _get_wav_duration_seconds(audio_bytes: bytes) -> float | None: - if not audio_bytes: - return None - try: - with closing(wave.open(io.BytesIO(audio_bytes), "rb")) as wf: - frame_rate = wf.getframerate() - frame_count = wf.getnframes() - if frame_rate <= 0: - return None - return round(frame_count / frame_rate, 2) - except wave.Error: - return None - - def _apply_tts(self, result: ChatResult, output_type: str, audio_output_dir: str | None = None) -> ChatResult: - if output_type not in ("audio", "both") or not audio_output_dir: - return result - os.makedirs(audio_output_dir, exist_ok=True) - for idx, gen in enumerate(result.generations): - if not isinstance(gen, ChatGeneration): - continue - msg = gen.message - if not isinstance(msg, AIMessage): - continue - text = assistant_text(msg.content) - if not text.strip(): - continue - try: - audio_bytes = self._text_to_audio(text) - timestamp = dt.now().strftime("%H%M%S") - audio_path = os.path.join(audio_output_dir, f"assistant_tts_{timestamp}_{idx}.wav") - with open(audio_path, "wb") as f: - f.write(audio_bytes) - duration_seconds = self._get_wav_duration_seconds(audio_bytes) - # 仅保存到实例属性，不再写入 additional_kwargs - if idx == 0: - self.last_audio_kwargs = { - "audio": { - "sample_rate": 24000, - "format": "wav", - "path": audio_path, - "duration_seconds": duration_seconds, - }, - "output_type": output_type, - } - if self.stream_play: - self.play_audio_bytes(audio_bytes) - if output_type == "audio": - msg.content = "" - except Exception as e: - print(f"[TTS ERROR] 合成语音失败: {e}") - # 发生错误时也清空 last_audio_kwargs，避免污染 - self.last_audio_kwargs = None - return result - - def _generate( - self, - messages: List[BaseMessage], - stop: Optional[List[str]] = None, - run_manager: Optional[Any] = None, - **kwargs: Any, - ) -> ChatResult: - metadata = {} - if run_manager: - metadata = getattr(run_manager, 'metadata', {}) - output_type = metadata.get("output_type", self.output_type) - audio_output_dir = metadata.get("audio_output_dir") - - # 移除自定义参数，防止它们传入 OpenAI API - kwargs.pop("output_type", None) - kwargs.pop("audio_output_dir", None) - - messages = self._preprocess_messages(messages) - - result = super()._generate(messages, stop=stop, run_manager=run_manager, **kwargs) - - if output_type in ("audio", "both"): - result = self._apply_tts(result, output_type, audio_output_dir=audio_output_dir) - if output_type == "audio": - for gen in result.generations: - if isinstance(gen, ChatGeneration) and isinstance(gen.message, AIMessage): - gen.message.content = "" - return result - - def _create_chat_result(self, response, generation_info=None) -> ChatResult: - # ---------- 打印原始 tool_calls ---------- - try: - raw = response if isinstance(response, dict) else response.model_dump() - print("[RAW_RESPONSE]", json.dumps(raw, indent=2, ensure_ascii=False)) - choices = raw.get("choices", []) - if choices: - msg = choices[0].get("message", {}) - tool_calls = msg.get("tool_calls") - if tool_calls: - print("[RAW_TOOLCALLS]", json.dumps(tool_calls, indent=2, ensure_ascii=False)) - except Exception: - pass - - result = super()._create_chat_result(response, generation_info) - try: - dumped = response if isinstance(response, dict) else response.model_dump(exclude={"choices": {"__all__": {"message": {"parsed"}}}}) - for i, choice in enumerate(dumped.get("choices") or []): - if i >= len(result.generations): - break - lc_msg = result.generations[i].message - msg = choice.get("message") or {} - ak = lc_msg.additional_kwargs - # 优先处理 reasoning 内容 - merged = "" - for key in ("reasoning_content", "reasoning_details", "reasoning"): - merged = assistant_text(msg.get(key)) - if merged: - break - if merged: - ak.setdefault("reasoning_content", merged) - raw_content = assistant_text(msg.get("content")) - if not raw_content and merged: - lc_msg.content = merged - elif raw_content: - lc_msg.content = raw_content - - content_text = lc_msg.content if isinstance(lc_msg.content, str) else "" - - # 匹配 ... 块 - pattern = re.compile(r'\s*(.*?)\s*', re.DOTALL) - matches = pattern.findall(content_text) - if matches: - tool_calls = [] - for idx, match in enumerate(matches): - # 先尝试 JSON 解析（兼容旧格式） - try: - call_data = json.loads(match) - tool_calls.append({ - "id": f"call_{idx}", - "type": "function", - "function": { - "name": call_data.get("name", ""), - "arguments": json.dumps(call_data.get("arguments", {})) - } - }) - continue # 解析成功，跳过 XML 尝试 - except json.JSONDecodeError: - pass - - # 再尝试 XML 解析 - func_match = re.search(r'', match) - if not func_match: - continue - func_name = func_match.group(1) - - params = {} - for param_match in re.finditer(r'\s*(.*?)\s*', match, re.DOTALL): - key = param_match.group(1) - value = param_match.group(2).strip() - # 可选：智能类型转换，先保持简单 - params[key] = value - - tool_calls.append({ - "id": f"call_{idx}", - "type": "function", - "function": { - "name": func_name, - "arguments": json.dumps(params) - } - }) - - if tool_calls: - ak["tool_calls"] = tool_calls - # 构造 LangChain 的 ToolCall 对象列表 - lc_tool_calls = [] - for idx, tc in enumerate(tool_calls): - lc_tool_calls.append( - ToolCall( - id=tc["id"], - name=tc["function"]["name"], - args=json.loads(tc["function"]["arguments"]) - ) - ) - lc_msg.tool_calls = lc_tool_calls - # 清理内容中的 XML/JSON 工具调用痕迹 - cleaned = pattern.sub("", content_text).strip() - lc_msg.content = cleaned if cleaned else "" - except Exception: - pass - return result - - def describe_image(self, image_path_or_url: str, prompt: str = "请详细描述这张图片的内容。") -> str: - messages = [ - SystemMessage(content=[{"type": "text", "text": VISION_SYSTEM_PROMPT}]), - HumanMessage(content=[ - {"type": "image_url", "image_url": {"url": image_path_or_url}}, - {"type": "text", "text": prompt} - ]) - ] - result = self.invoke(messages) - return result.content - - def describe_image_bytes(self, image_bytes: bytes, prompt: str = "请详细描述这张图片的内容。") -> str: - img_part = image_bytes_to_openai_image_url_part(image_bytes) - if img_part is None: - return "图片处理失败" - messages = [ - SystemMessage(content=[{"type": "text", "text": VISION_SYSTEM_PROMPT}]), - HumanMessage(content=[img_part, {"type": "text", "text": prompt}]) - ] - result = self.invoke(messages) - return result.content - -llm = ChatQwen() - -if __name__ == "__main__": - import sys - - # 使用原始字符串避免转义问题 - TEST_IMAGE_PATH = r"C:\Users\86166\Desktop\4.png" - - if not os.path.exists(TEST_IMAGE_PATH): - print(f"❌ 图片不存在: {TEST_IMAGE_PATH}") - sys.exit(1) - - # 测试1: 纯图片识别 - print("=" * 50) - print("测试1: 纯图片识别（无文本）") - print("=" * 50) - result1 = llm.invoke([ - HumanMessage(content=[{"type": "image_url", "image_url": {"url": TEST_IMAGE_PATH}}]) - ]) - print(f"返回类型: {type(result1)}") - print(f"返回内容: {result1.content}") - print(f"所有属性: {[attr for attr in dir(result1) if not attr.startswith('_')]}") - - # 测试2: 图片 + 文本 - print("\n" + "=" * 50) - print("测试2: 图文混合输入") - print("=" * 50) - result2 = llm.invoke([ - HumanMessage(content=[ - {"type": "image_url", "image_url": {"url": TEST_IMAGE_PATH}}, - {"type": "text", "text": "这张图片里有什么？请详细描述"} - ]) - ]) - print(f"回答: {result2.content}") +from agent.core.llm import ChatQwen, llm +__all__ = ["ChatQwen", "llm"] diff --git 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的身份，完成用户的摘要请求。 -""" +""".strip() VISION_SYSTEM_PROMPT = """ @@ -44,7 +44,7 @@ VISION_SYSTEM_PROMPT = """ 如果用户提供了多张图片，请分别编号描述(如「图1」「图2」等)，便于后续问答引用。 看不清的内容请如实说明，不要臆测。 其次请认真回答用户的问题，结合图片内容进行分析推理，给出具体、简短、清晰、实用的回复； -""" +""".strip() DB_SYSTEM_PROMT = """ 你是一位敏捷的数据库分析助手，能够高效利用内置功能帮助用户从PostgreSQL数据库中获取答案。 @@ -65,4 +65,9 @@ DB_SYSTEM_PROMT = """ 5. **非数据库问题快速响应**：遇到问候、常识性问题或超出数据库范围的内容时，可以直接友好回应，不必强制调用功能。 请记住，你是一个协作伙伴，目标是让用户以最小的学习成本从数据库中获得有价值的信息。开始工作吧！ -""" +""".strip() + +TEAM_MODE_PROMPT = """ +team 模式目前仅作为多 agent 协同的预留模式，尚未实现。 +请简短说明当前无法启动 team 协同，并建议用户改用 agent 模式。 +""".strip() diff --git a/src/agent/tools/__init__.py b/src/agent/tools/__init__.py index 17d29ff..d8191d6 100644 --- a/src/agent/tools/__init__.py +++ b/src/agent/tools/__init__.py @@ -1,34 +1,38 @@ from .tool import * -# from .tool_rag import RAGTool - -# from .tool_database import * -# from utils.database_utils import PostgreSQL_Manager - -# 创建数据库管理器实例 -# connection = "postgresql+psycopg2://postgres:12345678@localhost:5432/agent_controller" -# connection = "sqlite:///Agent/src/agent/history/data_base/chat_history.db" -# _db_manager = PostgreSQL_Manager(connection) - -# # 创建工具实例 -# _all_database_tools = [ -# Websearchclass(), -# DateQueryClass(), -# ListTableComment(db_manager=_db_manager), -# ListTableSchema(db_manager=_db_manager), -# SQLQuery(db_manager=_db_manager), -# SQLQueryChecker(db_manager=_db_manager) -# ] -# def get_sql_tools(): -# return _all_database_tools +from .tool_software_control import SoftwareControl +from .tool_rag import RAGTool _all_tools = [ WebSearch(), DateQuery(), ScreenCapture(), BrowserControl(), - # RAGTool(), + HumanInteraction(), + SoftwareControl(), + RAGTool(), DirectoryListing(), + TodoList(), ] +# 注册所有可用工具名称（供前端配置面板展示），从 _all_tools 实例推导 +_ALL_AVAILABLE_TOOL_CLASSES = {tool.name: type(tool) for tool in _all_tools} + + def get_tools(): + """返回当前 Agent 注册的全部 LangChain 工具列表。""" return _all_tools + + +def get_all_available_tool_names(): + """返回所有可用工具名称（含数据库工具，不管当前是否实例化）。""" + return list(_ALL_AVAILABLE_TOOL_CLASSES.keys()) + + +def get_all_available_tool_descriptions(): + """返回所有可用工具的名称和描述映射（用于前端展示）。""" + result = {} + for name, cls in _ALL_AVAILABLE_TOOL_CLASSES.items(): + inst = cls() + desc = getattr(inst, "description", "") + result[name] = (cls.__name__, (desc or "").split("\n")[0][:200]) + return result diff --git a/src/agent/tools/__pycache__/__init__.cpython-312.pyc b/src/agent/tools/__pycache__/__init__.cpython-312.pyc index d4f424723b971ebcf35b62b756c86585f01863e0..a75a70b1f2f12883bef7bfb0c44086bec22b2465 100644 GIT binary patch literal 1897 zcmZ`(TWk|Y6rEjmB>2ei$MPR)jMk&xv3aw?w)z< z+_~q@J%7~Ic@VT?t2gw2ZAa)KA5@yP0)wTyU?z})6hT8jivmZYCd9?w61Hi!IPS#) z5)rXamj4FqsKU-F?5e`gcOwrj1(oz+eeefbwZ_R=|t9WI2D%@lA16G zm7|85Ofa8b_WEp-i^?&{0I#e;Q7MTUNpPU4x}hdw%xxru@OqR3+fXQd~_WX=yT|(`ZEIz9T$7Jt6@Wsv$|Q4-7JQj2I;cv>A?H16_B4$)LF! z|1|c~Mi~4jXbclp&^$f}nv-BPL#&{Fc)-hB<+)6j_gfSlfL$euiX)lg#S!!LwfVET 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+from agent.utils.env_utils import GROUNDING_WIDTH, GROUNDING_HEIGHT \ No newline at end of file diff --git a/src/agent/tools/tool.py b/src/agent/tools/tool.py index 920b23e..49b9a77 100644 --- a/src/agent/tools/tool.py +++ b/src/agent/tools/tool.py @@ -2,6 +2,7 @@ from __future__ import annotations import os import io +import json from typing import Optional, Literal, Tuple import sys import time @@ -13,8 +14,8 @@ from pathlib import Path from langchain.tools import BaseTool from pydantic import BaseModel, Field, create_model -from agent.llm import llm -from utils.env_utils import BROWSER_MAP +from agent.core.llm import llm +from agent.utils.env_utils import BROWSER_MAP class WebSearch(BaseTool): @@ -24,6 +25,7 @@ class WebSearch(BaseTool): description : str = "一个网络搜索工具，可以根据用户的查询返回相关的搜索结果。仅当需要查询近期知识或已有知识无法解答需要联网查询时调用，其余任何情况严禁调用。" def _get_search_wrapper(self): + """懒加载 Google Serper 搜索客户端。""" from langchain_community.utilities import GoogleSerperAPIWrapper SERPER_API_KEY = "d6348dcce0c72bb8102f0eacfd2f536f797da0c5" return GoogleSerperAPIWrapper(serper_api_key=SERPER_API_KEY, content_size=2048) @@ -142,6 +144,7 @@ class BrowserControl(BaseTool): # ---------- 辅助方法（与原版相同，无改动） ---------- def _get_browser_info(self, browser_name: Optional[str]): + """解析浏览器名称，返回 webbrowser 控制器与窗口标题关键字。""" if browser_name is None: return webbrowser.get(), None name_lower = browser_name.lower().strip() @@ -155,6 +158,7 @@ class BrowserControl(BaseTool): return controller, title_keyword def _activate_window(self, title_keyword: str) -> bool: + """在 Windows 上按标题关键字激活浏览器窗口（非 Win32 返回 False）。""" if sys.platform != "win32": return False try: @@ -171,10 +175,12 @@ class BrowserControl(BaseTool): return False def _send_keys(self, *keys): + """通过 pyautogui 发送组合键。""" pyautogui.hotkey(*keys) # ---------- 核心操作（返回操作结果描述） ---------- def _open_action(self, url: Optional[str], browser: Optional[str]) -> str: + """执行打开首页、刷新当前页或打开指定 URL。""" controller, title_keyword = self._get_browser_info(browser) if url is None: controller.open("about:blank") @@ -199,6 +205,7 @@ class BrowserControl(BaseTool): return f"已尝试打开 URL: {url}" def _close_action(self, url: Optional[str], browser: Optional[str]) -> str: + """执行关闭浏览器窗口或当前标签页。""" _, title_keyword = self._get_browser_info(browser) if url is None: if title_keyword: @@ -298,6 +305,109 @@ class DirectoryListing(BaseTool): async def _arun(self, path: Optional[str] = None, show_hidden: bool = False) -> str: return await asyncio.to_thread(self._run, path, show_hidden) -if __name__ == "__main__": - # 这里可以进行一些简单的测试，例如创建工具实例并调用方法 - pass + +class HumanInteractionInput(BaseModel): + interaction_type: Literal["information", "selection", "approval"] = Field( + default="information", + description="交互类型：information=人工补充信息，selection=人工选择，approval=人工审批。", + ) + title: str = Field(default="需要人工处理", description="展示给人类的交互标题。") + prompt: str = Field(default="请处理后继续。", description="需要人类理解并响应的具体问题或说明。") + options: Optional[list[str] | str] = Field( + default=None, + description="selection/approval 可选项。优先传字符串数组；如果传 JSON 字符串，系统会自动解析。", + ) + suggested_response: Optional[str] = Field(default=None, description="可选的建议回复、默认选择或默认审批意见。") + + +class HumanInteraction(BaseTool): + """向人类请求补充信息、选择或审批；实际 UI 确认由图级人工干预层承接。""" + + args_schema: type[BaseModel] = HumanInteractionInput + name: str = "human_interaction" + description: str = ( + "当任务缺少关键信息、需要人类选择路径、或需要人类审批后才能继续时调用。" + "三种类型分别是：information=请求人类补充缺失信息；selection=让人类在多个选项中选择且可编辑；" + "approval=请求人类审批高影响决策或继续执行。" + "工具返回必须被视为用户原始意图的纯字符串反馈。其余情况严禁调用。" + ) + + @staticmethod + def normalize_options(options: Optional[list[str] | str]) -> list[str]: + """将 options 参数规范化为字符串列表（支持 JSON 与逗号分隔）。""" + if options is None: + return [] + if isinstance(options, str): + raw = options.strip() + if not raw: + return [] + try: + parsed = json.loads(raw) + except json.JSONDecodeError: + return [item.strip() for item in raw.replace("，", ",").split(",") if item.strip()] + if isinstance(parsed, list): + return [str(item) for item in parsed] + return [str(parsed)] + return [str(item) for item in options] + + def _run( + self, + interaction_type: str = "information", + title: str = "需要人工处理", + prompt: str = "请处理后继续。", + options: Optional[list[str] | str] = None, + suggested_response: Optional[str] = None, + ) -> str: + normalized_options = self.normalize_options(options) + if interaction_type == "approval": + default = (suggested_response or "").strip() + return default or "用户同意" + if interaction_type == "selection": + default = (suggested_response or "").strip() + selected = default or (normalized_options[0] if normalized_options else "") + return f"用户选择：{selected}" if selected else "用户选择：" + response = (suggested_response or "").strip() + return f"用户补充信息如下：{response}" if response else "用户没有补充信息" + + async def _arun( + self, + interaction_type: str = "information", + title: str = "需要人工处理", + prompt: str = "请处理后继续。", + options: Optional[list[str] | str] = None, + suggested_response: Optional[str] = None, + ) -> str: + return await asyncio.to_thread(self._run, interaction_type, title, prompt, options, suggested_response) + + +class TodoListInput(BaseModel): + steps: list[str] = Field(default_factory=list, description="任务步骤列表，每项为一步要做什么的描述") + step: int = Field(default=-1, description="已完成的步骤索引(从0开始)，-1表示无已完成步骤") + + +class TodoList(BaseTool): + """可选的任务列表工具，模型可主动调用以跟踪任务进度。""" + + args_schema: type[BaseModel] = TodoListInput + name: str = "todo_list" + description: str = ( + "可选的任务管理工具。当你需要拆分复杂任务为多个步骤并跟踪进度时调用。" + "输入 steps (list[str]) 为每一步的描述，step (int) 为已完成的步骤索引(从0开始)。" + "任务默认状态为 pending，根据 step 自动标记完成。工具返回当前 todo list 文本。" + "仅在需要跟踪多步骤任务进度时调用，简单任务不需要调用。" + ) + response_format: Literal["content", "content_and_artifact"] = "content" + + def _run(self, steps: list[str] | None = None, step: int = -1) -> str: + steps = steps or [] + step = max(-1, min(step, len(steps) - 1)) + lines = ["## 📋 任务列表"] + for i, s in enumerate(steps): + status = "✅ 已完成" if i <= step else "⏳ 待处理" + lines.append(f"- [{i+1}/{len(steps)}] {status}: {s}") + if not steps: + lines.append("（无任务）") + return "\n".join(lines) + + async def _arun(self, steps: list[str] | None = None, step: int = -1) -> str: + return self._run(steps, step) diff --git a/src/agent/tools/tool_agent_call.py b/src/agent/tools/tool_agent_call.py new file mode 100644 index 0000000..b705d4f --- /dev/null +++ b/src/agent/tools/tool_agent_call.py @@ -0,0 +1,52 @@ +"""Agent 委派工具：主 Agent 通过此工具将任务委派给子 Agent。 + +系统定位: + 作为主 Agent 工具集中的特殊工具，调用此工具将触发 + SupervisorGraph 将执行路由到对应子 Agent。 + +工具签名: + agent_call(agent_name, task_description) +""" +from __future__ import annotations + +from typing import Literal + +from langchain.tools import BaseTool +from pydantic import BaseModel, Field, create_model + + +class AgentCallInput(BaseModel): + """agent_call 工具的输入参数。""" + agent_name: str = Field( + description="要委派的子 Agent 名称，例如 'code_agent'、'research_agent'。" + ) + task_description: str = Field( + description="委派给子 Agent 的详细任务描述，子 Agent 将根据此描述执行任务。" + ) + + +class AgentCallTool(BaseTool): + """主 Agent 委派任务给子 Agent 的工具。 + + 调用后 SupervisorGraph 将暂停主 Agent、激活对应子 Agent， + 子 Agent 完成后结果回传给主 Agent。 + """ + + args_schema: type[BaseModel] = AgentCallInput + name: str = "agent_call" + description: str = ( + "将子任务委派给指定的子 Agent 执行。" + "仅在需要专业子 Agent 协助完成特定类型任务时调用。" + "可用的子 Agent 名称由系统配置决定。" + ) + + def _run(self, agent_name: str, task_description: str) -> str: + """占位实现：实际路由由 SupervisorGraph 处理。""" + return ( + f"已委派任务给子 Agent [{agent_name}]。\n" + f"任务描述: {task_description}\n" + f"等待子 Agent 返回结果..." + ) + + async def _arun(self, agent_name: str, task_description: str) -> str: + return self._run(agent_name, task_description) diff --git a/src/agent/tools/tool_rag.py b/src/agent/tools/tool_rag.py index a395a31..993d8e8 100644 --- a/src/agent/tools/tool_rag.py +++ b/src/agent/tools/tool_rag.py @@ -14,15 +14,16 @@ from langchain.tools import BaseTool from pydantic import BaseModel, Field, create_model from langchain_text_splitters import RecursiveCharacterTextSplitter -from utils.env_utils import RAG_BASE_URL, RAG_CHUNK_SIZE, RAG_CHUNK_OVERLAP -from utils.agent_utils import Documents_process -from ..llm import llm +from agent.utils.env_utils import RAG_BASE_URL, RAG_CHUNK_SIZE, RAG_CHUNK_OVERLAP +from agent.utils.agent_utils import Documents_process +from agent.core.llm import llm # ---------- 前置函数---------- ENDING_PUNCTUATION = set(".!?。！？…\"'”’）;:；：…") def has_ending_punctuation(text: str) -> bool: + """判断文本是否以句末标点结尾（空文本视为已结束）。""" stripped = text.rstrip() if not stripped: return True @@ -30,6 +31,7 @@ def has_ending_punctuation(text: str) -> bool: def pre_process(text_content: str, list_image_byte: List[bytes]) -> Tuple[str, List[bytes]]: + """合并图文行并为 占位符生成 Vision 描述，供 RAG 入库前预处理。""" lines = text_content.split('\n') new_lines = [] i = 0 @@ -115,6 +117,7 @@ GLOBAL_COLLECTION_NAME = "rag_global" def get_chroma_client(): + """获取或懒初始化 Chroma 持久化客户端单例。""" global _chroma_client if _chroma_client is None: _chroma_client = chromadb.PersistentClient(path=CHROMA_PERSIST_DIR) @@ -122,6 +125,7 @@ def get_chroma_client(): def get_global_collection(): + """获取或创建全局 RAG 向量集合（cosine 空间）。""" global _global_collection if _global_collection is None: client = get_chroma_client() @@ -138,6 +142,7 @@ def get_global_collection(): def embed_texts(queries: List[str], documents: List[str] = None) -> tuple: + """调用 RAG 嵌入服务，返回 (query_embeddings, doc_embeddings)。""" payload = { "task_description": "Given a web search query, retrieve relevant passages that answer the query", "queries": queries, @@ -153,6 +158,7 @@ def embed_texts(queries: List[str], documents: List[str] = None) -> tuple: def rerank_documents(query: str, documents: List[str]) -> List[float]: + """调用 RAG 重排序服务，返回与 documents 对齐的相关度分数列表。""" payload = { "task_description": "Given a web search query, retrieve relevant passages that answer the query", "query": query, @@ -167,6 +173,7 @@ def rerank_documents(query: str, documents: List[str]) -> List[float]: def split_into_chunks(input_data) -> List[dict]: + """将文本/图文元组切分为 chunk 列表，图片与表格块标记为 protected。""" if isinstance(input_data, str): text = input_data image_bytes_list = [] @@ -256,6 +263,7 @@ class RAGTool(BaseTool): return await asyncio.to_thread(self._run, **kwargs) def _get_batch_chunks(self, coll, batch_id: str) -> dict: + """按 batch_id 拉取同批次全部 chunk，以 chunk_index 为键索引。""" res = coll.get(where={"batch_id": batch_id}) batch_chunks = {} if res and res["metadatas"] and res["documents"]: @@ -273,6 +281,7 @@ class RAGTool(BaseTool): # ---------- 查询（返回 artifact）---------- def _handle_query(self, query: str, filter: Optional[Union[str, List[str]]]) -> Tuple[str, list]: + """向量检索 + rerank，返回拼接文本与图片 artifact 列表。""" if not query: return "查询内容不能为空。", [] @@ -376,6 +385,7 @@ class RAGTool(BaseTool): # ---------- 添加文档 ---------- def _handle_add(self, content: Optional[str], source: Optional[Union[str, List[str]]]) -> Tuple[str, dict]: + """解析文本或文件、分块嵌入后写入 Chroma 全局集合。""" if not content and not source: return "必须提供 content 或 source。", {} if content and source: @@ -508,6 +518,7 @@ class RAGTool(BaseTool): # ---------- 清除 ---------- def _handle_clear(self, filter: Optional[Union[str, List[str]]]) -> Tuple[str, dict]: + """按 source 过滤条件删除知识库记录及关联图片文件。""" if not filter: return "清除操作必须提供 filter 条件。", {} diff --git a/src/agent/tools/tool_software_control.py b/src/agent/tools/tool_software_control.py new file mode 100644 index 0000000..8f33d63 --- /dev/null +++ b/src/agent/tools/tool_software_control.py @@ -0,0 +1,519 @@ +# pyright: reportMissingImports=false, reportAttributeAccessIssue=false +from __future__ import annotations + +import json +import os +import re +import subprocess +import time +import csv +import ctypes +from dataclasses import asdict, dataclass, field +from difflib import SequenceMatcher +from pathlib import Path + +from langchain.tools import BaseTool +from pydantic import BaseModel, Field, create_model + +try: + import win32com.client as win32com_client # type: ignore +except Exception: + win32com_client = None + +_windll = getattr(ctypes, "windll", None) +user32: object | None = _windll.user32 if os.name == "nt" and _windll is not None else None +WM_CLOSE = 0x0010 + + +_INDEX_PATH = Path(__file__).resolve().parents[1] / "history" / "data_base" / "software_index.json" +_SHORTCUT_ROOTS = [ + Path(os.environ.get("ProgramData", "")) / "Microsoft" / "Windows" / "Start Menu" / "Programs", + Path(os.environ.get("APPDATA", "")) / "Microsoft" / "Windows" / "Start Menu" / "Programs", + Path(os.environ.get("USERPROFILE", "")) / "Desktop", + Path(os.environ.get("PUBLIC", "")) / "Desktop", +] +_APP_PATH_KEYS = [ + r"Software\Microsoft\Windows\CurrentVersion\App Paths", +] +_SKIP_KEYWORDS = ( + "卸载", + "uninstall", + "help", + "manual", + "documentation", + "release notes", + "readme", + "website", + "faq", + "license", + "update", +) + + +@dataclass +class SoftwareRecord: + display_name: str + aliases: list[str] = field(default_factory=list) + launch_type: str = "" + launch_target: str = "" + shortcut_path: str = "" + working_dir: str = "" + process_names: list[str] = field(default_factory=list) + source: str = "" + + +def _normalize(text: str) -> str: + value = text.casefold().strip() + value = re.sub(r"\.lnk$", "", value) + value = re.sub(r"\.exe$", "", value) + value = re.sub(r"[\s\-_()（）\[\]【】]+", "", value) + return value + + +def _should_skip(name: str) -> bool: + lowered = name.casefold() + return any(keyword in lowered for keyword in _SKIP_KEYWORDS) + + +def _add_aliases(*values: str) -> list[str]: + aliases: list[str] = [] + seen: set[str] = set() + for value in values: + if not value: + continue + cleaned = value.strip() + if not cleaned: + continue + for item in (cleaned, Path(cleaned).stem): + key = _normalize(item) + if not key or key in seen: + continue + seen.add(key) + aliases.append(item) + return aliases + + +def _resolve_shortcut(path: Path) -> tuple[str, str]: + if win32com_client is None: + return "", "" + try: + shell = win32com_client.Dispatch("WScript.Shell") + shortcut = shell.CreateShortCut(str(path)) + target = (shortcut.Targetpath or "").strip() + working_dir = (shortcut.WorkingDirectory or "").strip() + return target, working_dir + except Exception: + return "", "" + + +def _scan_shortcuts() -> list[SoftwareRecord]: + records: list[SoftwareRecord] = [] + for root in _SHORTCUT_ROOTS: + if not root.exists(): + continue + for shortcut in root.rglob("*.lnk"): + display_name = shortcut.stem.strip() + if not display_name or _should_skip(display_name): + continue + target, working_dir = _resolve_shortcut(shortcut) + process_names: list[str] = [] + if target: + process_names.append(Path(target).stem) + records.append( + SoftwareRecord( + display_name=display_name, + aliases=_add_aliases(display_name, Path(target).stem if target else ""), + launch_type="shortcut", + launch_target=str(shortcut), + shortcut_path=str(shortcut), + working_dir=working_dir, + process_names=process_names, + source="shortcut", + ) + ) + return records + + +def _scan_app_paths() -> list[SoftwareRecord]: + try: + import winreg as winreg_module # type: ignore + except Exception: + return [] + records: list[SoftwareRecord] = [] + for hive in (winreg_module.HKEY_LOCAL_MACHINE, winreg_module.HKEY_CURRENT_USER): + for subkey in _APP_PATH_KEYS: + try: + root = winreg_module.OpenKey(hive, subkey) + except OSError: + continue + try: + count = winreg_module.QueryInfoKey(root)[0] + for index in range(count): + name = winreg_module.EnumKey(root, index) + try: + child = winreg_module.OpenKey(root, name) + exe_path, _ = winreg_module.QueryValueEx(child, "") + except OSError: + continue + exe_path = str(exe_path).strip() + if not exe_path: + continue + display_name = Path(name).stem.strip() or Path(exe_path).stem.strip() + if not display_name or _should_skip(display_name): + continue + records.append( + SoftwareRecord( + display_name=display_name, + aliases=_add_aliases(display_name, Path(exe_path).stem), + launch_type="app_path", + launch_target=exe_path, + process_names=[Path(exe_path).stem], + source="app_path", + ) + ) + finally: + winreg_module.CloseKey(root) + return records + + +def _deduplicate(records: list[SoftwareRecord]) -> list[SoftwareRecord]: + merged: dict[tuple[str, str], SoftwareRecord] = {} + for record in records: + key = (record.launch_type, record.launch_target.casefold()) + existing = merged.get(key) + if existing is None: + merged[key] = record + continue + existing.aliases = sorted(set(existing.aliases + record.aliases), key=str.casefold) + existing.process_names = sorted(set(existing.process_names + record.process_names), key=str.casefold) + if not existing.display_name and record.display_name: + existing.display_name = record.display_name + return sorted(merged.values(), key=lambda item: item.display_name.casefold()) + + +def _write_index(records: list[SoftwareRecord]) -> None: + _INDEX_PATH.parent.mkdir(parents=True, exist_ok=True) + payload = { + "generated_at": int(time.time()), + "count": len(records), + "items": [asdict(record) for record in records], + } + _INDEX_PATH.write_text(json.dumps(payload, ensure_ascii=False, indent=2), encoding="utf-8") + + +def _load_index() -> list[SoftwareRecord]: + if not _INDEX_PATH.exists(): + return [] + try: + payload = json.loads(_INDEX_PATH.read_text(encoding="utf-8")) + except Exception: + return [] + items = payload.get("items") or [] + records: list[SoftwareRecord] = [] + for item in items: + if not isinstance(item, dict): + continue + records.append( + SoftwareRecord( + display_name=str(item.get("display_name") or ""), + aliases=list(item.get("aliases") or []), + launch_type=str(item.get("launch_type") or ""), + launch_target=str(item.get("launch_target") or ""), + shortcut_path=str(item.get("shortcut_path") or ""), + working_dir=str(item.get("working_dir") or ""), + process_names=list(item.get("process_names") or []), + source=str(item.get("source") or ""), + ) + ) + return records + + +def _build_index() -> list[SoftwareRecord]: + records = _deduplicate(_scan_shortcuts() + _scan_app_paths()) + _write_index(records) + return records + + +def _match_score(query: str, candidate: str) -> float: + q = _normalize(query) + c = _normalize(candidate) + if not q or not c: + return 0.0 + if q == c: + return 1.0 + if q in c: + return 0.95 + if c in q and len(c) >= 4 and len(c) / max(len(q), 1) >= 0.6: + return 0.9 + return SequenceMatcher(None, q, c).ratio() + + +def _rank_matches(query: str, records: list[SoftwareRecord]) -> list[tuple[float, SoftwareRecord]]: + ranked: list[tuple[float, SoftwareRecord]] = [] + for record in records: + candidates = [record.display_name, *record.aliases] + score = max((_match_score(query, candidate) for candidate in candidates), default=0.0) + if score >= 0.55: + ranked.append((score, record)) + ranked.sort(key=lambda item: (-item[0], item[1].display_name.casefold())) + return ranked + + +def _open_record(record: SoftwareRecord) -> str: + if not hasattr(os, "startfile"): + raise RuntimeError("software_control 目前仅支持 Windows 本机软件启动") + target = record.shortcut_path or record.launch_target + if not target: + raise RuntimeError(f"未找到 {record.display_name} 的启动入口") + if not Path(target).exists(): + raise RuntimeError(f"启动入口不存在：{target}") + os.startfile(target) # type: ignore[attr-defined] + return target + + +def _coerce_bool(value: object) -> bool: + if isinstance(value, bool): + return value + if isinstance(value, str): + lowered = value.strip().casefold() + if lowered in {"true", "1", "yes", "y", "on"}: + return True + if lowered in {"false", "0", "no", "n", "off", ""}: + return False + return bool(value) + + +def _list_running_processes() -> list[dict[str, str]]: + try: + output = subprocess.check_output( + ["tasklist", "/FO", "CSV", "/NH"], + text=True, + encoding="gbk", + errors="ignore", + ) + except Exception: + return [] + processes: list[dict[str, str]] = [] + for row in csv.reader(output.splitlines()): + if len(row) < 2: + continue + image_name = row[0].strip() + pid = row[1].strip() + if not image_name or not pid: + continue + processes.append( + { + "image_name": image_name, + "pid": pid, + "stem": Path(image_name).stem, + } + ) + return processes + + +def _find_running_pids(record: SoftwareRecord) -> list[int]: + process_keys = {_normalize(name) for name in record.process_names if name} + process_keys.add(_normalize(record.display_name)) + process_keys.update(_normalize(alias) for alias in record.aliases if alias) + matched: list[int] = [] + for proc in _list_running_processes(): + if _normalize(proc["stem"]) in process_keys: + try: + matched.append(int(proc["pid"])) + except ValueError: + continue + return sorted(set(matched)) + + +def _find_visible_windows_for_pids(pids: list[int]) -> list[tuple[int, int, str]]: + u32 = user32 + if u32 is None: + return [] + pid_set = set(pids) + results: list[tuple[int, int, str]] = [] + + @ctypes.WINFUNCTYPE(ctypes.c_bool, ctypes.c_void_p, ctypes.c_void_p) + def enum_proc(hwnd, lparam): + if not u32.IsWindowVisible(hwnd): + return True + length = u32.GetWindowTextLengthW(hwnd) + if length <= 0: + return True + buffer = ctypes.create_unicode_buffer(length + 1) + u32.GetWindowTextW(hwnd, buffer, length + 1) + title = buffer.value.strip() + if not title: + return True + pid = ctypes.c_ulong() + u32.GetWindowThreadProcessId(hwnd, ctypes.byref(pid)) + if pid.value in pid_set: + results.append((pid.value, int(hwnd), title)) + return True + + u32.EnumWindows(enum_proc, 0) + return results + + +def _wait_for_pids_exit(pids: list[int], timeout_seconds: float = 5.0) -> list[int]: + deadline = time.time() + timeout_seconds + remaining = set(pids) + while time.time() < deadline and remaining: + current = set() + for proc in _list_running_processes(): + try: + pid = int(proc["pid"]) + except ValueError: + continue + if pid in remaining: + current.add(pid) + remaining = current + if remaining: + time.sleep(0.4) + return sorted(remaining) + + +def _close_windows(windows: list[tuple[int, int, str]]) -> None: + u32 = user32 + if u32 is None: + return + for _, hwnd, _ in windows: + u32.PostMessageW(hwnd, WM_CLOSE, 0, 0) + + +def _force_kill_pids(pids: list[int]) -> list[int]: + killed: list[int] = [] + for pid in pids: + try: + subprocess.run( + ["taskkill", "/PID", str(pid), "/F"], + check=False, + capture_output=True, + text=True, + encoding="gbk", + errors="ignore", + ) + killed.append(pid) + except Exception: + continue + return killed + + +class SoftwareControl(BaseTool): + args_schema: type[BaseModel] = create_model( + "SoftwareControlInput", + action=(str, Field(default="open", description="操作类型：open 表示打开软件，close 表示正常关闭软件，force_close 表示强制关闭软件。")), + software_name=(str, Field(description="要操作的软件名称，例如微信、Visual Studio Code、MATLAB。")), + rescan=(bool, Field(default=False, description="是否强制重新扫描本机软件并刷新索引。")), + force=(bool, Field(default=False, description="兼容旧参数。当前仅当 action=force_close 时才会真正触发强制关闭。")), + ) + name: str = "software_control" + description: str = ( + "操作本机已安装的软件。工具会自动扫描 Windows 开始菜单快捷方式、桌面快捷方式和 App Paths，" + "建立软件索引后根据用户输入做模糊匹配。action=open 时打开软件；action=close 时仅做正常关闭；" + "action=force_close 时才允许在正常关闭失败后强制结束进程。仅当用户明确要求打开、关闭或强制关闭某个本机软件时调用。" + ) + + def _run(self, action: str = "open", software_name: str = "", rescan: bool = False, force: bool = False) -> str: + if os.name != "nt": + return "software_control 目前仅支持 Windows 本机软件操作。" + query = (software_name or "").strip() + if not query: + return "软件名称不能为空。" + action = (action or "open").strip().lower() + rescan = _coerce_bool(rescan) + force = _coerce_bool(force) + if action not in {"open", "close", "force_close"}: + return "action 仅支持 open、close 或 force_close。" + if force and action == "close": + action = "force_close" + force_requested = action == "force_close" + + records = _build_index() if rescan else _load_index() + if not records: + records = _build_index() + + ranked = _rank_matches(query, records) + if not ranked: + records = _build_index() + ranked = _rank_matches(query, records) + if not ranked: + return f"未找到与“{query}”匹配的软件，请换一个更具体的软件名。" + + best_score, best_record = ranked[0] + if best_score < 0.72: + return f"未找到与“{query}”足够接近的软件，请换一个更具体的软件名。" + if len(ranked) > 1 and best_score < 0.88 and (best_score - ranked[1][0]) < 0.08: + candidates = [ + f"{item.display_name} (score={score:.2f}, source={item.source})" + for score, item in ranked[:5] + ] + return ( + f"找到多个相近软件，暂不自动打开。你可以说得更具体一些。\n" + f"候选项：\n" + "\n".join(candidates) + ) + + if action == "open": + try: + opened_target = _open_record(best_record) + except Exception as exc: + return f"已匹配到软件“{best_record.display_name}”，但启动失败：{exc}" + + process_hint = ", ".join(best_record.process_names) if best_record.process_names else "unknown" + return ( + f"已打开软件：{best_record.display_name}\n" + f"匹配得分：{best_score:.2f}\n" + f"启动方式：{best_record.launch_type}\n" + f"启动入口：{opened_target}\n" + f"进程提示：{process_hint}\n" + f"索引文件：{_INDEX_PATH}" + ) + + running_pids = _find_running_pids(best_record) + if not running_pids: + process_hint = ", ".join(best_record.process_names) if best_record.process_names else "unknown" + return ( + f"已匹配到软件：{best_record.display_name}\n" + f"匹配得分：{best_score:.2f}\n" + f"但当前没有发现正在运行的对应进程。\n" + f"进程提示：{process_hint}" + ) + + windows = _find_visible_windows_for_pids(running_pids) + if windows: + _close_windows(windows) + remaining = _wait_for_pids_exit(running_pids, timeout_seconds=5.0) + if not remaining: + titles = "；".join(title for _, _, title in windows[:3]) + return ( + f"已正常关闭软件：{best_record.display_name}\n" + f"匹配得分：{best_score:.2f}\n" + f"关闭方式：窗口关闭请求\n" + f"涉及 PID：{', '.join(str(pid) for pid in running_pids)}\n" + f"窗口标题示例：{titles}" + ) + if not force_requested: + return ( + f"已向软件“{best_record.display_name}”发送正常关闭请求，但仍有进程未退出。\n" + f"剩余 PID：{', '.join(str(pid) for pid in remaining)}\n" + f"如需强制关闭，请明确使用 force_close 动作后重试。" + ) + killed = _force_kill_pids(remaining) + return ( + f"软件“{best_record.display_name}”正常关闭未完全成功，已执行强制关闭。\n" + f"强制结束 PID：{', '.join(str(pid) for pid in killed) if killed else 'none'}" + ) + + if not force_requested: + return ( + f"已找到软件“{best_record.display_name}”的运行进程，但未找到可见主窗口，暂未自动强制关闭。\n" + f"运行 PID：{', '.join(str(pid) for pid in running_pids)}\n" + f"如需强制关闭，请明确使用 force_close 动作后重试。" + ) + killed = _force_kill_pids(running_pids) + return ( + f"软件“{best_record.display_name}”未找到可见主窗口，已按进程强制关闭。\n" + f"强制结束 PID：{', '.join(str(pid) for pid in killed) if killed else 'none'}" + ) + + async def _arun(self, action: str = "open", software_name: str = "", rescan: bool = False, force: bool = False) -> str: + return self._run(action, software_name, rescan, force) diff --git a/src/agent/utils/__pycache__/__init__.cpython-313.pyc b/src/agent/utils/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 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+ """ os.makedirs(_DB_DIR, exist_ok=True) os.makedirs(MULTI_INPUTS_ROOT, exist_ok=True) # 迁移旧数据库 @@ -72,6 +97,18 @@ ensure_dirs_and_migrate_db() # ---------- 历史记录操作 ---------- def get_history(session_id: str) -> SQLChatMessageHistory: + """获取指定会话的 LangChain SQL 聊天历史对象。 + + 输入: + session_id: 会话唯一标识。 + + 输出: + SQLChatMessageHistory 实例，底层为 CHAT_HISTORY_DB_FS。 + + 系统定位: summarizer、save_messages_to_history 等读写对话记录。 + + 可扩展性: 可改为按 agent_name 分 session_id 命名空间。 + """ return SQLChatMessageHistory( session_id=session_id, connection="sqlite:///" + CHAT_HISTORY_DB_FS.replace("\\", "/"), @@ -79,6 +116,20 @@ def get_history(session_id: str) -> SQLChatMessageHistory: # ---------- session_meta 元数据管理 ---------- def get_session_meta(session_id: str, agent_name: str) -> dict: + """读取会话在指定 Agent 下的压缩元数据。 + + 输入: + session_id: 会话 ID。 + agent_name: Agent 名称（如 main）。 + + 输出: + dict: cursor_index, compressed_content, last_total_tokens； + 无记录时返回默认值（cursor=-1, 空摘要, tokens=0）。 + + 系统定位: summarizer 判断压缩档位与 runtime 更新 token 用量。 + + 可扩展性: 可增加 compress_version、embedding_id 等字段。 + """ conn = sqlite3.connect(CHAT_HISTORY_DB_FS) cur = conn.execute( "SELECT cursor_index, compressed_content, last_total_tokens FROM session_meta WHERE session_id=? AND agent_name=?", @@ -96,6 +147,20 @@ def get_session_meta(session_id: str, agent_name: str) -> dict: def update_session_meta(session_id: str, agent_name: str, cursor_index: int, compressed_content: str, last_total_tokens: int) -> None: + """写入或更新 session_meta 行（UPSERT）。 + + 输入: + session_id, agent_name: 联合主键。 + cursor_index: 已压缩消息截止索引。 + compressed_content: LLM 生成的历史摘要文本。 + last_total_tokens: 上一轮模型 reported total_tokens。 + + 输出: 无。 + + 系统定位: 压缩后与 execute_agent 记录 token 时调用。 + + 可扩展性: 可记录压缩时间戳便于调试与回滚。 + """ conn = sqlite3.connect(CHAT_HISTORY_DB_FS) conn.execute(""" INSERT INTO session_meta (session_id, agent_name, cursor_index, compressed_content, last_total_tokens) @@ -110,12 +175,37 @@ def update_session_meta(session_id: str, agent_name: str, # ---------- 工具函数 ---------- def _get_messages_since_cursor(history: SQLChatMessageHistory, cursor_index: int) -> List[BaseMessage]: + """返回游标之后未纳入摘要的消息切片。 + + 输入: + history: 聊天历史对象。 + cursor_index: 已压缩截止索引；-1 表示全部未压缩。 + + 输出: + BaseMessage 列表。 + + 系统定位: summarizer 构建发给模型的近期消息。 + + 可扩展性: 私有函数；对外行为通过 summarizer 暴露。 + """ all_msgs = history.messages if cursor_index < 0 or cursor_index >= len(all_msgs) - 1: return all_msgs return all_msgs[cursor_index + 1:] def _count_turns(messages: List[BaseMessage]) -> int: + """统计 human + ai/assistant 配对的对话轮数。 + + 输入: + messages: 消息列表。 + + 输出: + 整数轮数。 + + 系统定位: 轻量压缩时计算需保留的 CURRENT_TURNS 轮。 + + 可扩展性: 可将 tool 消息纳入轮次定义。 + """ turns = 0 i = 0 n = len(messages) @@ -132,7 +222,19 @@ def _count_turns(messages: List[BaseMessage]) -> int: # ---------- 压缩函数 ---------- def _lightweight_compress(llm, messages_to_compress: List[BaseMessage]) -> str: - """轻量压缩：保留关键事实、实体、决策，删除冗余""" + """轻量压缩：用 LLM 摘要待压缩消息，保留关键事实与决策。 + + 输入: + llm: 用于压缩的语言模型。 + messages_to_compress: 待压缩的 BaseMessage 列表。 + + 输出: + 摘要字符串。 + + 系统定位: summarizer 在 token 超过 MINI_COMPRESS_RATE 时触发。 + + 可扩展性: 可换用更小/专用摘要模型降低成本。 + """ prompt = ChatPromptTemplate.from_messages([ ("system", SUMMARY_SYSTEM_PROMPT), ("human", "请对以下对话进行轻量压缩，保留核心事实、实体、决策和对话流向。\n\n{chat}"), @@ -147,7 +249,20 @@ def _lightweight_compress(llm, messages_to_compress: List[BaseMessage]) -> str: return result.content def _hard_compress(llm, compressed_existing: str, new_chat_text: str) -> str: - """全量压缩：将已有压缩摘要和新对话内容融合为最终摘要""" + """全量压缩：合并已有摘要与游标后新对话为单一摘要。 + + 输入: + llm: 压缩用模型。 + compressed_existing: 先前摘要（可为空）。 + new_chat_text: 游标后对话的纯文本拼接。 + + 输出: + 融合后的最终摘要字符串。 + + 系统定位: token 超过 HARD_COMPRESS_RATE 时触发，游标移至末尾。 + + 可扩展性: 可分块摘要再合并，应对超长历史。 + """ prompt = ChatPromptTemplate.from_messages([ ("system", SUMMARY_SYSTEM_PROMPT), ("human", "这是之前的对话摘要：\n{previous_summary}\n\n以及后续对话内容：\n{new_chat}\n\n请生成包含所有信息的最终摘要，保留重要事实和决策。"), @@ -168,16 +283,25 @@ def summarizer( user_content: str, llm: Any ) -> Tuple[List[BaseMessage], bool]: - """ - 根据上下文窗口与上一轮 token 使用量，动态压缩历史消息并返回完整消息列表。 - :param session_id: 会话ID - :param agent_name: Agent 名称 - :param system_prompt: 系统提示词 - :param user_content: 当前用户输入文本（纯文本或已处理的多模态文本） - :param llm: 语言模型实例（用于压缩） - :return: (messages, context_exceeded) - messages: 构建好的消息列表 - context_exceeded: 是否超过 90% 窗口，需要提示新建会话 + """根据 token 占用动态压缩历史并组装本轮 LLM 输入消息列表。 + + 输入: + session_id: 会话 ID。 + agent_name: Agent 名称，用于 session_meta。 + system_prompt: 系统提示词（可追加历史摘要）。 + user_content: 当前用户输入文本。 + llm: 用于轻量/全量压缩的模型实例。 + + 输出: + (messages, context_exceeded): + - messages: SystemMessage + 未压缩近期消息 + 当前 HumanMessage + - context_exceeded: True 表示已超过 90% 窗口，应提示新建会话 + + 系统定位: + execute_agent 在调用 graph 前的上下文管理核心。 + + 可扩展性: + 阈值由 env_utils 配置；可接入 token 精确计数替代 last_total_tokens 估计。 """ meta = get_session_meta(session_id, agent_name) cursor_index = meta["cursor_index"] @@ -260,7 +384,20 @@ def summarizer( # ---------- 辅助函数 ---------- def assistant_text(value) -> str: - """把网关返回的 content、reasoning 等收成一段可见文本。""" + """将 LLM 响应的 content/reasoning 等多形态字段归一为可见纯文本。 + + 输入: + value: str | list | dict | None 等网关返回结构。 + + 输出: + 去首尾空白后的字符串；无法解析时 str(value) 或 JSON。 + + 系统定位: + runtime、llm、nodes 统一提取助手回复与 reasoning。 + + 可扩展性: + 新增厂商字段时在 key 列表中补充即可。 + """ if value is None: return "" if isinstance(value, str): @@ -290,13 +427,30 @@ def assistant_text(value) -> str: class Documents_process: - """文件分类与内容提取工具集""" + """多格式用户附件的分类、解析与多模态内容提取。 + + 系统定位: + core/llm._preprocess_messages 处理用户上传文件时调用 process()。 + + 可扩展性: + 新增格式：扩展 DOC_EXT_MAP 并实现 process_xxx 方法。 + """ @staticmethod def doc_classify(file_path: str) -> str: - """ - 根据扩展名和 MIME 类型返回内部处理类型标签。 - 可能的返回值：'plain_text'、'docx'、'pdf'、'image'、'tabular'。 + """根据扩展名与 MIME 返回内部类型标签。 + + 输入: + file_path: 本地文件路径。 + + 输出: + plain_text | docx | pdf | image | tabular。 + + 系统定位: + process() 分发入口。 + + 可扩展性: + 不支持类型抛出 ValueError，便于上层捕获并提示用户。 """ ext = pathlib.Path(file_path).suffix.lower() # 先检查文档映射表 @@ -320,12 +474,25 @@ class Documents_process: # ---------- 各类文件的具体处理方法 ---------- @staticmethod def process_plain_text(file_path: str) -> tuple[str, list]: + """读取 UTF-8 纯文本文件。 + + 输入: file_path 本地路径。 + 输出: (文本内容, 空图片列表)。 + 系统定位: plain_text 类型处理。 + 可扩展性: 可增加编码探测（chardet）。 + """ with open(file_path, "r", encoding="utf-8") as f: return f.read(), [] @staticmethod def process_docx(file_path: str) -> tuple[str, list]: - """按文档顺序提取文本、图片和表格，并将短标题与图表合并。""" + """按文档顺序提取 Word 文本、内嵌图片与表格。 + + 输入: file_path .docx 路径。 + 输出: (带 N、 标记的文本, 图片字节列表)。 + 系统定位: 用户上传 docx 附件解析。 + 可扩展性: 依赖 python-docx；可扩展页眉页脚、脚注。 + """ try: from docx import Document from docx.oxml.ns import qn @@ -434,6 +601,13 @@ class Documents_process: @staticmethod def process_pdf(file_path: str) -> tuple[str, list]: + """提取 PDF 文本与内嵌图片占位符。 + + 输入: file_path .pdf 路径。 + 输出: (分页拼接文本含 N, 图片字节列表)。 + 系统定位: PDF 附件入模前解析。 + 可扩展性: 依赖 pypdf；扫描件可接 OCR 管线。 + """ try: from pypdf import PdfReader except ImportError: @@ -468,10 +642,12 @@ class Documents_process: @staticmethod def process_tabular(file_path: str) -> tuple[str, list]: - """ - 处理 CSV 和 Excel(.xlsx) 文件。 - 将所有 sheet 的内容转化为
SheetName\n内容
格式的字符串。 - 图片列表固定为空。 + """将 CSV / xlsx 转为 标记的表格文本。 + + 输入: file_path .csv 或 .xlsx。 + 输出: (多 sheet 表格字符串, 空图片列表)。 + 系统定位: 结构化数据附件供 LLM 阅读。 + 可扩展性: 可支持 xls、parquet；大表可采样行数。 """ try: import csv @@ -507,12 +683,26 @@ class Documents_process: @staticmethod def process_image(file_path: str) -> tuple[str, list]: + """读取图片文件为字节，文本部分为空。 + + 输入: file_path 图片路径。 + 输出: ("", [图片字节])。 + 系统定位: 与 llm 中 image_url 分支配合多模态输入。 + 可扩展性: 可在此预调用 resize_image_if_needed。 + """ with open(file_path, "rb") as f: return "", [f.read()] # ---------- 统一处理入口 ---------- @staticmethod def process(file_path: str) -> tuple[str, list]: + """统一附件处理入口：分类后分发到对应 process_* 方法。 + + 输入: file_path 本地文件路径。 + 输出: (文本内容, 图片字节列表)。 + 系统定位: llm._preprocess_messages 文件 part 处理。 + 可扩展性: 新增类型在 doc_classify 与分支中注册。 + """ file_type = Documents_process.doc_classify(file_path) if file_type == "plain_text": @@ -527,13 +717,35 @@ class Documents_process: return Documents_process.process_tabular(file_path) else: raise ValueError(f"未处理的文件类型: {file_type}") -# 文本分块工具 + +###### 文本分块工具(多线程语音生成TTS) ###### SENT_SPLIT_PATTERN = re.compile(r'(?<=[。！？；，、])') REMOVE_PUNCT_PATTERN = re.compile(r'[。！？；，、：；“”（）《》【】\-\_\—\~\.\,\!\?\s·]') def effective_length(text: str) -> int: + """计算去除标点空白后的有效字符长度。 + + 输入: text 字符串。 + 输出: 整数长度。 + 系统定位: split_text 合并短句时的阈值判断。 + 可扩展性: 可按语言调整 REMOVE_PUNCT_PATTERN。 + """ return len(REMOVE_PUNCT_PATTERN.sub('', text)) + def split_text(text: str, min_effective_chars: int = 20) -> list[str]: + """按中文标点分句并合并过短片段，供 TTS 分块请求。 + + 输入: + text: 待切分文本。 + min_effective_chars: 每块最少有效字符数。 + + 输出: + 字符串列表，每块适合单次 TTS 调用。 + + 系统定位: core/llm._text_to_audio 并行合成前分块。 + + 可扩展性: 可改为按 token 或时长分块。 + """ # 先按标点粗分 raw_chunks = [c.strip() for c in SENT_SPLIT_PATTERN.split(text) if c.strip()] if not raw_chunks: @@ -555,7 +767,18 @@ def split_text(text: str, min_effective_chars: int = 20) -> list[str]: return merged def merge_wavs(audio_parts: list[bytes]) -> bytes: - """将多个 WAV 字节串按顺序拼接成一个 WAV""" + """将多段 WAV 二进制按顺序拼接为单个 WAV 文件字节。 + + 输入: + audio_parts: WAV 格式字节列表（采样率等参数以首段为准）。 + + 输出: + 合并后的 WAV bytes。 + + 系统定位: llm._text_to_audio 多块 TTS 结果合并。 + + 可扩展性: 参数不一致时可增加重采样；可支持 mp3 拼接。 + """ if len(audio_parts) == 1: return audio_parts[0] import io, wave diff --git a/src/agent/utils/database_utils.py b/src/agent/utils/database_utils.py index 6b45d36..d1197c1 100644 --- a/src/agent/utils/database_utils.py +++ b/src/agent/utils/database_utils.py @@ -1,3 +1,16 @@ +"""PostgreSQL 元数据查询与只读 SQL 执行（供 Agent 工具层使用）。 + +系统定位: + 为数据库相关 Tool 提供安全封装的 schema introspection 与 SELECT 查询， + 结果统一 JSON 序列化便于模型消费。 + +安全策略: + execute_query / validate_query 仅允许 SELECT 与 WITH 语句。 + +可扩展性: + - 可配置 fetch 行数上限、连接池参数 + - 注释掉的 rename_table / update_table_comment 可按需启用并加权限控制 +""" from sqlalchemy import create_engine, inspect, text import json from typing import Optional, List @@ -5,8 +18,15 @@ from uuid import UUID from datetime import datetime class JSONEncoder(json.JSONEncoder): - """自定义JSON编码器，处理非标准序列化类型""" + """扩展 json.JSONEncoder，支持 UUID、datetime 及带 __dict__ 的对象。 + + 系统定位: PostgreSQL_Manager 各方法 dumps 时的默认编码器。 + + 可扩展性: 可增加 Decimal、date、Enum 等类型分支。 + """ + def default(self, obj): + """将 UUID、datetime 及带 __dict__ 的对象转为 JSON 可编码值。""" if isinstance(obj, UUID): return str(obj) elif isinstance(obj, datetime): @@ -16,7 +36,20 @@ class JSONEncoder(json.JSONEncoder): return super().default(obj) def make_json_serializable(obj): - """递归转换对象为JSON序列化格式""" + """递归将嵌套结构转为 JSON 可序列化形式。 + + 输入: + obj: dict | list | UUID | datetime | 任意对象。 + + 输出: + 仅含 JSON 兼容类型的树结构。 + + 系统定位: + get_table_schema / get_table_comments 返回前清洗 SQLAlchemy 反射结果。 + + 可扩展性: + 与 JSONEncoder.default 保持类型处理一致。 + """ if isinstance(obj, dict): return {k: make_json_serializable(v) for k, v in obj.items()} elif isinstance(obj, (list, tuple)): @@ -31,21 +64,46 @@ def make_json_serializable(obj): return obj class PostgreSQL_Manager: + """PostgreSQL 连接池管理与只读元数据/查询 API。 + + 系统定位: + Agent 数据库工具的后端实现类。 + + 可扩展性: + 可注入 engine、增加读写分离、审计日志。 + """ + def __init__(self, connection_params): - """ - PostgreSQL数据库管理类,提供连接和操作数据库的功能 - Args: - connection_params (dict): 连接参数字符串，格式为： - postgresql+psycopg2://用户名:密码@主机:端口/数据库名 - 本机默认为:postgresql+psycopg2://postgres:12345678@localhost:5432/agent_controller + """创建 SQLAlchemy 引擎与连接池。 + + 输入: + connection_params: SQLAlchemy URL，如 + postgresql+psycopg2://user:pass@host:5432/dbname + + 输出: + 无（设置 self.engine）。 + + 系统定位: + 工具初始化时构造单例或按请求构造。 + + 可扩展性: + pool_size、pool_recycle 可改为构造参数或环境变量。 """ self.engine = create_engine(connection_params, pool_size=5, pool_recycle=7200) def get_table_names(self) -> list[str]: - """ - 获取数据库中的所有表名 - Returns: - list: 数据库中的表名列表 + """列出当前库中所有表名。 + + 输入: 无（使用 self.engine）。 + + 输出: + 表名字符串列表；异常时返回空列表并打印错误。 + + 系统定位: + Agent 探索库结构的第一步。 + + 可扩展性: + 可过滤 schema、排除系统表。 """ try: # 使用SQLAlchemy的inspect功能获取数据库表名 @@ -56,12 +114,19 @@ class PostgreSQL_Manager: return [] def get_table_comments(self, table_name: str = None) -> str: - """ - 获取指定表/所有表的注释信息,并整理成JSON格式 - Args: - table_name (str, optional): 表名,如果为None则返回所有表 - Returns: - str: JSON格式的表的注释信息 + """获取表 COMMENT 元数据并返回 JSON 字符串。 + + 输入: + table_name: 指定表名；None 表示全部表。 + + 输出: + JSON 字符串；错误时含 error 字段。 + + 系统定位: + 供 LLM 理解业务表含义。 + + 可扩展性: + 可合并列级 comment。 """ try: inspector = inspect(self.engine) @@ -80,12 +145,19 @@ class PostgreSQL_Manager: return json.dumps(error_msg, ensure_ascii=False, indent=2) def get_table_schema(self, table_names: Optional[List[str]] = None) -> str: - """ - 获取指定表/所有表的schema信息,并整理成JSON格式 - Args: - table_names (List[str], optional): 表名列表,如果为None则返回所有表 - Returns: - str: JSON格式的表的schema信息 + """反射表结构（列、主键、外键、索引）并返回 JSON。 + + 输入: + table_names: 表名列表；None 表示所有表。 + + 输出: + JSON 字符串，每表含 columns/primary_keys/foreign_keys/indexes。 + + 系统定位: + Text-to-SQL 或数据分析前的 schema 上下文。 + + 可扩展性: + 可增加视图、物化视图、枚举类型信息。 """ try: inspector = inspect(self.engine) @@ -111,12 +183,19 @@ class PostgreSQL_Manager: return json.dumps(error_msg, ensure_ascii=False, indent=2) def execute_query(self, query: str) -> str: - """ - 执行任意SQL查询,并返回结果 - Args: - query (str): SQL查询语句 - Returns: - str: JSON格式的查询结果或错误信息 + """执行只读 SQL 并返回最多 100 行结果的 JSON。 + + 输入: + query: 必须以 select 或 with 开头（大小写不敏感）。 + + 输出: + JSON 结果数组或 error/message 对象。 + + 系统定位: + Agent 安全查询数据；禁止 DML/DDL。 + + 可扩展性: + fetch 行数、超时、只读事务隔离级别可配置。 """ # 安全检查，防止执行危险的SQL语句，仅允许select和with查询语句 if not query.strip().lower().startswith(('select', 'with')): @@ -145,12 +224,19 @@ class PostgreSQL_Manager: return json.dumps(error_msg, ensure_ascii=False, indent=2) def validate_query(self, query: str) -> str: - """ - 验证SQL查询语句的合法性 - Args: - query (str): SQL查询语句 - Returns: - bool: 查询语句是否合法 + """用 EXPLAIN（PostgreSQL）或试执行验证 SQL 合法性。 + + 输入: + query: 只读 SQL 字符串。 + + 输出: + JSON：成功含 message；失败含 error。 + + 系统定位: + 执行前的预检，减少无效查询。 + + 可扩展性: + 非 PG 方言可切换验证策略。 """ if not query or not query.strip(): error_msg = {"error": f"Error occurred while validating query: Query is must not be empty."} diff --git a/src/agent/utils/env_utils.py b/src/agent/utils/env_utils.py index fb675f3..6b8dad4 100644 --- a/src/agent/utils/env_utils.py +++ b/src/agent/utils/env_utils.py @@ -1,11 +1,41 @@ -import os -from typing import Dict, Tuple +"""环境变量加载与全局配置常量。 -from dotenv import load_dotenv -load_dotenv(override=True) +系统定位: + 项目配置入口，模块导入时从 ``core/env_config.json`` 加载配置， + 并向 ``core/llm``、``utils/agent_utils``、``utils/image_utils`` 等模块 + 提供 LLM、压缩策略、RAG、浏览器等运行时参数。 + +可扩展性: + - 新增配置项时，在此定义常量并注明默认值与类型转换逻辑。 + - 可将 ``BROWSER_MAP`` 扩展为从环境变量或配置文件动态加载。 +""" +import json +import os +from pathlib import Path +from typing import Dict, Tuple def _get_env(name: str, default: str | None = None, cast: type | None = None, required: bool = False): + """读取并可选类型转换单个环境变量。 + + 功能描述: + 从 ``os.environ`` 取值，支持必填校验与类型转换。 + + 输入: + name: 环境变量名。 + default: 缺省值；与 required=True 互斥使用。 + cast: 转换类型（如 int、float）。 + required: 为 True 且变量缺失时抛出 ``EnvironmentError``。 + + 输出: + 原始字符串或 cast 后的值；缺失且无 default 时为 None。 + + 系统定位: + 本模块内部私有辅助，所有模块级常量均通过此函数初始化。 + + 可扩展性: + 可扩展 bool 解析、路径规范化、密钥脱敏日志等。 + """ value = os.getenv(name, default) if value is None and required: raise EnvironmentError(f"Missing required environment variable: {name}") @@ -17,25 +47,84 @@ def _get_env(name: str, default: str | None = None, cast: type | None = None, re raise ValueError(f"Invalid value for {name}: {value}") from exc -LLM_BASE_URL = _get_env("LLM_BASE_URL", required=True) -LLM_API_KEY = _get_env("LLM_API_KEY", required=True) -LLM_MODEL = _get_env("LLM_MODEL", required=True) -LLM_TIMEOUT = _get_env("LLM_TIMEOUT", default="60", cast=float) -# LLM_REQUESTS_PER_SECOND = _get_env("LLM_REQUESTS_PER_SECOND", default="10", cast=float) -# LLM_MAX_BUCKET_SIZE = _get_env("LLM_MAX_BUCKET_SIZE", default="10", cast=int) -# LLM_CHECK_EVERY_N_SECONDS = _get_env("LLM_CHECK_EVERY_N_SECONDS", default="0.1", cast=float) -# ASR_TTS_BASE_URL = _get_env("ASR_TTS_BASE_URL") -TTS_BASE_URL = _get_env("TTS_BASE_URL") -ASR_BASE_URL = _get_env("ASR_BASE_URL") -RAG_BASE_URL = _get_env("RAG_BASE_URL") -LLM_CONTEXT_WINDOW = int(os.getenv("LLM_CONTEXT_WINDOW", 131072)) -MINI_COMPRESS_RATE = float(os.getenv("MINI_COMPRESS_RATE", 0.5)) -HARD_COMPRESS_RATE = float(os.getenv("HARD_COMPRESS_RATE", 0.75)) -CURRENT_TURNS = int(os.getenv("CURRENT_TURNS", 4)) -IMG_SIZE = int(os.getenv("IMG_SIZE", 768)) -RAG_CHUNK_SIZE=int(os.getenv("RAG_CHUNK_SIZE", 500)) -RAG_CHUNK_OVERLAP=int(os.getenv("RAG_CHUNK_OVERLAP", 50)) +def _refresh_globals(): + """刷新模块级常量（热重载用）。""" + global LLM_BASE_URL, LLM_API_KEY, LLM_MODEL, LLM_TIMEOUT + global TTS_BASE_URL, ASR_BASE_URL, RAG_BASE_URL + global LLM_CONTEXT_WINDOW, MINI_COMPRESS_RATE, HARD_COMPRESS_RATE, CURRENT_TURNS + global IMG_SIZE, RAG_CHUNK_SIZE, RAG_CHUNK_OVERLAP, GROUNDING_WIDTH, GROUNDING_HEIGHT + LLM_BASE_URL = _get_env("LLM_BASE_URL", required=True) + LLM_API_KEY = _get_env("LLM_API_KEY", required=True) + LLM_MODEL = _get_env("LLM_MODEL", required=True) + LLM_TIMEOUT = _get_env("LLM_TIMEOUT", default="60", cast=float) + TTS_BASE_URL = _get_env("TTS_BASE_URL") + ASR_BASE_URL = _get_env("ASR_BASE_URL") + RAG_BASE_URL = _get_env("RAG_BASE_URL") + LLM_CONTEXT_WINDOW = int(os.getenv("LLM_CONTEXT_WINDOW", 131072)) + MINI_COMPRESS_RATE = float(os.getenv("MINI_COMPRESS_RATE", 0.5)) + HARD_COMPRESS_RATE = float(os.getenv("HARD_COMPRESS_RATE", 0.75)) + CURRENT_TURNS = int(os.getenv("CURRENT_TURNS", 4)) + IMG_SIZE = int(os.getenv("IMG_SIZE", 768)) + RAG_CHUNK_SIZE = int(os.getenv("RAG_CHUNK_SIZE", 500)) + RAG_CHUNK_OVERLAP = int(os.getenv("RAG_CHUNK_OVERLAP", 50)) + GROUNDING_WIDTH = int(os.getenv("GROUNDING_WIDTH", 1000)) + GROUNDING_HEIGHT = int(os.getenv("GROUNDING_HEIGHT", 1000)) + + +def _load_config() -> dict: + """加载 env_config.json，不存在时回退到 .env 文件。 + + 优先级: env_config.json > .env > 默认值 + """ + _config_path = Path(__file__).resolve().parents[1] / "core" / "env_config.json" + + if _config_path.is_file(): + try: + with open(_config_path, "r", encoding="utf-8") as f: + config = json.load(f) + if isinstance(config, dict): + # 将非 models 的值写入 os.environ + for key, value in config.items(): + if key != "models" and value is not None: + os.environ[key] = str(value) + # 从 models 列表中提取第一个模型的 LLM 参数 + models = config.get("models") + if isinstance(models, list) and models: + m = models[0] + if m.get("api_key"): os.environ["LLM_API_KEY"] = str(m["api_key"]) + if m.get("base_url"): os.environ["LLM_BASE_URL"] = str(m["base_url"]) + if m.get("model"): os.environ["LLM_MODEL"] = str(m["model"]) + if m.get("timeout") is not None: os.environ["LLM_TIMEOUT"] = str(m["timeout"]) + _refresh_globals() + return config + except (json.JSONDecodeError, OSError): + pass + + # 回退：读取 .env 文件 + _env_path = Path(__file__).resolve().parents[3] / ".env" + if _env_path.is_file(): + try: + with open(_env_path, "r", encoding="utf-8") as f: + for line in f: + line = line.strip() + if not line or line.startswith("#") or "=" not in line: + continue + key, _, value = line.partition("=") + key = key.strip() + value = value.strip().strip("'").strip('"') + if key not in os.environ: # 不覆盖已设置的值 + os.environ[key] = value + except OSError: + pass + + return {} + + +_load_config() +_refresh_globals() # 确保无论配置加载成功与否，都有默认值 + +# ---------- 浏览器工具映射（内部名 -> 可执行名, 显示名） ---------- BROWSER_MAP: Dict[str, Tuple[str, str]] = { "edge": ("microsoft-edge", "Microsoft Edge"), "chrome": ("chrome", "Google Chrome"), diff --git a/src/agent/utils/image_utils.py b/src/agent/utils/image_utils.py index 55e92b4..58e0a88 100644 --- a/src/agent/utils/image_utils.py +++ b/src/agent/utils/image_utils.py @@ -1,4 +1,13 @@ -"""图片缩放、体积控制与 OpenAI 风格多模态 image_url 片段（供 LLM / Agent 使用）。""" +"""图片缩放、体积控制与 OpenAI 风格多模态 image_url 片段构建。 + +系统定位: + 多模态输入适配层，被 ``core/llm``、``core/nodes``、``utils/agent_utils`` + 调用，将本地路径或内存字节转为 LLM 可消费的 data URL 内容块。 + +可扩展性: + - 可接入 WebP/AVIF 编码、按模型 token 预算动态调整 ``max_size``。 + - ``SupportsChatCompletion`` 协议可扩展为统一多模态网关接口。 +""" import base64 import io import os @@ -7,17 +16,37 @@ from PIL import Image from agent.utils.env_utils import IMG_SIZE + class SupportsChatCompletion(Protocol): - """具备 ``chat_completion(messages, **kwargs)`` 的 LLM（如 ``ChatQwen35OpenAI``）。""" + """具备 chat_completion 方法的 LLM 协议（如 ChatQwen）。 + + 系统定位: 类型提示用，便于静态检查与依赖注入。 + + 可扩展性: 可补充 stream、embed 等方法签名。 + """ def chat_completion(self, messages: list[Any], **kwargs: Any) -> Any: ... + # 小写扩展名（含点），用于判断路径是否为支持的栅格图 IMAGE_FILE_EXTENSIONS = frozenset({".png", ".jpg", ".jpeg", ".bmp", ".gif", ".webp"}) def guess_image_mime(extension_without_dot: str) -> str: - """根据扩展名返回 image/* MIME，未知则回退为 image/jpeg。""" + """根据文件扩展名推断 image/* MIME 类型。 + + 输入: + extension_without_dot: 扩展名，可带或不带点（如 png、.jpg）。 + + 输出: + MIME 字符串；未知扩展名回退为 ``image/jpeg``。 + + 系统定位: + ``image_path_to_openai_image_url_part`` 构造 data URL 时使用。 + + 可扩展性: + 可改用 ``mimetypes`` 或 magic bytes 双重校验。 + """ ext = extension_without_dot.lower().lstrip(".") if ext in ("png", "jpg", "jpeg", "gif", "bmp", "webp"): return "image/jpeg" if ext == "jpg" else f"image/{ext}" @@ -25,10 +54,25 @@ def guess_image_mime(extension_without_dot: str) -> str: def resize_image_if_needed(image_path_or_bytes: str | bytes, *, max_size: int = IMG_SIZE) -> bytes: - """ - 控制图片体积，避免 data-url base64 触发超长 token。 - - 若图片尺寸过大，等比例缩放（默认最大边 max_size） - - 优先压成 JPEG（透明图保底转白底 RGB） + """等比缩放并压缩图片，控制 base64 体积与 token 消耗。 + + 功能描述: + - 超长边超过 max_size 时等比缩放（LANCZOS） + - RGBA/LA 透明图转白底后优先输出 JPEG + - JPEG quality=70，PNG 保留透明通道场景外的优化输出 + + 输入: + image_path_or_bytes: 本地路径或原始图片字节。 + max_size: 最大边长像素，默认来自环境变量 IMG_SIZE。 + + 输出: + 处理后的图片二进制（JPEG 或 PNG）。 + + 系统定位: + 所有图片入模前的统一预处理入口。 + + 可扩展性: + 可按用途（vision / thumbnail）传入不同 quality 与 max_size 配置。 """ if isinstance(image_path_or_bytes, str): img = Image.open(image_path_or_bytes) @@ -77,16 +121,42 @@ def resize_image_if_needed(image_path_or_bytes: str | bytes, *, max_size: int = def image_to_base64(image_content: str | bytes) -> str | None: - """将图片路径或原始字节转为 base64 字符串；失败返回 None。""" + """将图片路径或字节转为 base64 字符串。 + + 输入: + image_content: 文件路径或原始字节。 + + 输出: + base64 编码字符串；处理失败返回 None。 + + 系统定位: + 供路径类 image_url 块与简单编码场景使用。 + + 可扩展性: + 失败时可返回错误码或记录日志，便于排查。 + """ try: processed = resize_image_if_needed(image_content) return base64.b64encode(processed).decode("utf-8") - except Exception as e: + except Exception: return None def _mime_from_magic(data: bytes) -> str: - """根据压缩后图片文件头推断 MIME，用于内存字节构造 data URL。""" + """根据文件头魔数推断图片 MIME。 + + 输入: + data: 压缩后的图片字节。 + + 输出: + ``image/jpeg`` | ``image/png`` | ``image/gif``；默认 jpeg。 + + 系统定位: + 内存字节构造 data URL 时，扩展名不可用时的回退。 + + 可扩展性: + 可补充 WebP、BMP 等魔数识别。 + """ if len(data) >= 2 and data[:2] == b"\xff\xd8": return "image/jpeg" if len(data) >= 8 and data[:8] == b"\x89PNG\r\n\x1a\n": @@ -101,10 +171,26 @@ def image_bytes_to_openai_image_url_part( *, detail: str | None = None, ) -> dict | None: - """将内存中的图片字节（如截屏 PNG）转为 OpenAI 风格的 image_url 块；失败返回 None。""" + """将内存图片字节转为 OpenAI Chat Completions 的 image_url 内容块。 + + 输入: + image_bytes: 原始或已编码图片字节（如工具截屏 PNG）。 + detail: 可选，传给模型的 detail 级别（auto/low/high）。 + + 输出: + ``{"type": "image_url", "image_url": {"url": "data:..."}}``； + 失败返回 None。 + + 系统定位: + ``core/nodes.process_tool_artifact`` 将工具截图注入对话； + ``core/llm`` 处理附件内嵌图片。 + + 可扩展性: + 可支持 URL 引用模式，避免大 base64 内联。 + """ try: processed = resize_image_if_needed(image_bytes) - except Exception as e: + except Exception: return None mime = _mime_from_magic(processed) b64_str = base64.b64encode(processed).decode("utf-8") @@ -120,9 +206,20 @@ def image_path_to_openai_image_url_part( *, detail: str | None = None, ) -> dict | None: - """ - 从本地图片路径构造 OpenAI Chat Completions 的 image_url 内容块。 - 无法编码时返回 None。 + """从本地图片路径构造 OpenAI 风格 image_url 内容块。 + + 输入: + file_path: 本地图片绝对或相对路径。 + detail: 可选 detail 级别。 + + 输出: + 与 ``image_bytes_to_openai_image_url_part`` 相同结构；失败 None。 + + 系统定位: + ``core/llm._process_image_content`` 将用户上传图片路径转为模型输入。 + + 可扩展性: + 可增加文件大小上限校验、病毒扫描钩子。 """ b64_str = image_to_base64(file_path) if not b64_str: diff --git a/src/agent/utils/log_utils.py b/src/agent/utils/log_utils.py index b36cb9e..f9c138e 100644 --- a/src/agent/utils/log_utils.py +++ b/src/agent/utils/log_utils.py @@ -1,3 +1,13 @@ +"""基于 loguru 的全局日志封装。 + +系统定位: + 提供项目级单例 ``log``，供各模块统一输出结构化调试日志； + 默认输出到 stdout，级别 DEBUG。 + +可扩展性: + - 可在 ``MyLogger.__init__`` 中增加文件轮转、按模块过滤等 handler。 + - 可封装 ``log.bind(session_id=...)`` 以支持请求级追踪。 +""" import sys from loguru import logger @@ -5,7 +15,19 @@ from loguru import logger class MyLogger: + """loguru 日志器包装类，统一格式与输出目标。""" + def __init__(self): + """初始化并配置 loguru：移除默认 handler，添加 stdout DEBUG 输出。 + + 输入: 无。 + + 输出: 无（副作用：配置全局 logger）。 + + 系统定位: 模块加载时由 ``log = MyLogger().get_logger()`` 实例化一次。 + + 可扩展性: 可增加环境变量控制日志级别、JSON 格式输出等。 + """ self.logger = logger self.logger.remove() self.logger.add( @@ -21,6 +43,16 @@ class MyLogger: ) def get_logger(self): + """返回已配置的 loguru Logger 实例。 + + 输入: 无。 + + 输出: loguru.Logger。 + + 系统定位: 供模块导出 ``log`` 单例。 + + 可扩展性: 可改为返回带默认 context 的 bound logger。 + """ return self.logger diff --git a/src/agent/utils/tool_call_utils.py b/src/agent/utils/tool_call_utils.py new file mode 100644 index 0000000..21f2dfc --- /dev/null +++ b/src/agent/utils/tool_call_utils.py @@ -0,0 +1,121 @@ +"""工具调用规范化与人工编辑解析工具。 + +系统定位: + 位于 Agent 工具链与人工确认（HITL）之间的适配层，被 ``core/routing``、 + ``core/runtime``、``core/approvals`` 调用，负责将 LangChain 工具调用对象 + 统一为字典格式，并解析用户在 UI 中编辑后的指令。 + +可扩展性: + - 新增工具参数格式时，可在 ``normalize_tool_call`` 中扩展 args 解析逻辑。 + - ``parse_edited_instruction`` 可支持更多用户输入格式（如 YAML、键值对等）。 +""" +from __future__ import annotations + +import json +from typing import Any + + +def json_safe(value: Any) -> Any: + """将任意 Python 对象递归转换为 JSON 可序列化结构。 + + 功能描述: + 尝试直接序列化；若遇到不可序列化类型，则递归处理 dict/list/tuple， + 其余类型转为字符串。 + + 输入: + value: 任意 Python 对象。 + + 输出: + JSON 兼容的 dict / list / str / 基本类型；结构与原值对应。 + + 系统定位: + 供 ``core/approvals.build_approval_meta`` 在展示待确认工具参数时使用， + 避免 UI 层因不可序列化对象而报错。 + + 可扩展性: + 可为 datetime、Decimal、Enum 等类型增加专用分支，而非一律 ``str()``。 + """ + try: + json.dumps(value, ensure_ascii=False) + return value + except TypeError: + if isinstance(value, dict): + return {str(k): json_safe(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [json_safe(v) for v in value] + return str(value) + + +def normalize_tool_call(tool_call: Any) -> dict[str, Any]: + """将 LangChain ToolCall 或原始 dict 规范化为统一字典。 + + 功能描述: + 兼容 dict 与对象两种来源，解析 args（支持 JSON 字符串）， + 输出固定字段 ``id``、``name``、``args``。 + + 输入: + tool_call: LangChain ``ToolCall`` 对象，或含 name/args/id 的 dict。 + + 输出: + ``{"id": str|None, "name": str|None, "args": dict}``。 + + 系统定位: + ``core/routing`` 在判断工具执行策略和提取待确认项时统一调用， + 屏蔽不同 LLM 返回格式的差异。 + + 可扩展性: + 可在此增加参数校验、默认值填充，或对接 JSON Schema 校验。 + """ + if isinstance(tool_call, dict): + name = tool_call.get("name") + args = tool_call.get("args") or {} + call_id = tool_call.get("id") + else: + name = getattr(tool_call, "name", None) + args = getattr(tool_call, "args", {}) or {} + call_id = getattr(tool_call, "id", None) + if isinstance(args, str): + try: + args = json.loads(args) + except Exception: + args = {"raw": args} + return {"id": call_id, "name": name, "args": args or {}} + + +def parse_edited_instruction(instruction: str, fallback: dict[str, Any]) -> dict[str, Any]: + """解析用户在「编辑」确认流程中提交的指令文本。 + + 功能描述: + 优先按 JSON 解析完整工具调用；失败则将原文本写入 ``args.instruction``； + 兼容 ``tool_name`` 别名字段。 + + 输入: + instruction: 用户编辑后的文本（可为 JSON 或纯文本）。 + fallback: 原始工具调用 dict，解析失败时作为回退。 + + 输出: + 规范化后的工具调用 dict，至少含 ``name`` 与 ``args``。 + + 系统定位: + ``core/runtime.continue_after_human_action`` 在 decision=edit 时调用， + 将 UI 编辑结果还原为可执行的工具调用。 + + 可扩展性: + 可增加 schema 校验、参数合并策略（仅覆盖用户修改的字段）。 + """ + raw = (instruction or "").strip() + if not raw: + return fallback + try: + parsed = json.loads(raw) + except json.JSONDecodeError: + updated = dict(fallback) + updated["args"] = {"instruction": raw} + return updated + if isinstance(parsed, dict): + if "name" not in parsed and "tool_name" in parsed: + parsed["name"] = parsed.get("tool_name") + if "args" not in parsed: + parsed["args"] = fallback.get("args") or {} + return parsed + return fallback diff --git a/src/agent/workspace/.todo b/src/agent/workspace/.todo new file mode 100644 index 0000000..9e86f67 --- /dev/null +++ b/src/agent/workspace/.todo @@ -0,0 +1,53 @@ +# Plan TodoList + +路径选择：路径一：使用浏览器控制 + +# 执行计划：读取 Bilibili 首页第一个视频标题并分析 + +**说明**：本计划旨在通过浏览器自动化控制访问 Bilibili 首页，提取首个视频标题，并提供基于当前网络热点和一般认知的理解。由于我无法直接“看到”实时变化的互联网内容，以下流程将指导如何获取最新数据并进行分析。在实际执行中，我会根据获取到的具体标题进行针对性解读。 + +## Todo List + +- [x] **步骤 1：环境准备与导航** · 状态：进行中 · 状态：已完成 + - **依赖**：无 + - **状态**：待开始 + - **任务描述**： + 1. 启动内置浏览器或模拟浏览器会话。 + 2. 导航至 Bilibili 首页 URL (`https://www.bilibili.com`)。 + 3. 等待页面完全加载，确保动态内容（如推荐流）已渲染完毕。 + +- [x] **步骤 2：定位首个视频元素** · 状态：进行中 · 状态：已完成 + - **依赖**：步骤 1 完成 + - **状态**：待开始 + - **任务描述**： + 1. 识别首页的主要视频推荐区域（通常是“推荐”标签页下的第一个卡片）。 + 2. 定位包含视频标题的 DOM 元素（通常位于视频封面下方或悬浮层中）。 + 3. *注意*：需排除广告位或非视频内容（如直播入口、专栏文章），确保选取的是标准的 UP 主投稿视频。 + +- [x] **步骤 3：提取视频标题文本** · 状态：进行中 · 状态：已完成 + - **依赖**：步骤 2 完成 + - **状态**：待开始 + - **任务描述**： + 1. 从定位到的元素中提取纯文本标题。 + 2. 清理多余的空白字符或隐藏的控制符。 + 3. 记录提取结果，若提取失败则重试或尝试备用选择器。 + +- [x] **步骤 4：信息分析与理解生成** · 状态：进行中 · 状态：已完成 + - **依赖**：步骤 3 完成 + - **状态**：待开始 + - **任务描述**： + 1. 基于提取的标题，结合当前的社会热点、B站社区文化及常见视频类型进行分析。 + 2. 判断视频可能的主题（如科技评测、生活 vlog、游戏实况、知识科普等）。 + 3. 评估标题是否具有吸引力（如是否使用悬念、热点词汇、情绪化表达）。 + 4. 给出简短的个性化理解和建议观看理由。 + +- [x] **步骤 5：输出最终报告** · 状态：进行中 · 状态：已完成 + - **依赖**：步骤 4 完成 + - **状态**：待开始 + - **任务描述**： + 1. 整合提取的标题和分析结果。 + 2. 以清晰的结构呈现给用户，包括“原始标题”、“内容推测”和“我的理解”。 + +--- + +**备注**：在执行过程中，我将根据实际抓取到的标题实时更新后续的分析内容。如果首页因登录状态或算法推荐显示不同内容，我将基于当时可见的第一个视频进行处理。 diff --git a/src/web/__pycache__/__init__.cpython-313.pyc b/src/web/__pycache__/__init__.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..1537bedc0af33b97e30c350485d9ea5667525d92 GIT binary patch literal 256 zcmey&%ge<81O)~UG6R70V-N=h7@>^M96-iYhG2#whIB?vrcw_eH#{{-;b~{@^VREL z&Yk(Jq4CAmmS?*rJlWCrY|fgeb2cbA26!qwo74NWYvKDn9WRzQDkLNnrxul^79}Jo zJX^8w>D=k>_jEpPX!6r!yu}_LpOT*(AHR~}GswnUuFh65p+MEeF&1WqW@a%ism0kP 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+.pending-action-title { + font-weight: 700; + color: rgba(15, 23, 42, 0.72); + margin-bottom: 0.35rem; +} + +.pending-action-params { + margin: 0 0 0.45rem; + padding: 0.5rem; + max-height: 160px; + overflow: auto; + border-radius: 10px; + background: rgba(255, 255, 255, 0.32); + color: rgba(30, 41, 59, 0.72); + font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; + font-size: 0.72rem; + white-space: pre-wrap; +} + +.pending-action-editor { + width: 100%; + resize: vertical; + border: 1px solid rgba(148, 163, 184, 0.42); + border-radius: 10px; + padding: 0.45rem 0.5rem; + background: rgba(255, 255, 255, 0.36); + color: rgba(15, 23, 42, 0.78); + font: inherit; + font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; + font-size: 0.72rem; +} + +.pending-action-buttons { + display: flex; + flex-wrap: wrap; + gap: 0.4rem; + margin-top: 0.5rem; +} + +.pending-action-btn { + border: 1px solid rgba(148, 163, 184, 0.45); + border-radius: 999px; + padding: 0.32rem 0.62rem; + background: rgba(255, 255, 255, 0.42); + color: #334155; + cursor: pointer; + font: inherit; + font-size: 0.75rem; +} + +.pending-action-btn:hover:not(:disabled) { + background: rgba(255, 255, 255, 0.7); +} + +.pending-action-btn--approve { + border-color: rgba(20, 184, 166, 0.45); + color: #0f766e; +} + +.pending-action-btn--reject { + border-color: rgba(248, 113, 113, 0.45); + color: #b91c1c; +} + /* 图片在气泡内留出呼吸空间 */ .msg-bubble img { display: block; @@ -1379,4 +1458,1346 @@ body { .chat-messages { -webkit-overflow-scrolling: touch; } -} \ No newline at end of file +} +/* ===== 人工交互 / 工具审批卡片 ===== */ +.pending-action-card { + margin-top: 0.75rem; + padding: 0.75rem; + border: 1px solid rgba(14, 116, 144, 0.22); + border-radius: 12px; + background: rgba(255, 255, 255, 0.42); + box-shadow: 0 8px 24px rgba(15, 23, 42, 0.08); +} + +.pending-action-card--resolved { + opacity: 0.68; +} + +.pending-action-title { + font-weight: 700; + margin-bottom: 0.45rem; + color: #0f172a; +} + +.pending-action-prompt { + margin-bottom: 0.55rem; + color: #334155; + line-height: 1.55; + white-space: pre-wrap; +} + +.pending-action-params { + margin: 0 0 0.55rem; + padding: 0.55rem; + max-height: 220px; + overflow: auto; + border-radius: 10px; + background: rgba(15, 23, 42, 0.88); + color: #e2e8f0; + font-size: 0.78rem; +} + +.pending-action-editor { + width: 100%; + resize: vertical; + border: 1px solid rgba(148, 163, 184, 0.55); + border-radius: 10px; + padding: 0.55rem 0.65rem; + font: inherit; + color: var(--text); + background: rgba(255, 255, 255, 0.65); +} + +.pending-action-editor:focus { + outline: 2px solid rgba(14, 116, 144, 0.28); + outline-offset: 1px; +} + +.pending-action-options { + display: flex; + flex-wrap: wrap; + gap: 0.4rem; + margin: 0.2rem 0 0.55rem; +} + +.pending-action-option, +.pending-action-btn { + border: 1px solid rgba(148, 163, 184, 0.55); + border-radius: 999px; + padding: 0.38rem 0.72rem; + font: inherit; + cursor: pointer; + background: rgba(255, 255, 255, 0.55); + color: #0f172a; +} + +.pending-action-option:hover:not(:disabled), +.pending-action-btn:hover:not(:disabled) { + background: rgba(255, 255, 255, 0.78); +} + +.pending-action-buttons { + display: flex; + flex-wrap: wrap; + gap: 0.45rem; + margin-top: 0.55rem; +} + +.pending-action-btn--approve { + background: linear-gradient(165deg, #14b8a6, #0e7490); + border-color: rgba(14, 116, 144, 0.7); + color: #fff; +} + +.pending-action-btn--reject { + background: rgba(254, 226, 226, 0.86); + border-color: rgba(220, 38, 38, 0.35); + color: #991b1b; +} + +.pending-action-btn--edit { + background: rgba(254, 243, 199, 0.86); + border-color: rgba(217, 119, 6, 0.35); + color: #92400e; +} + +.pending-action-card--information .pending-action-btn--approve, +.pending-action-card--selection .pending-action-btn--approve { + min-width: 2.35rem; + font-size: 1rem; + font-weight: 700; +} + +/* ===== 设置按钮 ===== */ +.settings-btn { + display: inline-flex; + align-items: center; + justify-content: center; + width: 2.2rem; + height: 2.2rem; + padding: 0; + border-radius: 999px; + border: 1px solid rgba(148, 163, 184, 0.45); + background: rgba(255, 255, 255, 0.35); + backdrop-filter: blur(10px); + -webkit-backdrop-filter: blur(10px); + cursor: pointer; + font-size: 1.15rem; + color: #334155; + transition: transform 0.15s, background 0.15s, box-shadow 0.15s; + box-shadow: 0 2px 8px rgba(15, 23, 42, 0.08); +} + +.settings-btn:hover { + background: rgba(255, 255, 255, 0.65); + box-shadow: 0 4px 14px rgba(15, 23, 42, 0.14); + transform: rotate(30deg); +} + +/* ===== 设置模态框 ===== */ +.settings-overlay { + position: fixed; + inset: 0; + z-index: 100; + display: flex; + align-items: center; + justify-content: center; + background: rgba(15, 23, 42, 0.55); + backdrop-filter: blur(6px); + -webkit-backdrop-filter: blur(6px); + animation: fadeIn 0.18s ease; +} + +.settings-overlay[hidden] { + display: none; +} + +.settings-dialog { + width: min(920px, 94vw); + max-height: 90vh; + max-height: 90dvh; + background: rgba(248, 250, 252, 0.96); + backdrop-filter: blur(24px); + -webkit-backdrop-filter: blur(24px); + border-radius: 18px; + border: 1px solid rgba(148, 163, 184, 0.35); + box-shadow: 0 24px 64px rgba(15, 23, 42, 0.22); + display: flex; + flex-direction: column; + overflow: hidden; + animation: slideUp 0.22s ease; +} + +.settings-header { + display: flex; + align-items: center; + justify-content: space-between; + padding: 0.85rem 1.15rem; + border-bottom: 1px solid rgba(148, 163, 184, 0.25); +} + +.settings-header h2 { + margin: 0; + font-size: 1.15rem; + color: #0f172a; +} + +.settings-close-btn { + width: 2rem; + height: 2rem; + padding: 0; + border-radius: 999px; + border: 1px solid rgba(148, 163, 184, 0.4); + background: rgba(255, 255, 255, 0.5); + cursor: pointer; + font-size: 1rem; + color: #475569; + display: inline-flex; + align-items: center; + justify-content: center; + transition: background 0.15s; +} + +.settings-close-btn:hover { + background: rgba(239, 68, 68, 0.15); + color: #991b1b; +} + +.settings-tabs { + display: flex; + gap: 0; + border-bottom: 1px solid rgba(148, 163, 184, 0.25); + padding: 0 1.15rem; + background: rgba(241, 245, 249, 0.6); +} + +.settings-tab { + padding: 0.55rem 1.15rem; + border: none; + border-bottom: 2px solid transparent; + background: none; + font: inherit; + font-size: 0.9rem; + font-weight: 600; + color: #64748b; + cursor: pointer; + transition: color 0.15s, border-color 0.15s; +} + +.settings-tab:hover { + color: #334155; +} + +.settings-tab.active { + color: var(--accent); + border-bottom-color: var(--accent); +} + +.settings-body { + flex: 1; + overflow-y: auto; + padding: 1rem 1.15rem; + min-height: 0; +} + +.settings-panel[hidden] { + display: none; +} + +.settings-toolbar { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 0.85rem; + flex-wrap: wrap; + gap: 0.5rem; +} + +.settings-path { + font-size: 0.72rem; + color: #94a3b8; + max-width: 320px; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; +} + +.settings-save-btn { + margin-top: 0.85rem; +} + +/* ===== Agent 配置卡片 ===== */ +.agent-config-card { + border: 1px solid rgba(148, 163, 184, 0.3); + border-radius: 14px; + padding: 1.05rem; + margin-bottom: 0.8rem; + background: rgba(255, 255, 255, 0.65); + box-shadow: 0 1px 4px rgba(15, 23, 42, 0.04); + transition: box-shadow 0.15s; +} + +.agent-config-card:hover { + box-shadow: 0 3px 12px rgba(15, 23, 42, 0.08); +} + +.agent-config-card + .agent-config-card { + margin-top: 0.8rem; +} + +.agent-card-header { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 0.75rem; + padding-bottom: 0.55rem; + border-bottom: 1px solid rgba(148, 163, 184, 0.15); +} + +.agent-card-header h4 { + margin: 0; + font-size: 0.95rem; + color: #0f172a; + font-weight: 700; +} + +.agent-card-remove { + width: 1.6rem; + height: 1.6rem; + padding: 0; + border-radius: 999px; + border: 1px solid rgba(220, 38, 38, 0.3); + background: rgba(254, 226, 226, 0.5); + cursor: pointer; + font-size: 0.8rem; + color: #991b1b; + display: inline-flex; + align-items: center; + justify-content: center; + transition: background 0.15s, color 0.15s; +} + +.agent-card-remove:hover { + background: rgba(220, 38, 38, 0.15); + color: #7f1d1d; +} + +.setting-field { + margin-bottom: 0.6rem; +} + +.setting-field label { + display: block; + font-size: 0.75rem; + font-weight: 700; + color: #475569; + margin-bottom: 0.25rem; + text-transform: uppercase; + letter-spacing: 0.03em; +} + +.setting-field input, +.setting-field select, +.setting-field textarea { + width: 100%; + border: 1px solid rgba(148, 163, 184, 0.45); + border-radius: 8px; + padding: 0.45rem 0.55rem; + font: inherit; + font-size: 0.85rem; + background: rgba(255, 255, 255, 0.8); + color: var(--text); + transition: border-color 0.15s, box-shadow 0.15s; + box-sizing: border-box; +} + +.setting-field input:focus, +.setting-field select:focus, +.setting-field textarea:focus { + border-color: var(--accent, #0e7490); + box-shadow: 0 0 0 3px rgba(14, 116, 144, 0.1); + outline: none; +} + +.setting-field textarea { + resize: vertical; + min-height: 68px; + font-size: 0.82rem; + line-height: 1.45; +} + +.setting-row { + display: flex; + gap: 0.55rem; +} + +.setting-row .setting-field { + flex: 1; + min-width: 0; +} + +/* ===== 工具配置卡片 ===== */ +.tool-config-card { + border: 1px solid rgba(148, 163, 184, 0.25); + border-radius: 12px; + padding: 0.8rem; + margin-bottom: 0.6rem; + background: rgba(255, 255, 255, 0.55); + transition: opacity 0.2s; +} + +.tool-config-card.disabled { + opacity: 0.45; +} + +.tool-config-card h4 { + margin: 0 0 0.4rem; + font-size: 0.9rem; + display: inline-flex; + align-items: center; + gap: 0.35rem; + color: #1e293b; +} + +.tool-card-desc { + font-size: 0.76rem; + color: #64748b; + margin-bottom: 0.5rem; + line-height: 1.4; +} + +.tool-card-row { + display: flex; + align-items: center; + gap: 0.6rem; + flex-wrap: wrap; +} + +.auto-rules-section { + border-top: 1px dashed rgba(148, 163, 184, 0.3); + padding-top: 0.35rem; + margin-top: 0.35rem; +} + +.auto-rules-list { + display: flex; + flex-direction: column; + gap: 0.12rem; +} + +/* ===== 环境变量配置 ===== */ +.env-config-item { + display: flex; + align-items: center; + gap: 0.6rem; + margin-bottom: 0.5rem; + flex-wrap: wrap; +} + +.env-config-item label { + min-width: 150px; + font-size: 0.8rem; + font-weight: 600; + color: #334155; + font-family: ui-monospace, SFMono-Regular, Menlo, Monaco, Consolas, monospace; +} + +.env-config-item input { + flex: 1; + min-width: 200px; + border: 1px solid rgba(148, 163, 184, 0.4); + border-radius: 8px; + padding: 0.4rem 0.5rem; + font: inherit; + font-size: 0.84rem; + background: rgba(255, 255, 255, 0.7); + transition: border-color 0.15s, box-shadow 0.15s; +} + +.env-config-item input:focus { + border-color: var(--accent, #0e7490); + box-shadow: 0 0 0 3px rgba(14, 116, 144, 0.08); + outline: none; +} + +/* ===== 设置页脚 ===== */ +.settings-footer { + display: flex; + align-items: center; + justify-content: space-between; + padding: 0.7rem 1.15rem; + border-top: 1px solid rgba(148, 163, 184, 0.2); + background: rgba(241, 245, 249, 0.55); +} + +.settings-status { + font-size: 0.8rem; + font-weight: 500; + color: #475569; +} + +.settings-status.success { + color: #16a34a; +} + +.settings-status.error { + color: #dc2626; +} + +/* ===== 设置面板内联按钮 ===== */ +.settings-btn-inline { + padding: 0.25rem 0.75rem; + font-size: 0.78rem; + border-radius: 6px; + border: 1px solid rgba(148, 163, 184, 0.3); + background: rgba(255, 255, 255, 0.7); + color: #475569; + cursor: pointer; + font-weight: 500; + transition: all 0.15s; +} + +.settings-btn-inline:hover { + background: #fff; + border-color: var(--accent, #0e7490); + color: var(--accent, #0e7490); +} + +.settings-btn-inline.danger { + color: #991b1b; +} + +.settings-btn-inline.danger:hover { + background: #fef2f2; + border-color: #dc2626; + color: #dc2626; +} + +/* ===== 设置面板标题区 ===== */ +.settings-section-title { + display: flex; + align-items: center; + justify-content: space-between; + margin-bottom: 0.65rem; +} + +.settings-section-title h4 { + margin: 0; + font-size: 0.92rem; + font-weight: 700; + color: #1e293b; +} + +.settings-section-title .btn { + padding: 0.3rem 0.8rem; + font-size: 0.78rem; +} + +/* ===== 模型编辑弹窗 ===== */ +.model-modal-overlay { + display: none; + position: fixed; + top: 0; left: 0; right: 0; bottom: 0; + background: rgba(0,0,0,0.4); + z-index: 2000; + align-items: center; + justify-content: center; +} +.model-modal-overlay.show { + display: flex; +} +.model-modal-box { + background: #fff; + border-radius: 12px; + padding: 1.5rem; + width: 480px; + max-width: 90vw; + box-shadow: 0 20px 60px rgba(0,0,0,0.2); + animation: fadeIn 0.2s ease; +} +.model-modal-box h3 { + margin: 0 0 1rem; + font-size: 1rem; + font-weight: 600; + color: #1e293b; +} +.model-modal-body .modal-field { + margin-bottom: 0.6rem; +} +.model-modal-body .modal-field label { + display: block; + font-size: 0.78rem; + font-weight: 600; + color: #475569; + margin-bottom: 0.15rem; +} +.model-modal-body .modal-field input { + width: 100%; + border: 1px solid rgba(148,163,184,0.5); + border-radius: 6px; + padding: 0.4rem 0.5rem; + font-size: 0.82rem; + box-sizing: border-box; + transition: border-color 0.15s; +} +.model-modal-body .modal-field input:focus { + outline: none; + border-color: var(--accent, #4f46e5); + box-shadow: 0 0 0 3px rgba(79,70,229,0.08); +} +.model-modal-actions { + display: flex; + justify-content: flex-end; + gap: 0.5rem; + margin-top: 1.2rem; +} + +/* ===== 模型表格 ===== */ +.model-table { + border: 1px solid rgba(148,163,184,0.3); + border-radius: 10px; + overflow: hidden; + background: rgba(255,255,255,0.65); +} + +.model-table-head { + display: flex; + background: rgba(241,245,249,0.8); + font-size: 0.73rem; + font-weight: 700; + color: #475569; + padding: 0.5rem 0.75rem; + text-transform: uppercase; + letter-spacing: 0.04em; + gap: 0.5rem; +} + +.model-table-row { + display: flex; + padding: 0.5rem 0.75rem; + border-top: 1px solid rgba(148,163,184,0.12); + align-items: center; + font-size: 0.8rem; + transition: background 0.12s; + gap: 0.5rem; +} + +.model-table-row:hover { + background: rgba(241,245,249,0.45); +} + +/* 模型表格列宽 */ +.model-table .col-name { flex: 1.5; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; font-weight: 600; color: #1e293b; } +.model-table .col-model { flex: 1.2; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; color: #475569; font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; font-size: 0.78rem; } +.model-table .col-url { flex: 2.5; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; color: #94a3b8; font-size: 0.72rem; } +.model-table .col-actions { flex: 0 0 90px; display: flex; gap: 0.35rem; justify-content: flex-end; align-items: center; } + +/* 表格内操作按钮 */ +.model-table .col-actions button { + padding: 0.25rem 0.55rem; + font-size: 0.72rem; + border: 1px solid rgba(148,163,184,0.25); + border-radius: 5px; + cursor: pointer; + background: rgba(255,255,255,0.8); + color: #475569; + font-weight: 500; + transition: all 0.15s; + white-space: nowrap; +} + +.model-table .col-actions button:hover { + background: #fff; + border-color: var(--accent, #0e7490); + color: var(--accent, #0e7490); +} + +.model-table .col-actions button.danger { + color: #991b1b; + border-color: rgba(220,38,38,0.2); + background: rgba(254,226,226,0.3); +} + +.model-table .col-actions button.danger:hover { + background: #fef2f2; + border-color: #dc2626; + color: #dc2626; +} + +/* ===== 开关控件 ===== */ +.toggle-switch { + position: relative; + display: inline-block; + width: 42px; + height: 22px; + flex-shrink: 0; +} + +.toggle-switch input { + opacity: 0; + width: 0; + height: 0; +} + +.toggle-slider { + position: absolute; + cursor: pointer; + top: 0; left: 0; right: 0; bottom: 0; + background: #cbd5e1; + border-radius: 22px; + transition: background 0.2s; +} + +.toggle-slider::before { + content: ""; + position: absolute; + height: 16px; + width: 16px; + left: 3px; + bottom: 3px; + background: #fff; + border-radius: 50%; + transition: transform 0.2s; + box-shadow: 0 1px 3px rgba(0,0,0,0.15); +} + +.toggle-switch input:checked + .toggle-slider { + background: var(--accent, #0e7490); +} + +.toggle-switch input:checked + .toggle-slider::before { + transform: translateX(20px); +} + +.toggle-row { + display: flex; + align-items: center; + gap: 0.5rem; +} + +.toggle-row .toggle-label { + font-size: 0.82rem; + font-weight: 600; + color: #475569; + cursor: pointer; + user-select: none; +} + +@keyframes fadeIn { + from { opacity: 0; } + to { opacity: 1; } +} + +@keyframes slideUp { + from { opacity: 0; transform: translateY(24px); } + to { opacity: 1; transform: translateY(0); } +} + +/* ===== 图谱切换按钮 ===== */ +.graph-toggle-btn { + margin-left: 0.15rem; +} + +.graph-toggle-btn.graph-active { + background: rgba(14, 116, 144, 0.18); + border-color: rgba(14, 116, 144, 0.5); + color: #0e7490; +} + +/* ===== 主内容区（chat + graph 并排） ===== */ +.main-content-wrap { + flex: 1; + min-height: 0; + display: flex; + gap: 0; + overflow: hidden; +} + +/* 图谱开启时聊天面板缩窄，圆角只保留左侧 */ +.main.has-graph .chat-shell { + flex: 1 1 58%; + min-width: 320px; + border-radius: var(--radius) 0 0 var(--radius); +} + +/* ===== 图谱侧面板 ===== */ +.graph-panel { + display: flex; + flex-direction: column; + width: 42%; + min-width: 340px; + max-width: 520px; + border-left: 1px solid rgba(148, 163, 184, 0.35); + border-radius: 0 var(--radius) var(--radius) 0; + background: rgba(255, 255, 255, 0.25); + backdrop-filter: blur(8px); + -webkit-backdrop-filter: blur(8px); + overflow: hidden; + box-shadow: -4px 0 24px rgba(15, 23, 42, 0.06); + transition: width 0.3s ease, min-width 0.3s ease, max-width 0.3s ease, opacity 0.25s ease; +} + +.graph-panel[hidden] { + display: none; +} + +.graph-toolbar { + flex-shrink: 0; + display: flex; + align-items: center; + gap: 0.4rem; + padding: 0.45rem 0.65rem; + border-bottom: 1px solid rgba(148, 163, 184, 0.22); + background: rgba(255, 255, 255, 0.15); +} + +.graph-toolbar-title { + font-size: 0.82rem; + font-weight: 700; + color: #1e293b; + margin-right: 0.25rem; +} + +.graph-btn { + font-size: 0.7rem; + padding: 0.2rem 0.45rem; + border-radius: 6px; +} + +.graph-legend { + margin-left: auto; + display: flex; + align-items: center; + gap: 0.5rem; + font-size: 0.65rem; + color: #64748b; +} + +.graph-close-btn { + width: 1.65rem; + height: 1.65rem; + padding: 0; + border: 1px solid rgba(148, 163, 184, 0.4); + border-radius: 6px; + background: rgba(255, 255, 255, 0.4); + color: #64748b; + font-size: 0.75rem; + cursor: pointer; + display: flex; + align-items: center; + justify-content: center; + flex-shrink: 0; +} + +.graph-close-btn:hover { + background: rgba(255, 255, 255, 0.65); + color: #1e293b; +} + +.legend-dot { + display: inline-block; + width: 10px; + height: 10px; + border-radius: 50%; + margin-right: 0.1rem; +} + +.legend-running { + background: #f59e0b; + box-shadow: 0 0 6px rgba(245, 158, 11, 0.5); + animation: legendPulse 1.2s ease-in-out infinite; +} + +.legend-done { + background: #22c55e; +} + +.legend-error { + background: #ef4444; +} + +.legend-idle { + background: #94a3b8; +} + +@keyframes legendPulse { + 0%, 100% { opacity: 0.7; box-shadow: 0 0 4px rgba(245, 158, 11, 0.3); } + 50% { opacity: 1; box-shadow: 0 0 10px rgba(245, 158, 11, 0.7); } +} + +/* 图谱画布 */ +.graph-canvas-wrap { + flex: 1; + min-height: 0; + overflow: hidden; + position: relative; + cursor: grab; + background: + radial-gradient(circle, rgba(148, 163, 184, 0.06) 1px, transparent 1px); + background-size: 24px 24px; +} + +.graph-canvas-wrap:active { + cursor: grabbing; +} + +.graph-canvas-wrap.panning { + cursor: grabbing; +} + +.graph-canvas { + width: 100%; + height: 100%; + display: block; + user-select: none; +} + +/* Agent 节点 */ +.graph-node { + cursor: pointer; + transition: transform 0.2s ease; +} + +.graph-node:hover .graph-node-rect { + filter: brightness(1.05); +} + +.graph-node-rect { + fill: rgba(255, 255, 255, 0.75); + stroke: rgba(148, 163, 184, 0.5); + stroke-width: 1.5; + rx: 16; + transition: fill 0.35s ease, stroke 0.35s ease, filter 0.35s ease; +} + +/* 状态颜色 */ +.graph-node.status-running .graph-node-rect { + fill: rgba(255, 200, 120, 0.45); + stroke: #f59e0b; + stroke-width: 2.5; + filter: drop-shadow(0 0 8px rgba(245, 158, 11, 0.35)); +} + +.graph-node.status-done .graph-node-rect { + fill: rgba(187, 247, 208, 0.45); + stroke: #22c55e; + stroke-width: 2; +} + +.graph-node.status-error .graph-node-rect { + fill: rgba(254, 202, 202, 0.5); + stroke: #ef4444; + stroke-width: 2.5; + filter: drop-shadow(0 0 6px rgba(239, 68, 68, 0.3)); +} + +.graph-node.status-idle .graph-node-rect { + fill: rgba(255, 255, 255, 0.5); + stroke: rgba(148, 163, 184, 0.4); +} + +/* Agent 节点内部文字 */ +.graph-node-icon { + font-size: 1.4rem; + text-anchor: middle; + dominant-baseline: central; + pointer-events: none; +} + +.graph-node-label { + font-size: 0.75rem; + font-weight: 600; + fill: #1e293b; + text-anchor: middle; + dominant-baseline: central; + pointer-events: none; +} + +.graph-node-role { + font-size: 0.6rem; + fill: #94a3b8; + text-anchor: middle; + dominant-baseline: central; + pointer-events: none; +} + +.graph-node-badge { + font-size: 0.6rem; + font-weight: 700; + text-anchor: middle; + dominant-baseline: central; + pointer-events: none; +} + +/* 连线 */ +.graph-edge { + transition: stroke 0.4s ease, stroke-width 0.4s ease, opacity 0.4s ease; +} + +.graph-edge.highlighted { + stroke: #f59e0b; + stroke-width: 2.5; + opacity: 1; + stroke-dasharray: none; +} + +.graph-edge.active { + stroke: #14b8a6; + stroke-width: 2; + opacity: 1; + stroke-dasharray: 6 3; + animation: edgeFlow 0.6s linear infinite; +} + +.graph-edge.normal { + stroke: rgba(148, 163, 184, 0.4); + stroke-width: 1.5; + opacity: 0.7; +} + +@keyframes edgeFlow { + from { stroke-dashoffset: 18; } + to { stroke-dashoffset: 0; } +} + +/* 工具调用面板 */ +/* 工具调用卡片 */ +.toolcall-card { + padding: 0.4rem 0.55rem; + border-radius: 10px; + border: 1px solid rgba(148, 163, 184, 0.3); + background: rgba(255, 255, 255, 0.5); + font-size: 0.72rem; +} + +.toolcall-card-head { + display: flex; + align-items: center; + gap: 0.4rem; + margin-bottom: 0.2rem; +} + +.toolcall-card-name { + font-weight: 700; + color: #1e293b; + font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; +} + +.toolcall-card-status { + font-size: 0.62rem; + padding: 0.1rem 0.35rem; + border-radius: 999px; + font-weight: 600; +} + +.toolcall-card-status.success { + background: rgba(34, 197, 94, 0.15); + color: #15803d; +} + +.toolcall-card-status.error { + background: rgba(239, 68, 68, 0.15); + color: #b91c1c; +} + +.toolcall-card-status.pending { + background: rgba(245, 158, 11, 0.15); + color: #b45309; +} + +.toolcall-card-args { + font-family: ui-monospace, SFMono-Regular, Menlo, Consolas, monospace; + font-size: 0.65rem; + color: #64748b; + white-space: pre-wrap; +} + +.toolcall-card-result { + margin-top: 0.25rem; + padding: 0.3rem 0.4rem; + border-radius: 6px; + background: rgba(241, 245, 249, 0.7); + font-size: 0.65rem; + color: #475569; + max-height: 48px; + overflow: hidden; + text-overflow: ellipsis; + white-space: pre-wrap; + word-break: break-all; +} + +/* 消息传递动画脉冲 */ +@keyframes msgPulse { + 0%, 100% { opacity: 0.4; } + 50% { opacity: 1; } +} + +.graph-edge.active { + animation: edgeFlow 0.6s linear infinite, msgPulse 0.8s ease-in-out infinite; +} + +/* ===== 工具调用气泡（chat 视图） ===== */ +.tool-call-bubble-wrap { + margin-top: 0.5rem; + border: 1px solid rgba(148, 163, 184, 0.3); + border-radius: 10px; + overflow: hidden; + background: rgba(255, 255, 255, 0.18); + transition: background 0.3s; +} + +.tool-call-toggle { + display: block; + width: 100%; + padding: 0.35rem 0.65rem; + border: none; + background: rgba(255, 255, 255, 0.3); + font-size: 0.72rem; + font-weight: 600; + color: #475569; + cursor: pointer; + text-align: left; + transition: background 0.2s; + font-family: inherit; +} + +.tool-call-toggle:hover { + background: rgba(255, 255, 255, 0.5); +} + +.tool-call-collapsed { + display: flex; + flex-wrap: wrap; + gap: 0.3rem; + padding: 0.4rem 0.65rem; +} + +.tool-call-bubble-wrap--expanded .tool-call-collapsed { + display: none; +} + +.tool-call-chip { + display: inline-block; + padding: 0.15rem 0.45rem; + border-radius: 8px; + background: rgba(79, 70, 229, 0.1); + font-size: 0.68rem; + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + color: #4f46e5; + font-weight: 500; + transition: opacity 0.5s; +} + +.tool-call-more { + display: inline-block; + padding: 0.15rem 0.45rem; + border-radius: 8px; + background: rgba(148, 163, 184, 0.15); + font-size: 0.65rem; + color: #94a3b8; + font-weight: 600; +} + +.tool-call-expanded { + display: none; + padding: 0.4rem 0.65rem 0.65rem; + max-height: 240px; + overflow-y: auto; +} + +.tool-call-bubble-wrap--expanded .tool-call-expanded { + display: block; +} + +.tool-call-detail { + margin-bottom: 0.5rem; + padding: 0.4rem 0.55rem; + border-radius: 8px; + background: rgba(241, 245, 249, 0.6); + border: 1px solid rgba(148, 163, 184, 0.2); +} + +.tool-call-detail-name { + font-weight: 700; + font-size: 0.72rem; + color: #1e293b; + margin-bottom: 0.25rem; +} + +.tool-call-detail-text { + margin: 0.2rem 0; + padding: 0.3rem 0.45rem; + border-radius: 6px; + background: rgba(148, 163, 184, 0.12); + font-size: 0.65rem; + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + color: #64748b; + white-space: pre-wrap; + word-break: break-all; + max-height: 80px; + overflow-y: auto; +} + +.tool-call-detail-result { + color: #334155; + background: rgba(148, 163, 184, 0.08); +} + +.tool-call-detail-img { + border-radius: 6px; + margin: 0.2rem; + max-width: 120px; + max-height: 90px; + object-fit: cover; + opacity: 0.6; + transition: opacity 0.3s; +} + +.tool-call-detail-img:hover { + opacity: 0.9; +} + +/* 淡出动画 */ +.tool-call-block--fading { + transition: opacity 0.8s ease-out; + opacity: 0 !important; + pointer-events: none; +} + +/* ===== 实时工具调用（thinking 气泡内） ===== */ +.tool-call-live-wrap { + display: flex; + flex-wrap: wrap; + gap: 0.3rem; + margin-top: 0.5rem; + padding: 0.4rem 0.5rem; + border-top: 1px solid rgba(148, 163, 184, 0.2); +} + +.tool-call-live-chip { + display: inline-flex; + align-items: center; + gap: 0.2rem; + padding: 0.15rem 0.5rem; + border-radius: 8px; + background: rgba(99, 102, 241, 0.08); + font-size: 0.68rem; + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + color: #6366f1; + font-weight: 500; + transition: all 0.3s; +} + +.tool-call-live-chip--running { + background: rgba(245, 158, 11, 0.1); + color: #d97706; + animation: livePulse 1.2s ease-in-out infinite; +} + +@keyframes livePulse { + 0%, 100% { opacity: 0.7; } + 50% { opacity: 1; } +} + +/* 任务完成提示 */ +.tool-call-done-badge { + display: inline-block; + padding: 0.2rem 0.5rem; + border-radius: 999px; + background: rgba(34, 197, 94, 0.15); + color: #15803d; + font-size: 0.68rem; + font-weight: 600; + margin-bottom: 0.4rem; +} + +/* ===== Graph 节点工具调用缩略 ===== */ +.graph-node-tool-chip { + font-size: 0.55rem; + font-family: ui-monospace, SFMono-Regular, Consolas, monospace; + fill: #6366f1; + font-weight: 600; + text-anchor: start; +} + +/* ===== Agent 详情弹窗 ===== */ +.agent-detail-overlay { + position: fixed; + inset: 0; + z-index: 999; + background: rgba(15, 23, 42, 0.55); + display: flex; + align-items: center; + justify-content: center; +} + +.agent-detail-overlay[hidden] { + display: none; +} + +.agent-detail-dialog { + background: rgba(255, 255, 255, 0.92); + backdrop-filter: blur(20px); + -webkit-backdrop-filter: blur(20px); + border-radius: 16px; + width: min(520px, 92vw); + max-height: 80vh; + overflow: hidden; + display: flex; + flex-direction: column; + box-shadow: 0 20px 60px rgba(0,0,0,0.2); +} + +.agent-detail-header { + display: flex; + align-items: center; + justify-content: space-between; + padding: 1rem 1.25rem; + border-bottom: 1px solid rgba(148, 163, 184, 0.25); +} + +.agent-detail-header h2 { + margin: 0; + font-size: 1.1rem; + font-weight: 700; + color: #1e293b; +} + +.agent-detail-close { + background: none; + border: none; + font-size: 1.5rem; + cursor: pointer; + color: #64748b; + line-height: 1; + padding: 0.2rem; +} + +.agent-detail-body { + padding: 1rem 1.25rem; + overflow-y: auto; + flex: 1; +} + +.agent-detail-section { + margin-bottom: 1.2rem; +} + +.agent-detail-section h3 { + margin: 0 0 0.5rem; + font-size: 0.85rem; + font-weight: 700; + color: #475569; + text-transform: uppercase; + letter-spacing: 0.03em; +} + +.agent-detail-section code { + padding: 0.1rem 0.3rem; + border-radius: 4px; + background: rgba(79, 70, 229, 0.08); + font-size: 0.75rem; + color: #4f46e5; +} + + diff --git a/src/web/app.js b/src/web/app.js index a17e553..346c69d 100644 --- a/src/web/app.js +++ b/src/web/app.js @@ -23,6 +23,7 @@ const drawerBackdrop = $("drawerBackdrop"); const bgImageLayer = $("bgImageLayer"); const outputModeSelect = $("outputModeSelect"); + const agentModeSelect = $("agentModeSelect"); const dofRange = $("dofRange"); const dofValue = $("dofValue"); const vignetteRange = $("vignetteRange"); @@ -31,7 +32,6 @@ const STORAGE_DOF = "minor_web_bg_dof"; const STORAGE_VIG = "minor_web_bg_vignette"; const STORAGE_HEADER_COLLAPSED = "minor_web_header_collapsed"; - /** @type {string} */ let sessionId = ""; /** @type {string[]} */ @@ -43,6 +43,7 @@ let recorderStream = null; let recordedChunks = []; let recordingMimeType = ""; + let sidebarCollapsed = false; // 自动语音输入相关变量 let autoInputEnabled = false; let audioContext = null; @@ -60,7 +61,6 @@ /** 最近一次 renderMessages 的 JSON 可序列化快照 */ let lastRenderedMessages = []; let activeAudioController = null; - async function api(path, opts = {}) { const r = await fetch(path, { ...opts, @@ -720,6 +720,10 @@ }); } + if (msg.meta && msg.meta.pending_action) { + bubble.appendChild(renderPendingActionCard(msg.meta.pending_action)); + } + scrollChatToBottom(); } @@ -748,6 +752,16 @@ return wrap; } + function showPendingActionInThinking(typingEl, msg) { + if (!typingEl || !msg || !msg.meta || !msg.meta.pending_action) return false; + const bubble = typingEl.querySelector(".msg-bubble"); + if (!bubble) return false; + bubble.appendChild(renderPendingActionCard(msg.meta.pending_action)); + lastRenderedMessages = lastRenderedMessages.concat([JSON.parse(JSON.stringify(msg))]); + scrollChatToBottom(); + return true; + } + function renderAudioBubble(part, options = {}) { const { autoplay = false, @@ -952,6 +966,336 @@ return div; } + function renderPendingActionCard(pending) { + const card = document.createElement("div"); + const resolved = Boolean(pending && pending.resolved); + const pendingType = pending && pending.type ? String(pending.type) : "tool_call"; + const interactionType = pending && pending.interaction_type ? String(pending.interaction_type) : ""; + card.className = "pending-action-card" + (resolved ? " pending-action-card--resolved" : "") + ` pending-action-card--${pendingType}` + (interactionType ? ` pending-action-card--${interactionType}` : ""); + const title = document.createElement("div"); + title.className = "pending-action-title"; + title.textContent = pending.title || "待确认执行"; + card.appendChild(title); + + if (pending.prompt) { + const prompt = document.createElement("div"); + prompt.className = "pending-action-prompt"; + prompt.textContent = pending.prompt; + card.appendChild(prompt); + } + + const textarea = document.createElement("textarea"); + textarea.className = "pending-action-editor"; + textarea.value = pending.instruction || ""; + textarea.disabled = resolved; + + const isTextChoiceCard = pendingType === "human_interaction"; + if (isTextChoiceCard) { + if (interactionType === "selection" && Array.isArray(pending.options) && pending.options.length > 0 && pendingType === "human_interaction") { + const optionWrap = document.createElement("div"); + optionWrap.className = "pending-action-options"; + pending.options.forEach((option, idx) => { + const btn = document.createElement("button"); + btn.type = "button"; + btn.className = "pending-action-option"; + btn.textContent = String(option); + btn.disabled = resolved; + btn.addEventListener("click", () => { + textarea.value = String(option); + textarea.focus(); + }); + if (idx === 0 && !textarea.value) textarea.value = String(option); + optionWrap.appendChild(btn); + }); + card.appendChild(optionWrap); + } + textarea.placeholder = interactionType === "information" ? "在这里补充信息..." : "可直接编辑选择内容..."; + textarea.rows = Math.min(8, Math.max(2, textarea.value.split("\n").length)); + card.appendChild(textarea); + + const actions = document.createElement("div"); + actions.className = "pending-action-buttons"; + const makeActionBtn = (label, decision, className = decision) => { + const btn = document.createElement("button"); + btn.type = "button"; + btn.className = `pending-action-btn pending-action-btn--${className}`; + btn.textContent = label; + btn.disabled = resolved; + btn.addEventListener("click", () => handleHumanAction(pending.id, decision, textarea.value, card).catch((e) => alert(e.message || String(e)))); + return btn; + }; + + if (interactionType === "approval") { + textarea.readOnly = true; + actions.appendChild(makeActionBtn("同意", "approve")); + actions.appendChild(makeActionBtn("拒绝", "reject")); + } else { + const okBtn = makeActionBtn("✓", interactionType === "selection" ? "edit" : "approve", "approve"); + okBtn.title = interactionType === "information" ? "提交补充信息" : "确认"; + actions.appendChild(okBtn); + } + card.appendChild(actions); + return card; + } + + textarea.rows = Math.min(10, Math.max(4, textarea.value.split("\n").length)); + textarea.readOnly = true; + card.appendChild(textarea); + + const actions = document.createElement("div"); + actions.className = "pending-action-buttons"; + + const makeBtn = (label, decision) => { + const btn = document.createElement("button"); + btn.type = "button"; + btn.className = `pending-action-btn pending-action-btn--${decision}`; + btn.textContent = label; + btn.disabled = resolved; + btn.addEventListener("click", () => handleHumanAction(pending.id, decision, textarea.value, card).catch((e) => alert(e.message || String(e)))); + return btn; + }; + actions.appendChild(makeBtn("同意", "approve")); + actions.appendChild(makeBtn("拒绝", "reject")); + card.appendChild(actions); + return card; + } + + async function handleHumanAction(approvalId, decision, instruction, card) { + if (!approvalId || sending) return; + sending = true; + sendBtn.disabled = true; + if (card) card.classList.add("pending-action-card--resolved"); + const fd = new FormData(); + fd.append("session_id", sessionId); + fd.append("approval_id", approvalId); + fd.append("decision", decision); + fd.append("instruction", instruction || ""); + fd.append("output_type", outputModeSelect ? outputModeSelect.value : "text"); + let typingEl = appendThinkingIndicator(); + try { + const done = await api("/api/human-action", { method: "POST", body: fd }); + if (typingEl && typingEl.parentNode) typingEl.remove(); + typingEl = null; + sessionId = done.sessionId; + sessions = done.sessions || []; + renderSessions(); + const messages = done.messages || []; + const last = messages[messages.length - 1]; + if (last && last.role === "assistant") { + renderMessages(messages.slice(0, -1)); + await streamAssistantMessage(last); + } else { + renderMessages(messages); + } + } finally { + if (typingEl && typingEl.parentNode) typingEl.remove(); + sending = false; + sendBtn.disabled = false; + } + } + + // ===== 工具调用展示（chat 视图） ===== + let toolCallDataCache = { recent: [], detail: [], complete: false }; + let toolCallTimer = null; + + async function fetchToolCallData() { + if (!sessionId) return; + try { + const [recentRes, detailRes, pendingRes] = await Promise.all([ + api(`/api/tool-calls/${encodeURIComponent(sessionId)}/recent`), + api(`/api/tool-calls/${encodeURIComponent(sessionId)}/detail`), + api(`/api/tool-calls/${encodeURIComponent(sessionId)}/has-pending`), + ]); + toolCallDataCache = { + recent: recentRes.tool_calls || [], + detail: detailRes.details || [], + complete: pendingRes.is_complete, + }; + } catch { + /* ignore */ + } + } + + function startToolCallPolling() { + fetchToolCallData(); + toolCallTimer = setInterval(fetchToolCallData, 2000); + } + + function stopToolCallPolling(keepDisplay = false) { + if (toolCallTimer) { + clearInterval(toolCallTimer); + toolCallTimer = null; + } + if (!keepDisplay) { + // 清除过期工具调用显示（渐变消失） + document.querySelectorAll(".tool-call-block--fading").forEach((el) => { + el.addEventListener("transitionend", () => el.remove()); + el.style.opacity = "0"; + }); + } + } + + function renderToolCallBlock(call, index, total, isComplete) { + const block = document.createElement("div"); + block.className = "tool-call-block" + (isComplete ? " tool-call-block--fading" : ""); + + const header = document.createElement("div"); + header.className = "tool-call-header"; + const name = document.createElement("span"); + name.className = "tool-call-name"; + name.textContent = `🔧 ${escapeHtml(call.name || "")}`; + const args = document.createElement("span"); + args.className = "tool-call-args"; + args.textContent = call.args_summary || ""; + const status = document.createElement("span"); + status.className = "tool-call-status"; + status.textContent = call.status === "done" ? "✓" : "…"; + header.appendChild(name); + header.appendChild(args); + header.appendChild(status); + block.appendChild(header); + + return block; + } + + function renderToolCallDetail(call) { + const detail = document.createElement("div"); + detail.className = "tool-call-detail"; + + const name = document.createElement("div"); + name.className = "tool-call-detail-name"; + name.textContent = `🔧 ${escapeHtml(call.name || "unknown")}`; + detail.appendChild(name); + + if (call.args && Object.keys(call.args).length > 0) { + const argsEl = document.createElement("pre"); + argsEl.className = "tool-call-detail-text"; + argsEl.textContent = JSON.stringify(call.args, null, 2); + detail.appendChild(argsEl); + } + + if (call.result_text) { + const resultEl = document.createElement("pre"); + resultEl.className = "tool-call-detail-text tool-call-detail-result"; + resultEl.textContent = call.result_text; + detail.appendChild(resultEl); + } + + if (call.result_images && call.result_images.length > 0) { + call.result_images.forEach((imgUrl) => { + const img = document.createElement("img"); + img.src = imgUrl; + img.className = "tool-call-detail-img"; + img.alt = "工具返回图片"; + img.style.maxWidth = "160px"; + img.style.opacity = "0.5"; + detail.appendChild(img); + }); + } + + return detail; + } + + function injectLiveToolCalls(typingEl, liveCalls) { + if (!typingEl || !liveCalls.length) return; + // 查找或创建工具调用容器 + let liveWrap = typingEl.querySelector(".tool-call-live-wrap"); + if (!liveWrap) { + liveWrap = document.createElement("div"); + liveWrap.className = "tool-call-live-wrap"; + typingEl.appendChild(liveWrap); + } + // 更新显示 + liveWrap.innerHTML = ""; + liveCalls.forEach((call, i) => { + const chip = document.createElement("span"); + chip.className = "tool-call-live-chip"; + const statusIcon = call.status === "running" ? "⏳" : "✓"; + chip.textContent = `${statusIcon} ${escapeHtml(call.name || "")}`; + chip.style.opacity = Math.max(0.3, 1 - i * 0.15); + if (call.status === "running") chip.classList.add("tool-call-live-chip--running"); + liveWrap.appendChild(chip); + }); + scrollChatToBottom(); + } + + function buildToolCallBubble() { + const data = toolCallDataCache; + if (!data.recent.length && !data.detail.length) return null; + + const total = data.recent.length; + const visibleCount = Math.min(2, total); + const moreCount = total - visibleCount; + + const wrap = document.createElement("div"); + wrap.className = "tool-call-bubble-wrap"; + + // 折叠按钮 + const toggleBtn = document.createElement("button"); + toggleBtn.type = "button"; + toggleBtn.className = "tool-call-toggle"; + toggleBtn.textContent = `🔧 工具调用 (${total}) ▸`; + toggleBtn.addEventListener("click", () => { + const expanded = wrap.classList.toggle("tool-call-bubble-wrap--expanded"); + toggleBtn.textContent = `🔧 工具调用 (${total}) ${expanded ? "▾" : "▸"}`; + }); + + // 折叠态：只显示最近2个工具名称，渐变透明度动态消失 + const collapsed = document.createElement("div"); + collapsed.className = "tool-call-collapsed"; + data.recent.slice(-visibleCount).forEach((call, i) => { + const chip = document.createElement("span"); + chip.className = "tool-call-chip"; + chip.textContent = call.name || ""; + // 越旧的越透明 + chip.style.opacity = Math.max(0.25, 1 - (visibleCount - 1 - i) * 0.35); + collapsed.appendChild(chip); + }); + if (moreCount > 0) { + const more = document.createElement("span"); + more.className = "tool-call-more"; + more.textContent = `+${moreCount}`; + more.style.cursor = "pointer"; + more.addEventListener("click", () => { + wrap.classList.add("tool-call-bubble-wrap--expanded"); + toggleBtn.textContent = `🔧 工具调用 (${total}) ▾`; + }); + collapsed.appendChild(more); + } + + // 展开态：显示所有详情 + const expanded = document.createElement("div"); + expanded.className = "tool-call-expanded"; + data.detail.forEach((call) => { + expanded.appendChild(renderToolCallDetail(call)); + }); + + wrap.appendChild(toggleBtn); + wrap.appendChild(collapsed); + wrap.appendChild(expanded); + + return wrap; + } + + function injectToolCallDisplay(typingEl) { + if (!typingEl) return; + if (!toolCallDataCache.recent.length && !toolCallDataCache.detail.length) return; + + const existingBlock = typingEl.querySelector(".tool-call-bubble-wrap"); + if (existingBlock) { + // 更新现有显示 + const newBlock = buildToolCallBubble(); + if (newBlock) existingBlock.replaceWith(newBlock); + return; + } + + const block = buildToolCallBubble(); + if (block) { + typingEl.appendChild(block); + scrollChatToBottom(); + } + } + function renderMessage(msg, allowAutoplay = false) { const wrap = document.createElement("div"); wrap.className = "msg " + (msg.role === "user" ? "user" : "assistant") + (isAudioOnlyAssistantMessage(msg) ? " msg--audio-only" : ""); @@ -991,6 +1335,9 @@ } else { bubble.textContent = c == null ? "" : String(c); } + if (msg.meta && msg.meta.pending_action) { + bubble.appendChild(renderPendingActionCard(msg.meta.pending_action)); + } wrap.appendChild(bubble); return wrap; } @@ -1130,12 +1477,33 @@ revokeBlobUrlStack(blobUrls); typingEl = appendThinkingIndicator(); + // 清空上轮工具调用缓存（新 turn） + toolCallDataCache = { recent: [], detail: [], complete: false }; + + // 启动实时工具调用轮询 + let livePollTimer = null; + let livePollActive = true; + const pollLiveToolCalls = async () => { + if (!livePollActive) return; + try { + const liveData = await api(`/api/tool-calls/${encodeURIComponent(sessionId)}/live`); + if (liveData.tool_calls && liveData.tool_calls.length > 0 && typingEl) { + injectLiveToolCalls(typingEl, liveData.tool_calls); + } + } catch { /* ignore */ } + if (livePollActive) livePollTimer = setTimeout(pollLiveToolCalls, 800); + }; + pollLiveToolCalls(); + const fd2 = new FormData(); fd2.append("session_id", sessionId); fd2.append("output_type", outputModeSelect ? outputModeSelect.value : "text"); + fd2.append("agent_mode", agentModeSelect ? agentModeSelect.value : "agent"); const done = await api("/api/chat/complete", { method: "POST", body: fd2 }); - if (typingEl && typingEl.parentNode) typingEl.remove(); - typingEl = null; + + // 停止轮询 + livePollActive = false; + if (livePollTimer) clearTimeout(livePollTimer); sessionId = done.sessionId; sessions = done.sessions || []; @@ -1143,14 +1511,28 @@ const messages = done.messages || []; const last = messages[messages.length - 1]; - if (last && last.role === "assistant") { - const head = messages.slice(0, -1); - renderMessages(head); - revokeBlobUrlStack(blobUrls); - await streamAssistantMessage(last); + if (last && last.role === "assistant" && last.meta && last.meta.pending_action && typingEl) { + showPendingActionInThinking(typingEl, last); + typingEl = null; } else { - renderMessages(messages); - revokeBlobUrlStack(blobUrls); + if (typingEl && typingEl.parentNode) typingEl.remove(); + typingEl = null; + if (last && last.role === "assistant") { + const head = messages.slice(0, -1); + renderMessages(head); + revokeBlobUrlStack(blobUrls); + await streamAssistantMessage(last); + // 获取工具调用数据并注入到最后一条助手消息 + await fetchToolCallData(); + const lastBubble = chatMessages.querySelector(".msg.assistant:last-child .msg-bubble"); + if (lastBubble && toolCallDataCache.recent.length) { + const toolBlock = buildToolCallBubble(); + if (toolBlock) lastBubble.appendChild(toolBlock); + } + } else { + renderMessages(messages); + revokeBlobUrlStack(blobUrls); + } } } catch (e) { revokeBlobUrlStack(blobUrls); @@ -1347,6 +1729,1115 @@ }); loadHeaderPref(); + // ===== 系统配置模态框 ===== + const settingsBtn = $("settingsBtn"); + const settingsOverlay = $("settingsOverlay"); + const settingsCloseBtn = $("settingsCloseBtn"); + const settingsCancelBtn = $("settingsCancelBtn"); + const settingsStatus = $("settingsStatus"); + const agentsListEl = $("agentsList"); + const toolsListEl = $("toolsList"); + const envListEl = $("envList"); + const agentConfigPath = $("agentConfigPath"); + const toolConfigPath = $("toolConfigPath"); + const envConfigPath = $("envConfigPath"); + const addAgentBtn = $("addAgentBtn"); + + let cachedAgentConfigs = []; + let cachedToolConfigs = []; + let cachedEnvConfig = {}; + let cachedModels = []; + let registeredToolNames = []; + + // 模型编辑状态 + let editingModelIdx = -1; + + function setStatus(msg, type) { + if (!settingsStatus) return; + settingsStatus.textContent = msg; + settingsStatus.className = "settings-status" + (type ? " " + type : ""); + if (type === "success" || type === "error") { + setTimeout(() => { if (settingsStatus.textContent === msg) setStatus(""); }, 3000); + } + } + + function openSettings() { + settingsOverlay.hidden = false; + loadAllConfigs().catch((e) => setStatus("加载配置失败: " + e.message, "error")); + } + + function closeSettings() { + settingsOverlay.hidden = true; + setStatus(""); + } + + // Tab 切换 + document.querySelectorAll(".settings-tab").forEach((tab) => { + tab.addEventListener("click", () => { + const target = tab.getAttribute("data-tab"); + document.querySelectorAll(".settings-tab").forEach((t) => t.classList.remove("active")); + tab.classList.add("active"); + document.querySelectorAll(".settings-panel").forEach((p) => p.hidden = p.getAttribute("data-panel") !== target); + // 切换时重新渲染对应面板 + if (target === "agent") renderAgentConfigs(); + else if (target === "tool") renderToolConfigs(); + else if (target === "env") renderEnvConfig(); + }); + }); + + // ===== 加载所有配置 ===== + async function loadAllConfigs() { + const [agentRes, toolRes, envRes] = await Promise.all([ + api("/api/config/agents"), + api("/api/config/tools"), + api("/api/config/env"), + ]); + cachedAgentConfigs = agentRes.agents || []; + cachedToolConfigs = toolRes.tools || []; + cachedEnvConfig = envRes.env || {}; + cachedModels = envRes.models || []; + registeredToolNames = toolRes.registered_tool_names || []; + window._toolParameters = toolRes.tool_parameters || {}; + window._forcedPermissions = toolRes.forced_permissions || {}; + if (agentConfigPath) agentConfigPath.textContent = agentRes.config_path || ""; + if (toolConfigPath) toolConfigPath.textContent = toolRes.config_path || ""; + if (envConfigPath) envConfigPath.textContent = envRes.config_path || ""; + renderAgentConfigs(); + renderToolConfigs(); + renderEnvConfig(); + } + + // ===== Agent 配置渲染 ===== + function renderAgentConfigs() { + agentsListEl.innerHTML = ""; + cachedAgentConfigs.forEach((agent, idx) => { + agentsListEl.appendChild(buildAgentCard(agent, idx)); + }); + } + + function getModelLabel(modelId) { + if (!modelId) return "— 未选择 —"; + const m = cachedModels.find((mod) => mod.id === modelId); + return m ? `${m.name} (${m.model})` : modelId; + } + + function buildAgentCard(agent, idx) { + const card = document.createElement("div"); + card.className = "agent-config-card"; + card.dataset.index = idx; + + // 头部 + const header = document.createElement("div"); + header.className = "agent-card-header"; + const h4 = document.createElement("h4"); + h4.textContent = `Agent: ${agent.name || "(未命名)"}`; + const removeBtn = document.createElement("button"); + removeBtn.className = "agent-card-remove"; + removeBtn.textContent = "✕"; + removeBtn.title = "删除此 Agent"; + removeBtn.addEventListener("click", () => { + const wasMain = agent.role === "main"; + cachedAgentConfigs.splice(idx, 1); + if (wasMain && cachedAgentConfigs.length > 0) { + // 如果删的是主 agent，第一个自动升为主 agent + cachedAgentConfigs[0].role = "main"; + } + renderAgentConfigs(); + }); + header.appendChild(h4); + header.appendChild(removeBtn); + + // Name + const nameField = makeField("名称", "text", agent.name || "", (v) => { agent.name = v; }); + // max_iterations + const maxIterField = makeField("最大迭代次数", "number", agent.max_iterations || 200, (v) => { agent.max_iterations = parseInt(v) || 200; }); + // system_prompt + const spField = makeField("系统提示词 (system_prompt)", "textarea", agent.system_prompt || "", (v) => { agent.system_prompt = v; }); + + // LLM 模型选择 + const llmField = document.createElement("div"); + llmField.className = "setting-field"; + const llmLabel = document.createElement("label"); + llmLabel.textContent = "LLM 模型"; + const llmRow = document.createElement("div"); + llmRow.style.cssText = "display:flex;gap:0.4rem;align-items:center;"; + const llmSelect = document.createElement("select"); + llmSelect.style.cssText = "flex:1;"; + const emptyOpt = document.createElement("option"); + emptyOpt.value = ""; + emptyOpt.textContent = "— 未选择 —"; + llmSelect.appendChild(emptyOpt); + cachedModels.forEach((m) => { + const opt = document.createElement("option"); + opt.value = m.id; + opt.textContent = `${m.name} (${m.model})`; + if (m.id === agent.llm_model_id) opt.selected = true; + llmSelect.appendChild(opt); + }); + if (!cachedModels.some((m) => m.id === agent.llm_model_id) && agent.llm_model_id) { + const opt = document.createElement("option"); + opt.value = agent.llm_model_id; + opt.textContent = agent.llm_model_id + " (已删除)"; + opt.selected = true; + llmSelect.appendChild(opt); + } + llmSelect.addEventListener("change", () => { + agent.llm_model_id = llmSelect.value; + }); + llmRow.appendChild(llmSelect); + llmField.appendChild(llmLabel); + llmField.appendChild(llmRow); + + // 角色与启用 + const metaRow = document.createElement("div"); + metaRow.className = "setting-field"; + metaRow.style.cssText = "display:flex;gap:1rem;align-items:center;flex-wrap:wrap;"; + + // 角色 + const roleWrap = document.createElement("div"); + roleWrap.style.cssText = "display:flex;align-items:center;gap:0.4rem;"; + const roleLabel = document.createElement("label"); + roleLabel.textContent = "角色:"; + roleLabel.style.cssText = "font-size:0.82rem;font-weight:600;"; + const roleSelect = document.createElement("select"); + roleSelect.innerHTML = ''; + roleSelect.value = agent.role || "sub"; + roleSelect.addEventListener("change", () => { + if (roleSelect.value === "main") { + // 将所有其他 agent 设为 sub + cachedAgentConfigs.forEach((a) => { a.role = "sub"; }); + agent.role = "main"; + renderAgentConfigs(); + } else { + agent.role = "sub"; + // 确保至少有一个 main + const hasMain = cachedAgentConfigs.some((a) => a.role === "main"); + if (!hasMain) { + // 找第一个非此 agent 的设为 main + const other = cachedAgentConfigs.find((a, i) => i !== idx); + if (other) other.role = "main"; + renderAgentConfigs(); + } + } + }); + roleWrap.appendChild(roleLabel); + roleWrap.appendChild(roleSelect); + + // 启用（开关） + const enabledWrap = document.createElement("div"); + enabledWrap.className = "toggle-row"; + const toggleLabel = document.createElement("label"); + toggleLabel.className = "toggle-switch"; + const toggleInput = document.createElement("input"); + toggleInput.type = "checkbox"; + toggleInput.checked = agent.enabled !== false; + toggleInput.addEventListener("change", () => { agent.enabled = toggleInput.checked; }); + const toggleSlider = document.createElement("span"); + toggleSlider.className = "toggle-slider"; + toggleLabel.appendChild(toggleInput); + toggleLabel.appendChild(toggleSlider); + const enabledLabel = document.createElement("span"); + enabledLabel.className = "toggle-label"; + enabledLabel.textContent = "激活"; + enabledLabel.addEventListener("click", () => { toggleInput.click(); }); + enabledWrap.appendChild(toggleLabel); + enabledWrap.appendChild(enabledLabel); + + metaRow.appendChild(roleWrap); + metaRow.appendChild(enabledWrap); + + // 工具选择（列表式：下拉 + 添加/删除） + const toolsField = document.createElement("div"); + toolsField.className = "setting-field"; + const toolsLabel = document.createElement("label"); + toolsLabel.textContent = "工具列表"; + toolsField.appendChild(toolsLabel); + + // 已选工具列表 + const selectedToolsList = document.createElement("div"); + selectedToolsList.style.cssText = "display:flex;flex-wrap:wrap;gap:0.25rem;margin-bottom:0.35rem;min-height:1.5rem;"; + + function renderSelectedTools() { + selectedToolsList.innerHTML = ""; + (agent.tools || []).forEach((tname) => { + const chip = document.createElement("span"); + chip.style.cssText = "display:inline-flex;align-items:center;gap:0.2rem;padding:0.15rem 0.4rem;background:rgba(79,70,229,0.12);border-radius:6px;font-size:0.78rem;"; + chip.textContent = tname; + const delBtn = document.createElement("span"); + delBtn.textContent = "×"; + delBtn.style.cssText = "cursor:pointer;color:#991b1b;font-weight:700;margin-left:0.15rem;"; + delBtn.title = "移除此工具"; + delBtn.addEventListener("click", () => { + agent.tools = (agent.tools || []).filter((t) => t !== tname); + renderSelectedTools(); + }); + chip.appendChild(delBtn); + selectedToolsList.appendChild(chip); + }); + } + renderSelectedTools(); + toolsField.appendChild(selectedToolsList); + + // 添加工具行 + const addToolRow = document.createElement("div"); + addToolRow.style.cssText = "display:flex;gap:0.4rem;align-items:center;"; + const toolSelect = document.createElement("select"); + toolSelect.style.cssText = "flex:1;"; + const emptyToolOpt = document.createElement("option"); + emptyToolOpt.value = ""; + emptyToolOpt.textContent = "选择工具..."; + toolSelect.appendChild(emptyToolOpt); + registeredToolNames.forEach((tname) => { + if (!(agent.tools || []).includes(tname)) { + const opt = document.createElement("option"); + opt.value = tname; + opt.textContent = tname; + toolSelect.appendChild(opt); + } + }); + const addToolBtn = document.createElement("button"); + addToolBtn.type = "button"; + addToolBtn.className = "btn btn-ghost"; + addToolBtn.style.cssText = "padding:0.2rem 0.6rem;font-size:0.78rem;white-space:nowrap;"; + addToolBtn.textContent = "+ 添加"; + addToolBtn.addEventListener("click", () => { + const val = toolSelect.value; + if (!val) return; + if (!agent.tools) agent.tools = []; + if (!agent.tools.includes(val)) { + agent.tools.push(val); + renderSelectedTools(); + // 移除已选项 + Array.from(toolSelect.options).forEach((o) => { if (o.value === val) o.remove(); }); + toolSelect.value = ""; + } + }); + addToolRow.appendChild(toolSelect); + addToolRow.appendChild(addToolBtn); + toolsField.appendChild(addToolRow); + + card.appendChild(header); + card.appendChild(nameField); + card.appendChild(maxIterField); + card.appendChild(spField); + card.appendChild(llmField); + card.appendChild(metaRow); + card.appendChild(toolsField); + return card; + } + + function makeField(label, type, value, onChange) { + const div = document.createElement("div"); + div.className = "setting-field"; + const lbl = document.createElement("label"); + lbl.textContent = label; + let input; + if (type === "textarea") { + input = document.createElement("textarea"); + input.rows = 3; + } else { + input = document.createElement("input"); + input.type = type; + } + input.value = value ?? ""; + input.addEventListener("input", () => onChange(input.value)); + if (type === "number") input.step = "any"; + div.appendChild(lbl); + div.appendChild(input); + return div; + } + + addAgentBtn.addEventListener("click", () => { + // 新 agent 默认为 sub，除非是第一个 + const isFirst = cachedAgentConfigs.length === 0; + cachedAgentConfigs.push({ + name: "new_agent", + system_prompt: "", + max_iterations: 200, + llm: { model: "", api_key: "", base_url: "", timeout: 60, max_retries: 0 }, + llm_model_id: cachedModels.length > 0 ? cachedModels[0].id : "", + tools: [], + enabled: true, + role: isFirst ? "main" : "sub", + }); + renderAgentConfigs(); + }); + + // ===== Tool 配置渲染 ===== + function renderToolConfigs() { + toolsListEl.innerHTML = ""; + cachedToolConfigs.forEach((tool, idx) => { + toolsListEl.appendChild(buildToolCard(tool, idx)); + }); + } + + function buildToolCard(tool, idx) { + const card = document.createElement("div"); + card.className = "tool-config-card" + (tool.enabled === false ? " disabled" : ""); + + const title = document.createElement("h4"); + const enabledCheck = document.createElement("input"); + enabledCheck.type = "checkbox"; + enabledCheck.checked = tool.enabled !== false; + enabledCheck.addEventListener("change", () => { + tool.enabled = enabledCheck.checked; + card.classList.toggle("disabled", !tool.enabled); + }); + title.appendChild(enabledCheck); + title.appendChild(document.createTextNode(` ${tool.display_name || tool.name}`)); + + const desc = document.createElement("div"); + desc.className = "tool-card-desc"; + desc.textContent = tool.description || ""; + + const row = document.createElement("div"); + row.className = "tool-card-row"; + + // 权限选择 + const forcedPerm = (window._forcedPermissions || {})[tool.name]; + const permLabel = document.createElement("label"); + permLabel.textContent = "权限: "; + permLabel.style.fontSize = "0.82rem"; + permLabel.style.fontWeight = "600"; + const permSelect = document.createElement("select"); + permSelect.innerHTML = ''; + permSelect.value = tool.permission || "confirm"; + if (forcedPerm) { + permSelect.disabled = true; + permSelect.value = forcedPerm; + tool.permission = forcedPerm; + permLabel.textContent = "权限（锁定）: "; + } + row.appendChild(permLabel); + row.appendChild(permSelect); + + card.appendChild(title); + card.appendChild(desc); + card.appendChild(row); + + // ---------- 自动执行规则（非强制 confirm 且含参数的工具） ---------- + const toolParams = (window._toolParameters || {})[tool.name] || []; + const hasParams = toolParams.length > 0; + const rulesWrap = document.createElement("div"); + + function updateRulesVisibility() { + const visible = !forcedPerm && tool.permission === "confirm" && hasParams; + rulesWrap.hidden = !visible; + } + + if (hasParams) { + rulesWrap.className = "auto-rules-section"; + const rulesLabel = document.createElement("div"); + rulesLabel.style.cssText = "font-size:0.78rem;font-weight:600;color:#475569;margin:0.5rem 0 0.3rem;"; + rulesLabel.textContent = "自动执行规则（满足任一条件则跳过确认）:"; + rulesWrap.appendChild(rulesLabel); + + const rulesList = document.createElement("div"); + rulesList.className = "auto-rules-list"; + + const renderRules = () => { + rulesList.innerHTML = ""; + (tool.auto_execute_rules || []).forEach((rule, ri) => { + const ruleRow = document.createElement("div"); + ruleRow.className = "auto-rule-row"; + ruleRow.style.cssText = "display:flex;align-items:center;gap:0.3rem;margin-bottom:0.25rem;flex-wrap:wrap;"; + + const paramLabel = document.createElement("span"); + paramLabel.style.cssText = "font-size:0.74rem;font-weight:600;"; + paramLabel.textContent = "当"; + ruleRow.appendChild(paramLabel); + + const paramSelect = document.createElement("select"); + paramSelect.style.cssText = "flex:0 0 auto;min-width:0;"; + toolParams.forEach((p) => { + const opt = document.createElement("option"); + opt.value = p.name; + opt.textContent = p.name; + if (p.type) opt.title = `类型: ${p.type}`; + if (p.name === (rule.parameter || "")) opt.selected = true; + paramSelect.appendChild(opt); + }); + if (rule.parameter && !toolParams.some((p) => p.name === rule.parameter)) { + const opt = document.createElement("option"); + opt.value = rule.parameter; + opt.textContent = rule.parameter; + opt.selected = true; + paramSelect.appendChild(opt); + } + paramSelect.addEventListener("change", () => { rule.parameter = paramSelect.value; }); + ruleRow.appendChild(paramSelect); + + const opLabel = document.createElement("span"); + opLabel.style.cssText = "font-size:0.74rem;"; + opLabel.textContent = "="; + ruleRow.appendChild(opLabel); + + const valInput = document.createElement("input"); + valInput.type = "text"; + valInput.value = rule.value || ""; + valInput.placeholder = "期望值"; + valInput.style.cssText = "flex:1;min-width:80px;border:1px solid rgba(148,163,184,0.5);border-radius:6px;padding:0.2rem 0.35rem;font-size:0.8rem;"; + valInput.addEventListener("input", () => { rule.value = valInput.value; }); + ruleRow.appendChild(valInput); + + const delBtn = document.createElement("button"); + delBtn.type = "button"; + delBtn.style.cssText = "border:none;background:none;cursor:pointer;color:#991b1b;font-size:0.9rem;padding:0 0.2rem;"; + delBtn.textContent = "×"; + delBtn.title = "删除此规则"; + delBtn.addEventListener("click", () => { + tool.auto_execute_rules = (tool.auto_execute_rules || []).filter((_, i) => i !== ri); + renderRules(); + }); + ruleRow.appendChild(delBtn); + + rulesList.appendChild(ruleRow); + }); + }; + renderRules(); + + const addRuleRow = document.createElement("div"); + addRuleRow.style.cssText = "margin-top:0.35rem;"; + const addRuleBtn = document.createElement("button"); + addRuleBtn.type = "button"; + addRuleBtn.className = "btn btn-ghost"; + addRuleBtn.style.cssText = "padding:0.15rem 0.5rem;font-size:0.75rem;"; + addRuleBtn.textContent = "+ 添加规则"; + addRuleBtn.addEventListener("click", () => { + const defaultParam = toolParams[0].name; + tool.auto_execute_rules = [...(tool.auto_execute_rules || []), { parameter: defaultParam, operator: "equals", value: "" }]; + renderRules(); + }); + addRuleRow.appendChild(addRuleBtn); + rulesWrap.appendChild(rulesList); + rulesWrap.appendChild(addRuleRow); + } + + card.appendChild(rulesWrap); + updateRulesVisibility(); + + permSelect.addEventListener("change", () => { + tool.permission = permSelect.value; + updateRulesVisibility(); + }); + + return card; + } + + // ===== Env 配置渲染 ===== + function renderEnvConfig() { + envListEl.innerHTML = ""; + + // ---------- 模型管理区 ---------- + const modelsSection = document.createElement("div"); + modelsSection.style.cssText = "margin-bottom:1.5rem;"; + + const modelsHeader = document.createElement("div"); + modelsHeader.className = "settings-section-title"; + const modelsTitle = document.createElement("h4"); + modelsTitle.textContent = "LLM 模型列表"; + const addModelBtn = document.createElement("button"); + addModelBtn.type = "button"; + addModelBtn.className = "btn btn-primary"; + addModelBtn.textContent = "+ 添加模型"; + addModelBtn.addEventListener("click", () => openModelModal(-1)); + modelsHeader.appendChild(modelsTitle); + modelsHeader.appendChild(addModelBtn); + modelsSection.appendChild(modelsHeader); + + if (cachedModels.length === 0) { + const emptyHint = document.createElement("p"); + emptyHint.style.cssText = "color:#94a3b8;font-size:0.8rem;"; + emptyHint.textContent = "暂无模型，请添加至少一个 LLM 模型。"; + modelsSection.appendChild(emptyHint); + } else { + const table = document.createElement("div"); + table.className = "model-table"; + const thead = document.createElement("div"); + thead.className = "model-table-head"; + thead.innerHTML = '名称ModelBase URL操作'; + table.appendChild(thead); + cachedModels.forEach((m, mi) => { + const row = document.createElement("div"); + row.className = "model-table-row"; + row.innerHTML = `${escapeHtml(m.name)} + ${escapeHtml(m.model)} + ${escapeHtml(m.base_url)}`; + const actions = document.createElement("span"); + actions.className = "col-actions"; + const editBtn = document.createElement("button"); + editBtn.type = "button"; + editBtn.textContent = "编辑"; + editBtn.addEventListener("click", () => openModelModal(mi)); + const delBtn = document.createElement("button"); + delBtn.type = "button"; + delBtn.className = "danger"; + delBtn.textContent = "删除"; + delBtn.addEventListener("click", () => { + if (!confirm(`确定删除模型 "${m.name}"？`)) return; + cachedModels.splice(mi, 1); + // 清除引用该模型的 agent + cachedAgentConfigs.forEach((a) => { if (a.llm_model_id === m.id) a.llm_model_id = ""; }); + // 同时保存模型变更 + api("/api/config/models", { method: "POST", body: JSON.stringify({ models: cachedModels }) }).catch(() => {}); + api("/api/config/reload", { method: "POST", body: JSON.stringify({}) }).catch(() => {}); + renderEnvConfig(); + renderAgentConfigs(); + }); + actions.appendChild(editBtn); + actions.appendChild(delBtn); + row.appendChild(actions); + table.appendChild(row); + }); + modelsSection.appendChild(table); + } + envListEl.appendChild(modelsSection); + + // ---------- 模型编辑弹窗（内嵌在 env 面板中） ---------- + const modalOverlay = document.createElement("div"); + modalOverlay.className = "model-modal-overlay"; + modalOverlay.addEventListener("click", (e) => { if (e.target === modalOverlay) closeModelModal(); }); + const modalBox = document.createElement("div"); + modalBox.className = "model-modal-box"; + modalBox.innerHTML = '

