commit 964eeaa8293c5674ffd44eeec9c186f8ac16500b Author: ThearchyHelios Date: Sun Sep 13 17:27:00 2026 +0800 AgentLab：可视化 Agent 编排实验台 在画布上拖拽编排 agent 工作流，并实时看着它执行。 后端 FastAPI + LangGraph：把画布 JSON 编译成 StateGraph，15 种节点 （模型/Agent/多Agent、工具/沙箱、分支/循环/子图、记忆/检索/整形、 人工介入/结构校验）。选 LangGraph 是因为可视化编排真正难的是暂停、 恢复和回放，而 checkpointer + interrupt() 正好把这几件事做在了底层。 前端 React + React Flow：节点元数据驱动属性面板与连接桩，运行时 节点高亮、边流动画、卡片内流式 token 全部由同一份事件流驱动。 关键实现： - Agent 内工具审批用 @task 缓存模型调用，interrupt 重放时不重复计费 - 沙箱保持只读根文件系统，代码经 exec+base64 写入（put_archive 会被 只读容器拒绝），断网、非 root、内存/CPU/超时限额 - 检索为向量与 BM25 混合，本地哈希 embedding 零配置可用 - 分支条件走 AST 白名单求值器；HTTP 工具逐 IP 拦截内网与元数据地址 - Mock provider 让无 API Key 也能跑通完整链路 7 个内置模板全部冒烟通过，含真实模型端到端验证。 diff --git a/.env.example b/.env.example new file mode 100644 index 00000000..84044066 --- /dev/null +++ b/.env.example @@ -0,0 +1,40 @@ +# 复制成 .env 后按需修改。所有项都可以留空——留空时用代码里的默认值。 +# API Key 也可以不写在这里，启动后在「设置 → 模型接入」页面填，会加密存进本地 SQLite。 + +# ---- 服务 ---- +# AGENTLAB_PORT=8000 +# AGENTLAB_WEB_PORT=5273 + +# ---- 沙箱 ---- +# auto（有 Docker 用 Docker，否则降级本地子进程）| docker | local | off +# AGENTLAB_SANDBOX_BACKEND=auto +# AGENTLAB_SANDBOX_IMAGE=python:3.12-slim +# AGENTLAB_SANDBOX_TIMEOUT=30 +# AGENTLAB_SANDBOX_MEMORY_MB=512 +# AGENTLAB_SANDBOX_NETWORK=false + +# ---- 执行护栏 ---- +# AGENTLAB_MAX_GRAPH_STEPS=200 +# AGENTLAB_MAX_AGENT_STEPS=25 +# AGENTLAB_MAX_CONCURRENT_RUNS=20 + +# ---- HTTP 工具 ---- +# 逗号分隔的域名白名单；留空表示只拦内网地址，不限制公网域名 +# AGENTLAB_HTTP_TOOL_ALLOWLIST=api.github.com,news.ycombinator.com + +# ---- 落库加密密钥 ---- +# 留空会自动生成并存到 data/.secret_key。换了这个值，之前存的 API Key 会解不开。 +# AGENTLAB_SECRET_KEY= + +# ---- 模型凭据（可选，首次启动会自动导入成 provider）---- +# ANTHROPIC_API_KEY=sk-ant-... +# 用中转网关时填这两个（会以 Authorization: Bearer 发送） +# ANTHROPIC_BASE_URL=https://your-gateway.example.com/api +# ANTHROPIC_AUTH_TOKEN=... +# OPENAI_API_KEY=sk-... + +# ---- 可选增强 ---- +# 配了就用 Tavily 搜索，否则 web_search 退回 DuckDuckGo +# TAVILY_API_KEY=tvly-... +# 打开后知识库用 OpenAI embedding 代替本地哈希向量（检索质量更好） +# AGENTLAB_USE_OPENAI_EMBEDDINGS=1 diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..b410e426 --- /dev/null +++ b/.gitignore @@ -0,0 +1,26 @@ +# 运行期数据：数据库、沙箱工作区、上传文件、自动生成的加密密钥 +/data/ + +# Python +__pycache__/ +*.py[cod] +.venv/ +*.egg-info/ +.pytest_cache/ +.ruff_cache/ + +# Node +node_modules/ +dist/ +*.tsbuildinfo + +# 环境变量（含 API Key，绝不入库） +.env +.env.* +!.env.example + +# 编辑器 / 系统 +.DS_Store +.idea/ +.vscode/ +*.swp diff --git a/README.md b/README.md new file mode 100644 index 00000000..8b8fe4d0 --- /dev/null +++ b/README.md @@ -0,0 +1,148 @@ +# AgentLab · 可视化 Agent 编排实验台 + +在画布上拖节点、连线，编出一条 agent 工作流，然后**看着它跑**——哪个节点在执行、 +模型正在吐什么字、调了什么工具、沙箱里的代码返回了什么、在哪一步停下来等你拍板。 + +不是 demo，是一套能真用的本地实验环境：多模型接入、隔离沙箱、工具链与 MCP、 +长期记忆与知识库、人工介入、断点续跑、结构化校验、运行追踪与成本核算。 + +``` +┌──────────┬──────────────────────────────────┬─────────────────┐ +│ 节点库 │ 画布 │ 运行 / 属性 │ +│ │ ┌────┐ ┌────┐ ┌────┐ │ ▸ 输入表单 │ +│ 模型 │ │输入│──▶│Agent│──▶│成果│ │ ▸ 实时时间线 │ +│ 执行 │ └────┘ └─┬──┘ └────┘ │ ▸ 人工审批 │ +│ 控制 │ ▼ 工具调用 │ ▸ 成果 / 用量 │ +│ 上下文 │ │ │ +│ 把关 │ │ │ +└──────────┴──────────────────────────────────┴─────────────────┘ +``` + +## 快速开始 + +需要 Python 3.12+（`uv` 会自动装）、Node 20+、pnpm；Docker 可选但强烈建议（代码沙箱靠它做隔离）。 + +```bash +./scripts/dev.sh +``` + +打开 http://localhost:5273 。 + +**不配任何 API Key 也能直接玩**——内置了一个 Mock provider，会产出假的流式回复、 +假的工具调用、符合 schema 的假结构化数据，整条编排链路（包括画布高亮、审批中断、 +沙箱执行）都会真实走一遍。想要真结果，去「设置 → 模型接入」填 Key 即可。 + +启动时如果环境里已有 `ANTHROPIC_API_KEY` / `ANTHROPIC_AUTH_TOKEN` / `OPENAI_API_KEY`， +会自动导入成 provider，不用手填。 + +内置 7 个模板，从「最小问答」到「多 Agent 协作」，既是能直接跑的例子，也是各类节点用法的活文档。 + +## 能做什么 + +| 能力 | 怎么体现 | +|---|---| +| **可视化编排** | 15 种节点拖拽连线；运行时节点实时高亮、边上数据流动画、卡片里直接看到流式 token | +| **多模型接入** | Anthropic / OpenAI / 任意 OpenAI 兼容服务（DeepSeek、Kimi、通义、智谱、硅基流动、Ollama…）+ Mock | +| **工具链** | 15 个内置工具 + 自定义工具（HTTP 模板 / 沙箱 Python）+ MCP server 接入 | +| **代码沙箱** | Docker 隔离：断网、只读根文件系统、非 root、内存/CPU/进程数限额、超时强杀 | +| **人工介入** | 图暂停并落盘，人在界面上批准/驳回/改稿，从断点继续——进程重启也不丢 | +| **记忆与知识库** | 跨运行的长期记忆（自动去重）+ 文档知识库（向量 + BM25 混合检索，可调配比） | +| **Skill 管理** | 把「怎么做事」抽成可复用的方法论，挂到节点上注入 system prompt | +| **自然语言编排** | Copilot：一句话生成或改写整张图，自动排版 + 校验 | +| **结构化成果** | JSON Schema 校验，不合格把错误喂回模型自动返工 | +| **追踪与成本** | 每步事件全量落库，可回放；逐节点耗时、token、按目录价折算的美元成本 | + +## 节点类型 + +| 分类 | 节点 | 说明 | +|---|---|---| +| 起止 | `input` `output` | 声明输入字段 / 收集结构化成果 | +| 模型 | `llm` `agent` `supervisor` | 单次调用 / 带工具循环 / 多 agent 协作 | +| 执行 | `tool` `code` | 直接调工具 / 沙箱里跑代码 | +| 控制 | `branch` `loop` `subgraph` | 条件或语义分支 / 遍历与条件循环 / 嵌套工作流 | +| 上下文 | `memory` `retrieve` `transform` | 长期记忆读写 / 知识检索 / 数据整形 | +| 把关 | `human` `validate` | 人工介入 / Schema 校验与自动返工 | + +节点之间用模板语法传数据： + +``` +{{ input.question }} 入口输入 +{{ vars.answer }} 某节点 assign_to 写入的变量 +{{ nodes.n1.text }} 指定节点的输出 +{{ vars.items | json }} 过滤器：json / compact / upper / length / first … +``` + +连线即并行：一个节点连出多条边就是 fan-out，多条边汇入同一节点会自动等待汇聚。 + +## 架构 + +``` +backend/ FastAPI + LangGraph + app/engine/ 画布 JSON → StateGraph 的编译与执行 + schema.py 图定义与静态校验 + compiler.py 编译：节点包装、事件、重试、条件路由 + runner.py 执行调度、事件总线、中断与恢复 + expressions.py 模板插值 + AST 白名单表达式求值 + nodes/ 各类节点的执行器 + app/providers/ 多模型接入与成本估算 + app/sandbox/ Docker / 本地子进程双后端 + app/tools/ 工具注册表、内置工具、MCP、自定义工具 + app/memory/ 记忆、知识库、混合检索 + app/api/ REST + WebSocket +frontend/ React 19 + React Flow + Zustand + src/canvas/nodeDefs.ts 节点元数据——属性面板、节点库、连接桩全由它驱动 + src/store/studio.ts 画布状态 + 事件流到高亮的映射 +scripts/dev.sh 一键启动 +scripts/e2e-check.mjs 浏览器冒烟测试 +``` + +### 几个值得说明的决定 + +**为什么用 LangGraph。** 可视化编排真正难的不是画框连线，是暂停、恢复、回放。 +LangGraph 的 checkpointer 和 `interrupt()` 正好把这几件事做在了底层：人工介入是 +一次中断 + 一次 `Command(resume=...)`，进程重启后仍能从断点继续；`aget_state_history()` +直接给出每一步的状态快照。自己实现这套语义的成本远高于接一个框架。 + +**Agent 内部的工具审批不会重复计费。** `interrupt()` 会让整个节点重放， +朴素实现会把之前的模型调用再跑一遍。Agent 节点里的模型调用和工具执行都包在 +LangGraph 的 `@task` 里，结果进 checkpoint，重放时直接取缓存。 + +**事件流是唯一的可视化数据源。** 节点通过 `get_stream_writer()` 发事件，runner 转成 +`RunEvent` 落库并广播。画布高亮、token 流、工具卡片、时间线全部由同一份事件驱动， +所以刷新页面或中途接入都能拿到完整过程（WebSocket 先补历史再接实时）。 + +**沙箱的隔离姿态。** 容器 `read_only` + tmpfs 工作区 + `cap_drop ALL` + +`no-new-privileges` + 非 root + 默认断网 + 内存/CPU/pids 限额 + 超时整容器销毁。 +注意 Docker 的 `put_archive` 在只读容器上会被拒，所以代码是用 exec + base64 管道写进去的 +——为的是不牺牲只读根文件系统。Docker 不可用时降级到子进程 + rlimit， +**那条路径没有内核级隔离**，界面上会明确标注。 + +**检索为什么是混合的。** 默认 embedding 是本地特征哈希，不联网不下模型，零配置可用， +但语义泛化弱；BM25 补上精确关键词匹配这一半。两者各自归一化后加权，权重在界面上可调， +命中结果会分别显示语义分和关键词分，方便调参。配了 OpenAI Key 可切换成真 embedding。 + +**安全边界。** 分支条件用 AST 白名单求值器而不是 `eval`；HTTP 工具解析域名后逐个 IP +检查，拦截内网、回环和云元数据地址，且不跟随重定向（逐跳重新校验）；文件工具的路径 +`resolve()` 后必须仍在工作区内；API Key 用 Fernet 加密落库，只回掩码给前端。 + +## 常见问题 + +**沙箱不可用？** 设置页「运行环境」会显示实际后端和原因。Docker 没启动时会自动降级到 +本地子进程，功能可用但没有隔离。首次执行会拉 `python:3.12-slim` 镜像，需要等一会儿。 + +**模型返回空内容？** Claude 4.6 之后的模型默认开着 thinking，`max_tokens` 给小了会出现 +「思考完就没额度写正文」。节点里把 max_tokens 调大，或把思考模式设为关闭。时间线里会有提示。 + +**中转网关报 401？** 有的网关只认 `Authorization: Bearer`，且多带一个 `x-api-key` 就会拒。 +在 provider 设置里勾上「用 Authorization: Bearer 认证」。 + +**端口冲突？** 前端默认 5273、后端 8000，用 `AGENTLAB_WEB_PORT` / `AGENTLAB_PORT` 改。 + +**数据在哪？** 全在 `data/`：`agentlab.db` 业务数据、`checkpoints.db` 执行断点、 +`workspace/` 沙箱文件、`.secret_key` 加密密钥。删掉 `data/` 就是恢复出厂设置。 + +## 冒烟测试 + +```bash +node scripts/e2e-check.mjs # 开浏览器跑一遍：打开画布 → 选模板 → 运行 → 看高亮 +``` diff --git a/backend/app/__init__.py b/backend/app/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/api/__init__.py b/backend/app/api/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/api/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/api/copilot.py b/backend/app/api/copilot.py new file mode 100644 index 00000000..ee661592 --- /dev/null +++ b/backend/app/api/copilot.py @@ -0,0 +1,289 @@ +from __future__ import annotations + +import json +from typing import Any + +from fastapi import APIRouter, Depends, HTTPException +from pydantic import BaseModel, Field +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.base import get_session +from app.db.models import Workflow +from app.engine.schema import GraphSpec, NodeType, validate_graph +from app.engine.state import message_text +from app.providers.factory import ModelSpec, ProviderNotConfigured, get_chat_model +from app.tools.registry import all_specs + +router = APIRouter(prefix="/api/copilot", tags=["copilot"]) + + +NODE_REFERENCE = """\ +可用节点类型（type 字段）： +- input：入口。config.fields = [{name, required, default, description}] +- output：出口，收集最终成果。config.fields = [{name, value}]，value 里写模板引用 +- llm：单次模型调用。config: {system, prompt, model, temperature, max_tokens, assign_to, output_schema} +- agent：带工具循环的 agent。config: {system, prompt, tools:[工具名], max_steps, approval, assign_to} +- supervisor：多 agent 协作。config: {goal, agents:[{name, description, system, tools, model}], max_rounds} +- tool：直接调一个工具。config: {tool: 工具名, args: {...}, assign_to} +- code：沙箱里跑代码。config: {language: python|bash|node, code, timeout, network, assign_to} +- branch：条件分支。config: {mode: expression|llm, cases:[{key, condition, label}]} +- loop：循环。config: {mode: foreach|while, items, item_var, condition, max_iterations} +- retrieve：知识库检索。config: {query, collection, limit, assign_to} +- memory：长期记忆读写。config: {action: recall|write, query, content, scope, assign_to} +- human：人工介入。config: {mode: approve|input|edit, title, message} +- validate：JSON Schema 校验，可自动让模型修复。config: {schema, source, max_retries} +- transform：数据整形。config: {mode: expression|template|json, expression/template, assign_to} +- subgraph：嵌套另一个工作流。config: {workflow_id, input} + +模板语法（在任意字符串里用）： +- {{ input.字段名 }} 入口输入 +- {{ vars.变量名 }} 某节点 assign_to 写入的变量 +- {{ nodes.节点id.text }} 某节点的输出 +- {{ last_message }} 最近一条消息文本 +- 过滤器：{{ vars.x | json }} + +连线规则： +- edges 里每条边 {source, target, sourceHandle?} +- branch 节点的出边必须带 sourceHandle，取值是 cases 里的 key，另外要有一条 sourceHandle="default" 兜底 +- loop 节点：sourceHandle="body" 连循环体，循环体最后一个节点再连回 loop 节点；sourceHandle="done" 连循环结束后的去向 +- human 节点（approve 模式）：sourceHandle 用 "approved" 和 "rejected" +- 一个节点连出多条普通边 = 并行执行，多条边汇入同一节点 = 等待汇聚 +""" + + +def auto_layout(spec: GraphSpec, *, x_gap: int = 300, y_gap: int = 150) -> GraphSpec: + """按拓扑层级排版。 + + 模型生成的图只有结构没有坐标，全堆在原点就没法看了。 + 这里按最长路径分层：同层的节点竖排，层与层横向铺开。 + """ + nodes = spec.node_map() + depth: dict[str, int] = {} + + def compute(node_id: str, seen: frozenset[str]) -> int: + if node_id in depth: + return depth[node_id] + if node_id in seen: # 环：就地截断，避免递归爆栈 + return 0 + incoming = [e.source for e in spec.incoming(node_id) if e.source in nodes] + value = 0 if not incoming else 1 + max( + compute(src, seen | {node_id}) for src in incoming + ) + depth[node_id] = value + return value + + for node in spec.nodes: + compute(node.id, frozenset()) + + by_level: dict[int, list[str]] = {} + for node_id, level in depth.items(): + by_level.setdefault(level, []).append(node_id) + + for level, ids in sorted(by_level.items()): + offset = -(len(ids) - 1) * y_gap / 2 + for i, node_id in enumerate(sorted(ids)): + node = nodes[node_id] + node.position.x = 80 + level * x_gap + node.position.y = 300 + offset + i * y_gap + return spec + + +class GenerateIn(BaseModel): + instruction: str = Field(min_length=1, description="用自然语言描述想要的工作流") + base_graph: dict[str, Any] | None = Field(default=None, description="在现有图上修改时传入") + provider: str | None = None + model: str | None = None + + +class GenerateOut(BaseModel): + graph: dict[str, Any] + explanation: str = "" + issues: list[dict[str, Any]] = Field(default_factory=list) + + +GRAPH_SCHEMA: dict[str, Any] = { + "type": "object", + "properties": { + "explanation": {"type": "string", "description": "一两句话说明这张图怎么跑"}, + "nodes": { + "type": "array", + "items": { + "type": "object", + "properties": { + "id": {"type": "string"}, + "type": {"type": "string", "enum": [t.value for t in NodeType]}, + "label": {"type": "string"}, + "config": {"type": "object"}, + }, + "required": ["id", "type"], + }, + }, + "edges": { + "type": "array", + "items": { + "type": "object", + "properties": { + "source": {"type": "string"}, + "target": {"type": "string"}, + "sourceHandle": {"type": "string"}, + }, + "required": ["source", "target"], + }, + }, + }, + "required": ["nodes", "edges"], +} + + +@router.post("/generate", response_model=GenerateOut) +async def generate( + payload: GenerateIn, session: AsyncSession = Depends(get_session) +) -> GenerateOut: + """用自然语言生成或改写一张工作流图。 + + 产物会经过和手工编排完全相同的校验与排版，所以生成完就是可运行、可读的， + 而不是一段还要人去修的 JSON。 + """ + tool_list = "\n".join( + f"- {name}（{spec.category}）：{spec.description}" + for name, spec in sorted(all_specs().items()) + ) + + system = ( + "你是一个 agent 工作流编排专家。根据用户需求产出一张可执行的工作流图。\n\n" + f"{NODE_REFERENCE}\n\n可用工具：\n{tool_list}\n\n" + "要求：\n" + "1. 必须有且只有一个 input 节点和至少一个 output 节点\n" + "2. 节点 id 用简短英文小写下划线，例如 fetch_data、summarize\n" + "3. 每个节点写清楚 label（中文），让人在画布上一眼看懂它干什么\n" + "4. 需要把结果传给下游时，用 assign_to 命名变量，下游用 {{ vars.变量名 }} 引用\n" + "5. 不要凭空发明工具名，只能用上面列出的\n" + "6. 结构尽量简单：能用 3 个节点解决就别堆 8 个" + ) + + if payload.base_graph and payload.base_graph.get("nodes"): + user = ( + "这是当前的工作流：\n" + f"{json.dumps(_slim(payload.base_graph), ensure_ascii=False, indent=2)}\n\n" + f"请按下面的要求修改它，返回完整的新图（不是补丁）：\n{payload.instruction}" + ) + else: + user = f"请设计一个工作流：{payload.instruction}" + + try: + model, _ = await get_chat_model( + session, ModelSpec(provider=payload.provider, model=payload.model, max_tokens=8192) + ) + except ProviderNotConfigured as e: + raise HTTPException(400, str(e)) from e + + messages = [("system", system), ("human", user)] + try: + raw = await model.with_structured_output(GRAPH_SCHEMA).ainvoke(messages) + if not isinstance(raw, dict): + raw = json.loads(message_text(raw)) + except Exception: # noqa: BLE001 - 不支持结构化输出的模型退回文本解析 + try: + text = message_text(await model.ainvoke(messages)) + raw = _extract_json(text) + except Exception as e: # noqa: BLE001 + raise HTTPException(502, f"模型没有返回可用的图：{type(e).__name__}: {e}") from e + + graph = { + "nodes": [ + { + "id": n.get("id") or f"node{i}", + "type": n.get("type") or "llm", + "position": {"x": 0, "y": 0}, + "data": {"label": n.get("label") or "", "config": n.get("config") or {}}, + } + for i, n in enumerate(raw.get("nodes") or []) + ], + "edges": [ + { + "source": e.get("source", ""), + "target": e.get("target", ""), + "sourceHandle": e.get("sourceHandle"), + } + for e in (raw.get("edges") or []) + ], + } + + try: + spec = GraphSpec.model_validate(graph) + except Exception as e: # noqa: BLE001 + raise HTTPException(502, f"生成的图结构非法：{e}") from e + + spec = auto_layout(spec) + report = validate_graph(spec) + return GenerateOut( + graph=spec.model_dump(mode="json"), + explanation=str(raw.get("explanation", "")), + issues=[i.model_dump() for i in report.issues], + ) + + +class ExplainIn(BaseModel): + graph: dict[str, Any] + provider: str | None = None + model: str | None = None + + +@router.post("/explain") +async def explain( + payload: ExplainIn, session: AsyncSession = Depends(get_session) +) -> dict[str, str]: + """用大白话讲清楚一张图在干什么。接手别人的编排时很有用。""" + try: + model, _ = await get_chat_model( + session, ModelSpec(provider=payload.provider, model=payload.model, max_tokens=2048) + ) + except ProviderNotConfigured as e: + raise HTTPException(400, str(e)) from e + + prompt = ( + "用中文解释下面这个 agent 工作流：它解决什么问题、数据怎么流动、" + "有哪些分支和风险点。用简洁的分点说明，不要复述 JSON。\n\n" + f"{json.dumps(_slim(payload.graph), ensure_ascii=False, indent=2)}" + ) + return {"explanation": message_text(await model.ainvoke(prompt))} + + +@router.post("/layout") +async def relayout(payload: ExplainIn) -> dict[str, Any]: + """一键整理画布。""" + spec = GraphSpec.model_validate(payload.graph) + return auto_layout(spec).model_dump(mode="json") + + +def _slim(graph: dict[str, Any]) -> dict[str, Any]: + """喂给模型时去掉坐标之类的噪音，省 token 也省得它被干扰。""" + return { + "nodes": [ + { + "id": n.get("id"), + "type": n.get("type"), + "label": (n.get("data") or {}).get("label"), + "config": (n.get("data") or {}).get("config"), + } + for n in graph.get("nodes") or [] + ], + "edges": [ + {k: v for k, v in e.items() if k in ("source", "target", "sourceHandle") and v} + for e in graph.get("edges") or [] + ], + } + + +def _extract_json(text: str) -> dict[str, Any]: + text = text.strip() + if text.startswith("```"): + lines = text.splitlines() + text = "\n".join(lines[1 : -1 if lines[-1].strip().startswith("```") else None]) + start = text.find("{") + for end in range(len(text), start, -1): + try: + return json.loads(text[start:end]) + except json.JSONDecodeError: + continue + raise ValueError("模型输出里找不到 JSON") diff --git a/backend/app/api/knowledge.py b/backend/app/api/knowledge.py new file mode 100644 index 00000000..d26ed6b3 --- /dev/null +++ b/backend/app/api/knowledge.py @@ -0,0 +1,248 @@ +from __future__ import annotations + +from typing import Any + +from fastapi import APIRouter, Depends, File, HTTPException, Query, UploadFile +from pydantic import BaseModel, Field +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.base import get_session +from app.db.models import Document, MemoryItem, Skill +from app.memory import kb, store + +# -------------------------------------------------------------------------- +# 长期记忆 +# -------------------------------------------------------------------------- + +memory_router = APIRouter(prefix="/api/memory", tags=["memory"]) + + +class MemoryIn(BaseModel): + content: str = Field(min_length=1) + scope: str = "default" + kind: str = "fact" + importance: float = Field(default=0.5, ge=0, le=1) + meta: dict[str, Any] = Field(default_factory=dict) + + +class MemoryOut(BaseModel): + id: str + scope: str + kind: str + content: str + importance: float + use_count: int + meta: dict[str, Any] + created_at: Any = None + + model_config = {"from_attributes": True} + + +@memory_router.get("/scopes") +async def memory_scopes(session: AsyncSession = Depends(get_session)) -> list[dict[str, Any]]: + return await store.list_scopes(session) + + +@memory_router.get("", response_model=list[MemoryOut]) +async def list_memory( + scope: str | None = None, + limit: int = Query(default=200, le=500), + session: AsyncSession = Depends(get_session), +) -> list[MemoryItem]: + return await store.list_memories(session, scope=scope, limit=limit) + + +@memory_router.post("", response_model=MemoryOut, status_code=201) +async def add_memory( + payload: MemoryIn, session: AsyncSession = Depends(get_session) +) -> MemoryItem: + return await store.remember( + session, + scope=payload.scope, + content=payload.content, + kind=payload.kind, + importance=payload.importance, + meta=payload.meta, + ) + + +@memory_router.get("/search") +async def search_memory( + q: str, + scope: str = "default", + limit: int = 10, + session: AsyncSession = Depends(get_session), +) -> dict[str, Any]: + hits = await store.recall(session, scope=scope, query=q, limit=limit) + return {"query": q, "scope": scope, "results": hits} + + +@memory_router.delete("/{memory_id}", status_code=204) +async def delete_memory( + memory_id: str, session: AsyncSession = Depends(get_session) +) -> None: + if not await store.forget(session, memory_id): + raise HTTPException(404, "记忆不存在") + + +@memory_router.delete("/scope/{scope}") +async def clear_scope(scope: str, session: AsyncSession = Depends(get_session)) -> dict[str, int]: + return {"removed": await store.clear_scope(session, scope)} + + +# -------------------------------------------------------------------------- +# 知识库 +# -------------------------------------------------------------------------- + +kb_router = APIRouter(prefix="/api/kb", tags=["knowledge"]) + + +class DocumentOut(BaseModel): + id: str + collection: str + title: str + source: str + mime: str + chunk_count: int + created_at: Any = None + + model_config = {"from_attributes": True} + + +@kb_router.get("/collections") +async def collections(session: AsyncSession = Depends(get_session)) -> list[dict[str, Any]]: + return await kb.list_collections(session) + + +@kb_router.get("/documents", response_model=list[DocumentOut]) +async def list_documents( + collection: str | None = None, + session: AsyncSession = Depends(get_session), +) -> list[Document]: + stmt = select(Document).order_by(Document.created_at.desc()).limit(300) + if collection: + stmt = stmt.where(Document.collection == collection) + return list((await session.execute(stmt)).scalars()) + + +class IngestIn(BaseModel): + collection: str = "default" + title: str = "" + content: str = Field(min_length=1) + source: str = "" + + +@kb_router.post("/documents", response_model=DocumentOut, status_code=201) +async def ingest(payload: IngestIn, session: AsyncSession = Depends(get_session)) -> Document: + return await kb.ingest_document( + session, + collection=payload.collection, + title=payload.title, + content=payload.content, + source=payload.source, + ) + + +@kb_router.post("/upload", response_model=DocumentOut, status_code=201) +async def upload( + file: UploadFile = File(...), + collection: str = "default", + session: AsyncSession = Depends(get_session), +) -> Document: + raw = await file.read() + if len(raw) > 10 * 1024 * 1024: + raise HTTPException(413, "文件超过 10MB") + try: + content = raw.decode("utf-8") + except UnicodeDecodeError: + raise HTTPException(415, "目前只支持 UTF-8 文本文件（txt / md / json / csv 等）") from None + return await kb.ingest_document( + session, + collection=collection, + title=file.filename or "上传文档", + content=content, + source=file.filename or "", + mime=file.content_type or "text/plain", + ) + + +@kb_router.get("/search") +async def search_kb( + q: str, + collection: str | None = None, + limit: int = 5, + alpha: float = Query(default=0.5, ge=0, le=1, description="1=纯向量, 0=纯关键词"), + session: AsyncSession = Depends(get_session), +) -> dict[str, Any]: + hits = await kb.search(session, collection=collection, query=q, limit=limit, alpha=alpha) + return {"query": q, "collection": collection, "results": hits} + + +@kb_router.delete("/documents/{doc_id}", status_code=204) +async def delete_document(doc_id: str, session: AsyncSession = Depends(get_session)) -> None: + if not await kb.delete_document(session, doc_id): + raise HTTPException(404, "文档不存在") + + +# -------------------------------------------------------------------------- +# Skill（方法论） +# -------------------------------------------------------------------------- + +skills_router = APIRouter(prefix="/api/skills", tags=["knowledge"]) + + +class SkillIn(BaseModel): + name: str = Field(min_length=1, max_length=100) + description: str = "" + instructions: str = "" + examples: list[dict[str, Any]] = Field(default_factory=list) + suggested_tools: list[str] = Field(default_factory=list) + tags: list[str] = Field(default_factory=list) + enabled: bool = True + + +class SkillOut(SkillIn): + id: str + created_at: Any = None + + model_config = {"from_attributes": True} + + +@skills_router.get("", response_model=list[SkillOut]) +async def list_skills(session: AsyncSession = Depends(get_session)) -> list[Skill]: + return list((await session.execute(select(Skill).order_by(Skill.created_at.desc()))).scalars()) + + +@skills_router.post("", response_model=SkillOut, status_code=201) +async def create_skill(payload: SkillIn, session: AsyncSession = Depends(get_session)) -> Skill: + if (await session.execute(select(Skill).where(Skill.name == payload.name))).scalar_one_or_none(): + raise HTTPException(409, "同名 Skill 已存在") + row = Skill(**payload.model_dump()) + session.add(row) + await session.commit() + await session.refresh(row) + return row + + +@skills_router.patch("/{skill_id}", response_model=SkillOut) +async def update_skill( + skill_id: str, payload: SkillIn, session: AsyncSession = Depends(get_session) +) -> Skill: + row = await session.get(Skill, skill_id) + if not row: + raise HTTPException(404, "Skill 不存在") + for key, value in payload.model_dump().items(): + setattr(row, key, value) + await session.commit() + await session.refresh(row) + return row + + +@skills_router.delete("/{skill_id}", status_code=204) +async def delete_skill(skill_id: str, session: AsyncSession = Depends(get_session)) -> None: + row = await session.get(Skill, skill_id) + if not row: + raise HTTPException(404, "Skill 不存在") + await session.delete(row) + await session.commit() diff --git a/backend/app/api/runs.py b/backend/app/api/runs.py new file mode 100644 index 00000000..31bb5804 --- /dev/null +++ b/backend/app/api/runs.py @@ -0,0 +1,352 @@ +from __future__ import annotations + +import asyncio +import contextlib +from typing import Any + +from fastapi import APIRouter, Depends, HTTPException, Query, WebSocket, WebSocketDisconnect +from pydantic import BaseModel, Field +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.bus import bus +from app.db.base import SessionLocal, get_session +from app.db.models import Approval, Run, RunEvent, Workflow +from app.engine.runner import run_manager + +router = APIRouter(prefix="/api/runs", tags=["runs"]) + + +class StartRunIn(BaseModel): + workflow_id: str | None = None + graph: dict[str, Any] | None = None # 直接跑一张未保存的图（画布上的"试运行"） + input: dict[str, Any] = Field(default_factory=dict) + memory_scope: str = "default" + collection: str = "default" + + +class RunOut(BaseModel): + id: str + workflow_id: str | None + workflow_name: str + status: str + input: dict[str, Any] + output: dict[str, Any] + error: str | None + usage: dict[str, Any] + created_at: Any = None + started_at: Any = None + finished_at: Any = None + + model_config = {"from_attributes": True} + + +@router.post("", response_model=RunOut, status_code=201) +async def start_run(payload: StartRunIn, session: AsyncSession = Depends(get_session)) -> Run: + graph = payload.graph + name = "临时图" + if payload.workflow_id: + workflow = await session.get(Workflow, payload.workflow_id) + if not workflow: + raise HTTPException(404, "工作流不存在") + graph = graph or workflow.graph + name = workflow.name + if not graph: + raise HTTPException(400, "需要提供 workflow_id 或 graph") + + try: + return await run_manager.start( + graph=graph, + input_payload=payload.input, + workflow_id=payload.workflow_id, + workflow_name=name, + memory_scope=payload.memory_scope, + collection=payload.collection, + ) + except ValueError as e: + raise HTTPException(400, str(e)) from e + + +@router.get("", response_model=list[RunOut]) +async def list_runs( + workflow_id: str | None = None, + status: str | None = None, + limit: int = Query(default=50, le=200), + session: AsyncSession = Depends(get_session), +) -> list[Run]: + stmt = select(Run).order_by(Run.created_at.desc()).limit(limit) + if workflow_id: + stmt = stmt.where(Run.workflow_id == workflow_id) + if status: + stmt = stmt.where(Run.status == status) + return list((await session.execute(stmt)).scalars()) + + +@router.get("/{run_id}", response_model=RunOut) +async def get_run(run_id: str, session: AsyncSession = Depends(get_session)) -> Run: + run = await session.get(Run, run_id) + if not run: + raise HTTPException(404, "运行记录不存在") + return run + + +@router.get("/{run_id}/events") +async def get_events( + run_id: str, + after: int = 0, + session: AsyncSession = Depends(get_session), +) -> list[dict[str, Any]]: + """拉历史事件。刷新页面后重建时间线用这个，再接 WebSocket 续上实时流。""" + rows = await session.execute( + select(RunEvent) + .where(RunEvent.run_id == run_id, RunEvent.seq > after) + .order_by(RunEvent.seq) + ) + return [ + { + "id": e.id, + "run_id": e.run_id, + "seq": e.seq, + "type": e.type, + "node_id": e.node_id, + "ts": e.ts, + "data": e.data, + } + for e in rows.scalars() + ] + + +@router.post("/{run_id}/cancel") +async def cancel_run(run_id: str) -> dict[str, Any]: + stopped = await run_manager.cancel(run_id) + if not stopped: + raise HTTPException(409, "该运行当前不在执行中") + return {"ok": True} + + +class ResumeIn(BaseModel): + response: Any = None + approval_id: str | None = None + + +@router.post("/{run_id}/resume", response_model=RunOut) +async def resume_run(run_id: str, payload: ResumeIn) -> Run: + try: + return await run_manager.resume(run_id, payload.response) + except KeyError as e: + raise HTTPException(404, str(e)) from e + except ValueError as e: + raise HTTPException(409, str(e)) from e + + +@router.get("/{run_id}/state") +async def get_state(run_id: str) -> dict[str, Any]: + """读取 checkpoint 里的当前状态。调试中断中的 run 时很有用。""" + from app.engine.compiler import compile_graph + from app.engine.context import RunContext + from app.engine.schema import GraphSpec + + async with SessionLocal() as session: + run = await session.get(Run, run_id) + if not run: + raise HTTPException(404, "运行记录不存在") + + spec = GraphSpec.model_validate(run.graph) + app = compile_graph(spec, RunContext(run_id=run_id, thread_id=run_id, spec=spec)).compile( + checkpointer=run_manager.checkpointer + ) + snapshot = await app.aget_state({"configurable": {"thread_id": run_id}}) + values = dict(snapshot.values or {}) + values.pop("messages", None) # 消息对象不好直接序列化，前端也用不到 + return { + "values": _jsonable(values), + "next": list(snapshot.next or ()), + "interrupts": [ + {"id": getattr(i, "id", None), "value": _jsonable(getattr(i, "value", None))} + for i in (snapshot.interrupts or ()) + ], + } + + +@router.get("/{run_id}/history") +async def get_history(run_id: str, limit: int = 50) -> list[dict[str, Any]]: + """checkpoint 历史 —— 可以看到每一步之后的状态快照，用来做时间旅行式调试。""" + from app.engine.compiler import compile_graph + from app.engine.context import RunContext + from app.engine.schema import GraphSpec + + async with SessionLocal() as session: + run = await session.get(Run, run_id) + if not run: + raise HTTPException(404, "运行记录不存在") + + spec = GraphSpec.model_validate(run.graph) + app = compile_graph(spec, RunContext(run_id=run_id, thread_id=run_id, spec=spec)).compile( + checkpointer=run_manager.checkpointer + ) + out: list[dict[str, Any]] = [] + async for snapshot in app.aget_state_history({"configurable": {"thread_id": run_id}}): + values = dict(snapshot.values or {}) + values.pop("messages", None) + out.append( + { + "checkpoint_id": snapshot.config.get("configurable", {}).get("checkpoint_id"), + "next": list(snapshot.next or ()), + "created_at": snapshot.created_at, + "trail": _jsonable(values.get("trail") or [])[-1:], + "nodes_done": list((values.get("nodes") or {}).keys()), + } + ) + if len(out) >= limit: + break + return out + + +@router.delete("/{run_id}", status_code=204) +async def delete_run(run_id: str, session: AsyncSession = Depends(get_session)) -> None: + run = await session.get(Run, run_id) + if not run: + raise HTTPException(404, "运行记录不存在") + await session.delete(run) + await session.commit() + + +# -------------------------------------------------------------------------- +# 实时事件流 +# -------------------------------------------------------------------------- + + +@router.websocket("/{run_id}/stream") +async def stream_run(websocket: WebSocket, run_id: str) -> None: + """把一次运行的事件推给前端。 + + 先补历史再接实时，所以中途打开页面、或者刷新之后，时间线都是完整的。 + """ + await websocket.accept() + after = int(websocket.query_params.get("after", 0) or 0) + + try: + async with SessionLocal() as session: + rows = await session.execute( + select(RunEvent) + .where(RunEvent.run_id == run_id, RunEvent.seq > after) + .order_by(RunEvent.seq) + ) + for event in rows.scalars(): + await websocket.send_json( + { + "seq": event.seq, + "type": event.type, + "node_id": event.node_id, + "ts": event.ts, + "data": event.data, + "replay": True, + } + ) + run = await session.get(Run, run_id) + + # 已经结束的 run 没有后续事件，补完历史就关掉 + if run and run.status in ("succeeded", "failed", "cancelled"): + await websocket.send_json({"type": "stream.end", "status": run.status}) + await websocket.close() + return + + stream = bus.subscribe(run_id) + pump = asyncio.create_task(_pump(websocket, stream)) + try: + # 客户端断开时读操作会抛异常，用它来结束这条连接 + while True: + await websocket.receive_text() + except WebSocketDisconnect: + pass + finally: + pump.cancel() + with contextlib.suppress(asyncio.CancelledError): + await pump + except WebSocketDisconnect: + return + except Exception: # noqa: BLE001 + with contextlib.suppress(Exception): + await websocket.close() + + +async def _pump(websocket: WebSocket, stream: Any) -> None: + try: + async for event in stream: + await websocket.send_json(event.to_wire()) + except (WebSocketDisconnect, RuntimeError): + return + + +# -------------------------------------------------------------------------- +# 审批 +# -------------------------------------------------------------------------- + +approvals_router = APIRouter(prefix="/api/approvals", tags=["approvals"]) + + +class ApprovalOut(BaseModel): + id: str + run_id: str + node_id: str + mode: str + title: str + payload: dict[str, Any] + status: str + response: dict[str, Any] + created_at: Any = None + + model_config = {"from_attributes": True} + + +@approvals_router.get("", response_model=list[ApprovalOut]) +async def list_approvals( + status: str = "pending", + run_id: str | None = None, + session: AsyncSession = Depends(get_session), +) -> list[Approval]: + stmt = select(Approval).order_by(Approval.created_at.desc()).limit(100) + if status != "all": + stmt = stmt.where(Approval.status == status) + if run_id: + stmt = stmt.where(Approval.run_id == run_id) + return list((await session.execute(stmt)).scalars()) + + +class DecideIn(BaseModel): + approved: bool = True + note: str = "" + value: Any = None + args: dict[str, Any] | None = None + + +@approvals_router.post("/{approval_id}/decide", response_model=RunOut) +async def decide( + approval_id: str, payload: DecideIn, session: AsyncSession = Depends(get_session) +) -> Run: + approval = await session.get(Approval, approval_id) + if not approval: + raise HTTPException(404, "审批请求不存在") + if approval.status != "pending": + raise HTTPException(409, "该请求已经处理过了") + + response: dict[str, Any] = {"approved": payload.approved, "note": payload.note} + if payload.value is not None: + response["value"] = payload.value + if payload.args is not None: + response["args"] = payload.args + + try: + return await run_manager.resume(approval.run_id, response) + except ValueError as e: + raise HTTPException(409, str(e)) from e + + +def _jsonable(value: Any) -> Any: + if isinstance(value, dict): + return {str(k): _jsonable(v) for k, v in value.items()} + if isinstance(value, (list, tuple)): + return [_jsonable(v) for v in value] + if isinstance(value, (str, int, float, bool)) or value is None: + return value + return str(value) diff --git a/backend/app/api/sandbox.py b/backend/app/api/sandbox.py new file mode 100644 index 00000000..3283d023 --- /dev/null +++ b/backend/app/api/sandbox.py @@ -0,0 +1,49 @@ +from __future__ import annotations + +from typing import Any + +from fastapi import APIRouter +from pydantic import BaseModel, Field + +from app.sandbox.base import SandboxLimits +from app.sandbox.manager import sandbox_manager + +router = APIRouter(prefix="/api/sandbox", tags=["sandbox"]) + + +@router.get("/health") +async def health() -> dict[str, Any]: + return await sandbox_manager.health() + + +class ExecIn(BaseModel): + code: str = Field(min_length=1) + language: str = "python" + timeout: int = Field(default=30, ge=1, le=300) + memory_mb: int = Field(default=512, ge=64, le=4096) + network: bool = False + session_id: str = "playground" + files: dict[str, str] = Field(default_factory=dict) + + +@router.post("/exec") +async def execute(payload: ExecIn) -> dict[str, Any]: + """沙箱试验台。在画布外面直接跑一段代码，用来试限额、试依赖。""" + result = await sandbox_manager.run( + payload.code, + language=payload.language, + limits=SandboxLimits( + timeout=payload.timeout, + memory_mb=payload.memory_mb, + network=payload.network, + ), + session_id=payload.session_id, + files=payload.files, + ) + return result.model_dump() + + +@router.post("/cleanup/{session_id}") +async def cleanup(session_id: str) -> dict[str, bool]: + await sandbox_manager.cleanup(session_id) + return {"ok": True} diff --git a/backend/app/api/settings.py b/backend/app/api/settings.py new file mode 100644 index 00000000..8db5fcae --- /dev/null +++ b/backend/app/api/settings.py @@ -0,0 +1,262 @@ +from __future__ import annotations + +import os +from typing import Any + +from fastapi import APIRouter, Depends, HTTPException +from pydantic import BaseModel, Field +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.config import settings as app_settings +from app.core.crypto import encrypt, mask +from app.db.base import get_session +from app.db.models import Provider, Setting +from app.providers import catalog +from app.providers.factory import ModelSpec, ProviderNotConfigured, build_chat_model + +router = APIRouter(prefix="/api", tags=["settings"]) + + +# -------------------------------------------------------------------------- +# Provider +# -------------------------------------------------------------------------- + + +class ProviderIn(BaseModel): + name: str = Field(min_length=1, max_length=100) + kind: str = "anthropic" + base_url: str | None = None + api_key: str | None = None + models: list[dict[str, Any]] = Field(default_factory=list) + default_model: str | None = None + enabled: bool = True + extra: dict[str, Any] = Field(default_factory=dict) + + +class ProviderPatch(BaseModel): + name: str | None = None + base_url: str | None = None + api_key: str | None = None # 传空字符串表示清空；不传表示不动 + models: list[dict[str, Any]] | None = None + default_model: str | None = None + enabled: bool | None = None + extra: dict[str, Any] | None = None + + +class ProviderOut(BaseModel): + id: str + name: str + kind: str + base_url: str | None + default_model: str | None + models: list[dict[str, Any]] + enabled: bool + extra: dict[str, Any] + # 只回掩码，明文 key 永远不出后端 + api_key_masked: str = "" + has_key: bool = False + + +def _to_out(row: Provider) -> ProviderOut: + return ProviderOut( + id=row.id, + name=row.name, + kind=row.kind, + base_url=row.base_url, + default_model=row.default_model, + models=row.models or [], + enabled=row.enabled, + extra={k: v for k, v in (row.extra or {}).items() if k != "headers"} | ( + {"headers": {k: "***" for k in (row.extra or {}).get("headers", {})}} + if (row.extra or {}).get("headers") + else {} + ), + api_key_masked=mask(row.api_key), + has_key=bool(row.api_key), + ) + + +@router.get("/providers", response_model=list[ProviderOut]) +async def list_providers(session: AsyncSession = Depends(get_session)) -> list[ProviderOut]: + rows = (await session.execute(select(Provider).order_by(Provider.created_at))).scalars() + return [_to_out(r) for r in rows] + + +@router.get("/providers/catalog") +async def provider_catalog() -> dict[str, Any]: + """内置的 provider 预设和模型目录，前端用它渲染"添加 Provider"表单。""" + return { + "kinds": catalog.PROVIDER_KINDS, + "env_detected": { + "ANTHROPIC_API_KEY": bool(os.environ.get("ANTHROPIC_API_KEY")), + "ANTHROPIC_AUTH_TOKEN": bool(os.environ.get("ANTHROPIC_AUTH_TOKEN")), + "ANTHROPIC_BASE_URL": os.environ.get("ANTHROPIC_BASE_URL") or None, + "OPENAI_API_KEY": bool(os.environ.get("OPENAI_API_KEY")), + "TAVILY_API_KEY": bool(os.environ.get("TAVILY_API_KEY")), + }, + } + + +@router.post("/providers", response_model=ProviderOut, status_code=201) +async def create_provider( + payload: ProviderIn, session: AsyncSession = Depends(get_session) +) -> ProviderOut: + exists = ( + await session.execute(select(Provider).where(Provider.name == payload.name)) + ).scalar_one_or_none() + if exists: + raise HTTPException(409, f"已经有叫 {payload.name!r} 的 provider 了") + + row = Provider( + name=payload.name, + kind=payload.kind, + base_url=payload.base_url or None, + api_key=encrypt(payload.api_key), + models=payload.models, + default_model=payload.default_model, + enabled=payload.enabled, + extra=payload.extra, + ) + session.add(row) + await session.commit() + await session.refresh(row) + return _to_out(row) + + +@router.patch("/providers/{provider_id}", response_model=ProviderOut) +async def update_provider( + provider_id: str, payload: ProviderPatch, session: AsyncSession = Depends(get_session) +) -> ProviderOut: + row = await session.get(Provider, provider_id) + if not row: + raise HTTPException(404, "provider 不存在") + + data = payload.model_dump(exclude_unset=True) + if "api_key" in data: + row.api_key = encrypt(data.pop("api_key")) if data["api_key"] else None + data.pop("api_key", None) + for key, value in data.items(): + setattr(row, key, value) + await session.commit() + await session.refresh(row) + return _to_out(row) + + +@router.delete("/providers/{provider_id}", status_code=204) +async def delete_provider( + provider_id: str, session: AsyncSession = Depends(get_session) +) -> None: + row = await session.get(Provider, provider_id) + if not row: + raise HTTPException(404, "provider 不存在") + await session.delete(row) + await session.commit() + + +class TestIn(BaseModel): + model: str | None = None + prompt: str = "用一句话介绍你自己" + + +@router.post("/providers/{provider_id}/test") +async def test_provider( + provider_id: str, payload: TestIn, session: AsyncSession = Depends(get_session) +) -> dict[str, Any]: + """真发一次请求验证配置。设置页的"测试连接"按钮调它。""" + row = await session.get(Provider, provider_id) + if not row: + raise HTTPException(404, "provider 不存在") + + import time + + from app.engine.state import message_text + + try: + model = build_chat_model( + row, ModelSpec(model=payload.model or row.default_model, max_tokens=256) + ) + except ProviderNotConfigured as e: + return {"ok": False, "error": str(e)} + + started = time.perf_counter() + try: + reply = await model.ainvoke(payload.prompt) + except Exception as e: # noqa: BLE001 + return {"ok": False, "error": f"{type(e).