模型配置

'; + const modalBody = document.createElement("div"); + modalBody.className = "model-modal-body"; + const modalFields = {}; + ["name", "model", "api_key", "base_url", "timeout"].forEach((key) => { + const div = document.createElement("div"); + div.className = "modal-field"; + const lbl = document.createElement("label"); + lbl.textContent = { name: "显示名称", model: "模型名 (model)", api_key: "API Key", base_url: "Base URL", timeout: "Timeout (秒)" }[key]; + const inp = document.createElement("input"); + inp.type = key === "timeout" ? "number" : key === "api_key" ? "password" : "text"; + if (key === "timeout") inp.step = "any"; + div.appendChild(lbl); + div.appendChild(inp); + modalFields[key] = inp; + modalBody.appendChild(div); + }); + modalBox.appendChild(modalBody); + const modalActions = document.createElement("div"); + modalActions.className = "model-modal-actions"; + const cancelBtn = document.createElement("button"); + cancelBtn.type = "button"; + cancelBtn.className = "btn btn-secondary"; + cancelBtn.textContent = "取消"; + cancelBtn.addEventListener("click", closeModelModal); + const saveBtn = document.createElement("button"); + saveBtn.type = "button"; + saveBtn.className = "btn btn-primary"; + saveBtn.textContent = "保存"; + saveBtn.addEventListener("click", async () => { + const data = { + id: editingModelIdx >= 0 && cachedModels[editingModelIdx] ? cachedModels[editingModelIdx].id : makeId(), + name: modalFields.name.value.trim(), + model: modalFields.model.value.trim(), + api_key: modalFields.api_key.value.trim(), + base_url: modalFields.base_url.value.trim(), + timeout: parseFloat(modalFields.timeout.value) || 60, + max_retries: 0, + }; + if (!data.name || !data.model) { alert("名称和模型名不能为空"); return; } + if (editingModelIdx >= 0) { + cachedModels[editingModelIdx] = data; + } else { + cachedModels.push(data); + } + await api("/api/config/models", { method: "POST", body: JSON.stringify({ models: cachedModels }) }); + closeModelModal(); + renderEnvConfig(); + renderAgentConfigs(); + setStatus("模型已保存", "success"); + }); + modalActions.appendChild(cancelBtn); + modalActions.appendChild(saveBtn); + modalBox.appendChild(modalActions); + modalOverlay.appendChild(modalBox); + envListEl.appendChild(modalOverlay); + + window._modelModalOverlay = modalOverlay; + window._modelModalFields = modalFields; + + // ---------- 分隔线 ---------- + const divider = document.createElement("div"); + divider.style.cssText = "border-top:1px solid rgba(148,163,184,0.25);margin:1rem 0;"; + envListEl.appendChild(divider); + + // ---------- 其他环境变量 ---------- + const otherTitle = document.createElement("h4"); + otherTitle.textContent = "其他环境变量"; + otherTitle.style.cssText = "margin:0 0 0.65rem;font-size:0.92rem;font-weight:700;color:#1e293b;"; + envListEl.appendChild(otherTitle); + + const entries = Object.entries(cachedEnvConfig); + entries.forEach(([key, val]) => { + const row = document.createElement("div"); + row.className = "env-config-item"; + const lbl = document.createElement("label"); + lbl.textContent = key; + const inp = document.createElement("input"); + inp.type = "text"; + inp.value = val ?? ""; + inp.addEventListener("input", () => { cachedEnvConfig[key] = inp.value; }); + row.appendChild(lbl); + row.appendChild(inp); + envListEl.appendChild(row); + }); + } + + function openModelModal(idx) { + editingModelIdx = idx; + const overlay = window._modelModalOverlay; + const fields = window._modelModalFields; + if (!overlay || !fields) return; + if (idx >= 0 && cachedModels[idx]) { + const m = cachedModels[idx]; + fields.name.value = m.name || ""; + fields.model.value = m.model || ""; + fields.api_key.value = m.api_key || ""; + fields.base_url.value = m.base_url || ""; + fields.timeout.value = m.timeout || 60; + } else { + fields.name.value = ""; + fields.model.value = ""; + fields.api_key.value = ""; + fields.base_url.value = ""; + fields.timeout.value = 60; + } + overlay.classList.add("show"); + } + + function closeModelModal() { + const overlay = window._modelModalOverlay; + if (overlay) overlay.classList.remove("show"); + } + + function makeId() { + return Math.random().toString(36).substring(2, 10); + } + + // ===== 保存回调 ===== + document.querySelectorAll(".settings-save-btn").forEach((btn) => { + btn.addEventListener("click", async () => { + const saveType = btn.getAttribute("data-save"); + setStatus("正在保存...", ""); + try { + if (saveType === "agents") { + const agents = cachedAgentConfigs.map((a) => ({ + name: a.name, + system_prompt: a.system_prompt, + max_iterations: a.max_iterations, + llm: { + model: (a.llm || {}).model || "", + api_key: (a.llm || {}).api_key || "", + base_url: (a.llm || {}).base_url || "", + timeout: (a.llm || {}).timeout || 60, + max_retries: (a.llm || {}).max_retries || 0, + }, + llm_model_id: a.llm_model_id || "", + tools: a.tools || [], + enabled: a.enabled !== false, + role: a.role || "sub", + })); + await api("/api/config/agents", { method: "POST", body: JSON.stringify({ agents }) }); + await api("/api/config/reload", { method: "POST", body: JSON.stringify({}) }); + } else if (saveType === "tools") { + const tools = cachedToolConfigs.map((t) => ({ + name: t.name, + display_name: t.display_name, + description: t.description, + permission: t.permission, + auto_execute_rules: t.auto_execute_rules || [], + enabled: t.enabled, + })); + await api("/api/config/tools", { method: "POST", body: JSON.stringify({ tools }) }); + await api("/api/config/reload", { method: "POST", body: JSON.stringify({}) }); + } else if (saveType === "env") { + // 保存 env + models + await api("/api/config/models", { method: "POST", body: JSON.stringify({ models: cachedModels }) }); + await api("/api/config/env", { method: "POST", body: JSON.stringify({ env: cachedEnvConfig }) }); + await api("/api/config/reload", { method: "POST", body: JSON.stringify({}) }); + } + await loadAllConfigs(); + setStatus("保存成功！配置已实时生效。", "success"); + } catch (e) { + setStatus("保存失败: " + e.message, "error"); + } + }); + }); + + // 事件绑定 + settingsBtn.addEventListener("click", openSettings); + settingsCloseBtn.addEventListener("click", closeSettings); + settingsCancelBtn.addEventListener("click", closeSettings); + settingsOverlay.addEventListener("click", (e) => { + if (e.target === settingsOverlay) closeSettings(); + }); + document.addEventListener("keydown", (e) => { + if (e.key === "Escape" && !settingsOverlay.hidden) closeSettings(); + if (e.key === "Escape" && agentDetailOverlay && !agentDetailOverlay.hidden) closeAgentDetailModal(); + }); + + // ========== 图谱侧面板 ========== + const graphToggleBtn = $("graphToggleBtn"); + const graphPanel = $("graphPanel"); + const graphCloseBtn = $("graphCloseBtn"); + const graphCanvas = $("graphCanvas"); + const graphCanvasWrap = $("graphCanvasWrap"); + const graphEdgesLayer = $("graphEdgesLayer"); + const graphNodesLayer = $("graphNodesLayer"); + const graphRefreshBtn = $("graphRefreshBtn"); + const graphFitBtn = $("graphFitBtn"); + + // 图谱数据与状态 + let graphData = { agents: [], edges: [], events: [], tool_calls: {} }; + let graphOpen = false; + // 可拖拽偏移 + let panOffset = { x: 80, y: 60 }; + let isPanning = false; + let panStart = { x: 0, y: 0 }; + let panStartOffset = { x: 0, y: 0 }; + // 节点布局参数 + const NODE_W = 160; + const NODE_H = 100; + + // 图谱开关（始终可聊天） + function toggleGraph(open) { + graphOpen = open; + graphPanel.hidden = !open; + graphToggleBtn.classList.toggle("graph-active", open); + appRoot.classList.toggle("has-graph", open); + if (open) { + loadGraphData(); + } + } + + graphToggleBtn.addEventListener("click", () => toggleGraph(!graphOpen)); + graphCloseBtn.addEventListener("click", () => toggleGraph(false)); + + // Agent 详情弹窗事件 + const agentDetailOverlay = document.getElementById("agentDetailOverlay"); + const agentDetailClose = document.getElementById("agentDetailClose"); + if (agentDetailClose) agentDetailClose.addEventListener("click", closeAgentDetailModal); + if (agentDetailOverlay) agentDetailOverlay.addEventListener("click", (e) => { + if (e.target === agentDetailOverlay) closeAgentDetailModal(); + }); + + // 加载图谱数据 + async function loadGraphData() { + try { + const snap = await api(`/api/graph/snapshot?session_id=${encodeURIComponent(sessionId)}`); + graphData = snap; + renderGraph(); + } catch (e) { + console.error("加载图谱数据失败:", e); + } + } + + // 自动布局：主 agent 在中心，子 agent 环绕 + function computeLayout() { + const agents = graphData.agents || []; + if (agents.length === 0) return {}; + + const positions = {}; + const wrapW = graphCanvasWrap.clientWidth || 600; + const wrapH = graphCanvasWrap.clientHeight || 400; + const cx = wrapW / 2; + const cy = wrapH / 2; + + // 主 agent 在中心 + const mainAgent = agents.find(a => a.role === "main"); + if (mainAgent) { + positions[mainAgent.name] = { x: cx - NODE_W / 2, y: cy - NODE_H / 2 }; + } + + // 子 agent 均匀环绕 + const subs = agents.filter(a => a.role !== "main"); + const radius = Math.min(wrapW, wrapH) * 0.32; + subs.forEach((agent, i) => { + const angle = (2 * Math.PI * i) / subs.length - Math.PI / 2; + positions[agent.name] = { + x: cx + radius * Math.cos(angle) - NODE_W / 2, + y: cy + radius * Math.sin(angle) - NODE_H / 2, + }; + }); + + return positions; + } + + // 状态到 CSS 类 + 文字 + function statusInfo(status) { + switch (status) { + case "running": return { cls: "status-running", badge: "执行中" }; + case "done": return { cls: "status-done", badge: "已完成" }; + case "error": return { cls: "status-error", badge: "出错" }; + default: return { cls: "status-idle", badge: "等待" }; + } + } + + // 渲染图谱 + function renderGraph() { + const agents = graphData.agents || []; + const edges = graphData.edges || []; + const positions = computeLayout(); + + // 计算 SVG viewBox + const wrapW = graphCanvasWrap.clientWidth || 600; + const wrapH = graphCanvasWrap.clientHeight || 400; + graphCanvas.setAttribute("viewBox", `0 0 ${wrapW} ${wrapH}`); + + // --- 渲染连线 --- + graphEdgesLayer.innerHTML = ""; + edges.forEach((edge, i) => { + const srcPos = positions[edge.source]; + const tgtPos = positions[edge.target]; + if (!srcPos || !tgtPos) return; + + const x1 = srcPos.x + NODE_W / 2; + const y1 = srcPos.y + NODE_H / 2; + const x2 = tgtPos.x + NODE_W / 2; + const y2 = tgtPos.y + NODE_H / 2; + + // 计算从边缘出发（而非中心） + const dx = x2 - x1; + const dy = y2 - y1; + const dist = Math.sqrt(dx * dx + dy * dy) || 1; + const nx = dx / dist; + const ny = dy / dist; + + const sx = x1 + nx * (NODE_W / 2); + const sy = y1 + ny * (NODE_H / 2); + const ex = x2 - nx * (NODE_W / 2); + const ey = y2 - ny * (NODE_H / 2); + + let edgeClass = "normal"; + let markerEnd = "url(#arrowNormal)"; + if (edge.highlighted) { + edgeClass = "highlighted"; + markerEnd = "url(#arrowHighlight)"; + } + + const line = document.createElementNS("http://www.w3.org/2000/svg", "line"); + line.setAttribute("x1", sx + panOffset.x); + line.setAttribute("y1", sy + panOffset.y); + line.setAttribute("x2", ex + panOffset.x); + line.setAttribute("y2", ey + panOffset.y); + line.setAttribute("class", `graph-edge ${edgeClass}`); + line.setAttribute("marker-end", markerEnd); + line.setAttribute("data-source", edge.source); + line.setAttribute("data-target", edge.target); + graphEdgesLayer.appendChild(line); + }); + + // --- 渲染节点 --- + graphNodesLayer.innerHTML = ""; + agents.forEach((agent) => { + const pos = positions[agent.name]; + if (!pos) return; + + const sinfo = statusInfo(agent.status || "idle"); + const g = document.createElementNS("http://www.w3.org/2000/svg", "g"); + g.setAttribute("class", `graph-node ${sinfo.cls}`); + g.setAttribute("transform", `translate(${pos.x + panOffset.x}, ${pos.y + panOffset.y})`); + g.setAttribute("data-agent", agent.name); + + // 圆角矩形 + const rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + rect.setAttribute("width", NODE_W); + rect.setAttribute("height", NODE_H); + rect.setAttribute("class", "graph-node-rect"); + g.appendChild(rect); + + // 图标 + const iconText = document.createElementNS("http://www.w3.org/2000/svg", "text"); + iconText.setAttribute("x", 28); + iconText.setAttribute("y", NODE_H / 2); + iconText.setAttribute("class", "graph-node-icon"); + iconText.textContent = agent.icon || "🧠"; + g.appendChild(iconText); + + // 名称 + const labelText = document.createElementNS("http://www.w3.org/2000/svg", "text"); + labelText.setAttribute("x", 90); + labelText.setAttribute("y", NODE_H / 2 - 8); + labelText.setAttribute("class", "graph-node-label"); + labelText.textContent = agent.name; + g.appendChild(labelText); + + // 角色 + const roleText = document.createElementNS("http://www.w3.org/2000/svg", "text"); + roleText.setAttribute("x", 90); + roleText.setAttribute("y", NODE_H / 2 + 12); + roleText.setAttribute("class", "graph-node-role"); + roleText.textContent = agent.role === "main" ? "主 Agent" : "子 Agent"; + g.appendChild(roleText); + + // 状态标签（右上角） + const badgeText = document.createElementNS("http://www.w3.org/2000/svg", "text"); + badgeText.setAttribute("x", NODE_W - 8); + badgeText.setAttribute("y", 14); + badgeText.setAttribute("class", "graph-node-badge"); + const badgeColor = agent.status === "running" ? "#f59e0b" : agent.status === "done" ? "#22c55e" : agent.status === "error" ? "#ef4444" : "#94a3b8"; + badgeText.setAttribute("fill", badgeColor); + badgeText.textContent = sinfo.badge; + g.appendChild(badgeText); + + // 工具调用缩略（仅最近2轮，渐变透明） + const agentToolCalls = (graphData.tool_calls || {})[agent.name] || []; + const recentCalls = agentToolCalls.slice(0, 4); + recentCalls.forEach((tc, idx) => { + const chip = document.createElementNS("http://www.w3.org/2000/svg", "text"); + chip.setAttribute("x", 12 + idx * 38); + chip.setAttribute("y", NODE_H - 10); + chip.setAttribute("class", "graph-node-tool-chip"); + chip.setAttribute("opacity", Math.max(0.2, 1 - idx * 0.35)); + chip.textContent = (tc.tool_name || "").substring(0, 5); + g.appendChild(chip); + }); + + // 点击事件 → 弹出 Agent 详情 modal + g.style.cursor = "pointer"; + g.addEventListener("click", () => openAgentDetailModal(agent)); + + graphNodesLayer.appendChild(g); + }); + } + + // 渲染工具调用列表 → 已移至 Agent 点击弹窗 + function openAgentDetailModal(agent) { + const overlay = document.getElementById("agentDetailOverlay"); + const stateDiv = document.getElementById("agentDetailState"); + const configDiv = document.getElementById("agentDetailConfig"); + const toolCallsDiv = document.getElementById("agentDetailToolCalls"); + const titleEl = document.getElementById("agentDetailTitle"); + if (!overlay || !stateDiv || !configDiv || !toolCallsDiv) return; + + titleEl.textContent = `Agent: ${agent.name}`; + const sinfo = statusInfo(agent.status || "idle"); + + // 状态 + stateDiv.innerHTML = ` +