__name__}: {e}"} + + usage = getattr(reply, "usage_metadata", None) or {} + return { + "ok": True, + "latency_ms": int((time.perf_counter() - started) * 1000), + "model": payload.model or row.default_model, + "reply": message_text(reply)[:500], + "usage": usage, + } + + +# -------------------------------------------------------------------------- +# 用户设置 +# -------------------------------------------------------------------------- + +DEFAULT_SETTINGS: dict[str, Any] = { + "ui": {"theme": "system", "language": "zh", "canvas_snap": True, "autosave": True}, + "run": { + "default_memory_scope": "default", + "default_collection": "default", + "stream_tokens": True, + "confirm_dangerous_tools": True, + }, + "limits": { + "max_graph_steps": app_settings.max_graph_steps, + "max_agent_steps": app_settings.max_agent_steps, + "max_run_seconds": app_settings.max_run_seconds, + }, +} + + +@router.get("/settings") +async def get_settings(session: AsyncSession = Depends(get_session)) -> dict[str, Any]: + rows = (await session.execute(select(Setting))).scalars() + stored = {r.key: r.value for r in rows} + merged = {k: {**v, **(stored.get(k) or {})} for k, v in DEFAULT_SETTINGS.items()} + for key, value in stored.items(): + merged.setdefault(key, value) + return merged + + +class SettingsIn(BaseModel): + values: dict[str, Any] + + +@router.put("/settings") +async def put_settings( + payload: SettingsIn, session: AsyncSession = Depends(get_session) +) -> dict[str, Any]: + for key, value in payload.values.items(): + row = await session.get(Setting, key) + if row: + row.value = value + else: + session.add(Setting(key=key, value=value)) + await session.commit() + return await get_settings(session) + + +@router.get("/system") +async def system_info() -> dict[str, Any]: + """给设置页顶部展示的运行环境概览。""" + import sys + + from app.sandbox.manager import sandbox_manager + + return { + "python": sys.version.split()[0], + "data_dir": str(app_settings.data_dir), + "sandbox": await sandbox_manager.health(), + "limits": { + "max_graph_steps": app_settings.max_graph_steps, + "max_agent_steps": app_settings.max_agent_steps, + "max_concurrent_runs": app_settings.max_concurrent_runs, + }, + } diff --git a/backend/app/api/tools.py b/backend/app/api/tools.py new file mode 100644 index 00000000..ad5286bf --- /dev/null +++ b/backend/app/api/tools.py @@ -0,0 +1,255 @@ +from __future__ import annotations + +from typing import Any + +from fastapi import APIRouter, Depends, HTTPException +from pydantic import BaseModel, Field +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.base import get_session +from app.db.models import CustomTool, McpServer +from app.tools.custom import run_custom_tool +from app.tools.mcp_manager import mcp_manager +from app.tools.registry import ToolContext, all_specs, call_tool + +router = APIRouter(prefix="/api/tools", tags=["tools"]) + + +@router.get("") +async def list_tools(session: AsyncSession = Depends(get_session)) -> list[dict[str, Any]]: + """把内置、自定义、MCP 三类工具拉平成一个列表，前端节点面板直接用。""" + out: list[dict[str, Any]] = [] + for name, spec in sorted(all_specs().items()): + out.append( + { + "id": name, + "name": name, + "description": spec.description, + "category": spec.category, + "source": "builtin", + "dangerous": spec.dangerous, + "schema": spec.json_schema(), + } + ) + + rows = ( + await session.execute(select(CustomTool).where(CustomTool.enabled.is_(True))) + ).scalars() + for row in rows: + out.append( + { + "id": row.name, + "name": row.name, + "description": row.description, + "category": f"自定义 · {row.kind}", + "source": "custom", + "dangerous": True, + "schema": row.parameters or {}, + } + ) + + try: + out.extend({**t, "source": "mcp", "dangerous": True} for t in await mcp_manager.list_tools()) + except Exception: # noqa: BLE001 - MCP 连不上不该让整个工具列表挂掉 + pass + return out + + +class RunToolIn(BaseModel): + args: dict[str, Any] = Field(default_factory=dict) + sandbox_session: str = "playground" + + +@router.post("/{tool_name:path}/run") +async def run_tool( + tool_name: str, payload: RunToolIn, session: AsyncSession = Depends(get_session) +) -> dict[str, Any]: + """直接试跑一个工具。工具面板上的"试一下"用它，不用为了测工具去搭一张图。""" + ctx = ToolContext(run_id="playground", node_id="playground", + sandbox_session=payload.sandbox_session) + import time + + started = time.perf_counter() + try: + result = await call_tool(tool_name, payload.args, ctx, session=session) + except KeyError as e: + raise HTTPException(404, str(e)) from e + except Exception as e: # noqa: BLE001 + return { + "ok": False, + "error": f"{type(e).__name__}: {e}", + "duration_ms": int((time.perf_counter() - started) * 1000), + } + return { + "ok": True, + "result": result, + "duration_ms": int((time.perf_counter() - started) * 1000), + } + + +# -------------------------------------------------------------------------- +# 自定义工具 +# -------------------------------------------------------------------------- + +custom_router = APIRouter(prefix="/api/custom-tools", tags=["tools"]) + + +class CustomToolIn(BaseModel): + name: str = Field(min_length=1, max_length=100, pattern=r"^[a-zA-Z_][a-zA-Z0-9_]*$") + description: str = "" + kind: str = "http" # http | python + parameters: dict[str, Any] = Field(default_factory=dict) + config: dict[str, Any] = Field(default_factory=dict) + enabled: bool = True + + +class CustomToolOut(CustomToolIn): + id: str + + model_config = {"from_attributes": True} + + +@custom_router.get("", response_model=list[CustomToolOut]) +async def list_custom(session: AsyncSession = Depends(get_session)) -> list[CustomTool]: + return list((await session.execute(select(CustomTool))).scalars()) + + +@custom_router.post("", response_model=CustomToolOut, status_code=201) +async def create_custom( + payload: CustomToolIn, session: AsyncSession = Depends(get_session) +) -> CustomTool: + if (await session.execute(select(CustomTool).where(CustomTool.name == payload.name))).scalar_one_or_none(): + raise HTTPException(409, "同名工具已存在") + if payload.name in all_specs(): + raise HTTPException(409, "这个名字和内置工具冲突") + row = CustomTool(**payload.model_dump()) + session.add(row) + await session.commit() + await session.refresh(row) + return row + + +@custom_router.patch("/{tool_id}", response_model=CustomToolOut) +async def update_custom( + tool_id: str, payload: CustomToolIn, session: AsyncSession = Depends(get_session) +) -> CustomTool: + row = await session.get(CustomTool, tool_id) + if not row: + raise HTTPException(404, "工具不存在") + for key, value in payload.model_dump().items(): + setattr(row, key, value) + await session.commit() + await session.refresh(row) + return row + + +@custom_router.delete("/{tool_id}", status_code=204) +async def delete_custom(tool_id: str, session: AsyncSession = Depends(get_session)) -> None: + row = await session.get(CustomTool, tool_id) + if not row: + raise HTTPException(404, "工具不存在") + await session.delete(row) + await session.commit() + + +@custom_router.post("/{tool_id}/test") +async def test_custom( + tool_id: str, payload: RunToolIn, session: AsyncSession = Depends(get_session) +) -> dict[str, Any]: + row = await session.get(CustomTool, tool_id) + if not row: + raise HTTPException(404, "工具不存在") + ctx = ToolContext(run_id="playground", node_id="test", sandbox_session="playground") + try: + result = await run_custom_tool(session, row.name, payload.args, ctx) + except Exception as e: # noqa: BLE001 + return {"ok": False, "error": f"{type(e).__name__}: {e}"} + return {"ok": True, "result": result} + + +# -------------------------------------------------------------------------- +# MCP +# -------------------------------------------------------------------------- + +mcp_router = APIRouter(prefix="/api/mcp", tags=["tools"]) + + +class McpIn(BaseModel): + name: str = Field(min_length=1, max_length=100) + transport: str = "stdio" # stdio | http + command: str | None = None + args: list[str] = Field(default_factory=list) + env: dict[str, str] = Field(default_factory=dict) + url: str | None = None + enabled: bool = True + + +class McpOut(McpIn): + id: str + status: str + last_error: str | None + tools_cache: list[Any] + + model_config = {"from_attributes": True} + + +@mcp_router.get("/servers", response_model=list[McpOut]) +async def list_servers(session: AsyncSession = Depends(get_session)) -> list[McpServer]: + return list((await session.execute(select(McpServer))).scalars()) + + +@mcp_router.post("/servers", response_model=McpOut, status_code=201) +async def create_server( + payload: McpIn, session: AsyncSession = Depends(get_session) +) -> McpServer: + if (await session.execute(select(McpServer).where(McpServer.name == payload.name))).scalar_one_or_none(): + raise HTTPException(409, "同名 MCP server 已存在") + row = McpServer(**payload.model_dump()) + session.add(row) + await session.commit() + await session.refresh(row) + return row + + +@mcp_router.patch("/servers/{server_id}", response_model=McpOut) +async def update_server( + server_id: str, payload: McpIn, session: AsyncSession = Depends(get_session) +) -> McpServer: + row = await session.get(McpServer, server_id) + if not row: + raise HTTPException(404, "MCP server 不存在") + for key, value in payload.model_dump().items(): + setattr(row, key, value) + await session.commit() + await session.refresh(row) + return row + + +@mcp_router.delete("/servers/{server_id}", status_code=204) +async def delete_server(server_id: str, session: AsyncSession = Depends(get_session)) -> None: + row = await session.get(McpServer, server_id) + if not row: + raise HTTPException(404, "MCP server 不存在") + await session.delete(row) + await session.commit() + + +@mcp_router.post("/servers/{server_id}/probe") +async def probe_server( + server_id: str, session: AsyncSession = Depends(get_session) +) -> dict[str, Any]: + row = await session.get(McpServer, server_id) + if not row: + raise HTTPException(404, "MCP server 不存在") + result = await mcp_manager.probe(row) + row.status = "ok" if result.get("ok") else "error" + row.last_error = result.get("error") + row.tools_cache = [t["name"] for t in result.get("tools", [])] + await session.commit() + return result + + +@mcp_router.post("/refresh") +async def refresh_mcp() -> dict[str, Any]: + return {"servers": await mcp_manager.refresh()} diff --git a/backend/app/api/workflows.py b/backend/app/api/workflows.py new file mode 100644 index 00000000..4f94ce93 --- /dev/null +++ b/backend/app/api/workflows.py @@ -0,0 +1,220 @@ +from __future__ import annotations + +from typing import Any + +from fastapi import APIRouter, Depends, HTTPException +from pydantic import BaseModel, Field +from sqlalchemy import func, select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.base import get_session +from app.db.models import Run, Workflow, WorkflowVersion +from app.engine.schema import GraphSpec, validate_graph + +router = APIRouter(prefix="/api/workflows", tags=["workflows"]) + + +class WorkflowIn(BaseModel): + name: str = Field(min_length=1, max_length=200) + description: str = "" + graph: dict[str, Any] = Field(default_factory=dict) + tags: list[str] = Field(default_factory=list) + + +class WorkflowPatch(BaseModel): + name: str | None = None + description: str | None = None + graph: dict[str, Any] | None = None + tags: list[str] | None = None + note: str = "" + + +class WorkflowOut(BaseModel): + id: str + name: str + description: str + graph: dict[str, Any] + tags: list[str] + version: int + is_template: bool + created_at: Any = None + updated_at: Any = None + run_count: int = 0 + + model_config = {"from_attributes": True} + + +@router.get("", response_model=list[WorkflowOut]) +async def list_workflows(session: AsyncSession = Depends(get_session)) -> list[WorkflowOut]: + rows = list( + (await session.execute(select(Workflow).order_by(Workflow.updated_at.desc()))).scalars() + ) + counts = dict( + (await session.execute(select(Run.workflow_id, func.count(Run.id)).group_by(Run.workflow_id))).all() + ) + out: list[WorkflowOut] = [] + for row in rows: + item = WorkflowOut.model_validate(row) + item.run_count = counts.get(row.id, 0) + out.append(item) + return out + + +@router.post("", response_model=WorkflowOut, status_code=201) +async def create_workflow( + payload: WorkflowIn, session: AsyncSession = Depends(get_session) +) -> Workflow: + workflow = Workflow( + name=payload.name, + description=payload.description, + graph=payload.graph, + tags=payload.tags, + ) + session.add(workflow) + await session.flush() + session.add( + WorkflowVersion(workflow_id=workflow.id, version=1, graph=payload.graph, note="初始版本") + ) + await session.commit() + await session.refresh(workflow) + return workflow + + +@router.get("/{workflow_id}", response_model=WorkflowOut) +async def get_workflow( + workflow_id: str, session: AsyncSession = Depends(get_session) +) -> Workflow: + workflow = await session.get(Workflow, workflow_id) + if not workflow: + raise HTTPException(404, "工作流不存在") + return workflow + + +@router.patch("/{workflow_id}", response_model=WorkflowOut) +async def update_workflow( + workflow_id: str, payload: WorkflowPatch, session: AsyncSession = Depends(get_session) +) -> Workflow: + workflow = await session.get(Workflow, workflow_id) + if not workflow: + raise HTTPException(404, "工作流不存在") + + if payload.name is not None: + workflow.name = payload.name + if payload.description is not None: + workflow.description = payload.description + if payload.tags is not None: + workflow.tags = payload.tags + if payload.graph is not None and payload.graph != workflow.graph: + # 图有实际变化才留快照，避免改个名字就堆一堆版本 + workflow.graph = payload.graph + workflow.version += 1 + session.add( + WorkflowVersion( + workflow_id=workflow.id, + version=workflow.version, + graph=payload.graph, + note=payload.note, + ) + ) + await session.commit() + await session.refresh(workflow) + return workflow + + +@router.delete("/{workflow_id}", status_code=204) +async def delete_workflow( + workflow_id: str, session: AsyncSession = Depends(get_session) +) -> None: + workflow = await session.get(Workflow, workflow_id) + if not workflow: + raise HTTPException(404, "工作流不存在") + await session.delete(workflow) + await session.commit() + + +@router.post("/{workflow_id}/duplicate", response_model=WorkflowOut, status_code=201) +async def duplicate_workflow( + workflow_id: str, session: AsyncSession = Depends(get_session) +) -> Workflow: + source = await session.get(Workflow, workflow_id) + if not source: + raise HTTPException(404, "工作流不存在") + copy = Workflow( + name=f"{source.name} 副本", + description=source.description, + graph=source.graph, + tags=list(source.tags or []), + ) + session.add(copy) + await session.commit() + await session.refresh(copy) + return copy + + +class VersionOut(BaseModel): + id: str + version: int + note: str + created_at: Any = None + + model_config = {"from_attributes": True} + + +@router.get("/{workflow_id}/versions", response_model=list[VersionOut]) +async def list_versions( + workflow_id: str, session: AsyncSession = Depends(get_session) +) -> list[WorkflowVersion]: + rows = await session.execute( + select(WorkflowVersion) + .where(WorkflowVersion.workflow_id == workflow_id) + .order_by(WorkflowVersion.version.desc()) + ) + return list(rows.scalars()) + + +@router.post("/{workflow_id}/versions/{version}/restore", response_model=WorkflowOut) +async def restore_version( + workflow_id: str, version: int, session: AsyncSession = Depends(get_session) +) -> Workflow: + workflow = await session.get(Workflow, workflow_id) + if not workflow: + raise HTTPException(404, "工作流不存在") + snapshot = ( + await session.execute( + select(WorkflowVersion).where( + WorkflowVersion.workflow_id == workflow_id, + WorkflowVersion.version == version, + ) + ) + ).scalar_one_or_none() + if not snapshot: + raise HTTPException(404, "版本不存在") + + workflow.graph = snapshot.graph + workflow.version += 1 + session.add( + WorkflowVersion( + workflow_id=workflow.id, + version=workflow.version, + graph=snapshot.graph, + note=f"回滚到 v{version}", + ) + ) + await session.commit() + await session.refresh(workflow) + return workflow + + +class ValidateIn(BaseModel): + graph: dict[str, Any] + + +@router.post("/validate") +async def validate(payload: ValidateIn) -> dict[str, Any]: + """画布上的实时校验。前端每次改动都会调它来标红。""" + try: + spec = GraphSpec.model_validate(payload.graph) + except Exception as e: # noqa: BLE001 + return {"ok": False, "issues": [{"level": "error", "message": f"图结构非法：{e}"}]} + result = validate_graph(spec) + return result.model_dump() diff --git a/backend/app/core/__init__.py b/backend/app/core/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/core/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/core/bus.py b/backend/app/core/bus.py new file mode 100644 index 00000000..d46439f9 --- /dev/null +++ b/backend/app/core/bus.py @@ -0,0 +1,78 @@ +from __future__ import annotations + +import asyncio +import contextlib +from collections import defaultdict +from typing import AsyncIterator + +from app.core.events import RunEventModel + +# 单个订阅者的积压上限。慢消费者（比如卡住的浏览器标签）不应该拖垮整个 run， +# 所以队列满了就丢最旧的事件并打标记，而不是阻塞发布方。 +_QUEUE_MAXSIZE = 2048 + + +class _Subscriber: + def __init__(self) -> None: + self.queue: asyncio.Queue[RunEventModel | None] = asyncio.Queue(maxsize=_QUEUE_MAXSIZE) + self.dropped = 0 + + +class EventBus: + """按 run_id 分频道的内存 pub/sub。 + + 只负责实时分发；历史事件由 DB 承担，订阅者接入时先读库补齐再接实时流， + 这样刷新页面或中途接入都能看到完整时间线。 + """ + + def __init__(self) -> None: + self._subs: dict[str, set[_Subscriber]] = defaultdict(set) + self._seq: dict[str, int] = defaultdict(int) + self._lock = asyncio.Lock() + + def next_seq(self, run_id: str) -> int: + self._seq[run_id] += 1 + return self._seq[run_id] + + def set_seq(self, run_id: str, value: int) -> None: + """恢复一个已存在的 run 时，把序号接上，保证前端排序不乱。""" + self._seq[run_id] = max(self._seq[run_id], value) + + async def publish(self, event: RunEventModel) -> None: + for sub in list(self._subs.get(event.run_id, ())): + try: + sub.queue.put_nowait(event) + except asyncio.QueueFull: + with contextlib.suppress(asyncio.QueueEmpty): + sub.queue.get_nowait() + sub.dropped += 1 + with contextlib.suppress(asyncio.QueueFull): + sub.queue.put_nowait(event) + + async def close(self, run_id: str) -> None: + """给所有订阅者发结束哨兵，让 WebSocket 干净地收尾。""" + for sub in list(self._subs.get(run_id, ())): + with contextlib.suppress(asyncio.QueueFull): + sub.queue.put_nowait(None) + + async def subscribe(self, run_id: str) -> AsyncIterator[RunEventModel]: + sub = _Subscriber() + async with self._lock: + self._subs[run_id].add(sub) + try: + while True: + item = await sub.queue.get() + if item is None: + break + yield item + finally: + async with self._lock: + self._subs[run_id].discard(sub) + if not self._subs[run_id]: + self._subs.pop(run_id, None) + + def subscriber_count(self, run_id: str) -> int: + return len(self._subs.get(run_id, ())) + + +bus = EventBus() diff --git a/backend/app/core/config.py b/backend/app/core/config.py new file mode 100644 index 00000000..9051f943 --- /dev/null +++ b/backend/app/core/config.py @@ -0,0 +1,88 @@ +from __future__ import annotations + +from functools import lru_cache +from pathlib import Path + +from pydantic_settings import BaseSettings, SettingsConfigDict + +BACKEND_DIR = Path(__file__).resolve().parents[2] +PROJECT_DIR = BACKEND_DIR.parent + + +class Settings(BaseSettings): + """全局配置。所有项都可以用 AGENTLAB_ 前缀的环境变量覆盖。""" + + model_config = SettingsConfigDict( + env_prefix="AGENTLAB_", + env_file=(PROJECT_DIR / ".env", BACKEND_DIR / ".env"), + extra="ignore", + ) + + # --- 服务 --- + host: str = "127.0.0.1" + port: int = 8000 + cors_origins: list[str] = ["http://localhost:5273", "http://127.0.0.1:5273"] + + # --- 存储 --- + data_dir: Path = PROJECT_DIR / "data" + + # --- 密钥加密 --- + # 用于加密落库的 provider api key。留空则自动生成并存到 data_dir/.secret_key。 + secret_key: str = "" + + # --- 沙箱 --- + sandbox_backend: str = "auto" # auto | docker | local | off + sandbox_image: str = "python:3.12-slim" + sandbox_timeout: int = 30 + sandbox_memory_mb: int = 512 + sandbox_cpus: float = 1.0 + sandbox_network: bool = False + sandbox_max_output: int = 20_000 + + # --- 执行引擎护栏 --- + max_run_seconds: int = 600 + max_graph_steps: int = 200 + max_agent_steps: int = 25 + max_concurrent_runs: int = 20 + + # --- 工具 --- + http_tool_timeout: int = 20 + http_tool_max_bytes: int = 2_000_000 + # 逗号分隔的域名白名单；为空表示不限制（仅拦截内网地址） + http_tool_allowlist: list[str] = [] + + @property + def db_path(self) -> Path: + return self.data_dir / "agentlab.db" + + @property + def db_url(self) -> str: + return f"sqlite+aiosqlite:///{self.db_path}" + + @property + def checkpoint_path(self) -> Path: + """LangGraph checkpointer 用独立的库文件，避免和业务表抢写锁。""" + return self.data_dir / "checkpoints.db" + + @property + def workspace_dir(self) -> Path: + """沙箱与文件工具的可写根目录，一切文件访问都被限制在这里面。""" + return self.data_dir / "workspace" + + @property + def uploads_dir(self) -> Path: + return self.data_dir / "uploads" + + def ensure_dirs(self) -> None: + for d in (self.data_dir, self.workspace_dir, self.uploads_dir): + d.mkdir(parents=True, exist_ok=True) + + +@lru_cache +def get_settings() -> Settings: + s = Settings() + s.ensure_dirs() + return s + + +settings = get_settings() diff --git a/backend/app/core/crypto.py b/backend/app/core/crypto.py new file mode 100644 index 00000000..abca0b07 --- /dev/null +++ b/backend/app/core/crypto.py @@ -0,0 +1,60 @@ +from __future__ import annotations + +import base64 +import hashlib +import os + +from cryptography.fernet import Fernet, InvalidToken + +from app.core.config import settings + +_PREFIX = "enc::" + + +def _load_key() -> bytes: + """取加密密钥：优先用配置里的 secret_key，否则在 data_dir 生成一个持久化的。""" + raw = settings.secret_key + if not raw: + key_file = settings.data_dir / ".secret_key" + if key_file.exists(): + raw = key_file.read_text().strip() + else: + raw = base64.urlsafe_b64encode(os.urandom(32)).decode() + key_file.write_text(raw) + key_file.chmod(0o600) + # 任意字符串都归一化成 32 字节 Fernet key + digest = hashlib.sha256(raw.encode()).digest() + return base64.urlsafe_b64encode(digest) + + +_fernet = Fernet(_load_key()) + + +def encrypt(value: str | None) -> str | None: + """加密明文。已经是密文的原样返回，方便重复保存同一条记录。""" + if not value: + return value + if value.startswith(_PREFIX): + return value + return _PREFIX + _fernet.encrypt(value.encode()).decode() + + +def decrypt(value: str | None) -> str | None: + if not value: + return value + if not value.startswith(_PREFIX): + return value # 兼容历史明文 + try: + return _fernet.decrypt(value[len(_PREFIX) :].encode()).decode() + except InvalidToken: + return None + + +def mask(value: str | None) -> str: + """给前端展示用的掩码，永远不把明文 key 发回浏览器。""" + if not value: + return "" + plain = decrypt(value) or "" + if len(plain) <= 8: + return "••••" + return f"{plain[:4]}••••{plain[-4:]}" diff --git a/backend/app/core/events.py b/backend/app/core/events.py new file mode 100644 index 00000000..70c992ce --- /dev/null +++ b/backend/app/core/events.py @@ -0,0 +1,65 @@ +from __future__ import annotations + +import time +import uuid +from enum import StrEnum +from typing import Any + +from pydantic import BaseModel, Field + + +class EventType(StrEnum): + """前端与后端之间的实时协议。