名称: ${escapeHtml(agent.name)}

+

角色: ${agent.role === "main" ? "主 Agent" : "子 Agent"}

+

状态: ${sinfo.badge}

+ ${agent.error_message ? `

错误: ${escapeHtml(agent.error_message)}

` : ""} + `; + + // 配置（只读） + const tools = agent.tools || []; + configDiv.innerHTML = ` +

工具列表: ${tools.length ? tools.map(t => `${escapeHtml(t)}`).join(", ") : "（无）"}

+ `; + + // 工具调用记录 + const calls = (graphData.tool_calls || {})[agent.name] || []; + if (calls.length === 0) { + toolCallsDiv.innerHTML = `

暂无工具调用记录

`; + } else { + toolCallsDiv.innerHTML = [...calls].reverse().slice(0, 6).map(c => ` +
+
+ ${escapeHtml(c.tool_name)} + ${c.status || 'success'} +
+ ${c.tool_args && Object.keys(c.tool_args).length > 0 ? `
${escapeHtml(JSON.stringify(c.tool_args, null, 1))}
` : ""} + ${c.result_preview ? `
${escapeHtml(c.result_preview)}
` : ""} +
+ `).join(""); + } + + overlay.hidden = false; + } + + // 关闭 Agent 详情弹窗 + function closeAgentDetailModal() { + const overlay = document.getElementById("agentDetailOverlay"); + if (overlay) overlay.hidden = true; + } + + + // 画布拖拽 + graphCanvasWrap.addEventListener("mousedown", (e) => { + if (e.target === graphCanvas || e.target === graphCanvasWrap || e.target.closest("svg")) { + isPanning = true; + panStart = { x: e.clientX, y: e.clientY }; + panStartOffset = { ...panOffset }; + graphCanvasWrap.classList.add("panning"); + e.preventDefault(); + } + }); + + window.addEventListener("mousemove", (e) => { + if (!isPanning) return; + const dx = e.clientX - panStart.x; + const dy = e.clientY - panStart.y; + panOffset = { + x: panStartOffset.x + dx, + y: panStartOffset.y + dy, + }; + renderGraph(); + }); + + window.addEventListener("mouseup", () => { + if (isPanning) { + isPanning = false; + graphCanvasWrap.classList.remove("panning"); + } + }); + + // 触摸拖拽 + graphCanvasWrap.addEventListener("touchstart", (e) => { + if (e.touches.length === 1) { + isPanning = true; + panStart = { x: e.touches[0].clientX, y: e.touches[0].clientY }; + panStartOffset = { ...panOffset }; + graphCanvasWrap.classList.add("panning"); + } + }, { passive: false }); + + graphCanvasWrap.addEventListener("touchmove", (e) => { + if (!isPanning || e.touches.length !== 1) return; + const dx = e.touches[0].clientX - panStart.x; + const dy = e.touches[0].clientY - panStart.y; + panOffset = { + x: panStartOffset.x + dx, + y: panStartOffset.y + dy, + }; + renderGraph(); + e.preventDefault(); + }, { passive: false }); + + graphCanvasWrap.addEventListener("touchend", () => { + if (isPanning) { + isPanning = false; + graphCanvasWrap.classList.remove("panning"); + } + }); + + // 鼠标滚轮缩放（可选） + + // 按钮事件 + graphRefreshBtn.addEventListener("click", () => { + loadGraphData(); + }); + + graphFitBtn.addEventListener("click", () => { + panOffset = { x: 80, y: 60 }; + renderGraph(); + }); + + // 窗口大小变化时重新渲染 + let resizeGraphTimer = null; + window.addEventListener("resize", () => { + clearTimeout(resizeGraphTimer); + resizeGraphTimer = setTimeout(() => { + if (graphOpen) { + renderGraph(); + } + }, 200); + }); + bootstrap().catch((e) => { chatPlaceholder.hidden = false; chatPlaceholder.innerHTML = `

无法连接服务

${escapeHtml(e.message)}

请从 Agent 目录执行：uvicorn web.server:app --reload --host 127.0.0.1 --port 8765，并保证 PYTHONPATH 包含 src。

`; diff --git a/src/web/index.html b/src/web/index.html index cd18cf1..ac59600 100644 --- a/src/web/index.html +++ b/src/web/index.html @@ -77,15 +77,69 @@ > ▴ + + -
-
-