画布高亮、时间线、token 流全部基于这套事件。""" + + # run 级 + RUN_STARTED = "run.started" + RUN_FINISHED = "run.finished" + RUN_FAILED = "run.failed" + RUN_CANCELLED = "run.cancelled" + RUN_INTERRUPTED = "run.interrupted" # 等待人工介入 + RUN_RESUMED = "run.resumed" + + # 节点级 + NODE_STARTED = "node.started" + NODE_FINISHED = "node.finished" + NODE_FAILED = "node.failed" + NODE_SKIPPED = "node.skipped" + EDGE_TAKEN = "edge.taken" # 条件分支实际走了哪条边 + + # 模型级 + LLM_START = "llm.start" + LLM_TOKEN = "llm.token" + LLM_THINKING = "llm.thinking" + LLM_END = "llm.end" + + # 工具 / 沙箱 + TOOL_START = "tool.start" + TOOL_END = "tool.end" + TOOL_ERROR = "tool.error" + SANDBOX_START = "sandbox.start" + SANDBOX_END = "sandbox.end" + + # 多 agent 协作中的单个 agent 步骤 + AGENT_STEP_START = "agent.step.start" + AGENT_STEP_END = "agent.step.end" + + # 人工介入 + HUMAN_REQUESTED = "human.requested" + HUMAN_RESOLVED = "human.resolved" + + # 其他 + LOG = "log" + USAGE = "usage" # token / 成本累计 + + +class RunEventModel(BaseModel): + id: str = Field(default_factory=lambda: uuid.uuid4().hex) + run_id: str + seq: int = 0 + type: EventType + node_id: str | None = None + ts: float = Field(default_factory=time.time) + data: dict[str, Any] = Field(default_factory=dict) + + def to_wire(self) -> dict[str, Any]: + return self.model_dump(mode="json") diff --git a/backend/app/db/__init__.py b/backend/app/db/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/db/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/db/base.py b/backend/app/db/base.py new file mode 100644 index 00000000..b7edfdd9 --- /dev/null +++ b/backend/app/db/base.py @@ -0,0 +1,69 @@ +from __future__ import annotations + +import uuid +from datetime import datetime, timezone +from typing import Any, AsyncIterator + +from sqlalchemy import JSON, DateTime, MetaData, event +from sqlalchemy.ext.asyncio import AsyncSession, async_sessionmaker, create_async_engine +from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column + +from app.core.config import settings + +NAMING_CONVENTION = { + "ix": "ix_%(column_0_label)s", + "uq": "uq_%(table_name)s_%(column_0_name)s", + "fk": "fk_%(table_name)s_%(column_0_name)s", + "pk": "pk_%(table_name)s", +} + + +def utcnow() -> datetime: + return datetime.now(timezone.utc) + + +def new_id() -> str: + return uuid.uuid4().hex + + +class Base(DeclarativeBase): + metadata = MetaData(naming_convention=NAMING_CONVENTION) + + # 图定义、节点配置、事件负载都是任意形状的 JSON，统一映射到 SQLite 的 JSON 列 + type_annotation_map = {dict[str, Any]: JSON, list[Any]: JSON} + + +class TimestampMixin: + created_at: Mapped[datetime] = mapped_column(DateTime(timezone=True), default=utcnow) + updated_at: Mapped[datetime] = mapped_column( + DateTime(timezone=True), default=utcnow, onupdate=utcnow + ) + + +engine = create_async_engine(settings.db_url, echo=False, future=True) + + +@event.listens_for(engine.sync_engine, "connect") +def _set_sqlite_pragma(dbapi_conn, _record): # pragma: no cover - 驱动回调 + """WAL + 外键约束。WAL 让读写不互相阻塞，事件写入和前端轮询能并行。""" + cur = dbapi_conn.cursor() + cur.execute("PRAGMA journal_mode=WAL") + cur.execute("PRAGMA foreign_keys=ON") + cur.execute("PRAGMA busy_timeout=5000") + cur.close() + + +SessionLocal = async_sessionmaker(engine, expire_on_commit=False, class_=AsyncSession) + + +async def get_session() -> AsyncIterator[AsyncSession]: + """FastAPI 依赖注入用的会话。""" + async with SessionLocal() as session: + yield session + + +async def init_db() -> None: + from app.db import models # noqa: F401 确保模型已注册到 metadata + + async with engine.begin() as conn: + await conn.run_sync(Base.metadata.create_all) diff --git a/backend/app/db/models.py b/backend/app/db/models.py new file mode 100644 index 00000000..114869f0 --- /dev/null +++ b/backend/app/db/models.py @@ -0,0 +1,246 @@ +from __future__ import annotations + +from datetime import datetime +from typing import Any + +from sqlalchemy import Float, ForeignKey, Index, Integer, LargeBinary, String, Text, UniqueConstraint +from sqlalchemy.orm import Mapped, mapped_column, relationship + +from app.db.base import Base, TimestampMixin, new_id + +# -------------------------------------------------------------------------- +# 设置：模型供应商 / 用户偏好 / MCP / 自定义工具 +# -------------------------------------------------------------------------- + + +class Provider(Base, TimestampMixin): + """一个模型接入点。api_key 落库前经 app.core.crypto 加密。""" + + __tablename__ = "providers" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + name: Mapped[str] = mapped_column(String(100), unique=True) + kind: Mapped[str] = mapped_column(String(32)) # anthropic | openai | openai_compatible | mock + base_url: Mapped[str | None] = mapped_column(String(500), default=None) + api_key: Mapped[str | None] = mapped_column(Text, default=None) # 密文 + models: Mapped[list[Any]] = mapped_column(default=list) # [{id, label, context, pricing}] + default_model: Mapped[str | None] = mapped_column(String(200), default=None) + enabled: Mapped[bool] = mapped_column(default=True) + extra: Mapped[dict[str, Any]] = mapped_column(default=dict) + + +class Setting(Base, TimestampMixin): + """键值形式的用户设置，前端 Settings 页直接读写。""" + + __tablename__ = "settings" + + key: Mapped[str] = mapped_column(String(100), primary_key=True) + value: Mapped[dict[str, Any]] = mapped_column(default=dict) + + +class McpServer(Base, TimestampMixin): + __tablename__ = "mcp_servers" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + name: Mapped[str] = mapped_column(String(100), unique=True) + transport: Mapped[str] = mapped_column(String(20), default="stdio") # stdio | http + command: Mapped[str | None] = mapped_column(String(500), default=None) + args: Mapped[list[Any]] = mapped_column(default=list) + env: Mapped[dict[str, Any]] = mapped_column(default=dict) + url: Mapped[str | None] = mapped_column(String(500), default=None) + enabled: Mapped[bool] = mapped_column(default=True) + # 最近一次探活的结果与工具清单缓存 + status: Mapped[str] = mapped_column(String(20), default="unknown") + last_error: Mapped[str | None] = mapped_column(Text, default=None) + tools_cache: Mapped[list[Any]] = mapped_column(default=list) + + +class CustomTool(Base, TimestampMixin): + """用户自定义工具：HTTP 调用模板，或跑在沙箱里的一段 Python。""" + + __tablename__ = "custom_tools" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + name: Mapped[str] = mapped_column(String(100), unique=True) + description: Mapped[str] = mapped_column(Text, default="") + kind: Mapped[str] = mapped_column(String(20), default="http") # http | python + parameters: Mapped[dict[str, Any]] = mapped_column(default=dict) # JSON Schema + config: Mapped[dict[str, Any]] = mapped_column(default=dict) + enabled: Mapped[bool] = mapped_column(default=True) + + +# -------------------------------------------------------------------------- +# 工作流与版本 +# -------------------------------------------------------------------------- + + +class Workflow(Base, TimestampMixin): + __tablename__ = "workflows" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + name: Mapped[str] = mapped_column(String(200)) + description: Mapped[str] = mapped_column(Text, default="") + graph: Mapped[dict[str, Any]] = mapped_column(default=dict) # {nodes, edges, viewport} + tags: Mapped[list[Any]] = mapped_column(default=list) + version: Mapped[int] = mapped_column(Integer, default=1) + is_template: Mapped[bool] = mapped_column(default=False) + + versions: Mapped[list["WorkflowVersion"]] = relationship( + back_populates="workflow", cascade="all, delete-orphan", lazy="selectin" + ) + + +class WorkflowVersion(Base, TimestampMixin): + """每次保存留一份快照，便于回滚和对比不同编排的效果。""" + + __tablename__ = "workflow_versions" + __table_args__ = (UniqueConstraint("workflow_id", "version"),) + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + workflow_id: Mapped[str] = mapped_column(ForeignKey("workflows.id", ondelete="CASCADE")) + version: Mapped[int] = mapped_column(Integer) + graph: Mapped[dict[str, Any]] = mapped_column(default=dict) + note: Mapped[str] = mapped_column(Text, default="") + + workflow: Mapped[Workflow] = relationship(back_populates="versions") + + +# -------------------------------------------------------------------------- +# 运行、事件、审批 +# -------------------------------------------------------------------------- + + +class Run(Base, TimestampMixin): + __tablename__ = "runs" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + workflow_id: Mapped[str | None] = mapped_column( + ForeignKey("workflows.id", ondelete="SET NULL"), default=None + ) + workflow_name: Mapped[str] = mapped_column(String(200), default="") + # LangGraph checkpointer 的 thread_id —— 持久化与恢复的锚点 + thread_id: Mapped[str] = mapped_column(String(64), index=True, default=new_id) + # queued | running | interrupted | succeeded | failed | cancelled + status: Mapped[str] = mapped_column(String(20), default="queued", index=True) + graph: Mapped[dict[str, Any]] = mapped_column(default=dict) # 执行时的图快照 + input: Mapped[dict[str, Any]] = mapped_column(default=dict) + output: Mapped[dict[str, Any]] = mapped_column(default=dict) + error: Mapped[str | None] = mapped_column(Text, default=None) + usage: Mapped[dict[str, Any]] = mapped_column(default=dict) # tokens / cost / 各节点耗时 + started_at: Mapped[datetime | None] = mapped_column(default=None) + finished_at: Mapped[datetime | None] = mapped_column(default=None) + last_seq: Mapped[int] = mapped_column(Integer, default=0) + + events: Mapped[list["RunEvent"]] = relationship( + back_populates="run", cascade="all, delete-orphan" + ) + + +class RunEvent(Base): + """完整事件流落库 —— 这既是 trace，也是页面刷新后重建时间线的依据。""" + + __tablename__ = "run_events" + __table_args__ = (Index("ix_run_events_run_seq", "run_id", "seq"),) + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + run_id: Mapped[str] = mapped_column(ForeignKey("runs.id", ondelete="CASCADE")) + seq: Mapped[int] = mapped_column(Integer, default=0) + type: Mapped[str] = mapped_column(String(40)) + node_id: Mapped[str | None] = mapped_column(String(64), default=None) + ts: Mapped[float] = mapped_column(Float) + data: Mapped[dict[str, Any]] = mapped_column(default=dict) + + run: Mapped[Run] = relationship(back_populates="events") + + +class Approval(Base, TimestampMixin): + """一次人工介入请求。对应 LangGraph 的 interrupt。""" + + __tablename__ = "approvals" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + run_id: Mapped[str] = mapped_column(ForeignKey("runs.id", ondelete="CASCADE"), index=True) + node_id: Mapped[str] = mapped_column(String(64)) + interrupt_id: Mapped[str | None] = mapped_column(String(64), default=None) + mode: Mapped[str] = mapped_column(String(20), default="approve") # approve | input | edit + title: Mapped[str] = mapped_column(String(300), default="") + payload: Mapped[dict[str, Any]] = mapped_column(default=dict) # 给人看的上下文 + schema_: Mapped[dict[str, Any]] = mapped_column("schema", default=dict) + status: Mapped[str] = mapped_column(String(20), default="pending", index=True) + response: Mapped[dict[str, Any]] = mapped_column(default=dict) + resolved_at: Mapped[datetime | None] = mapped_column(default=None) + + +# -------------------------------------------------------------------------- +# 记忆、知识库、Skill +# -------------------------------------------------------------------------- + + +class MemoryItem(Base, TimestampMixin): + """长期记忆。scope 用来隔离不同 agent / 会话的记忆空间。""" + + __tablename__ = "memories" + __table_args__ = (Index("ix_memories_scope_kind", "scope", "kind"),) + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + scope: Mapped[str] = mapped_column(String(100), default="default") + kind: Mapped[str] = mapped_column(String(32), default="fact") # fact | preference | episode + content: Mapped[str] = mapped_column(Text) + meta: Mapped[dict[str, Any]] = mapped_column(default=dict) + embedding: Mapped[bytes | None] = mapped_column(LargeBinary, default=None) + importance: Mapped[float] = mapped_column(Float, default=0.5) + last_used_at: Mapped[datetime | None] = mapped_column(default=None) + use_count: Mapped[int] = mapped_column(Integer, default=0) + + +class Document(Base, TimestampMixin): + """知识库里的一份原始文档。""" + + __tablename__ = "documents" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + collection: Mapped[str] = mapped_column(String(100), default="default", index=True) + title: Mapped[str] = mapped_column(String(300), default="") + source: Mapped[str] = mapped_column(String(500), default="") # 文件名 / URL + mime: Mapped[str] = mapped_column(String(100), default="text/plain") + content: Mapped[str] = mapped_column(Text, default="") + meta: Mapped[dict[str, Any]] = mapped_column(default=dict) + chunk_count: Mapped[int] = mapped_column(Integer, default=0) + + chunks: Mapped[list["Chunk"]] = relationship( + back_populates="document", cascade="all, delete-orphan" + ) + + +class Chunk(Base): + __tablename__ = "chunks" + __table_args__ = (Index("ix_chunks_collection", "collection"),) + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + document_id: Mapped[str] = mapped_column(ForeignKey("documents.id", ondelete="CASCADE")) + collection: Mapped[str] = mapped_column(String(100), default="default") + ordinal: Mapped[int] = mapped_column(Integer, default=0) + content: Mapped[str] = mapped_column(Text) + embedding: Mapped[bytes | None] = mapped_column(LargeBinary, default=None) + meta: Mapped[dict[str, Any]] = mapped_column(default=dict) + + document: Mapped[Document] = relationship(back_populates="chunks") + + +class Skill(Base, TimestampMixin): + """可复用的方法论：一段指令 + 可选的示例和推荐工具。 + + 节点可以挂载 skill，运行时把内容注入 system prompt —— 把"怎么做事" + 从工作流结构里抽出来单独管理和迭代。 + """ + + __tablename__ = "skills" + + id: Mapped[str] = mapped_column(String(32), primary_key=True, default=new_id) + name: Mapped[str] = mapped_column(String(100), unique=True) + description: Mapped[str] = mapped_column(Text, default="") + instructions: Mapped[str] = mapped_column(Text, default="") + examples: Mapped[list[Any]] = mapped_column(default=list) + suggested_tools: Mapped[list[Any]] = mapped_column(default=list) + tags: Mapped[list[Any]] = mapped_column(default=list) + enabled: Mapped[bool] = mapped_column(default=True) diff --git a/backend/app/engine/__init__.py b/backend/app/engine/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/engine/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/engine/compiler.py b/backend/app/engine/compiler.py new file mode 100644 index 00000000..55aa9898 --- /dev/null +++ b/backend/app/engine/compiler.py @@ -0,0 +1,230 @@ +from __future__ import annotations + +import asyncio +import time +from typing import Any, Awaitable, Callable + +from langgraph.errors import GraphBubbleUp +from langgraph.graph import END, START, StateGraph + +from app.core.events import EventType +from app.engine.context import NodeContext, NodeError, RunContext +from app.engine.nodes import control, human, io, knowledge, llm, multi, tools +from app.engine.schema import GraphNode, GraphSpec, NodeType +from app.engine.state import GraphState + +NodeRunner = Callable[[GraphState, NodeContext], Awaitable[dict[str, Any]]] + +# 节点类型 -> 执行器。加一种新节点只需要在这里登记一行。 +RUNNERS: dict[NodeType, NodeRunner] = { + NodeType.INPUT: io.run_input, + NodeType.OUTPUT: io.run_output, + NodeType.TRANSFORM: io.run_transform, + NodeType.LLM: llm.run_llm, + NodeType.AGENT: llm.run_agent, + NodeType.SUPERVISOR: multi.run_supervisor, + NodeType.SUBGRAPH: multi.run_subgraph, + NodeType.TOOL: tools.run_tool, + NodeType.CODE: tools.run_code, + NodeType.BRANCH: control.run_branch, + NodeType.LOOP: control.run_loop, + NodeType.HUMAN: human.run_human, + NodeType.VALIDATE: human.run_validate, + NodeType.MEMORY: knowledge.run_memory, + NodeType.RETRIEVE: knowledge.run_retrieve, +} + +# 这些节点的出边由"决定"路由，不能直接连死 +ROUTING_TYPES = {NodeType.BRANCH, NodeType.LOOP} + + +def _wrap(node: GraphNode, run_ctx: RunContext) -> Callable[[GraphState], Awaitable[dict[str, Any]]]: + """给节点执行器套上事件、计时、重试和错误处理。 + + 画布上看到的每一次高亮、每一条耗时，都来自这层包装； + 执行器本身只管自己那点业务逻辑。 + """ + runner = RUNNERS.get(node.type) + ctx = NodeContext(node=node, run=run_ctx) + + async def _execute(state: GraphState) -> dict[str, Any]: + if runner is None: + raise NodeError(node.id, f"没有实现的节点类型：{node.type}") + + # 允许节点配置跳过条件，不用为了"有时不执行"专门加一个分支节点 + skip_if = node.config.get("skip_if") + if skip_if: + from app.engine.expressions import eval_condition + from app.engine.state import template_context + + if eval_condition(skip_if, template_context(state)): + ctx.emit(EventType.NODE_SKIPPED, reason=f"skip_if 成立：{skip_if}") + return {"nodes": {node.id: {"skipped": True}}} + + retries = int(node.config.get("retries", 0) or 0) + backoff = float(node.config.get("retry_backoff", 1.0) or 1.0) + started = time.perf_counter() + ctx.emit(EventType.NODE_STARTED, node_type=str(node.type), label=node.title) + + last_error: Exception | None = None + for attempt in range(retries + 1): + try: + updates = await runner(state, ctx) + elapsed = int((time.perf_counter() - started) * 1000) + ctx.emit( + EventType.NODE_FINISHED, + duration_ms=elapsed, + attempt=attempt + 1, + preview=_preview(updates, node.id), + ) + trail = { + "node_id": node.id, + "type": str(node.type), + "duration_ms": elapsed, + "attempt": attempt + 1, + "ts": time.time(), + } + return {**updates, "trail": [trail]} + except GraphBubbleUp: + # interrupt() / Command 靠异常向上传递控制流，不是执行失败： + # 必须原样放行，否则人工介入会被当成节点报错吞掉。 + raise + except asyncio.CancelledError: + raise + except NodeError: + raise + except Exception as e: # noqa: BLE001 + last_error = e + if attempt < retries: + ctx.emit( + EventType.LOG, + level="warn", + message=f"第 {attempt + 1} 次失败（{type(e).__name__}: {e}），准备重试", + ) + await asyncio.sleep(backoff * (2**attempt)) + continue + break + + elapsed = int((time.perf_counter() - started) * 1000) + message = f"{type(last_error).__name__}: {last_error}" + ctx.emit(EventType.NODE_FAILED, error=message, duration_ms=elapsed) + + # 容错模式：记下错误继续往下走，而不是让整张图挂掉 + if node.config.get("on_error") == "continue": + return { + "nodes": {node.id: {"error": message, "failed": True}}, + "trail": [{"node_id": node.id, "error": message, "ts": time.time()}], + } + raise NodeError(node.id, message) from last_error + + async def _entry(state: GraphState) -> dict[str, Any]: + return await _execute(state) + + return _entry + + +def _preview(updates: dict[str, Any], node_id: str) -> Any: + """给前端卡片用的输出摘要，避免把整坨数据塞进事件流。""" + payload = (updates.get("nodes") or {}).get(node_id) + if payload is None: + return None + if isinstance(payload, dict): + out: dict[str, Any] = {} + for key, value in payload.items(): + if isinstance(value, str): + out[key] = value[:1200] + elif isinstance(value, (int, float, bool, type(None))): + out[key] = value + elif isinstance(value, list): + out[key] = f"[{len(value)} 项]" + else: + out[key] = str(value)[:300] + return out + return str(payload)[:1200] + + +def _make_router( + node: GraphNode, spec: GraphSpec +) -> tuple[Callable[[GraphState], list[str] | str], dict[str, str]]: + """按节点写下的 __decision__ 把执行导向对应的边。 + + 路由函数必须是纯函数 —— LangGraph 会调用它决定下一跳， + 所以真正的判断逻辑放在节点执行阶段，这里只查表。 + """ + mapping: dict[str, list[str]] = {} + for edge in spec.outgoing(node.id): + key = edge.sourceHandle or "default" + mapping.setdefault(key, []).append(edge.target) + + default_targets = mapping.get("default") or [] + + def route(state: GraphState) -> list[str] | str: + payload = (state.get("nodes") or {}).get(node.id) or {} + decision = payload.get("__decision__") if isinstance(payload, dict) else None + targets = mapping.get(str(decision)) or default_targets + return targets if targets else END + + path_map = {key: key for key in mapping} + return route, path_map + + +def compile_graph(spec: GraphSpec, run_ctx: RunContext) -> StateGraph: + """把图定义编译成未 compile 的 StateGraph。 + + 调用方负责挂 checkpointer 后再 .compile()，这样主图能带持久化、 + 子图可以不带。 + """ + builder = StateGraph(GraphState) + node_map = spec.node_map() + + for node in spec.nodes: + builder.add_node(node.id, _wrap(node, run_ctx)) + + # 入口 + entries = spec.entry_nodes() + if not entries: + raise ValueError("这张图没有入口节点") + for node in entries: + builder.add_edge(START, node.id) + + # 边 + for node in spec.nodes: + outgoing = spec.outgoing(node.id) + if not outgoing: + builder.add_edge(node.id, END) + continue + + handles = {e.sourceHandle for e in outgoing if e.sourceHandle} + needs_routing = node.type in ROUTING_TYPES or ( + node.type == NodeType.HUMAN and handles & {"approved", "rejected"} + ) + + if needs_routing: + route, path_map = _make_router(node, spec) + targets = {t for edge in outgoing for t in [edge.target] if t in node_map} + # path_map 用实际目标节点名，LangGraph 才知道有哪些可达分支 + builder.add_conditional_edges( + node.id, + route, + sorted(targets) + [END], + ) + else: + for edge in outgoing: + if edge.target in node_map: + builder.add_edge(node.id, edge.target) + + return builder + + +def initial_state(payload: dict[str, Any] | None = None) -> GraphState: + data = dict(payload or {}) + return { + "input": data, + "vars": dict(data), + "nodes": {}, + "output": {}, + "messages": [], + "usage": {}, + "trail": [], + "loops": {}, + } diff --git a/backend/app/engine/context.py b/backend/app/engine/context.py new file mode 100644 index 00000000..edff3309 --- /dev/null +++ b/backend/app/engine/context.