开始对话

-

输入文字，或上传附件 后点击发送。

-

支持用户音频输入、助手文本/语音/双模输出、会话本地媒体持久化。

-
- -
+
+ +
+
+

开始对话

+

输入文字，或上传附件 后点击发送。

+

支持用户音频输入、助手文本/语音/双模输出、会话本地媒体持久化。

+
+ +
+ + +
@@ -111,6 +165,11 @@ + +
+ + + + diff --git a/src/web/server.py b/src/web/server.py index 5268b20..840c7b9 100644 --- a/src/web/server.py +++ b/src/web/server.py @@ -1,3 +1,4 @@ +# server.py """ 为 ``web`` 目录下的静态前端提供 API：会话列表、多模态发送（与 ``chatbot_agent`` 一致）。 @@ -8,6 +9,7 @@ from __future__ import annotations import base64 +import json import mimetypes import os import shutil @@ -27,6 +29,14 @@ if str(_SRC) not in sys.path: from agent import chatbot_agent as ca # noqa: E402 from web import ui_session as ui # noqa: E402 from agent.utils.image_utils import IMAGE_FILE_EXTENSIONS # noqa: E402 +from agent.core.config_manager import ( # noqa: E402 + load_agent_configs, save_agent_configs, + load_tool_configs, save_tool_configs, + load_env_config, save_env_config, + load_models, save_models, + get_registered_tool_names, + AGENT_CONFIG_PATH, TOOL_CONFIG_PATH, ENV_CONFIG_PATH, +) _WEB_DIR = Path(__file__).resolve().parent _IMAGE_DIR = _WEB_DIR / "image" @@ -35,17 +45,21 @@ _AUDIO_EXT = frozenset({".wav", ".mp3", ".m4a", ".flac", ".ogg", ".webm"}) _IMAGE_EXT = frozenset(IMAGE_FILE_EXTENSIONS) -def _infer_upload_kind(fp: str) -> str | None: - ext = os.path.splitext(fp)[1].lower() - if ext in _IMAGE_EXT: - return "image" - if ext in _AUDIO_EXT: - return "audio" - return "file" - - def _data_url_for_file(fp: str) -> str | None: - """将本地文件读入为 data URL，供前端直接展示（不单独提供文件下载路由）。""" + """将本地文件读入为 data URL，供前端直接展示（不单独提供文件下载路由）。 + + 输入: + fp: 本地文件路径。 + + 输出: + data URL 字符串，失败返回 None。 + + 系统定位: + _serialize_content 将附件转为内联 data URL 以嵌入 API 响应。 + + 可扩展性: + 可增加缓存、大小限制、权限检查。 + """ if not fp or not os.path.isfile(fp): return None mime, _ = mimetypes.guess_type(fp) @@ -75,7 +89,22 @@ def _data_url_for_file(fp: str) -> str | None: def _serialize_content(content: Any, *, meta: dict[str, Any] | None = None, role: str | None = None) -> Any: - """将一条消息中的 content 转为 JSON 友好结构；文件以内联 data URL 输出。""" + """将一条消息中的 content 转为 JSON 友好结构；文件以内联 data URL 输出。 + + 输入: + content: 原始 content（str、list 或 dict）。 + meta: 消息的 meta 信息（如音频时长、自动播放等）。 + role: 消息角色（user/assistant），用于控制自动播放行为。 + + 输出: + 序列化后的 content（str 或 list of dict）。 + + 系统定位: + _serialize_messages 中逐条转换，供前端直接渲染。 + + 可扩展性: + 可支持更多文件类型（如视频）的 data URL 输出。 + """ meta = meta or {} if isinstance(content, str): return content @@ -121,6 +150,20 @@ def _serialize_content(content: Any, *, meta: dict[str, Any] | None = None, role def _serialize_messages(hist: list[dict[str, Any]] | None) -> list[dict[str, Any]]: + """将 chat_history 转为 API 响应格式，附件内联为 data URL。 + + 输入: + hist: 聊天历史（dict 列表）。 + + 输出: + 序列化后的消息列表，每条消息包含 role、content、meta。 + + 系统定位: + 所有返回给前端的 API 响应中，messages 字段均经过此函数转换。 + + 可扩展性: + 可支持更细粒度的权限控制或裁剪。 + """ hist = hist or [] # 找到最后一条 assistant 消息的索引，用于控制自动播放 last_assistant_idx = -1 @@ -178,6 +221,19 @@ def _serialize_messages(hist: list[dict[str, Any]] | None) -> list[dict[str, Any def _bootstrap() -> tuple[str, list[str], list[dict[str, Any]]]: + """初始化默认会话：取最近会话或新建时间戳 ID 及其消息列表。 + + 输入: 无。 + + 输出: + (session_id, sessions列表, messages列表) + + 系统定位: + API 启动时/删除会话后回退到默认会话。 + + 可扩展性: + 可增加用户偏好默认会话逻辑。 + """ ids = ui.list_session_ids_ordered() if ids: sid = ids[0] @@ -201,12 +257,35 @@ app.add_middleware( @app.get("/api/bootstrap") def api_bootstrap() -> dict[str, Any]: + """获取初始会话信息（默认会话 ID、会话列表、消息历史）。 + + 输入: 无。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + 前端页面加载时调用的第一个 API，用于初始化界面。 + + 可扩展性: + 可返回用户配置、主题等元信息。 + """ sid, ids, hist = _bootstrap() return {"sessionId": sid, "sessions": ids, "messages": _serialize_messages(hist)} @app.post("/api/sessions/new") def api_new_session() -> dict[str, Any]: + """创建新会话（基于时间戳），返回会话信息。 + + 输入: 无。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": []} + + 系统定位: + 前端点击“新建会话”时调用。 + """ sid = ui.new_timestamp_session_id() ids = ui.list_session_ids_ordered() ids = [sid] + [x for x in ids if x != sid] @@ -215,6 +294,17 @@ def api_new_session() -> dict[str, Any]: @app.get("/api/sessions/{session_id}/messages") def api_session_messages(session_id: str) -> dict[str, Any]: + """切换会话：加载指定会话的消息列表与全局会话列表。 + + 输入: + session_id: 路径参数，目标会话 ID。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + 前端切换会话时调用。 + """ if not session_id: raise HTTPException(status_code=400, detail="缺少 session_id") hist = ui.load_ui_messages_for_session(session_id) @@ -226,6 +316,17 @@ def api_session_messages(session_id: str) -> dict[str, Any]: @app.post("/api/sessions/clear") def api_clear_session(session_id: str = Form(...)) -> dict[str, Any]: + """清空当前会话的所有消息（重置会话）。 + + 输入: + session_id: 表单字段，会话 ID。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": []} + + 系统定位: + 前端“清空对话”按钮调用。 + """ if not session_id: raise HTTPException(status_code=400, detail="缺少 session_id") messages, _ = ui.clear_ui_and_manifest(session_id) @@ -237,6 +338,17 @@ def api_clear_session(session_id: str = Form(...)) -> dict[str, Any]: @app.post("/api/sessions/delete") def api_delete_session(session_id: str = Form(...)) -> dict[str, Any]: + """删除整个会话（含文件与历史），并回退到默认会话。 + + 输入: + session_id: 表单字段，会话 ID。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + 前端“删除会话”按钮调用。 + """ if not session_id: raise HTTPException(status_code=400, detail="缺少 session_id") ui.delete_session(session_id) @@ -250,7 +362,24 @@ def _materialize_uploads_to_temp( audio: UploadFile | None, text: str, ) -> tuple[dict[str, Any], str]: - """将上传文件落盘到临时目录，返回 ``(query_dict, tmp_root)``；调用方须在 finally 中删除 ``tmp_root``。""" + """将上传文件落盘到临时目录，返回 ``(query_dict, tmp_root)``；调用方须在 finally 中删除 ``tmp_root``。 + + 输入: + images: 前端上传的图片文件列表。 + files: 前端上传的普通文件列表。 + audio: 前端上传的音频文件。 + text: 用户输入的文本。 + + 输出: + (query_dict, tmp_root) + query_dict 包含 "text", "files"(路径列表), "file_names"(原始文件名列表)。 + + 系统定位: + API 层处理用户多模态输入的统一临时落盘逻辑。 + + 可扩展性: + 可增加文件大小限制、病毒扫描钩子。 + """ tmp_root = tempfile.mkdtemp(prefix="web_mm_") file_paths: list[str] = [] file_names: list[str] = [] @@ -287,7 +416,21 @@ def _materialize_uploads_to_temp( def _chat_start_core(session_id: str, query: dict[str, Any]) -> dict[str, Any]: - """追加 user、复制附件到会话目录并写 manifest（不调用模型）。""" + """追加 user、复制附件到会话目录并写 manifest（不调用模型）。 + + 输入: + session_id: 会话 ID。 + query: {"text": str, "files": list[str], "file_names": list[str]} + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + /api/chat/start 与 /api/chat（兼容模式）的后端核心逻辑。 + + 可扩展性: + 可增加消息预处理钩子。 + """ hist = ui.load_ui_messages_for_session(session_id) before = len(hist or []) new_hist, cleared = ui.add_message(hist or [], query) @@ -309,15 +452,31 @@ def _chat_start_core(session_id: str, query: dict[str, Any]) -> dict[str, Any]: } -def _chat_complete_core(session_id: str, output_type: str = "text") -> dict[str, Any]: - """对 manifest 中末条 user 执行视觉解析与 ``chain_with_history.invoke``。""" +def _chat_complete_core(session_id: str, output_type: str = "text", agent_mode: str = "agent") -> dict[str, Any]: + """对 manifest 中末条 user 执行视觉解析与 ``chain_with_history.invoke``。 + + 输入: + session_id: 会话 ID。 + output_type: text | audio | both。 + agent_mode: agent | plan | team。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + /api/chat/complete 与 /api/chat（兼容模式）的后端核心逻辑。 + + 可扩展性: + 可支持异步执行、流式输出。 + """ hist = ui.load_ui_messages_for_session(session_id) if not hist or hist[-1].get("role") != "user": raise HTTPException( status_code=400, detail="没有待完成的用户消息，请先调用 /api/chat/start", ) - new_hist = ca.execute_agent([*hist], session_id, output_type=output_type) + normalized_mode = agent_mode if agent_mode in {"agent", "plan", "team"} else "agent" + new_hist = ca.execute_agent([*hist], session_id, output_type=output_type, agent_mode=normalized_mode) ui.prune_memory_session_ids() ids = ui.list_session_ids_ordered() if session_id not in ids: @@ -340,6 +499,19 @@ async def api_chat_start( """ 仅追加用户消息并持久化到 manifest（含附件落盘）；不调用模型。 前端应先调用本接口再调用 ``/api/chat/complete``。 + + 输入: + session_id: 表单字段，会话 ID。 + text: 用户输入的文本。 + images: 上传的图片文件列表。 + files: 上传的普通文件列表。 + audio: 上传的音频文件。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + 两步式对话的第一步，用于提前上传文件并显示用户消息。 """ if not session_id: raise HTTPException(status_code=400, detail="缺少 session_id") @@ -352,14 +524,27 @@ async def api_chat_start( @app.post("/api/chat/complete") -async def api_chat_complete( +def api_chat_complete( session_id: str = Form(...), output_type: str = Form("text"), + agent_mode: str = Form("agent"), ) -> dict[str, Any]: - """对当前会话最近一次 ``/api/chat/start`` 提交执行模型推理并返回完整消息列表。""" + """对当前会话最近一次 ``/api/chat/start`` 提交执行模型推理并返回完整消息列表。 + + 输入: + session_id: 表单字段，会话 ID。 + output_type: text | audio | both。 + agent_mode: agent | plan | team。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + 两步式对话的第二步，执行 Agent 并返回结果。 + """ if not session_id: raise HTTPException(status_code=400, detail="缺少 session_id") - return _chat_complete_core(session_id, output_type=output_type) + return _chat_complete_core(session_id, output_type=output_type, agent_mode=agent_mode) @app.post("/api/chat") @@ -370,9 +555,25 @@ async def api_chat( files: list[UploadFile] | None = File(None), audio: UploadFile | None = File(None), output_type: str = Form("text"), + agent_mode: str = Form("agent"), ) -> dict[str, Any]: """ 兼容旧客户端：等价于依次 ``/api/chat/start`` + ``/api/chat/complete``（仍阻塞至推理结束）。 + + 输入: + session_id: 表单字段，会话 ID。 + text: 用户输入的文本。 + images: 上传的图片文件列表。 + files: 上传的普通文件列表。 + audio: 上传的音频文件。 + output_type: text | audio | both。 + agent_mode: agent | plan | team。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + 向后兼容的一次性对话接口。 """ if not session_id: raise HTTPException(status_code=400, detail="缺少 session_id") @@ -380,20 +581,351 @@ async def api_chat( query, tmp_root = _materialize_uploads_to_temp(images, files, audio, text) try: _chat_start_core(session_id, query) - return _chat_complete_core(session_id, output_type=output_type) + return _chat_complete_core(session_id, output_type=output_type, agent_mode=agent_mode) finally: shutil.rmtree(tmp_root, ignore_errors=True) +@app.post("/api/human-action") +def api_human_action( + session_id: str = Form(...), + approval_id: str = Form(...), + decision: str = Form(...), + instruction: str = Form(""), + output_type: str = Form("text"), +) -> dict[str, Any]: + """用户对待确认操作（HITL）做出决策后，继续执行 Agent。 + + 输入: + session_id: 会话 ID。 + approval_id: 待确认项的 ID。 + decision: approve | reject | edit。 + instruction: 用户输入的补充文本（用于 edit 或 selection）。 + output_type: 回复的输出类型。 + + 输出: + {"sessionId": str, "sessions": list[str], "messages": list[dict]} + + 系统定位: + 前端确认框提交决策时调用的回调接口。 + """ + if not session_id: + raise HTTPException(status_code=400, detail="缺少 session_id") + if decision not in {"approve", "reject", "edit"}: + raise HTTPException(status_code=400, detail="不支持的人工处理结果") + hist = ui.load_ui_messages_for_session(session_id) + new_hist = ca.continue_after_human_action( + [*hist], + session_id=session_id, + approval_id=approval_id, + decision=decision, + instruction=instruction, + output_type=output_type, + ) + ids = ui.list_session_ids_ordered() + if session_id not in ids: + ids = [session_id, *ids] + return {"sessionId": session_id, "sessions": ids, "messages": _serialize_messages(new_hist)} + + +# ---------- 配置管理 API ---------- +@app.get("/api/config/agents") +def api_get_agent_configs() -> dict[str, Any]: + """获取所有 agent 配置列表。 + + 输出: {"agents": list[dict], "config_path": str} + """ + configs = load_agent_configs() + return { + "agents": [{"name": c.name, "system_prompt": c.system_prompt, "max_iterations": c.max_iterations, + "llm": {"model": c.llm.model, "api_key": c.llm.api_key, "base_url": c.llm.base_url, + "timeout": c.llm.timeout, "max_retries": c.llm.max_retries}, + "llm_model_id": c.llm_model_id, + "tools": c.tools, + "enabled": c.enabled, + "role": c.role} for c in configs], + "config_path": str(AGENT_CONFIG_PATH), + } + + +@app.post("/api/config/agents") +def api_save_agent_configs(data: dict[str, Any]) -> dict[str, Any]: + """保存 agent 配置列表。 + + 输入: {"agents": list[dict]} + 输出: {"success": bool, "config_path": str} + """ + agents = data.get("agents") if isinstance(data, dict) else None + if not isinstance(agents, list): + raise HTTPException(status_code=400, detail="缺少 agents 字段或格式不正确") + success = save_agent_configs(agents) + return {"success": success, "config_path": str(AGENT_CONFIG_PATH)} + + +@app.get("/api/config/tools") +def api_get_tool_configs() -> dict[str, Any]: + """获取所有工具配置列表。 + + 输出: {"tools": list[dict], "registered_tool_names": list[str], + "tool_parameters": dict, "forced_permissions": dict, "config_path": str} + """ + from agent.core.config_manager import _get_tool_parameters, get_tool_forced_permissions + configs = load_tool_configs() + return { + "tools": [{"name": c.name, "display_name": c.display_name, "description": c.description, + "permission": c.permission, "auto_execute_rules": c.auto_execute_rules, "enabled": c.enabled} + for c in configs], + "registered_tool_names": get_registered_tool_names(), + "tool_parameters": _get_tool_parameters(), + "forced_permissions": get_tool_forced_permissions(), + "config_path": str(TOOL_CONFIG_PATH), + } + + +@app.post("/api/config/tools") +def api_save_tool_configs(data: dict[str, Any]) -> dict[str, Any]: + """保存工具配置列表。 + + 输入: {"tools": list[dict]} + 输出: {"success": bool, "config_path": str} + """ + tools = data.get("tools") if isinstance(data, dict) else None + if not isinstance(tools, list): + raise HTTPException(status_code=400, detail="缺少 tools 字段或格式不正确") + success = save_tool_configs(tools) + # 保存后清除 tool_policy 的规则缓存，使新配置生效 + try: + from agent.core.tool_policy import reload_auto_execute_rules + reload_auto_execute_rules() + except Exception: + pass + return {"success": success, "config_path": str(TOOL_CONFIG_PATH)} + + +# ---------- 模型管理 API ---------- +@app.get("/api/config/models") +def api_get_models() -> dict[str, Any]: + """获取所有 LLM 模型配置。 + + 输出: {"models": list[dict]} + """ + return {"models": load_models()} + + +@app.post("/api/config/models") +def api_save_models(data: dict[str, Any]) -> dict[str, Any]: + """保存 LLM 模型配置列表。 + + 输入: {"models": list[dict]} + 输出: {"success": bool} + """ + models = data.get("models") if isinstance(data, dict) else None + if not isinstance(models, list): + raise HTTPException(status_code=400, detail="缺少 models 字段或格式不正确") + success = save_models(models) + return {"success": success} + + +@app.get("/api/config/env") +def api_get_env_config() -> dict[str, Any]: + """获取环境变量配置。 + + 输出: {"env": dict, "models": list[dict], "config_path": str} + """ + config = load_env_config() + models = config.get("models", []) + env_only = {k: v for k, v in config.items() if k != "models"} + return {"env": env_only, "models": models, "config_path": str(ENV_CONFIG_PATH)} + + +@app.post("/api/config/env") +def api_save_env_config(data: dict[str, Any]) -> dict[str, Any]: + """保存环境变量配置。 + + 输入: {"env": dict} + 输出: {"success": bool, "config_path": str} + """ + env = data.get("env") if isinstance(data, dict) else None + if not isinstance(env, dict): + raise HTTPException(status_code=400, detail="缺少 env 字段或格式不正确") + success = save_env_config(env) + return {"success": success, "config_path": str(ENV_CONFIG_PATH)} + # 先注册 API，再挂静态文件，避免 ``/`` 抢路由 @app.get("/api/health") def health() -> dict[str, str]: + """健康检查接口。 + + 输入: 无。 + + 输出: {"status": "ok"} + """ return {"status": "ok"} +@app.post("/api/config/reload") +def api_reload_configs() -> dict[str, Any]: + """热重载所有配置并重建 Agent 运行时，无需重启服务。 + + 输入: 无（或 {}）。 + + 输出: {"success": bool, "agent": dict, "detail": str} + """ + import os as _os + try: + # 1. 重新读取 env_config.json 并写入 os.environ + from agent.utils.env_utils import _load_config + _load_config() + + # 2. 重建 LLM 实例 + from agent.core.llm import reload_llm + reload_llm() + + # 3. 清除并重新加载 tool_policy 缓存 + from agent.core.tool_policy import reload_auto_execute_rules + reload_auto_execute_rules() + + # 4. 重建主 Agent 运行时 + from agent.agents.main_agent import reload_main_agent_runtime + agent_info = reload_main_agent_runtime() + + return {"success": True, "agent": agent_info, "detail": "配置已热重载"} + except Exception as e: + return {"success": False, "agent": {}, "detail": str(e)} + + +# ---------- 多 Agent 图面板 API ---------- +@app.get("/api/graph/snapshot") +def api_get_graph_snapshot(session_id: str = "") -> dict[str, Any]: + """获取当前多 Agent 运行时的图结构快照，供前端 graph 面板渲染。""" + try: + from agent.agents.multi_agent_runtime import get_agent_graph_snapshot, build_multi_agent_runtime + from agent.agents.main_agent import main_agent_runtime + from agent.history.tool_call_recorder import get_tool_calls_for_latest_turn + runtime = build_multi_agent_runtime(main_runtime=main_agent_runtime) + result = get_agent_graph_snapshot(runtime) + if session_id: + recorded = get_tool_calls_for_latest_turn(session_id) + if recorded: + existing = result.get("tool_calls", {}) or {} + main_calls = existing.get("main", []) + for r in recorded: + main_calls.append({ + "tool_name": r.get("name", ""), + "tool_args": {}, + "result_preview": r.get("result_text", "")[:80], + "status": r.get("status", "done"), + }) + result["tool_calls"] = {**existing, "main": main_calls} + return result + except Exception as e: + return { + "agents": [ + {"name": "main", "role": "main", "icon": "🧠", "status": "idle", "error_message": ""} + ], + "edges": [], + "events": [], + "tool_calls": {}, + "error": str(e), + } + + +@app.get("/api/graph/config") +def api_get_graph_config() -> dict[str, Any]: + """获取 graph 面板的初始配置（从 agent_config.json 读取各 agent 的 role/icon 等）。 + + 输出: + {"agents": [{"name": str, "role": str, "icon": str}, ...]} + """ + from agent.core.config_manager import load_agent_configs + configs = load_agent_configs() + role_icons = {"main": "🧠", "code": "💻", "research": "🔍", "doc": "📄", "gui": "🖥"} + agents_list = [] + for cfg in configs: + if cfg.enabled: + agents_list.append({ + "name": cfg.name, + "role": cfg.role, + "icon": role_icons.get(cfg.name, "🔧"), + "tools": cfg.tools, + }) + if not agents_list: + agents_list = [{"name": "main", "role": "main", "icon": "🧠", "tools": []}] + return {"agents": agents_list} + + +# ---------- 工具调用历史 API ---------- +@app.get("/api/tool-calls/{session_id}/live") +def api_get_live_tool_calls(session_id: str) -> dict[str, Any]: + """获取当前进行中的工具调用（图表执行期间实时轮询）。""" + try: + from agent.history.tool_call_recorder import get_live_tool_calls + return {"tool_calls": get_live_tool_calls(session_id)} + except Exception as e: + return {"tool_calls": [], "error": str(e)} + + +@app.get("/api/tool-calls/{session_id}/recent") +def api_get_recent_tool_calls(session_id: str) -> dict[str, Any]: + """获取最新 turn 的工具调用缩略（仅名称，无返回值）。""" + try: + from agent.history.tool_call_recorder import get_tool_calls_for_latest_turn + calls = get_tool_calls_for_latest_turn(session_id) + result = [] + for c in calls: + result.append({ + "name": c.get("name", ""), + "args_summary": json.dumps(c.get("args", {}), ensure_ascii=False)[:40], + "status": "done", + "turn_id": c.get("turn_id", ""), + }) + return {"tool_calls": result} + except Exception as e: + return {"tool_calls": [], "error": str(e)} + + +@app.get("/api/tool-calls/{session_id}/detail") +def api_get_tool_call_detail(session_id: str) -> dict[str, Any]: + """获取最新 turn 的工具调用详情（含返回值和图片 base64）。""" + try: + from agent.history.tool_call_recorder import get_tool_calls_for_latest_turn + details = get_tool_calls_for_latest_turn(session_id) + # 图片文件转 data URL + import base64 + for d in details: + imgs = [] + for fp in d.get("result_files_full", []): + if os.path.isfile(fp): + with open(fp, "rb") as f: + b64 = base64.standard_b64encode(f.read()).decode("ascii") + ext = os.path.splitext(fp)[1].lower() + mime_map = {".png": "image/png", ".jpg": "image/jpeg", ".jpeg": "image/jpeg", ".gif": "image/gif"} + mime = mime_map.get(ext, "image/png") + imgs.append(f"data:{mime};base64,{b64}") + d["result_images"] = imgs + return {"details": details} + except Exception as e: + return {"details": [], "error": str(e)} + + +@app.get("/api/tool-calls/{session_id}/has-pending") +def api_has_pending_tool_calls(session_id: str) -> dict[str, Any]: + """检查是否有待处理的工具调用（thinking 状态时使用）。""" + try: + from agent.history.tool_call_recorder import list_turns + turns = list_turns(session_id) + has_tools = bool(turns) + # 判断最新 turn 是否已完成（有 assistant_response） + latest_complete = turns[0].get("assistant_response", "") if turns else "" + return {"has_recent": has_tools, "is_complete": bool(latest_complete)} + except Exception as e: + return {"has_recent": False, "is_complete": True, "error": str(e)} + + @app.get("/favicon.ico") def favicon() -> FileResponse: + """返回网站图标（favicon.ico/png/svg）。""" for name, mime in ( ("favicon.ico", "image/x-icon"), ("favicon.png", "image/png"), @@ -407,6 +939,14 @@ def favicon() -> FileResponse: @app.get("/image/{asset_name}") def serve_image_asset(asset_name: str) -> FileResponse: + """提供 web/image 目录下的静态图片资源。 + + 输入: + asset_name: 图片文件名。 + + 输出: + 图片文件响应。 + """ path = (_IMAGE_DIR / asset_name).resolve() try: path.relative_to(_IMAGE_DIR.resolve()) @@ -420,14 +960,17 @@ def serve_image_asset(asset_name: str) -> FileResponse: @app.get("/") def index_page() -> FileResponse: + """返回前端主页面 index.html。""" return FileResponse(_WEB_DIR / "index.html") @app.get("/app.css") def serve_css() -> FileResponse: + """返回样式文件 app.css。""" return FileResponse(_WEB_DIR / "app.css") @app.get("/app.js") def serve_js() -> FileResponse: + """返回前端脚本 app.js。""" return FileResponse(_WEB_DIR / "app.js") \ No newline at end of file diff --git a/src/web/ui_session.py b/src/web/ui_session.py index e18d021..099618f 100644 --- a/src/web/ui_session.py +++ b/src/web/ui_session.py @@ -1,3 +1,4 @@ +# ui_session.py """ Web/API 与会话 UI 状态：会话 id 列表、manifest、多模态 user 消息结构、附件落盘。 与 LangChain 链解耦；execute_agent 仍在 chatbot_agent 中，此处提供其依赖的同步与解析函数。 @@ -14,23 +15,58 @@ from agent.utils.agent_utils import ( CHAT_HISTORY_DB_FS, MULTI_INPUTS_ROOT, assistant_text, + get_history, ) from agent.utils.image_utils import IMAGE_FILE_EXTENSIONS -from agent.chatbot_agent import get_history _AUDIO_EXTENSIONS = {".wav", ".mp3", ".m4a", ".flac", ".ogg"} _memory_session_ids: set[str] = set() def manifest_path(session_id: str) -> str: + """返回会话 UI manifest 文件（ui_messages.json）的绝对路径。 + + 输入: + session_id: 会话 ID。 + + 输出: + manifest 文件的绝对路径字符串。 + + 系统定位: + 持久化 UI 层消息结构的唯一文件路径。 + + 可扩展性: + 可改为支持不同版本的 manifest 文件（如 version 4）。 + """ return os.path.join(MULTI_INPUTS_ROOT, session_id, "ui_messages.json") def session_dir(session_id: str) -> str: + """返回会话附件与 TTS 音频的存储目录绝对路径。 + + 输入: + session_id: 会话 ID。 + + 输出: + 会话目录的绝对路径字符串。 + + 系统定位: + 所有用户上传文件和助手生成的音频文件存放于此目录下。 + """ return os.path.join(MULTI_INPUTS_ROOT, session_id) def list_session_ids_ordered() -> list[str]: + """从 SQLite 按最近消息时间倒序列出所有 session_id。 + + 输入: 无。 + + 输出: + session_id 列表，按最后活跃时间降序排列。 + + 系统定位: + 用于前端展示会话列表，最近对话的会话排在前面。 + """ if not os.path.isfile(CHAT_HISTORY_DB_FS): return [] conn = sqlite3.connect(CHAT_HISTORY_DB_FS) @@ -44,6 +80,16 @@ def list_session_ids_ordered() -> list[str]: def new_timestamp_session_id() -> str: + """生成基于时间戳的唯一会话 ID，冲突时追加序号后缀。 + + 输入: 无。 + + 输出: + 新的会话 ID 字符串（如 "20260609_143022" 或 "20260609_143022_1"）。 + + 系统定位: + 前端点击“新建会话”时调用的 ID 生成器。 + """ base = datetime.now().strftime("%Y%m%d_%H%M%S") existing = set(list_session_ids_ordered()) | _memory_session_ids if base not in existing: @@ -59,6 +105,15 @@ def new_timestamp_session_id() -> str: def prune_memory_session_ids() -> None: + """移除已写入数据库的内存 session_id 缓存，避免集合无限增长。 + + 输入: 无。 + + 输出: 无。 + + 系统定位: + 每次会话操作后调用，清理过期的内存缓存。 + """ persisted = set(list_session_ids_ordered()) for sid in list(_memory_session_ids): if sid in persisted: @@ -66,6 +121,16 @@ def prune_memory_session_ids() -> None: def delete_session(session_id: str) -> None: + """删除会话的 SQLite 消息、内存缓存、manifest 与附件目录。 + + 输入: + session_id: 要删除的会话 ID。 + + 输出: 无。 + + 系统定位: + API 层删除会话时彻底清理所有相关数据。 + """ if not session_id: return if os.path.isfile(CHAT_HISTORY_DB_FS): @@ -80,6 +145,17 @@ def delete_session(session_id: str) -> None: def _lc_content_for_ui_messages(content) -> str | list: + """将 LangChain 消息的 content 转为 UI 可展示的 str 或 list。 + + 输入: + content: LangChain 消息的 content 字段。 + + 输出: + str 或 list，供前端渲染。 + + 系统定位: + 从 SQLite 历史重建 UI 消息时的降级方案（无 manifest 时使用）。 + """ if isinstance(content, str): return content if isinstance(content, list): @@ -89,6 +165,17 @@ def _lc_content_for_ui_messages(content) -> str | list: def _load_ui_messages_from_db(session_id: str) -> list: + """从 SQLChatMessageHistory 重建简化版 UI 消息列表（无 manifest 元数据）。 + + 输入: + session_id: 会话 ID。 + + 输出: + 消息列表，每条包含 role 和 content（无 meta）。 + + 系统定位: + 当 manifest 文件缺失时，回退到 SQLite 历史展示。 + """ if not session_id: return [] rows: list[dict] = [] @@ -102,7 +189,17 @@ def _load_ui_messages_from_db(session_id: str) -> list: def _build_user_chat_content(query) -> str | list | None: - """根据上传文件扩展名直接生成多模态 content（与 LLM 输入格式一致）""" + """根据上传文件扩展名直接生成多模态 content（与 LLM 输入格式一致）。 + + 输入: + query: {"text": str, "files": list[str], "file_names": list[str]} + + 输出: + content 结构（str 或 list），无有效内容时返回 None。 + + 系统定位: + add_message 中将前端上传的原始 query 转为 LangChain 可识别的 content。 + """ parts: list = [] file_names = query.get("file_names") or [] for idx, filepath in enumerate(query.get("files", []) or []): @@ -127,6 +224,17 @@ def _build_user_chat_content(query) -> str | list | None: def _sanitize_filename_stem(name: str) -> str: + """清理文件名主干中的非法字符，空名回退为 file。 + + 输入: + name: 原始文件名。 + + 输出: + 清洗后的文件名主干（不含扩展名）。 + + 系统定位: + _next_session_copy_name 中生成安全的文件名前缀。 + """ stem = os.path.splitext(name or "")[0].strip() if not stem: return "file" @@ -141,6 +249,20 @@ def _sanitize_filename_stem(name: str) -> str: def _next_session_copy_name(dest_dir: str, session_id: str, original_name: str, ext: str) -> str: + """在会话目录内生成不冲突的附件副本文件名（含 session_id 与递增序号）。 + + 输入: + dest_dir: 会话目录。 + session_id: 会话 ID。 + original_name: 原始文件名。 + ext: 扩展名（含点）。 + + 输出: + 不冲突的文件名字符串。 + + 系统定位: + rewrite_user_message_files_to_session_dir 中为每个附件生成唯一副本名。 + """ ext = ext or "" base_name = _sanitize_filename_stem(original_name) prefix = f"{base_name}_{session_id}_" @@ -159,6 +281,17 @@ def _next_session_copy_name(dest_dir: str, session_id: str, original_name: str, def rewrite_user_message_files_to_session_dir(user_msg: dict, session_id: str) -> None: + """将 user 消息中的外部附件复制到会话目录并原地更新路径引用。 + + 输入: + user_msg: 用户消息字典（原地修改）。 + session_id: 会话 ID。 + + 输出: 无。 + + 系统定位: + 将临时上传的文件移动到持久化的会话目录，并修正消息中的路径。 + """ dest_dir = session_dir(session_id) os.makedirs(dest_dir, exist_ok=True) raw = user_msg.get("content") @@ -202,7 +335,18 @@ def rewrite_user_message_files_to_session_dir(user_msg: dict, session_id: str) - user_msg["content"] = new_list -def _serialize_user_msg_for_manifest(user_msg: dict) -> dict: +def _serialize_user_msg_for_manifest(user_msg: dict) -> dict: + """将 user 消息序列化为 manifest 可持久化的 content 列表结构。 + + 输入: + user_msg: 用户消息字典。 + + 输出: + 用于写入 manifest 的 user 条目字典（含相对路径引用）。 + + 系统定位: + sync_manifest_from_chat_history 中将 user 消息转为可持久化格式。 + """ raw = user_msg.get("content") content_list: list[dict] = [] if isinstance(raw, str): @@ -239,17 +383,26 @@ def _serialize_user_msg_for_manifest(user_msg: dict) -> dict: "relpath": rel.replace("\\", "/"), "name": piece.get("name") or os.path.basename(fp), }) - # 不再写入 image_description 字段 return {"content": content_list} def _manifest_user_entry_to_content(user_entry: dict) -> str | list: + """将 manifest 中的 user 条目还原为 UI/LLM 使用的 content（含附件绝对路径）。 + + 输入: + user_entry: manifest 中读取的 user 条目字典。 + + 输出: + content 结构（str 或 list），用于展示或模型输入。 + + 系统定位: + load_ui_messages_for_session 中从 manifest 重建用户消息。 + """ root_norm = os.path.normpath(MULTI_INPUTS_ROOT) parts_out: list = [] any_image_missing = False had_image_parts = False - # 新格式直接取 "content" content_data = user_entry.get("content") if content_data is None: return "" @@ -290,7 +443,6 @@ def _manifest_user_entry_to_content(user_entry: dict) -> str | list: else: parts_out.append({"type": "text", "text": f"[附件已丢失: {display_name}]"}) - # 不再处理 image_description（新格式中没有） if had_image_parts and any_image_missing: parts_out.append({"type": "text", "text": "[图片已丢失]"}) @@ -302,6 +454,14 @@ def _manifest_user_entry_to_content(user_entry: dict) -> str | list: def _serialize_assistant_entry(assistant_msg: dict | None) -> dict: + """将 assistant 消息及其 meta 序列化为 manifest assistant 条目。 + + 输入: + assistant_msg: assistant 消息字典。 + + 输出: + 用于写入 manifest 的 assistant 条目字典。 + """ ac = (assistant_msg or {}).get("content", "") meta = dict((assistant_msg or {}).get("meta") or {}) if isinstance(ac, str): @@ -323,36 +483,64 @@ def _serialize_assistant_entry(assistant_msg: dict | None) -> dict: "autoplay": bool(meta.get("autoplay") or (audio or {}).get("autoplay")), "audio_duration_seconds": (audio or {}).get("duration_seconds") if isinstance(audio, dict) else None, "audio_relpath": audio_relpath.replace("\\", "/") if audio_relpath else None, + "pending_action": meta.get("pending_action"), + "agent_mode": meta.get("agent_mode"), } +def _serialize_message_for_manifest(msg: dict) -> dict | None: + """按 role 分发单条 chat_history 消息到 user/assistant manifest 结构。 + + 输入: + msg: 消息字典。 + + 输出: + manifest 格式的消息条目，或 None（无法识别）。 + """ + role = msg.get("role") + if role == "user": + return {"role": "user", "user": _serialize_user_msg_for_manifest(msg)} + if role == "assistant": + return {"role": "assistant", "assistant": _serialize_assistant_entry(msg)} + return None + + def sync_manifest_from_chat_history(session_id: str, chat_history: list, latest_user_image_description: str) -> None: + """将当前 chat_history 全量写入 ui_messages.json（version 3 messages 格式）。 + + 输入: + session_id: 会话 ID。 + chat_history: 当前聊天历史列表。 + latest_user_image_description: 最新用户图片描述（保留参数，当前未使用）。 + + 输出: 无。 + + 系统定位: + 每轮对话结束后调用，持久化 UI 层消息结构。 + """ if not session_id: return mp = manifest_path(session_id) - turns_out: list[dict] = [] - ch = chat_history or [] - i = 0 - while i < len(ch): - if ch[i].get("role") != "user": - i += 1 - continue - user_msg = ch[i] - assistant_msg = ch[i + 1] if i + 1 < len(ch) and ch[i + 1].get("role") == "assistant" else None - - turns_out.append( - { - "user": _serialize_user_msg_for_manifest(user_msg), - "assistant": _serialize_assistant_entry(assistant_msg), - } - ) - i += 2 if assistant_msg else 1 + messages_out: list[dict] = [] + for msg in chat_history or []: + item = _serialize_message_for_manifest(msg) + if item is not None: + messages_out.append(item) os.makedirs(os.path.dirname(mp), exist_ok=True) with open(mp, "w", encoding="utf-8") as f: - json.dump({"version": 2, "turns": turns_out}, f, ensure_ascii=False, indent=2) + json.dump({"version": 3, "messages": messages_out}, f, ensure_ascii=False, indent=2) + def _assistant_manifest_entry_to_content(entry: dict) -> str | list: + """将 manifest assistant 条目还原为 UI content（文本与/或音频部件）。 + + 输入: + entry: manifest 中的 assistant 条目字典。 + + 输出: + content 结构（str 或 list），用于前端展示。 + """ text = (entry.get("text") or "").strip() output_type = entry.get("output_type") or "text" autoplay = bool(entry.get("autoplay")) @@ -386,7 +574,53 @@ def _assistant_manifest_entry_to_content(entry: dict) -> str | list: return parts +def _assistant_manifest_entry_to_message(asst_entry: dict) -> dict | None: + """将 manifest assistant 条目还原为完整 assistant 消息 dict（含 meta）。 + + 输入: + asst_entry: manifest 中的 assistant 条目字典。 + + 输出: + 消息字典，包含 role、content、meta；无有效内容时返回 None。 + """ + if not isinstance(asst_entry, dict): + asst_entry = {"text": str(asst_entry)} + asst_content = _assistant_manifest_entry_to_content(asst_entry) + if not str(asst_entry.get("text") or "").strip() and asst_content == "": + return None + + meta = { + "output_type": asst_entry.get("output_type", "text"), + "autoplay": asst_entry.get("autoplay", False), + } + if asst_entry.get("pending_action"): + meta["pending_action"] = asst_entry.get("pending_action") + if asst_entry.get("agent_mode"): + meta["agent_mode"] = asst_entry.get("agent_mode") + audio_relpath = asst_entry.get("audio_relpath") + if audio_relpath: + root_norm = os.path.normpath(MULTI_INPUTS_ROOT) + audio_path = os.path.normpath(os.path.join(root_norm, audio_relpath.replace("/", os.sep))) + if os.path.isfile(audio_path): + meta["audio"] = { + "path": audio_path, + "autoplay": asst_entry.get("autoplay", False), + "duration_seconds": asst_entry.get("audio_duration_seconds"), + "format": "wav", + "sample_rate": 24000, + } + return {"role": "assistant", "content": asst_content, "meta": meta} + + def _load_ui_messages_from_manifest(session_id: str) -> list | None: + """从 ui_messages.json 加载 UI 消息；文件缺失或解析失败返回 None。 + + 输入: + session_id: 会话 ID。 + + 输出: + 消息列表，或 None（表示无有效 manifest）。 + """ mp = manifest_path(session_id) if not os.path.isfile(mp): return None @@ -395,6 +629,19 @@ def _load_ui_messages_from_manifest(session_id: str) -> list | None: data = json.load(f) except (json.JSONDecodeError, OSError): return None + if "messages" in data: + out: list[dict] = [] + for item in data.get("messages") or []: + if not isinstance(item, dict): + continue + role = item.get("role") + if role == "user": + out.append({"role": "user", "content": _manifest_user_entry_to_content(item.get("user") or {})}) + elif role == "assistant": + assistant_msg = _assistant_manifest_entry_to_message(item.get("assistant") or {}) + if assistant_msg is not None: + out.append(assistant_msg) + return out if "turns" not in data: return None turns = data.get("turns") or [] @@ -405,34 +652,23 @@ def _load_ui_messages_from_manifest(session_id: str) -> list | None: for idx, t in enumerate(turns): out.append({"role": "user", "content": _manifest_user_entry_to_content(t.get("user") or {})}) asst_entry = t.get("assistant") or {} - if not isinstance(asst_entry, dict): - asst_entry = {"text": str(asst_entry)} - asst_content = _assistant_manifest_entry_to_content(asst_entry) - if idx == n - 1 and not str(asst_entry.get("text") or "").strip() and asst_content == "": + assistant_msg = _assistant_manifest_entry_to_message(asst_entry) + if assistant_msg is None and idx == n - 1: continue - - # 从扁平存储重建与 execute_agent 产物相同的 meta 字典 - meta = { - "output_type": asst_entry.get("output_type", "text"), - "autoplay": asst_entry.get("autoplay", False), - } - audio_relpath = asst_entry.get("audio_relpath") - if audio_relpath: - root_norm = os.path.normpath(MULTI_INPUTS_ROOT) - audio_path = os.path.normpath(os.path.join(root_norm, audio_relpath.replace("/", os.sep))) - if os.path.isfile(audio_path): - meta["audio"] = { - "path": audio_path, - "autoplay": asst_entry.get("autoplay", False), - "duration_seconds": asst_entry.get("audio_duration_seconds"), - "format": "wav", - "sample_rate": 24000, - } - out.append({"role": "assistant", "content": asst_content, "meta": meta}) + if assistant_msg is not None: + out.append(assistant_msg) return out def load_ui_messages_for_session(session_id: str) -> list: + """加载会话 UI 消息：优先 manifest，回退 SQLite 历史。 + + 输入: + session_id: 会话 ID。 + + 输出: + 消息列表（总是有效列表，可能为空）。 + """ if not session_id: return [] m = _load_ui_messages_from_manifest(session_id) @@ -442,11 +678,35 @@ def load_ui_messages_for_session(session_id: str) -> list: def rewrite_user_files_for_last_turn(chat_history: list, session_id: str) -> None: + """若末条为 user 消息，将其附件复制到会话目录。 + + 输入: + chat_history: 聊天历史列表。 + session_id: 会话 ID。 + + 输出: 无。 + + 系统定位: + API 层添加用户消息后立即调用，落盘附件。 + """ if session_id and chat_history and chat_history[-1].get("role") == "user": rewrite_user_message_files_to_session_dir(chat_history[-1], session_id) def add_message(chat_history, query): + """将上传 query 转为 user 消息追加到 chat_history；无有效内容时返回 None。 + + 输入: + chat_history: 现有聊天历史。 + query: {"text": str, "files": list[str], "file_names": list[str]} + + 输出: + (new_chat_history, cleared_query) 元组； + cleared_query 在旧 API 兼容中保留，新调用可忽略。 + + 系统定位: + API 层构造用户消息的核心函数。 + """ chat_history = chat_history or [] user_content = _build_user_chat_content(query) if user_content is None: @@ -456,6 +716,17 @@ def add_message(chat_history, query): def clear_ui_and_manifest(session_id: str): + """清空会话 manifest 并返回空 chat_history 与重置后的 query 状态。 + + 输入: + session_id: 会话 ID。 + + 输出: + (empty_chat_history, reset_query) 元组。 + + 系统定位: + 清空会话时重置 UI 状态。 + """ if session_id: mp = manifest_path(session_id) os.makedirs(os.path.dirname(mp), exist_ok=True)