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +import time +from dataclasses import dataclass, field +from typing import Any + +from langgraph.config import get_stream_writer + +from app.core.events import EventType +from app.engine.schema import GraphNode, GraphSpec +from app.engine.state import GraphState, template_context +from app.engine.expressions import render_deep, render_template + + +@dataclass +class RunContext: + """一次运行的全局上下文，编译时闭包进每个节点。""" + + run_id: str + thread_id: str + spec: GraphSpec + workflow_id: str | None = None + # 深度 > 0 表示当前在子图里，用来防止子图无限递归 + depth: int = 0 + memory_scope: str = "default" + collection: str = "default" + extra: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class NodeContext: + """单个节点执行时能拿到的东西。""" + + node: GraphNode + run: RunContext + + @property + def config(self) -> dict[str, Any]: + return self.node.data.config + + def emit(self, event_type: EventType | str, **data: Any) -> None: + """往 LangGraph 的 custom 流里发一条事件。 + + runner 在另一端消费，转成落库的 RunEvent 并广播给前端 —— 画布上的节点高亮、 + token 流、工具调用卡片全都来自这里。 + """ + try: + writer = get_stream_writer() + except Exception: # noqa: BLE001 - 不在图执行上下文里（比如单元测试） + return + writer( + { + "__agentlab__": True, + "type": str(event_type), + "node_id": self.node.id, + "ts": time.time(), + "data": data, + } + ) + + def render(self, value: Any, state: GraphState) -> Any: + """用当前状态渲染配置里的模板。""" + return render_deep(value, template_context(state)) + + def render_str(self, value: str | None, state: GraphState) -> str: + return render_template(value or "", template_context(state)) + + def cfg(self, key: str, default: Any = None) -> Any: + """读节点配置，没有就回退到图级 defaults。""" + value = self.config.get(key) + if value in (None, ""): + return self.run.spec.defaults.get(key, default) + return value + + +class NodeError(RuntimeError): + """节点执行失败。带上 node_id 方便前端定位到具体的卡片。""" + + def __init__(self, node_id: str, message: str, *, retryable: bool = False) -> None: + super().__init__(message) + self.node_id = node_id + self.retryable = retryable diff --git a/backend/app/engine/expressions.py b/backend/app/engine/expressions.py new file mode 100644 index 00000000..def89713 --- /dev/null +++ b/backend/app/engine/expressions.py @@ -0,0 +1,274 @@ +from __future__ import annotations + +import ast +import json +import operator +import re +from typing import Any + +# -------------------------------------------------------------------------- +# 变量插值： {{ input.query }} / {{ nodes.n1.text }} / {{ vars.items | json }} +# -------------------------------------------------------------------------- + +_TEMPLATE_RE = re.compile(r"\{\{(.*?)\}\}", re.DOTALL) + + +def resolve_path(ctx: dict[str, Any], path: str) -> Any: + """按点号路径取值，支持 a.b[0].c。取不到返回 None 而不是抛错 —— 编排里 + 半成品状态很常见，让它渲染成空字符串比中断整个 run 有用。""" + cur: Any = ctx + for raw in path.split("."): + raw = raw.strip() + if not raw: + continue + # 拆出 name[0][1] 形式的下标 + m = re.match(r"^([^\[\]]*)((?:\[[^\[\]]+\])*)$", raw) + if not m: + return None + name, idx_part = m.group(1), m.group(2) + if name: + if isinstance(cur, dict): + cur = cur.get(name) + else: + cur = getattr(cur, name, None) + if cur is None: + return None + for idx in re.findall(r"\[([^\[\]]+)\]", idx_part): + idx = idx.strip().strip("'\"") + try: + if isinstance(cur, (list, tuple, str)): + cur = cur[int(idx)] + elif isinstance(cur, dict): + cur = cur.get(idx) + else: + return None + except (ValueError, IndexError, KeyError, TypeError): + return None + return cur + + +_FILTERS = { + "json": lambda v: json.dumps(v, ensure_ascii=False, indent=2, default=str), + "compact": lambda v: json.dumps(v, ensure_ascii=False, separators=(",", ":"), default=str), + "upper": lambda v: str(v).upper(), + "lower": lambda v: str(v).lower(), + "trim": lambda v: str(v).strip(), + "length": lambda v: len(v) if hasattr(v, "__len__") else 0, + "first": lambda v: v[0] if isinstance(v, (list, tuple)) and v else None, + "last": lambda v: v[-1] if isinstance(v, (list, tuple)) and v else None, +} + + +def _stringify(value: Any) -> str: + if value is None: + return "" + if isinstance(value, str): + return value + if isinstance(value, (dict, list, tuple)): + return json.dumps(value, ensure_ascii=False, default=str) + return str(value) + + +def render_template(text: str, ctx: dict[str, Any]) -> str: + """把字符串里的 {{ ... }} 全部替换掉。支持 `路径 | 过滤器` 语法。""" + if not text or "{{" not in text: + return text or "" + + def _sub(match: re.Match[str]) -> str: + expr = match.group(1).strip() + if not expr: + return "" + parts = [p.strip() for p in expr.split("|")] + value = resolve_path(ctx, parts[0]) + for filt in parts[1:]: + fn = _FILTERS.get(filt) + if fn: + try: + value = fn(value) + except Exception: # noqa: BLE001 - 过滤器不该让 run 挂掉 + value = None + return _stringify(value) + + return _TEMPLATE_RE.sub(_sub, text) + + +def render_deep(value: Any, ctx: dict[str, Any]) -> Any: + """递归渲染嵌套结构里的所有模板字符串。""" + if isinstance(value, str): + return render_template(value, ctx) + if isinstance(value, dict): + return {k: render_deep(v, ctx) for k, v in value.items()} + if isinstance(value, list): + return [render_deep(v, ctx) for v in value] + return value + + +# -------------------------------------------------------------------------- +# 安全表达式求值（分支条件用） +# -------------------------------------------------------------------------- + +_BIN_OPS = { + ast.Add: operator.add, + ast.Sub: operator.sub, + ast.Mult: operator.mul, + ast.Div: operator.truediv, + ast.FloorDiv: operator.floordiv, + ast.Mod: operator.mod, + ast.Pow: operator.pow, +} +_CMP_OPS = { + ast.Eq: operator.eq, + ast.NotEq: operator.ne, + ast.Lt: operator.lt, + ast.LtE: operator.le, + ast.Gt: operator.gt, + ast.GtE: operator.ge, + ast.In: lambda a, b: a in b, + ast.NotIn: lambda a, b: a not in b, + ast.Is: operator.is_, + ast.IsNot: operator.is_not, +} +_UNARY_OPS = {ast.Not: operator.not_, ast.USub: operator.neg, ast.UAdd: operator.pos} + +_SAFE_FUNCS: dict[str, Any] = { + "len": len, + "str": str, + "int": int, + "float": float, + "bool": bool, + "abs": abs, + "min": min, + "max": max, + "sum": sum, + "round": round, + "sorted": sorted, + "any": any, + "all": all, + "lower": lambda s: str(s).lower(), + "upper": lambda s: str(s).upper(), + "contains": lambda hay, needle: needle in hay if hay is not None else False, + "startswith": lambda s, p: str(s).startswith(p), + "endswith": lambda s, p: str(s).endswith(p), + "matches": lambda s, p: bool(re.search(p, str(s))), + "get": lambda obj, key, default=None: (obj or {}).get(key, default), +} + +_MAX_NODES = 200 + + +class ExpressionError(ValueError): + pass + + +def eval_expression(expr: str, ctx: dict[str, Any]) -> Any: + """求值一个受限的 Python 表达式。 + + 只放行字面量、比较、布尔/算术运算、下标、属性访问和一小撮白名单函数。 + 没有 import、没有属性魔法、没有函数定义，所以用户在画布上写条件是安全的。 + """ + expr = (expr or "").strip() + if not expr: + return None + try: + tree = ast.parse(expr, mode="eval") + except SyntaxError as e: + raise ExpressionError(f"表达式语法错误：{e.msg}") from e + + if sum(1 for _ in ast.walk(tree)) > _MAX_NODES: + raise ExpressionError("表达式太复杂") + + def _eval(node: ast.AST) -> Any: + if isinstance(node, ast.Expression): + return _eval(node.body) + if isinstance(node, ast.Constant): + return node.value + if isinstance(node, ast.Name): + if node.id in ctx: + return ctx[node.id] + if node.id in _SAFE_FUNCS: + return _SAFE_FUNCS[node.id] + if node.id in ("true", "True"): + return True + if node.id in ("false", "False"): + return False + if node.id in ("null", "None"): + return None + return None + if isinstance(node, ast.Attribute): + base = _eval(node.value) + if isinstance(base, dict): + return base.get(node.attr) + if node.attr.startswith("_"): + raise ExpressionError("不允许访问私有属性") + return getattr(base, node.attr, None) + if isinstance(node, ast.Subscript): + base = _eval(node.value) + key = _eval(node.slice) + try: + return base[key] + except (KeyError, IndexError, TypeError): + return None + if isinstance(node, ast.BinOp): + op = _BIN_OPS.get(type(node.op)) + if not op: + raise ExpressionError("不支持的运算符") + return op(_eval(node.left), _eval(node.right)) + if isinstance(node, ast.UnaryOp): + op = _UNARY_OPS.get(type(node.op)) + if not op: + raise ExpressionError("不支持的一元运算符") + return op(_eval(node.operand)) + if isinstance(node, ast.BoolOp): + # Python 的 and/or 返回的是操作数本身而不是布尔值，并且短路求值。 + # `vars.x or '默认值'` 是模板里最常见的写法，返回 True 就全错了。 + is_and = isinstance(node.op, ast.And) + result: Any = None + for i, operand in enumerate(node.values): + result = _eval(operand) + truthy = bool(result) + if i < len(node.values) - 1 and (truthy is not is_and): + return result # and 遇假、or 遇真：短路返回该操作数 + return result + if isinstance(node, ast.Compare): + left = _eval(node.left) + for op_node, comparator in zip(node.ops, node.comparators): + op = _CMP_OPS.get(type(op_node)) + if not op: + raise ExpressionError("不支持的比较运算符") + right = _eval(comparator) + try: + if not op(left, right): + return False + except TypeError: + return False + left = right + return True + if isinstance(node, ast.IfExp): + return _eval(node.body) if _eval(node.test) else _eval(node.orelse) + if isinstance(node, ast.Call): + if not isinstance(node.func, ast.Name) or node.func.id not in _SAFE_FUNCS: + raise ExpressionError("只能调用白名单函数") + args = [_eval(a) for a in node.args] + kwargs = {kw.arg: _eval(kw.value) for kw in node.keywords if kw.arg} + return _SAFE_FUNCS[node.func.id](*args, **kwargs) + if isinstance(node, (ast.List, ast.Tuple)): + return [_eval(e) for e in node.elts] + if isinstance(node, ast.Dict): + return { + _eval(k): _eval(v) + for k, v in zip(node.keys, node.values) + if k is not None + } + raise ExpressionError(f"表达式里不允许出现 {type(node).__name__}") + + return _eval(tree) + + +def eval_condition(expr: str, ctx: dict[str, Any]) -> bool: + """求值成布尔。条件写错时返回 False 而不是炸掉整个 run。""" + try: + return bool(eval_expression(expr, ctx)) + except ExpressionError: + raise + except Exception: # noqa: BLE001 + return False diff --git a/backend/app/engine/nodes/__init__.py b/backend/app/engine/nodes/__init__.py new file mode 100644 index 00000000..b0c0d711 --- /dev/null +++ b/backend/app/engine/nodes/__init__.py @@ -0,0 +1 @@ +from app.engine.nodes import control, human, io, knowledge, llm, multi, tools # noqa: F401 diff --git a/backend/app/engine/nodes/control.py b/backend/app/engine/nodes/control.py new file mode 100644 index 00000000..7bf2cc0a --- /dev/null +++ b/backend/app/engine/nodes/control.py @@ -0,0 +1,183 @@ +from __future__ import annotations + +import json +from typing import Any + +from app.core.events import EventType +from app.db.base import SessionLocal +from app.engine.context import NodeContext, NodeError +from app.engine.expressions import ExpressionError, eval_condition +from app.engine.state import GraphState, message_text, template_context +from app.providers.factory import ModelSpec, get_chat_model + +# 分支和循环的"决定"写在 nodes[node_id] 里，编译器的路由函数再读出来。 +# 路由函数本身不能有副作用，所以判断必须发生在节点执行阶段。 +DECISION_KEY = "__decision__" + + +async def run_branch(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """条件分支。两种模式：表达式判断，或让模型做语义分类。""" + cases = ctx.cfg("cases", []) or [] + if not cases: + raise NodeError(ctx.node.id, "分支节点没有配置任何条件") + + mode = ctx.cfg("mode", "expression") + tctx = template_context(state) + chosen = "default" + reason = "" + + if mode == "llm": + chosen, reason = await _classify_with_llm(state, ctx, cases) + else: + for case in cases: + expr = case.get("condition", "") + key = case.get("key") or case.get("label") or "case" + if not expr: + continue + try: + if eval_condition(expr, tctx): + chosen, reason = key, f"命中条件：{expr}" + break + except ExpressionError as e: + raise NodeError(ctx.node.id, f"分支条件写错了（{expr}）：{e}") from e + else: + reason = "所有条件都不满足，走 default" + + ctx.emit(EventType.EDGE_TAKEN, branch=chosen, reason=reason, mode=mode) + return { + "nodes": {ctx.node.id: {DECISION_KEY: chosen, "branch": chosen, "reason": reason}}, + } + + +async def _classify_with_llm( + state: GraphState, ctx: NodeContext, cases: list[dict[str, Any]] +) -> tuple[str, str]: + """让模型在给定选项里挑一个。用结构化输出保证它只会吐出合法的 key。""" + options = [ + {"key": c.get("key") or f"case{i}", "描述": c.get("label") or c.get("condition") or ""} + for i, c in enumerate(cases) + ] + keys = [o["key"] for o in options] + ["default"] + subject = ctx.render_str(ctx.cfg("input", "{{ last_message }}"), state) + instruction = ctx.render_str(ctx.cfg("instruction", ""), state) + + prompt = ( + f"{instruction}\n\n" if instruction else "" + ) + ( + "请把下面的内容归入最合适的一类。\n\n" + f"可选类别：\n{json.dumps(options, ensure_ascii=False, indent=2)}\n\n" + f"内容：\n{subject}\n\n" + "只返回 JSON：{\"key\": <类别key>, \"reason\": <一句话理由>}" + ) + + async with SessionLocal() as session: + model, _ = await get_chat_model( + session, + ModelSpec( + provider=ctx.cfg("provider"), + model=ctx.cfg("model"), + max_tokens=512, + ), + ) + + schema = { + "type": "object", + "properties": { + "key": {"type": "string", "enum": keys}, + "reason": {"type": "string"}, + }, + "required": ["key"], + } + try: + value = await model.with_structured_output(schema).ainvoke(prompt) + if not isinstance(value, dict): + value = json.loads(message_text(value)) + except Exception: # noqa: BLE001 - 结构化不支持就退回自由文本再解析 + raw = message_text(await model.ainvoke(prompt)) + value = {} + for key in keys: + if key in raw: + value = {"key": key, "reason": raw[:200]} + break + + chosen = str(value.get("key") or "default") + if chosen not in keys: + chosen = "default" + return chosen, str(value.get("reason", "")) + + +async def run_loop(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """循环控制。 + + 画布上的接法：loop 节点的 `body` 出口连向循环体，循环体最后一个节点再连回 loop 节点； + `done` 出口连向循环结束后要走的分支。每进一次这个节点就推进一次计数。 + """ + mode = ctx.cfg("mode", "foreach") + node_id = ctx.node.id + loops = dict(state.get("loops") or {}) + cursor = int((loops.get(node_id) or {}).get("index", 0)) if isinstance(loops.get(node_id), dict) else 0 + max_iter = int(ctx.cfg("max_iterations", 10) or 10) + + tctx = template_context(state) + item_var = ctx.cfg("item_var", "item") + updates_vars: dict[str, Any] = {} + keep_going = False + current_item: Any = None + total = None + + if mode == "foreach": + items = ctx.render(ctx.cfg("items", ""), state) + if isinstance(items, str): + try: + items = json.loads(items) + except json.JSONDecodeError: + items = [line for line in items.splitlines() if line.strip()] + if not isinstance(items, list): + items = [items] if items not in (None, "") else [] + total = len(items) + keep_going = cursor < len(items) and cursor < max_iter + if keep_going: + current_item = items[cursor] + updates_vars = {item_var: current_item, f"{item_var}_index": cursor} + else: # while + condition = ctx.cfg("condition", "") + if cursor >= max_iter: + keep_going = False + elif not condition: + keep_going = False + else: + try: + keep_going = eval_condition(condition, tctx) + except ExpressionError as e: + raise NodeError(ctx.node.id, f"循环条件写错了：{e}") from e + + hit_limit = cursor >= max_iter + if hit_limit: + ctx.emit( + EventType.LOG, level="warn", + message=f"循环达到上限 {max_iter} 次，强制退出", + ) + + decision = "body" if keep_going else "done" + ctx.emit( + EventType.EDGE_TAKEN, + branch=decision, + iteration=cursor, + total=total, + mode=mode, + ) + + result = { + DECISION_KEY: decision, + "iteration": cursor, + "total": total, + "item": current_item, + "finished": not keep_going, + } + updates: dict[str, Any] = { + "nodes": {node_id: result}, + "loops": {node_id: {"index": cursor + 1 if keep_going else cursor}}, + } + if updates_vars: + updates["vars"] = updates_vars + return updates diff --git a/backend/app/engine/nodes/human.py b/backend/app/engine/nodes/human.py new file mode 100644 index 00000000..9a1f7a04 --- /dev/null +++ b/backend/app/engine/nodes/human.py @@ -0,0 +1,172 @@ +from __future__ import annotations + +import json +from typing import Any + +from langgraph.types import interrupt + +from app.core.events import EventType +from app.db.base import SessionLocal +from app.engine.context import NodeContext, NodeError +from app.engine.state import GraphState, message_text, template_context +from app.providers.factory import ModelSpec, get_chat_model + + +async def run_human(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """人工介入节点。 + + 执行到这里会挂起整张图并落盘 checkpoint；进程重启也不影响， + 人在前端回复之后从断点继续，而不是从头重跑。 + """ + mode = ctx.cfg("mode", "approve") # approve | input | edit + payload = { + "kind": "human_node", + "node_id": ctx.node.id, + "mode": mode, + "title": ctx.render_str(ctx.cfg("title", "需要你确认"), state), + "message": ctx.render_str(ctx.cfg("message", ""), state), + "schema": ctx.cfg("schema") or {}, + "context": ctx.render(ctx.cfg("context", {}) or {}, state), + } + if mode == "edit": + payload["draft"] = ctx.render(ctx.cfg("draft", ""), state) + + ctx.emit(EventType.HUMAN_REQUESTED, **payload) + response = interrupt(payload) + ctx.emit(EventType.HUMAN_RESOLVED, response=response) + + if mode == "approve": + approved = _truthy(response) + result: dict[str, Any] = { + "approved": approved, + "note": _note_of(response), + "__decision__": "approved" if approved else "rejected", + } + if not approved and ctx.cfg("stop_on_reject", False): + raise NodeError(ctx.node.id, f"人工拒绝：{result['note'] or '未说明原因'}") + elif mode == "edit": + value = response.get("value") if isinstance(response, dict) else response + result = {"value": value, "note": _note_of(response), "__decision__": "approved"} + else: # input + value = response.get("value") if isinstance(response, dict) else response + result = {"value": value, "__decision__": "approved"} + + updates: dict[str, Any] = {"nodes": {ctx.node.id: result}} + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: result.get("value", result.get("approved"))} + return updates + + +def _truthy(value: Any) -> bool: + if isinstance(value, bool): + return value + if isinstance(value, dict): + if "approved" in value: + return bool(value["approved"]) + return str(value.get("decision", "")).lower() in ("approve", "yes", "true", "同意") + if isinstance(value, str): + return value.strip().lower() in ("yes", "y", "true", "approve", "ok", "同意", "通过") + return bool(value) + + +def _note_of(value: Any) -> str: + if isinstance(value, dict): + return str(value.get("note", "") or value.get("comment", "")) + return "" + + +# -------------------------------------------------------------------------- +# 结构化校验 +# -------------------------------------------------------------------------- + + +async def run_validate(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """按 JSON Schema 校验上游产出。 + + 校验失败时可以把错误原样喂回模型让它改 —— 这比在 prompt 里祈祷它输出正确格式可靠得多。 + """ + import jsonschema + + schema = ctx.cfg("schema") + if not schema: + raise NodeError(ctx.node.id, "校验节点需要一个 JSON Schema") + + source_cfg = ctx.cfg("source", "{{ last_message }}") + raw = ctx.render(source_cfg, state) if not isinstance(source_cfg, str) else ctx.render_str(source_cfg, state) + max_retries = int(ctx.cfg("max_retries", 2) or 0) + repair = bool(ctx.cfg("repair_with_llm", True)) + + attempt = 0 + errors: list[str] = [] + value: Any = raw + + while True: + value, parse_error = _coerce_json(raw) + if parse_error: + errors = [parse_error] + else: + try: + jsonschema.validate(value, schema) + ctx.emit(EventType.LOG, level="info", message=f"校验通过（第 {attempt + 1} 次尝试）") + result = {"valid": True, "data": value, "attempts": attempt + 1, "errors": []} + updates: dict[str, Any] = {"nodes": {ctx.node.id: result}} + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: value} + return updates + except jsonschema.ValidationError as e: + path = "/".join(str(p) for p in e.absolute_path) or "(根)" + errors = [f"{path}: {e.message}"] + + ctx.emit(EventType.LOG, level="warn", message=f"第 {attempt + 1} 次校验失败：{errors[0]}") + attempt += 1 + if attempt > max_retries or not repair: + break + raw = await _repair(raw, schema, errors, ctx, state) + + result = {"valid": False, "data": value, "attempts": attempt, "errors": errors} + if ctx.cfg("fail_fast", True): + raise NodeError(ctx.node.id, f"结构化校验未通过：{'; '.join(errors)}") + return {"nodes": {ctx.node.id: result}} + + +def _coerce_json(raw: Any) -> tuple[Any, str | None]: + """尽量把模型输出解析成 JSON：剥掉 ``` 围栏，再退一步找第一个完整对象。""" + if isinstance(raw, (dict, list)): + return raw, None + text = str(raw or "").strip() + if text.startswith("```"): + lines = text.splitlines() + text = "\n".join(lines[1 : -1 if lines[-1].strip().startswith("```") else None]).strip() + try: + return json.loads(text), None + except json.JSONDecodeError: + pass + start = min( + (i for i in (text.find("{"), text.find("[")) if i >= 0), default=-1 + ) + if start >= 0: + for end in range(len(text), start, -1): + try: + return json.loads(text[start:end]), None + except json.JSONDecodeError: + continue + return text, "输出不是合法 JSON" + + +async def _repair(raw: Any, schema: dict[str, Any], errors: list[str], ctx: NodeContext, + state: GraphState) -> str: + async with SessionLocal() as session: + model, _ = await get_chat_model( + session, + ModelSpec(provider=ctx.cfg("provider"), model=ctx.cfg("model"), max_tokens=4096), + ) + prompt = ( + "下面这段输出没有通过 JSON Schema 校验，请修正后重新输出。\n\n" + f"Schema：\n{json.dumps(schema, ensure_ascii=False, indent=2)}\n\n" + f"错误：\n- " + "\n- ".join(errors) + "\n\n" + f"原始输出：\n{str(raw)[:6000]}\n\n" + "只输出修正后的 JSON，不要任何解释或代码围栏。" + ) + return message_text(await model.ainvoke(prompt)) diff --git a/backend/app/engine/nodes/io.py b/backend/app/engine/nodes/io.py new file mode 100644 index 00000000..8eaf4f87 --- /dev/null +++ b/backend/app/engine/nodes/io.py @@ -0,0 +1,82 @@ +from __future__ import annotations + +import json +from typing import Any + +from app.core.events import EventType +from app.engine.context import NodeContext, NodeError +from app.engine.expressions import ExpressionError, eval_expression +from app.engine.state import GraphState, template_context + + +async def run_input(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """入口节点。把运行输入按声明的字段整理进变量池，补上默认值。""" + raw = dict(state.get("input") or {}) + fields = ctx.cfg("fields", []) or [] + resolved: dict[str, Any] = dict(raw) + + for field in fields: + name = field.get("name") + if not name: + continue + if name not in resolved or resolved[name] in (None, ""): + if field.get("required") and field.get("default") in (None, ""): + raise NodeError(ctx.node.id, f"缺少必填输入：{name}") + resolved[name] = field.get("default") + + return { + "input": resolved, + "vars": resolved, + "nodes": {ctx.node.id: resolved}, + } + + +async def run_output(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """出口节点。按配置把散落在各节点的结果收成一个结构化成果。""" + mapping = ctx.cfg("fields", None) + tctx = template_context(state) + + if mapping: + result: dict[str, Any] = {} + for field in mapping: + name = field.get("name") + if not name: + continue + result[name] = ctx.render(field.get("value", ""), state) + else: + # 没配映射就把最后一条消息当作结果，让新建的图开箱即用 + result = {"result": tctx.get("last_message", "")} + + return {"output": result, "nodes": {ctx.node.id: result}} + + +async def run_transform(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """数据整形。用受限表达式把上游输出揉成下游要的形状，不用为此专门起一个 LLM。""" + tctx = template_context(state) + mode = ctx.cfg("mode", "expression") + output: Any + + if mode == "expression": + expr = ctx.cfg("expression", "") + if not expr: + raise NodeError(ctx.node.id, "整形节点没有填表达式") + try: + output = eval_expression(expr, tctx) + except ExpressionError as e: + raise NodeError(ctx.node.id, f"表达式错误：{e}") from e + elif mode == "template": + output = ctx.render_str(ctx.cfg("template", ""), state) + elif mode == "json": + rendered = ctx.render_str(ctx.cfg("template", "{}"), state) + try: + output = json.loads(rendered) + except json.JSONDecodeError as e: + raise NodeError(ctx.node.id, f"渲染结果不是合法 JSON：{e}") from e + else: + raise NodeError(ctx.node.id, f"未知的整形模式：{mode}") + + updates: dict[str, Any] = {"nodes": {ctx.node.id: output}} + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: output} + return updates diff --git a/backend/app/engine/nodes/knowledge.py b/backend/app/engine/nodes/knowledge.py new file mode 100644 index 00000000..8b0002e1 --- /dev/null +++ b/backend/app/engine/nodes/knowledge.py @@ -0,0 +1,102 @@ +from __future__ import annotations + +import json +from typing import Any + +from app.core.events import EventType +from app.db.base import SessionLocal +from app.engine.context import NodeContext, NodeError +from app.engine.state import GraphState, template_context +from app.memory import kb, store + + +async def run_memory(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """长期记忆读写节点。 + + 把记忆做成画布上的一个节点，而不是藏在 agent 内部，是为了让"什么时候记、 + 记什么、记到哪个 scope"变成可见、可调的编排决策。 + """ + action = ctx.cfg("action", "recall") # recall | write | clear + scope = ctx.render_str(ctx.cfg("scope", "") or ctx.run.memory_scope, state) + + async with SessionLocal() as session: + if action == "recall": + query = ctx.render_str(ctx.cfg("query", "{{ last_message }}"), state) + limit = int(ctx.cfg("limit", 5) or 5) + hits = await store.recall( + session, scope=scope, query=query, limit=limit, kind=ctx.cfg("kind") or None + ) + text = "\n".join(f"- {h['content']}" for h in hits) + result: dict[str, Any] = { + "memories": hits, + "count": len(hits), + "text": text, + "scope": scope, + } + elif action == "write": + content = ctx.render_str(ctx.cfg("content", ""), state) + if not content.strip(): + raise NodeError(ctx.node.id, "记忆写入节点没有内容") + item = await store.remember( + session, + scope=scope, + content=content, + kind=ctx.cfg("kind", "fact") or "fact", + importance=float(ctx.cfg("importance", 0.5) or 0.5), + meta={"run_id": ctx.run.run_id, "node_id": ctx.node.id}, + ) + result = {"id": item.id, "saved": True, "scope": scope, "content": item.content} + elif action == "clear": + removed = await store.clear_scope(session, scope) + result = {"cleared": removed, "scope": scope} + else: + raise NodeError(ctx.node.id, f"未知的记忆操作：{action}") + + ctx.emit(EventType.LOG, level="info", + message=f"记忆 {action} @ {scope}：{result.get('count', result.get('cleared', 1))} 条") + + updates: dict[str, Any] = {"nodes": {ctx.node.id: result}} + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: result.get("text", result)} + return updates + + +async def run_retrieve(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """知识库检索节点。输出既有结构化命中列表，也有拼好的上下文文本。""" + query = ctx.render_str(ctx.cfg("query", "{{ last_message }}"), state) + if not query.strip(): + raise NodeError(ctx.node.id, "检索节点没有查询内容") + + collection = ctx.render_str(ctx.cfg("collection", "") or ctx.run.collection, state) + limit = int(ctx.cfg("limit", 5) or 5) + alpha = float(ctx.cfg("alpha", 0.5) or 0.5) # 1=纯向量，0=纯关键词 + + async with SessionLocal() as session: + hits = await kb.search( + session, collection=collection, query=query, limit=limit, alpha=alpha + ) + + min_score = float(ctx.cfg("min_score", 0.0) or 0.0) + hits = [h for h in hits if h["score"] >= min_score] + + # 拼成可以直接塞进 prompt 的上下文，带编号方便模型引用出处 + context_text = "\n\n".join( + f"[{i + 1}] {h['title']}（片段 {h['ordinal']}）\n{h['content']}" + for i, h in enumerate(hits) + ) + result = { + "hits": hits, + "count": len(hits), + "text": context_text, + "query": query, + "collection": collection, + } + ctx.emit(EventType.LOG, level="info", + message=f"检索 {collection}：命中 {len(hits)} 条，最高分 {hits[0]['score'] if hits else 0}") + + updates: dict[str, Any] = {"nodes": {ctx.node.id: result}} + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: context_text} + return updates diff --git a/backend/app/engine/nodes/llm.py b/backend/app/engine/nodes/llm.py new file mode 100644 index 00000000..4197e2d8 --- /dev/null +++ b/backend/app/engine/nodes/llm.py @@ -0,0 +1,378 @@ +from __future__ import annotations + +import json +import time +from typing import Any + +from langchain_core.messages import AIMessage, BaseMessage, HumanMessage, SystemMessage, ToolMessage +from langchain_core.tools import BaseTool +from langgraph.func import task +from langgraph.types import interrupt +from sqlalchemy import select + +from app.core.config import settings +from app.core.events import EventType +from app.db.base import SessionLocal +from app.db.models import Skill +from app.engine.context import NodeContext, NodeError +from app.engine.state import GraphState, message_text, template_context, thinking_text +from app.providers import catalog +from app.providers.factory import ModelSpec, ProviderNotConfigured, get_chat_model +from app.tools.registry import ToolContext, build_tools, get_spec + +# -------------------------------------------------------------------------- +# 公共部分 +# -------------------------------------------------------------------------- + + +def _model_spec(ctx: NodeContext) -> ModelSpec: + return ModelSpec( + provider=ctx.cfg("provider"), + model=ctx.cfg("model"), + temperature=ctx.cfg("temperature"), + max_tokens=ctx.cfg("max_tokens"), + thinking=ctx.cfg("thinking"), + effort=ctx.cfg("effort"), + ) + + +async def _load_skills(names: list[str]) -> str: + """把挂载的 Skill 拼成一段 system 指令。 + + Skill 把"怎么做事"从图结构里抽出来单独管理 —— 改方法论不用动编排。 + """ + if not names: + return "" + async with SessionLocal() as session: + rows = list( + ( + await session.execute( + select(Skill).where(Skill.name.in_(names), Skill.enabled.is_(True)) + ) + ).scalars() + ) + if not rows: + return "" + blocks: list[str] = [] + for skill in rows: + block = f"## {skill.name}\n{skill.instructions.strip()}" + if skill.examples: + examples = "\n".join( + f"- 输入：{e.get('input', '')}\n 输出：{e.get('output', '')}" + for e in skill.examples + if isinstance(e, dict) + ) + if examples: + block += f"\n\n示例：\n{examples}" + blocks.append(block) + return "# 适用的方法论\n\n" + "\n\n".join(blocks) + + +async def _build_messages(state: GraphState, ctx: NodeContext) -> list[BaseMessage]: + tctx = template_context(state) + messages: list[BaseMessage] = [] + + system_parts: list[str] = [] + system = ctx.render_str(ctx.cfg("system", ""), state) + if system: + system_parts.append(system) + skill_text = await _load_skills(ctx.cfg("skills", []) or []) + if skill_text: + system_parts.append(skill_text) + if system_parts: + messages.append(SystemMessage(content="\n\n".join(system_parts))) + + # 是否把之前节点的对话历史带上 + if ctx.cfg("use_history", False): + messages.extend(state.get("messages") or []) + + prompt = ctx.render_str(ctx.cfg("prompt", ""), state) + if not prompt and not ctx.cfg("use_history", False): + prompt = str(tctx.get("last_message") or json.dumps(tctx.get("input"), ensure_ascii=False)) + if prompt: + messages.append(HumanMessage(content=prompt)) + if not messages: + raise NodeError(ctx.node.id, "没有可发送的内容：system 和 prompt 都是空的") + return messages + + +def _usage_of(message: BaseMessage, model_id: str) -> dict[str, Any]: + meta = getattr(message, "usage_metadata", None) or {} + inp = int(meta.get("input_tokens") or 0) + out = int(meta.get("output_tokens") or 0) + return { + "input_tokens": inp, + "output_tokens": out, + "total_tokens": inp + out, + "cost_usd": round(catalog.estimate_cost(model_id, inp, out), 6), + "calls": 1, + } + + +async def _invoke_streaming(model: Any, messages: list[BaseMessage], ctx: NodeContext) -> AIMessage: + """流式调用并把 token 实时推给前端。 + + 失败时回退到非流式 —— 有些兼容网关不支持 SSE，不该因此让整个节点挂掉。 + """ + ctx.emit(EventType.LLM_START, message_count=len(messages)) + chunks: list[Any] = [] + try: + async for chunk in model.astream(messages): + chunks.append(chunk) + text = message_text(chunk) + if text: + ctx.emit(EventType.LLM_TOKEN, delta=text) + reasoning = thinking_text(chunk) + if reasoning: + ctx.emit(EventType.LLM_THINKING, delta=reasoning) + except NotImplementedError: + return await model.ainvoke(messages) + except Exception as e: # noqa: BLE001 + ctx.emit(EventType.LOG, level="warn", message=f"流式失败，回退非流式：{e}") + return await model.ainvoke(messages) + + if not chunks: + return await model.ainvoke(messages) + merged = chunks[0] + for chunk in chunks[1:]: + merged = merged + chunk + return merged + + +# -------------------------------------------------------------------------- +# LLM 节点：单次调用 +# -------------------------------------------------------------------------- + + +async def run_llm(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + spec = _model_spec(ctx) + async with SessionLocal() as session: + try: + model, model_id = await get_chat_model(session, spec) + except ProviderNotConfigured as e: + raise NodeError(ctx.node.id, str(e)) from e + + messages = await _build_messages(state, ctx) + schema = ctx.cfg("output_schema") + started = time.perf_counter() + + if schema: + # 要结构化结果就走原生 structured output，这条路径拿不到 token 流 + ctx.emit(EventType.LLM_START, structured=True, message_count=len(messages)) + try: + structured = model.with_structured_output(schema) + value = await structured.ainvoke(messages) + except Exception as e: # noqa: BLE001 + raise NodeError(ctx.node.id, f"结构化输出失败：{type(e).__name__}: {e}") from e + payload = value if isinstance(value, (dict, list)) else getattr(value, "model_dump", lambda: value)() + output = {"data": payload, "text": json.dumps(payload, ensure_ascii=False, indent=2)} + response: BaseMessage = AIMessage(content=output["text"]) + else: + response = await _invoke_streaming(model, messages, ctx) + text = message_text(response) + reasoning = thinking_text(response) + if not text and reasoning: + # 思考型模型把额度全花在推理上了，正文是空的 —— 说清楚而不是抛个空结果给下游 + ctx.emit(EventType.LOG, level="warn", + message="模型只输出了思考内容，正文为空。把 max_tokens 调大，或把 thinking 设为 off。") + output = {"text": text, "thinking": reasoning} + + usage = _usage_of(response, model_id) + elapsed = int((time.perf_counter() - started) * 1000) + ctx.emit( + EventType.LLM_END, + model=model_id, + duration_ms=elapsed, + **usage, + ) + + result = {**output, "model": model_id, "duration_ms": elapsed} + updates: dict[str, Any] = { + "nodes": {ctx.node.id: result}, + "usage": usage, + } + if ctx.cfg("emit_message", True): + updates["messages"] = [response] + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: output.get("data", output.get("text"))} + return updates + + +# -------------------------------------------------------------------------- +# Agent 节点：带工具循环 +# -------------------------------------------------------------------------- + + +async def _resolve_tools(ctx: NodeContext, state: GraphState) -> list[BaseTool]: + names = ctx.cfg("tools", []) or [] + tool_ctx = ToolContext( + run_id=ctx.run.run_id, + node_id=ctx.node.id, + sandbox_session=ctx.run.thread_id, + memory_scope=ctx.cfg("memory_scope") or ctx.run.memory_scope, + collection=ctx.cfg("collection") or ctx.run.collection, + ) + async with SessionLocal() as session: + return await build_tools(names, tool_ctx, session=session) + + +def _needs_approval(ctx: NodeContext, tool_name: str) -> bool: + mode = ctx.cfg("approval", "dangerous") # never | dangerous | always + if mode == "always": + return True + if mode == "never": + return False + spec = get_spec(tool_name) + return bool(spec and spec.dangerous) + + +async def run_agent(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """ReAct 循环：模型想调工具就调，拿到结果继续想，直到给出最终答案。 + + 这里没有用 prebuilt 的 create_react_agent，因为需要在循环内部做三件它不做的事： + 每步工具调用都往画布推事件、对危险工具逐个弹人工审批、以及步数护栏。 + """ + model_spec = _model_spec(ctx) + async with SessionLocal() as session: + try: + base_model, model_id = await get_chat_model(session, model_spec) + except ProviderNotConfigured as e: + raise NodeError(ctx.node.id, str(e)) from e + + tools = await _resolve_tools(ctx, state) + tool_map = {t.name: t for t in tools} + model = base_model.bind_tools(tools) if tools else base_model + + messages = await _build_messages(state, ctx) + max_steps = min(int(ctx.cfg("max_steps", 8) or 8), settings.max_agent_steps) + total_usage: dict[str, Any] = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0} + transcript: list[dict[str, Any]] = [] + + # @task 的返回值会进 checkpoint：人工审批导致节点重放时， + # 之前已经完成的模型调用和工具执行不会重跑，也就不会重复计费。 + @task + async def llm_step(step: int, payload: list[BaseMessage]) -> BaseMessage: + return await _invoke_streaming(model, payload, ctx) + + @task + async def tool_step(step: int, name: str, args: dict[str, Any]) -> str: + tool = tool_map[name] + result = await tool.ainvoke(args) + return result if isinstance(result, str) else json.dumps(result, ensure_ascii=False, default=str) + + final_text = "" + for step in range(max_steps): + response = await llm_step(step, messages) + messages.append(response) + usage = _usage_of(response, model_id) + for key in ("input_tokens", "output_tokens", "calls"): + total_usage[key] += usage[key] + total_usage["cost_usd"] = round(total_usage["cost_usd"] + usage["cost_usd"], 6) + + tool_calls = getattr(response, "tool_calls", None) or [] + if not tool_calls: + final_text = message_text(response) + break + + for call in tool_calls: + name = call.get("name", "") + args = call.get("args", {}) or {} + call_id = call.get("id") or f"{ctx.node.id}-{step}-{name}" + + if name not in tool_map: + messages.append( + ToolMessage(content=f"错误：没有名为 {name} 的工具", tool_call_id=call_id) + ) + continue + + if _needs_approval(ctx, name): + ctx.emit( + EventType.HUMAN_REQUESTED, + mode="approve", + tool=name, + args=args, + title=f"Agent 想调用工具 {name}", + ) + decision = interrupt( + { + "kind": "tool_approval", + "node_id": ctx.node.id, + "tool": name, + "args": args, + "title": f"是否允许调用 {name}？", + } + ) + approved, note, override = _parse_decision(decision) + ctx.emit(EventType.HUMAN_RESOLVED, tool=name, approved=approved, note=note) + if not approved: + messages.append( + ToolMessage( + content=f"用户拒绝了这次调用。原因：{note or '未说明'}。请换一种方式。", + tool_call_id=call_id, + ) + ) + transcript.append({"tool": name, "args": args, "denied": True, "note": note}) + continue + if isinstance(override, dict) and override: + args = override + + ctx.emit(EventType.TOOL_START, tool=name, args=args, call_id=call_id) + started = time.perf_counter() + try: + content = await tool_step(step, name, args) + ok = True + except Exception as e: # noqa: BLE001 - 工具失败要喂回模型，让它自己纠错 + content = f"工具执行失败：{type(e).__name__}: {e}" + ok = False + elapsed = int((time.perf_counter() - started) * 1000) + ctx.emit( + EventType.TOOL_END if ok else EventType.TOOL_ERROR, + tool=name, + call_id=call_id, + duration_ms=elapsed, + preview=content[:2000], + ) + transcript.append( + {"tool": name, "args": args, "ok": ok, "duration_ms": elapsed, "result": content[:4000]} + ) + messages.append(ToolMessage(content=content, tool_call_id=call_id)) + else: + final_text = message_text(messages[-1]) if messages else "" + ctx.emit( + EventType.LOG, + level="warn", + message=f"agent 达到最大步数 {max_steps}，提前收尾", + ) + + total_usage["total_tokens"] = total_usage["input_tokens"] + total_usage["output_tokens"] + result = { + "text": final_text, + "steps": len(transcript), + "tool_calls": transcript, + "model": model_id, + } + updates: dict[str, Any] = { + "nodes": {ctx.node.id: result}, + "usage": total_usage, + } + if ctx.cfg("emit_message", True): + updates["messages"] = [AIMessage(content=final_text or "(空)")] + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: final_text} + return updates + + +def _parse_decision(decision: Any) -> tuple[bool, str, dict[str, Any] | None]: + """人工审批的返回值兼容几种写法：布尔、字符串、或带备注的结构体。""" + if isinstance(decision, bool): + return decision, "", None + if isinstance(decision, str): + return decision.lower() in ("yes", "y", "true", "approve", "ok", "同意"), decision, None + if isinstance(decision, dict): + approved = decision.get("approved") + if approved is None: + approved = decision.get("decision") in ("approve", "yes", True) + return bool(approved), str(decision.get("note", "")), decision.get("args") + return bool(decision), "", None diff --git a/backend/app/engine/nodes/multi.py b/backend/app/engine/nodes/multi.py new file mode 100644 index 00000000..3fbf89b1 --- /dev/null +++ b/backend/app/engine/nodes/multi.py @@ -0,0 +1,284 @@ +from __future__ import annotations + +import json +import time +from typing import Any + +from langchain_core.messages import AIMessage, HumanMessage, SystemMessage, ToolMessage +from langgraph.func import task + +from app.core.config import settings +from app.core.events import EventType +from app.db.base import SessionLocal +from app.db.models import Workflow +from app.engine.context import NodeContext, NodeError +from app.engine.state import GraphState, message_text, template_context +from app.providers import catalog +from app.providers.factory import ModelSpec, get_chat_model +from app.tools.registry import ToolContext, build_tools + +_MAX_DEPTH = 3 + + +# -------------------------------------------------------------------------- +# Supervisor：一个调度者 + 若干专家 +# -------------------------------------------------------------------------- + + +async def run_supervisor(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """多 agent 协作。 + + 调度者每轮只做一件事：看当前进展，决定交给哪个专家、并给它一句具体指令， + 或者宣布收工。专家各自带自己的 system prompt 和工具集。 + 把整个协作塞进一个节点，是为了让画布保持可读 —— 展开细节看运行时间线就够了。 + """ + agents = ctx.cfg("agents", []) or [] + if not agents: + raise NodeError(ctx.node.id, "supervisor 节点至少要配一个 agent") + + names = [a.get("name") or f"agent{i}" for i, a in enumerate(agents)] + agent_map = {n: a for n, a in zip(names, agents)} + max_rounds = min(int(ctx.cfg("max_rounds", 6) or 6), settings.max_agent_steps) + + goal = ctx.render_str(ctx.cfg("goal", "{{ last_message }}"), state) + if not goal.strip(): + goal = json.dumps(template_context(state).get("input"), ensure_ascii=False) + + async with SessionLocal() as session: + supervisor_model, sup_model_id = await get_chat_model( + session, + ModelSpec( + provider=ctx.cfg("provider"), + model=ctx.cfg("model"), + max_tokens=int(ctx.cfg("max_tokens", 2048) or 2048), + ), + ) + + roster = "\n".join( + f"- {n}：{agent_map[n].get('description') or agent_map[n].get('system', '')[:100]}" + for n in names + ) + route_schema = { + "type": "object", + "properties": { + "next": {"type": "string", "enum": names + ["FINISH"]}, + "instruction": {"type": "string"}, + "reason": {"type": "string"}, + }, + "required": ["next"], + } + + transcript: list[dict[str, Any]] = [] + usage_total = {"input_tokens": 0, "output_tokens": 0, "cost_usd": 0.0, "calls": 0} + + def _accumulate(msg: Any, model_id: str) -> None: + meta = getattr(msg, "usage_metadata", None) or {} + i, o = int(meta.get("input_tokens") or 0), int(meta.get("output_tokens") or 0) + usage_total["input_tokens"] += i + usage_total["output_tokens"] += o + usage_total["calls"] += 1 + usage_total["cost_usd"] = round( + usage_total["cost_usd"] + catalog.estimate_cost(model_id, i, o), 6 + ) + + @task + async def route(round_no: int, progress: str) -> dict[str, Any]: + prompt = ( + f"你是团队调度者。目标：\n{goal}\n\n" + f"可用成员：\n{roster}\n\n" + f"当前进展：\n{progress or '(还没有任何进展)'}\n\n" + "决定下一步交给谁处理，并给出一句明确的指令。" + "如果目标已经达成，next 返回 FINISH。" + ) + try: + value = await supervisor_model.with_structured_output(route_schema).ainvoke(prompt) + if not isinstance(value, dict): + value = json.loads(message_text(value)) + except Exception: # noqa: BLE001 + raw = message_text(await supervisor_model.ainvoke(prompt)) + picked = next((n for n in names if n in raw), "FINISH") + value = {"next": picked, "instruction": raw[:500], "reason": ""} + return value + + @task + async def work(round_no: int, name: str, instruction: str, progress: str) -> dict[str, Any]: + cfg = agent_map[name] + async with SessionLocal() as session: + model, model_id = await get_chat_model( + session, + ModelSpec( + provider=cfg.get("provider") or ctx.cfg("provider"), + model=cfg.get("model") or ctx.cfg("model"), + max_tokens=int(cfg.get("max_tokens") or 4096), + ), + ) + tool_ctx = ToolContext( + run_id=ctx.run.run_id, + node_id=f"{ctx.node.id}:{name}", + sandbox_session=ctx.run.thread_id, + memory_scope=ctx.run.memory_scope, + collection=ctx.run.collection, + ) + tools = await build_tools(cfg.get("tools", []) or [], tool_ctx, session=session) + + bound = model.bind_tools(tools) if tools else model + tool_map = {t.name: t for t in tools} + messages: list[Any] = [ + SystemMessage(content=cfg.get("system") or f"你是 {name}。"), + HumanMessage(content=f"团队目标：{goal}\n\n已有进展：\n{progress or '(无)'}\n\n你的任务：{instruction}"), + ] + calls: list[dict[str, Any]] = [] + for _ in range(int(cfg.get("max_steps") or 4)): + reply = await bound.ainvoke(messages) + _accumulate(reply, model_id) + messages.append(reply) + tool_calls = getattr(reply, "tool_calls", None) or [] + if not tool_calls: + return {"text": message_text(reply), "tool_calls": calls} + for call in tool_calls: + tname = call.get("name", "") + targs = call.get("args", {}) or {} + cid = call.get("id") or f"{name}-{tname}" + ctx.emit(EventType.TOOL_START, tool=tname, args=targs, agent=name, call_id=cid) + if tname not in tool_map: + content = f"错误：没有名为 {tname} 的工具" + else: + try: + raw = await tool_map[tname].ainvoke(targs) + content = raw if isinstance(raw, str) else json.dumps(raw, ensure_ascii=False, default=str) + except Exception as e: # noqa: BLE001 + content = f"工具失败：{type(e).__name__}: {e}" + ctx.emit(EventType.TOOL_END, tool=tname, agent=name, call_id=cid, preview=content[:1500]) + calls.append({"tool": tname, "args": targs, "result": content[:2000]}) + messages.append(ToolMessage(content=content, tool_call_id=cid)) + return {"text": message_text(messages[-1]), "tool_calls": calls} + + progress_lines: list[str] = [] + final_text = "" + for round_no in range(max_rounds): + progress = "\n\n".join(progress_lines) + decision = await route(round_no, progress) + _accumulate(AIMessage(content=""), sup_model_id) # route 用了一次调用 + nxt = str(decision.get("next") or "FINISH") + reason = str(decision.get("reason", "")) + ctx.emit( + EventType.LOG, level="info", round=round_no, + message=f"调度 → {nxt}" + (f"（{reason}）" if reason else ""), + ) + if nxt == "FINISH" or nxt not in agent_map: + final_text = progress_lines[-1].split("：", 1)[-1] if progress_lines else "" + break + + instruction = str(decision.get("instruction") or goal) + ctx.emit(EventType.AGENT_STEP_START, agent=nxt, instruction=instruction[:300], + round=round_no) + started = time.perf_counter() + outcome = await work(round_no, nxt, instruction, progress) + elapsed = int((time.perf_counter() - started) * 1000) + ctx.emit(EventType.AGENT_STEP_END, agent=nxt, duration_ms=elapsed, + round=round_no, preview=outcome["text"][:500]) + + progress_lines.append(f"【{nxt}】{outcome['text']}") + transcript.append( + {"round": round_no, "agent": nxt, "instruction": instruction, **outcome, + "duration_ms": elapsed} + ) + final_text = outcome["text"] + + usage_total["total_tokens"] = usage_total["input_tokens"] + usage_total["output_tokens"] + result = { + "text": final_text, + "rounds": len(transcript), + "transcript": transcript, + "agents": names, + } + updates: dict[str, Any] = { + "nodes": {ctx.node.id: result}, + "usage": usage_total, + } + if ctx.cfg("emit_message", True): + updates["messages"] = [AIMessage(content=final_text or "(空)")] + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: final_text} + return updates + + +# -------------------------------------------------------------------------- +# 子图：把另一张工作流当成一个节点 +# -------------------------------------------------------------------------- + + +async def run_subgraph(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """嵌套执行另一个工作流。 + + 子图共享同一个 run 的事件流（节点 id 会加前缀），所以在时间线上能看到它内部 + 每一步；但它有独立的状态，不会污染父图的变量池。 + """ + from app.engine.compiler import compile_graph # 延迟导入，避免循环依赖 + from app.engine.schema import GraphSpec + + if ctx.run.depth >= _MAX_DEPTH: + raise NodeError(ctx.node.id, f"子图嵌套超过 {_MAX_DEPTH} 层，已阻止") + + workflow_id = ctx.cfg("workflow_id") + if not workflow_id: + raise NodeError(ctx.node.id, "子图节点没有选择工作流") + + async with SessionLocal() as session: + workflow = await session.get(Workflow, workflow_id) + if not workflow: + raise NodeError(ctx.node.id, f"找不到工作流 {workflow_id}") + + sub_input = ctx.render(ctx.cfg("input", {}) or {}, state) + if not isinstance(sub_input, dict): + sub_input = {"input": sub_input} + + sub_spec = GraphSpec.model_validate(workflow.graph) + sub_run = type(ctx.run)( + run_id=ctx.run.run_id, + thread_id=f"{ctx.run.thread_id}:{ctx.node.id}", + spec=sub_spec, + workflow_id=workflow.id, + depth=ctx.run.depth + 1, + memory_scope=ctx.run.memory_scope, + collection=ctx.run.collection, + extra={**ctx.run.extra, "node_prefix": f"{ctx.node.id}/"}, + ) + + ctx.emit(EventType.LOG, level="info", + message=f"进入子图「{workflow.name}」（{len(sub_spec.nodes)} 个节点）") + + compiled = compile_graph(sub_spec, sub_run) + # 子图不带 checkpointer：它的断点由父图的 checkpoint 统一承载 + app = compiled.compile() + result_state = await app.ainvoke( + { + "input": sub_input, + "vars": sub_input, + "nodes": {}, + "output": {}, + "messages": [], + "usage": {}, + "trail": [], + "loops": {}, + }, + {"recursion_limit": settings.max_graph_steps}, + ) + + output = result_state.get("output") or {} + result = { + "output": output, + "workflow": workflow.name, + "text": json.dumps(output, ensure_ascii=False) if output else "", + } + ctx.emit(EventType.LOG, level="info", message=f"子图「{workflow.name}」完成") + + updates: dict[str, Any] = { + "nodes": {ctx.node.id: result}, + "usage": result_state.get("usage") or {}, + } + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: output} + return updates diff --git a/backend/app/engine/nodes/tools.py b/backend/app/engine/nodes/tools.py new file mode 100644 index 00000000..72ff00c1 --- /dev/null +++ b/backend/app/engine/nodes/tools.py @@ -0,0 +1,149 @@ +from __future__ import annotations + +import json +import time +from typing import Any + +from langgraph.types import interrupt + +from app.core.events import EventType +from app.db.base import SessionLocal +from app.engine.context import NodeContext, NodeError +from app.engine.state import GraphState +from app.sandbox.base import SandboxLimits +from app.sandbox.manager import sandbox_manager +from app.tools.registry import ToolContext, call_tool, get_spec + + +def _tool_ctx(ctx: NodeContext) -> ToolContext: + return ToolContext( + run_id=ctx.run.run_id, + node_id=ctx.node.id, + sandbox_session=ctx.run.thread_id, + memory_scope=ctx.cfg("memory_scope") or ctx.run.memory_scope, + collection=ctx.cfg("collection") or ctx.run.collection, + ) + + +async def run_tool(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """直接调用一个工具。参数里的 {{ }} 会先用当前状态渲染。""" + name = ctx.cfg("tool", "") + if not name: + raise NodeError(ctx.node.id, "工具节点没有选择工具") + + args = ctx.render(ctx.cfg("args", {}) or {}, state) + if not isinstance(args, dict): + raise NodeError(ctx.node.id, "工具参数必须是对象") + + spec = get_spec(name) + approval = ctx.cfg("approval", "dangerous") + if approval == "always" or (approval == "dangerous" and spec and spec.dangerous): + ctx.emit(EventType.HUMAN_REQUESTED, mode="approve", tool=name, args=args, + title=f"是否允许调用 {name}？") + decision = interrupt( + {"kind": "tool_approval", "node_id": ctx.node.id, "tool": name, "args": args, + "title": f"是否允许调用 {name}？"} + ) + approved = decision if isinstance(decision, bool) else bool( + (decision or {}).get("approved") if isinstance(decision, dict) else decision + ) + if isinstance(decision, dict) and isinstance(decision.get("args"), dict): + args = decision["args"] + ctx.emit(EventType.HUMAN_RESOLVED, tool=name, approved=approved) + if not approved: + raise NodeError(ctx.node.id, f"用户拒绝执行工具 {name}") + + ctx.emit(EventType.TOOL_START, tool=name, args=args) + started = time.perf_counter() + try: + async with SessionLocal() as session: + result = await call_tool(name, args, _tool_ctx(ctx), session=session) + except KeyError as e: + raise NodeError(ctx.node.id, str(e)) from e + except Exception as e: # noqa: BLE001 + ctx.emit(EventType.TOOL_ERROR, tool=name, error=f"{type(e).__name__}: {e}") + if ctx.cfg("fail_fast", True): + raise NodeError(ctx.node.id, f"工具 {name} 执行失败：{e}") from e + result = {"error": f"{type(e).__name__}: {e}"} + + elapsed = int((time.perf_counter() - started) * 1000) + ctx.emit(EventType.TOOL_END, tool=name, duration_ms=elapsed, + preview=json.dumps(result, ensure_ascii=False, default=str)[:2000]) + + updates: dict[str, Any] = {"nodes": {ctx.node.id: result}} + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: result} + return updates + + +async def run_code(state: GraphState, ctx: NodeContext) -> dict[str, Any]: + """沙箱代码节点。代码本身支持模板插值，可以把上游结果直接嵌进去。""" + code = ctx.render_str(ctx.cfg("code", ""), state) + if not code.strip(): + raise NodeError(ctx.node.id, "代码节点是空的") + + language = ctx.cfg("language", "python") + limits = SandboxLimits( + timeout=int(ctx.cfg("timeout", 30) or 30), + memory_mb=int(ctx.cfg("memory_mb", 512) or 512), + network=bool(ctx.cfg("network", False)), + ) + + if ctx.cfg("approval", "never") == "always": + ctx.emit(EventType.HUMAN_REQUESTED, mode="approve", title="是否执行这段代码？", + code=code[:4000], language=language) + decision = interrupt( + {"kind": "code_approval", "node_id": ctx.node.id, "code": code, + "language": language, "title": "是否执行这段代码？"} + ) + approved = decision if isinstance(decision, bool) else bool( + (decision or {}).get("approved") if isinstance(decision, dict) else decision + ) + if isinstance(decision, dict) and decision.get("code"): + code = decision["code"] # 允许人工改完再跑 + ctx.emit(EventType.HUMAN_RESOLVED, approved=approved) + if not approved: + raise NodeError(ctx.node.id, "用户拒绝执行代码") + + ctx.emit(EventType.SANDBOX_START, language=language, limits=limits.model_dump()) + result = await sandbox_manager.run( + code, + language=language, + limits=limits, + session_id=ctx.run.thread_id, + files=ctx.render(ctx.cfg("files", {}) or {}, state), + ) + ctx.emit( + EventType.SANDBOX_END, + ok=result.ok, + exit_code=result.exit_code, + duration_ms=result.duration_ms, + backend=result.backend, + stdout=result.stdout[:4000], + stderr=result.stderr[:2000], + ) + + if not result.ok and ctx.cfg("fail_fast", True): + raise NodeError( + ctx.node.id, + f"代码执行失败（exit={result.exit_code}）：{result.error or result.stderr[:500]}", + ) + + payload = result.model_dump() + # 如果 stdout 是 JSON，顺手解析出来，下游就能直接用字段而不是再写解析 + parsed: Any = None + text = result.stdout.strip() + if text.startswith(("{", "[")): + try: + parsed = json.loads(text) + except json.JSONDecodeError: + parsed = None + payload["data"] = parsed + payload["text"] = result.stdout + + updates: dict[str, Any] = {"nodes": {ctx.node.id: payload}} + var_name = ctx.cfg("assign_to", "") + if var_name: + updates["vars"] = {var_name: parsed if parsed is not None else result.stdout} + return updates diff --git a/backend/app/engine/runner.py b/backend/app/engine/runner.py new file mode 100644 index 00000000..e849d992 --- /dev/null +++ b/backend/app/engine/runner.py @@ -0,0 +1,371 @@ +from __future__ import annotations + +import asyncio +import contextlib +import logging +import time +from datetime import datetime, timezone +from typing import Any + +from langgraph.checkpoint.sqlite.aio import AsyncSqliteSaver +from langgraph.types import Command +from sqlalchemy import select, update + +from app.core.bus import bus +from app.core.config import settings +from app.core.events import EventType, RunEventModel +from app.db.base import SessionLocal +from app.db.models import Approval, Run, RunEvent, Workflow +from app.engine.compiler import compile_graph, initial_state +from app.engine.context import RunContext +from app.engine.schema import GraphSpec, validate_graph + +logger = logging.getLogger(__name__) + +# token 事件量太大，只做实时推送不落库；回放时用节点的输出摘要重建时间线 +_EPHEMERAL = {EventType.LLM_TOKEN} + + +class RunManager: + """负责一次运行的完整生命周期：启动、串流、中断、恢复、取消。""" + + def __init__(self) -> None: + self._tasks: dict[str, asyncio.Task[None]] = {} + self._checkpointer: AsyncSqliteSaver | None = None + self._cm: Any = None + self._semaphore = asyncio.Semaphore(settings.max_concurrent_runs) + + # ---------------- 生命周期 ---------------- + + async def setup(self) -> None: + settings.ensure_dirs() + self._cm = AsyncSqliteSaver.from_conn_string(str(settings.checkpoint_path)) + self._checkpointer = await self._cm.__aenter__() + # 上次进程被强杀时留下的 running 记录，标成中断态而不是永远转圈 + async with SessionLocal() as session: + await session.execute( + update(Run) + .where(Run.status.in_(["running", "queued"])) + .values(status="interrupted", error="服务重启，运行已挂起，可从断点恢复") + ) + await session.commit() + + async def shutdown(self) -> None: + for task in list(self._tasks.values()): + task.cancel() + await asyncio.gather(*self._tasks.values(), return_exceptions=True) + if self._cm is not None: + with contextlib.suppress(Exception): + await self._cm.__aexit__(None, None, None) + + @property + def checkpointer(self) -> AsyncSqliteSaver: + if self._checkpointer is None: + raise RuntimeError("RunManager 还没初始化") + return self._checkpointer + + # ---------------- 事件 ---------------- + + async def _emit( + self, + run_id: str, + event_type: EventType | str, + *, + node_id: str | None = None, + data: dict[str, Any] | None = None, + persist: bool = True, + ) -> None: + event = RunEventModel( + run_id=run_id, + seq=bus.next_seq(run_id), + type=EventType(str(event_type)), + node_id=node_id, + data=data or {}, + ) + await bus.publish(event) + if persist and event.type not in _EPHEMERAL: + async with SessionLocal() as session: + session.add( + RunEvent( + run_id=run_id, + seq=event.seq, + type=str(event.type), + node_id=event.node_id, + ts=event.ts, + data=event.data, + ) + ) + await session.execute( + update(Run).where(Run.id == run_id).values(last_seq=event.seq) + ) + await session.commit() + + # ---------------- 启动 ---------------- + + async def start( + self, + *, + graph: dict[str, Any], + input_payload: dict[str, Any], + workflow_id: str | None = None, + workflow_name: str = "", + memory_scope: str = "default", + collection: str = "default", + ) -> Run: + spec = GraphSpec.model_validate(graph) + report = validate_graph(spec) + if not report.ok: + raise ValueError( + "图校验未通过：" + "；".join(i.message for i in report.issues if i.level == "error") + ) + + async with SessionLocal() as session: + run = Run( + workflow_id=workflow_id, + workflow_name=workflow_name, + status="queued", + graph=graph, + input=input_payload, + ) + run.thread_id = run.id # 一个 run 一条 checkpoint 线程 + session.add(run) + await session.commit() + await session.refresh(run) + + self._tasks[run.id] = asyncio.create_task( + self._drive( + run.id, + spec, + initial_state(input_payload), + workflow_id=workflow_id, + memory_scope=memory_scope, + collection=collection, + ) + ) + return run + + async def resume(self, run_id: str, response: Any) -> Run: + """从人工介入的断点继续。""" + async with SessionLocal() as session: + run = await session.get(Run, run_id) + if not run: + raise KeyError(f"找不到运行 {run_id}") + if run.status not in ("interrupted",): + raise ValueError(f"当前状态是 {run.status}，无法恢复") + spec = GraphSpec.model_validate(run.graph) + bus.set_seq(run_id, run.last_seq) + run.status = "running" + run.error = None + + # 把这次恢复对应的待审批记录结掉，前端的待办列表才不会一直挂着 + pending = ( + await session.execute( + select(Approval).where( + Approval.run_id == run_id, Approval.status == "pending" + ) + ) + ).scalars() + for approval in pending: + approval.status = "resolved" + approval.response = _safe(response) if isinstance(response, dict) else {"value": _safe(response)} + approval.resolved_at = datetime.now(timezone.utc) + + await session.commit() + await session.refresh(run) + + await self._emit(run_id, EventType.RUN_RESUMED, data={"response": _safe(response)}) + self._tasks[run_id] = asyncio.create_task( + self._drive(run_id, spec, Command(resume=response), workflow_id=run.workflow_id) + ) + return run + + async def cancel(self, run_id: str) -> bool: + task = self._tasks.get(run_id) + if task and not task.done(): + task.cancel() + return True + return False + + def is_active(self, run_id: str) -> bool: + task = self._tasks.get(run_id) + return bool(task and not task.done()) + + # ---------------- 执行 ---------------- + + async def _drive( + self, + run_id: str, + spec: GraphSpec, + payload: Any, + *, + workflow_id: str | None = None, + memory_scope: str = "default", + collection: str = "default", + ) -> None: + started = time.perf_counter() + status = "succeeded" + error: str | None = None + final_state: dict[str, Any] = {} + + async with self._semaphore: + try: + async with SessionLocal() as session: + await session.execute( + update(Run) + .where(Run.id == run_id) + .values(status="running", started_at=datetime.now(timezone.utc)) + ) + await session.commit() + await self._emit( + run_id, + EventType.RUN_STARTED, + data={"nodes": len(spec.nodes), "resumed": isinstance(payload, Command)}, + ) + + run_ctx = RunContext( + run_id=run_id, + thread_id=run_id, + spec=spec, + workflow_id=workflow_id, + memory_scope=memory_scope, + collection=collection, + ) + app = compile_graph(spec, run_ctx).compile(checkpointer=self.checkpointer) + config = { + "configurable": {"thread_id": run_id}, + "recursion_limit": settings.max_graph_steps, + } + + interrupted = False + async for mode, chunk in app.astream( + payload, config=config, stream_mode=["custom", "updates"] + ): + if mode == "custom": + await self._handle_custom(run_id, chunk) + elif mode == "updates": + if await self._handle_updates(run_id, chunk): + interrupted = True + + snapshot = await app.aget_state(config) + final_state = dict(snapshot.values or {}) + if interrupted or snapshot.interrupts: + status = "interrupted" + elif snapshot.next: + status = "interrupted" + + except asyncio.CancelledError: + status = "cancelled" + error = "用户取消" + await self._emit(run_id, EventType.RUN_CANCELLED, data={}) + raise + except Exception as e: # noqa: BLE001 + status = "failed" + error = f"{type(e).__name__}: {e}" + logger.exception("run %s 失败", run_id) + await self._emit(run_id, EventType.RUN_FAILED, data={"error": error}) + finally: + elapsed = int((time.perf_counter() - started) * 1000) + await self._finalize(run_id, status, error, final_state, elapsed) + self._tasks.pop(run_id, None) + + async def _handle_custom(self, run_id: str, chunk: Any) -> None: + """节点通过 get_stream_writer 发来的事件。""" + if not isinstance(chunk, dict) or not chunk.get("__agentlab__"): + return + await self._emit( + run_id, + chunk.get("type", EventType.LOG), + node_id=chunk.get("node_id"), + data=chunk.get("data") or {}, + ) + + async def _handle_updates(self, run_id: str, chunk: Any) -> bool: + """LangGraph 的状态更新。这里只关心中断信号 —— 节点级事件已经由 custom 流覆盖。""" + if not isinstance(chunk, dict): + return False + interrupts = chunk.get("__interrupt__") + if not interrupts: + return False + + for item in interrupts: + value = getattr(item, "value", item) + interrupt_id = getattr(item, "id", None) + payload = value if isinstance(value, dict) else {"value": value} + async with SessionLocal() as session: + existing = ( + await session.execute( + select(Approval).where( + Approval.run_id == run_id, + Approval.interrupt_id == interrupt_id, + Approval.status == "pending", + ) + ) + ).scalar_one_or_none() + if existing is None: + session.add( + Approval( + run_id=run_id, + node_id=str(payload.get("node_id") or ""), + interrupt_id=interrupt_id, + mode=str(payload.get("mode") or "approve"), + title=str(payload.get("title") or "需要你确认"), + payload=payload, + schema_=payload.get("schema") or {}, + ) + ) + await session.commit() + await self._emit( + run_id, + EventType.RUN_INTERRUPTED, + node_id=payload.get("node_id"), + data={"interrupt_id": interrupt_id, "payload": _safe(payload)}, + ) + return True + + async def _finalize( + self, + run_id: str, + status: str, + error: str | None, + state: dict[str, Any], + elapsed_ms: int, + ) -> None: + usage = dict(state.get("usage") or {}) + usage["duration_ms"] = elapsed_ms + output = dict(state.get("output") or {}) + + async with SessionLocal() as session: + run = await session.get(Run, run_id) + if run: + run.status = status + run.error = error + run.output = output + run.usage = usage + if status != "interrupted": + run.finished_at = datetime.now(timezone.utc) + await session.commit() + + if status == "succeeded": + await self._emit( + run_id, + EventType.RUN_FINISHED, + data={"output": _safe(output), "usage": usage, "duration_ms": elapsed_ms}, + ) + if status in ("succeeded", "failed", "cancelled"): + await bus.close(run_id) + + +def _safe(value: Any, limit: int = 4000) -> Any: + """把任意对象裁剪成能安全放进事件负载的形状。""" + if isinstance(value, str): + return value[:limit] + if isinstance(value, dict): + return {k: _safe(v, limit // 2) for k, v in list(value.items())[:50]} + if isinstance(value, list): + return [_safe(v, limit // 2) for v in value[:50]] + if isinstance(value, (int, float, bool)) or value is None: + return value + return str(value)[:limit] + + +run_manager = RunManager() diff --git a/backend/app/engine/schema.py b/backend/app/engine/schema.py new file mode 100644 index 00000000..d26547c2 --- /dev/null +++ b/backend/app/engine/schema.py @@ -0,0 +1,217 @@ +from __future__ import annotations + +from enum import StrEnum +from typing import Any, Literal + +from pydantic import BaseModel, Field, field_validator, model_validator + + +class NodeType(StrEnum): + """画布上可用的节点类型。新增类型 = 加一个枚举值 + 一个执行器 + 一个前端渲染。""" + + INPUT = "input" + OUTPUT = "output" + + LLM = "llm" # 单次模型调用 + AGENT = "agent" # 带工具循环的 ReAct agent + SUPERVISOR = "supervisor" # 多 agent 调度 + + TOOL = "tool" # 直接调用一个工具 + CODE = "code" # 沙箱里跑代码 + + BRANCH = "branch" # 条件分支 + LOOP = "loop" # 循环 / 迭代 + SUBGRAPH = "subgraph" # 嵌套另一个工作流 + + MEMORY = "memory" # 长期记忆读写 + RETRIEVE = "retrieve" # 知识库检索 + TRANSFORM = "transform" # 数据整形 + + HUMAN = "human" # 人工介入 + VALIDATE = "validate" # 结构化校验 + 自动重试 + + +class Position(BaseModel): + x: float = 0 + y: float = 0 + + +class NodeData(BaseModel): + """节点上的可编辑内容。config 保持松散 dict，各执行器自己解析成强类型模型， + 这样前端加字段不会让整张图反序列化失败。""" + + label: str = "" + description: str = "" + config: dict[str, Any] = Field(default_factory=dict) + + +class GraphNode(BaseModel): + id: str + type: NodeType + position: Position = Field(default_factory=Position) + data: NodeData = Field(default_factory=NodeData) + + @field_validator("id") + @classmethod + def _valid_id(cls, v: str) -> str: + if not v or not v.replace("_", "").replace("-", "").isalnum(): + raise ValueError(f"节点 id 只能包含字母数字、下划线和连字符：{v!r}") + # LangGraph 保留了这两个名字 + if v in ("__start__", "__end__"): + raise ValueError(f"{v!r} 是保留节点名") + return v + + @property + def config(self) -> dict[str, Any]: + return self.data.config + + @property + def title(self) -> str: + return self.data.label or self.id + + +class GraphEdge(BaseModel): + id: str = "" + source: str + target: str + # 分支节点用 sourceHandle 区分走哪条路："true"/"false" 或自定义 case key + sourceHandle: str | None = None + targetHandle: str | None = None + label: str = "" + + @model_validator(mode="after") + def _fill_id(self) -> "GraphEdge": + if not self.id: + self.id = f"{self.source}:{self.sourceHandle or ''}->{self.target}" + return self + + @property + def branch_key(self) -> str | None: + return self.sourceHandle if self.sourceHandle not in (None, "out", "source") else None + + +class GraphSpec(BaseModel): + """一整张可执行的图。前端 React Flow 的画布状态直接序列化成它。""" + + nodes: list[GraphNode] = Field(default_factory=list) + edges: list[GraphEdge] = Field(default_factory=list) + viewport: dict[str, Any] = Field(default_factory=dict) + # 图级别的默认值，节点没配 provider/model 时回退到这里 + defaults: dict[str, Any] = Field(default_factory=dict) + + def node_map(self) -> dict[str, GraphNode]: + return {n.id: n for n in self.nodes} + + def outgoing(self, node_id: str) -> list[GraphEdge]: + return [e for e in self.edges if e.source == node_id] + + def incoming(self, node_id: str) -> list[GraphEdge]: + return [e for e in self.edges if e.target == node_id] + + def entry_nodes(self) -> list[GraphNode]: + """入口 = input 节点；没有 input 节点时退化为所有无入边的节点。""" + inputs = [n for n in self.nodes if n.type == NodeType.INPUT] + if inputs: + return inputs + return [n for n in self.nodes if not self.incoming(n.id)] + + def terminal_nodes(self) -> list[GraphNode]: + outputs = [n for n in self.nodes if n.type == NodeType.OUTPUT] + if outputs: + return outputs + return [n for n in self.nodes if not self.outgoing(n.id)] + + +# -------------------------------------------------------------------------- +# 校验 +# -------------------------------------------------------------------------- + + +class ValidationIssue(BaseModel): + level: Literal["error", "warning"] = "error" + node_id: str | None = None + edge_id: str | None = None + message: str + + +class ValidationResult(BaseModel): + ok: bool = True + issues: list[ValidationIssue] = Field(default_factory=list) + + def add(self, message: str, *, level: str = "error", node_id: str | None = None, + edge_id: str | None = None) -> None: + self.issues.append( + ValidationIssue(level=level, message=message, node_id=node_id, edge_id=edge_id) # type: ignore[arg-type] + ) + if level == "error": + self.ok = False + + +def validate_graph(spec: GraphSpec) -> ValidationResult: + """编译前的静态检查。错误会挡住运行，警告只在画布上提示。""" + result = ValidationResult() + nodes = spec.node_map() + + if not spec.nodes: + result.add("图是空的，先拖一个节点进来") + return result + + # 引用完整性 + for edge in spec.edges: + if edge.source not in nodes: + result.add(f"边指向了不存在的源节点 {edge.source!r}", edge_id=edge.id) + if edge.target not in nodes: + result.add(f"边指向了不存在的目标节点 {edge.target!r}", edge_id=edge.id) + + if not spec.entry_nodes(): + result.add("找不到入口：每个节点都有入边，图里存在环且没有起点") + + # 节点级必填项 + for node in spec.nodes: + cfg = node.config + if node.type == NodeType.BRANCH: + cases = cfg.get("cases") or [] + if not cases: + result.add("分支节点至少要配一个条件分支", node_id=node.id) + handles = {e.sourceHandle for e in spec.outgoing(node.id)} + for case in cases: + key = case.get("key") + if key and key not in handles: + result.add( + f"分支 {key!r} 没有连出去的边", level="warning", node_id=node.id + ) + if "default" not in handles: + result.add( + "建议给分支节点连一条 default 边，兜住所有条件都不满足的情况", + level="warning", + node_id=node.id, + ) + elif node.type == NodeType.TOOL: + if not cfg.get("tool"): + result.add("工具节点还没选工具", node_id=node.id) + elif node.type == NodeType.SUBGRAPH: + if not cfg.get("workflow_id"): + result.add("子图节点还没选要嵌套的工作流", node_id=node.id) + elif node.type == NodeType.VALIDATE: + if not cfg.get("schema"): + result.add("校验节点需要一个 JSON Schema", node_id=node.id) + elif node.type == NodeType.LOOP: + if not spec.outgoing(node.id): + result.add("循环节点没有循环体", node_id=node.id) + + if node.type in (NodeType.LLM, NodeType.AGENT, NodeType.SUPERVISOR): + if not (cfg.get("model") or spec.defaults.get("model")): + result.add( + "没有指定模型，将回退到默认 provider", + level="warning", + node_id=node.id, + ) + + # 孤儿节点 + for node in spec.nodes: + if node.type in (NodeType.INPUT, NodeType.OUTPUT): + continue + if not spec.incoming(node.id) and not spec.outgoing(node.id): + result.add("节点没有任何连线，不会被执行", level="warning", node_id=node.id) + + return result diff --git a/backend/app/engine/state.py b/backend/app/engine/state.py new file mode 100644 index 00000000..f3110de2 --- /dev/null +++ b/backend/app/engine/state.py @@ -0,0 +1,103 @@ +from __future__ import annotations + +import operator +from typing import Annotated, Any, TypedDict + +from langchain_core.messages import AnyMessage +from langgraph.graph import add_messages + + +def merge_dict(left: dict[str, Any] | None, right: dict[str, Any] | None) -> dict[str, Any]: + """浅合并。并行分支各自写不同的 key 时不会互相覆盖。""" + out = dict(left or {}) + out.update(right or {}) + return out + + +def merge_usage(left: dict[str, Any] | None, right: dict[str, Any] | None) -> dict[str, Any]: + """累加 token / 成本，其余字段取新值。""" + out = dict(left or {}) + for key, value in (right or {}).items(): + if isinstance(value, (int, float)) and isinstance(out.get(key), (int, float)): + out[key] = out[key] + value + else: + out[key] = value + return out + + +class GraphState(TypedDict, total=False): + """所有节点共享的状态。 + + - messages：对话历史，agent 节点之间靠它传递上下文 + - nodes：node_id -> 该节点的结构化输出，模板里用 {{ nodes.xxx.text }} 引用 + - vars：用户显式写入的变量池 + - output：output 节点收集的最终成果 + """ + + messages: Annotated[list[AnyMessage], add_messages] + input: dict[str, Any] + vars: Annotated[dict[str, Any], merge_dict] + nodes: Annotated[dict[str, Any], merge_dict] + output: Annotated[dict[str, Any], merge_dict] + usage: Annotated[dict[str, Any], merge_usage] + trail: Annotated[list[dict[str, Any]], operator.add] + # 循环计数器：node_id -> 已迭代次数，防止无限循环 + loops: Annotated[dict[str, Any], merge_dict] + + +def template_context(state: GraphState) -> dict[str, Any]: + """把状态摊平成模板/表达式可以引用的命名空间。""" + return { + "input": state.get("input") or {}, + "nodes": state.get("nodes") or {}, + "vars": state.get("vars") or {}, + "output": state.get("output") or {}, + "loops": state.get("loops") or {}, + "usage": state.get("usage") or {}, + "messages": [ + {"role": getattr(m, "type", "?"), "content": _text_of(m)} + for m in (state.get("messages") or []) + ], + "last_message": _text_of((state.get("messages") or [None])[-1]), + } + + +def _text_of(message: Any) -> str: + """把 LangChain 的多模态 content 压成纯文本。""" + if message is None: + return "" + content = getattr(message, "content", message) + if isinstance(content, str): + return content + if isinstance(content, list): + parts: list[str] = [] + for block in content: + if isinstance(block, str): + parts.append(block) + elif isinstance(block, dict): + if block.get("type") == "text": + parts.append(block.get("text", "")) + elif "text" in block: + parts.append(str(block["text"])) + return "".join(parts) + return str(content) + + +def message_text(message: Any) -> str: + return _text_of(message) + + +def thinking_text(message: Any) -> str: + """抽出思考块的内容。 + + 开了 adaptive thinking 的模型（Claude 4.6+）返回的 content 里混着 thinking 块， + 正文提取必须跳过它们，但它本身对"看清楚模型怎么想的"很有价值，所以单独取出来。 + """ + content = getattr(message, "content", message) + if not isinstance(content, list): + return "" + parts: list[str] = [] + for block in content: + if isinstance(block, dict) and block.get("type") == "thinking": + parts.append(str(block.get("thinking") or "")) + return "".join(parts) diff --git a/backend/app/main.py b/backend/app/main.py new file mode 100644 index 00000000..730268b8 --- /dev/null +++ b/backend/app/main.py @@ -0,0 +1,81 @@ +from __future__ import annotations + +import logging +from contextlib import asynccontextmanager +from typing import AsyncIterator + +from fastapi import FastAPI, Request +from fastapi.middleware.cors import CORSMiddleware +from fastapi.responses import JSONResponse + +from app.api import copilot, knowledge, runs, sandbox, settings as settings_api, tools, workflows +from app.core.config import settings +from app.db.base import init_db +from app.engine.runner import run_manager +from app.seed import seed_defaults + +logging.basicConfig( + level=logging.INFO, + format="%(asctime)s %(levelname)-7s %(name)s | %(message)s", + datefmt="%H:%M:%S", +) +logger = logging.getLogger("agentlab") + + +@asynccontextmanager +async def lifespan(_: FastAPI) -> AsyncIterator[None]: + settings.ensure_dirs() + await init_db() + await seed_defaults() + await run_manager.setup() + logger.info("AgentLab 就绪 · 数据目录 %s", settings.data_dir) + try: + yield + finally: + await run_manager.shutdown() + from app.sandbox.manager import sandbox_manager + + await sandbox_manager.close() + logger.info("已停止") + + +app = FastAPI( + title="AgentLab", + description="可视化 Agent 编排实验台", + version="0.1.0", + lifespan=lifespan, +) + +app.add_middleware( + CORSMiddleware, + allow_origins=settings.cors_origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +for router in ( + workflows.router, + runs.router, + runs.approvals_router, + settings_api.router, + tools.router, + tools.custom_router, + tools.mcp_router, + knowledge.memory_router, + knowledge.kb_router, + knowledge.skills_router, + sandbox.router, + copilot.router, +): + app.include_router(router) + + +@app.exception_handler(ValueError) +async def value_error_handler(_: Request, exc: ValueError) -> JSONResponse: + return JSONResponse(status_code=400, content={"detail": str(exc)}) + + +@app.get("/api/health") +async def health() -> dict[str, str]: + return {"status": "ok", "service": "agentlab"} diff --git a/backend/app/memory/__init__.py b/backend/app/memory/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/memory/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/memory/embeddings.py b/backend/app/memory/embeddings.py new file mode 100644 index 00000000..73d3e34a --- /dev/null +++ b/backend/app/memory/embeddings.py @@ -0,0 +1,192 @@ +from __future__ import annotations + +import hashlib +import math +import os +import re +from typing import Sequence + +import numpy as np + +_DIM = 512 +_TOKEN_RE = re.compile(r"[a-zA-Z0-9_]+|[一-鿿]") + + +def _tokens(text: str) -> list[str]: + """英文按词、中文按字切，再补 2-gram。 + + 中文没有空格分词，只用单字召回太糊；加上相邻二字组合能显著提升短语匹配。 + """ + raw = _TOKEN_RE.findall(text.lower()) + grams = [a + b for a, b in zip(raw, raw[1:]) if len(a) == 1 and len(b) == 1] + return raw + grams + + +def _hash_index(token: str) -> int: + return int.from_bytes(hashlib.blake2b(token.encode(), digest_size=4).digest(), "big") % _DIM + + +class LocalEmbedder: + """特征哈希 + TF 的本地向量器。 + + 不联网、不下模型、零配置就能用。语义泛化能力当然不如真 embedding 模型， + 所以检索侧用它和 BM25 做混合，把关键词精确匹配的能力补回来。 + """ + + dim = _DIM + name = "local-hashing" + + def embed(self, text: str) -> np.ndarray: + vec = np.zeros(_DIM, dtype=np.float32) + toks = _tokens(text) + if not toks: + return vec + for tok in toks: + vec[_hash_index(tok)] += 1.0 + # 次线性缩放，压掉高频词的统治力 + vec = np.sign(vec) * np.log1p(np.abs(vec)) + norm = np.linalg.norm(vec) + return vec / norm if norm > 0 else vec + + def embed_many(self, texts: Sequence[str]) -> np.ndarray: + return np.vstack([self.embed(t) for t in texts]) if texts else np.zeros((0, _DIM), np.float32) + + +class OpenAIEmbedder: + """配了 key 就用真 embedding 模型，检索质量明显更好。""" + + name = "openai" + + def __init__(self, model: str = "text-embedding-3-small", api_key: str | None = None, + base_url: str | None = None) -> None: + from langchain_openai import OpenAIEmbeddings + + self.dim = 1536 + self._impl = OpenAIEmbeddings( + model=model, + api_key=api_key or os.environ.get("OPENAI_API_KEY"), + base_url=base_url, + ) + + async def aembed(self, text: str) -> np.ndarray: + vec = await self._impl.aembed_query(text) + return np.array(vec, dtype=np.float32) + + async def aembed_many(self, texts: Sequence[str]) -> np.ndarray: + vecs = await self._impl.aembed_documents(list(texts)) + return np.array(vecs, dtype=np.float32) + + +_local = LocalEmbedder() + + +def get_embedder() -> LocalEmbedder | OpenAIEmbedder: + """默认走本地；显式打开 AGENTLAB_USE_OPENAI_EMBEDDINGS 且有 key 时才用远端。""" + if os.environ.get("AGENTLAB_USE_OPENAI_EMBEDDINGS") and os.environ.get("OPENAI_API_KEY"): + try: + return OpenAIEmbedder() + except Exception: # noqa: BLE001 + return _local + return _local + + +async def embed_text(text: str) -> np.ndarray: + emb = get_embedder() + if isinstance(emb, OpenAIEmbedder): + return await emb.aembed(text) + return emb.embed(text) + + +async def embed_texts(texts: Sequence[str]) -> np.ndarray: + emb = get_embedder() + if isinstance(emb, OpenAIEmbedder): + return await emb.aembed_many(texts) + return emb.embed_many(texts) + + +def to_blob(vec: np.ndarray) -> bytes: + return vec.astype(np.float32).tobytes() + + +def from_blob(blob: bytes | None, dim: int | None = None) -> np.ndarray | None: + if not blob: + return None + vec = np.frombuffer(blob, dtype=np.float32) + if dim and vec.shape[0] != dim: + return None + return vec + + +def cosine(a: np.ndarray, b: np.ndarray) -> float: + na, nb = np.linalg.norm(a), np.linalg.norm(b) + if na == 0 or nb == 0: + return 0.0 + return float(np.dot(a, b) / (na * nb)) + + +# -------------------------------------------------------------------------- +# BM25：关键词侧的一半 +# -------------------------------------------------------------------------- + + +class BM25: + def __init__(self, corpus: Sequence[str], k1: float = 1.5, b: float = 0.75) -> None: + self.k1, self.b = k1, b + self.docs = [_tokens(d) for d in corpus] + self.n = len(self.docs) + self.avg_len = sum(len(d) for d in self.docs) / self.n if self.n else 0.0 + self.df: dict[str, int] = {} + for doc in self.docs: + for tok in set(doc): + self.df[tok] = self.df.get(tok, 0) + 1 + + def score(self, query: str) -> list[float]: + q_tokens = _tokens(query) + scores = [0.0] * self.n + for tok in q_tokens: + df = self.df.get(tok, 0) + if df == 0: + continue + idf = math.log(1 + (self.n - df + 0.5) / (df + 0.5)) + for i, doc in enumerate(self.docs): + tf = doc.count(tok) + if tf == 0: + continue + denom = tf + self.k1 * (1 - self.b + self.b * len(doc) / (self.avg_len or 1)) + scores[i] += idf * (tf * (self.k1 + 1)) / denom + return scores + + +def hybrid_rank( + query: str, + texts: Sequence[str], + vectors: Sequence[np.ndarray | None], + query_vec: np.ndarray, + *, + alpha: float = 0.5, +) -> list[tuple[int, float, dict[str, float]]]: + """向量相似度和 BM25 各归一化后加权合并。 + + 纯向量会漏掉精确的专有名词，纯 BM25 又抓不住同义表达， + 混合排序在本地小知识库上稳定优于任何一个单独使用。 + """ + if not texts: + return [] + bm = BM25(texts) + kw_scores = bm.score(query) + vec_scores = [cosine(query_vec, v) if v is not None else 0.0 for v in vectors] + + def _norm(xs: list[float]) -> list[float]: + lo, hi = min(xs), max(xs) + if hi - lo < 1e-9: + return [0.0 for _ in xs] + return [(x - lo) / (hi - lo) for x in xs] + + kw_n, vec_n = _norm(kw_scores), _norm(vec_scores) + ranked = [ + (i, alpha * vec_n[i] + (1 - alpha) * kw_n[i], + {"vector": vec_scores[i], "keyword": kw_scores[i]}) + for i in range(len(texts)) + ] + ranked.sort(key=lambda t: t[1], reverse=True) + return ranked diff --git a/backend/app/memory/kb.py b/backend/app/memory/kb.py new file mode 100644 index 00000000..13c61243 --- /dev/null +++ b/backend/app/memory/kb.py @@ -0,0 +1,169 @@ +from __future__ import annotations + +import re +from typing import Any + +from sqlalchemy import func, select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.models import Chunk, Document +from app.memory.embeddings import embed_text, embed_texts, from_blob, hybrid_rank, to_blob + +_TARGET = 800 +_OVERLAP = 120 + + +def chunk_text(text: str, target: int = _TARGET, overlap: int = _OVERLAP) -> list[str]: + """按语义边界切块。 + + 先按空行分段，段落塞不下再按句子切，实在超长才硬切。 + 这样切出来的块不会把一句话拦腰截断，检索命中后读起来是完整的。 + """ + text = text.replace("\r\n", "\n").strip() + if not text: + return [] + + paragraphs = [p.strip() for p in re.split(r"\n\s*\n", text) if p.strip()] + blocks: list[str] = [] + for para in paragraphs: + if len(para) <= target: + blocks.append(para) + continue + sentences = re.split(r"(?<=[。！？.!?\n])\s*", para) + current = "" + for sentence in sentences: + if not sentence: + continue + if len(current) + len(sentence) <= target: + current += sentence + else: + if current: + blocks.append(current) + while len(sentence) > target: + blocks.append(sentence[:target]) + sentence = sentence[target:] + current = sentence + if current: + blocks.append(current) + + # 把相邻小块合并到接近目标长度，并留一点重叠保住上下文 + merged: list[str] = [] + buf = "" + for block in blocks: + if len(buf) + len(block) + 1 <= target: + buf = f"{buf}\n{block}" if buf else block + else: + if buf: + merged.append(buf) + tail = buf[-overlap:] if overlap else "" + buf = f"{tail}\n{block}" if tail else block + else: + merged.append(block) + buf = "" + if buf: + merged.append(buf) + return [m.strip() for m in merged if m.strip()] + + +async def ingest_document( + session: AsyncSession, + *, + collection: str, + title: str, + content: str, + source: str = "", + mime: str = "text/plain", + meta: dict[str, Any] | None = None, +) -> Document: + doc = Document( + collection=collection, + title=title or source or "未命名文档", + source=source, + mime=mime, + content=content, + meta=meta or {}, + ) + session.add(doc) + await session.flush() + + pieces = chunk_text(content) + if pieces: + vectors = await embed_texts(pieces) + for i, (piece, vec) in enumerate(zip(pieces, vectors)): + session.add( + Chunk( + document_id=doc.id, + collection=collection, + ordinal=i, + content=piece, + embedding=to_blob(vec), + meta={"title": doc.title}, + ) + ) + doc.chunk_count = len(pieces) + await session.commit() + await session.refresh(doc) + return doc + + +async def search( + session: AsyncSession, + *, + collection: str | None, + query: str, + limit: int = 5, + alpha: float = 0.5, +) -> list[dict[str, Any]]: + stmt = select(Chunk) + if collection: + stmt = stmt.where(Chunk.collection == collection) + rows = list((await session.execute(stmt)).scalars()) + if not rows: + return [] + + query_vec = await embed_text(query) + ranked = hybrid_rank( + query, + [r.content for r in rows], + [from_blob(r.embedding) for r in rows], + query_vec, + alpha=alpha, + ) + out: list[dict[str, Any]] = [] + for idx, score, parts in ranked[:limit]: + row = rows[idx] + out.append( + { + "chunk_id": row.id, + "document_id": row.document_id, + "title": (row.meta or {}).get("title", ""), + "ordinal": row.ordinal, + "content": row.content, + "score": round(score, 4), + "signals": {k: round(v, 4) for k, v in parts.items()}, + } + ) + return out + + +async def delete_document(session: AsyncSession, doc_id: str) -> bool: + doc = await session.get(Document, doc_id) + if not doc: + return False + await session.delete(doc) + await session.commit() + return True + + +async def list_collections(session: AsyncSession) -> list[dict[str, Any]]: + rows = await session.execute( + select( + Document.collection, + func.count(Document.id), + func.sum(Document.chunk_count), + ).group_by(Document.collection) + ) + return [ + {"collection": name, "documents": docs, "chunks": chunks or 0} + for name, docs, chunks in rows + ] diff --git a/backend/app/memory/store.py b/backend/app/memory/store.py new file mode 100644 index 00000000..387695f5 --- /dev/null +++ b/backend/app/memory/store.py @@ -0,0 +1,140 @@ +from __future__ import annotations + +from datetime import datetime, timezone +from typing import Any + +from sqlalchemy import delete, func, select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.models import MemoryItem +from app.memory.embeddings import embed_text, from_blob, hybrid_rank, to_blob + + +async def remember( + session: AsyncSession, + *, + scope: str, + content: str, + kind: str = "fact", + meta: dict[str, Any] | None = None, + importance: float = 0.5, + dedupe: bool = True, +) -> MemoryItem: + """写一条长期记忆。 + + dedupe 会先看同 scope 下有没有近乎重复的内容 —— agent 很容易把同一件事 + 反复记下来，不去重的话记忆库几轮就被同义句灌满了。 + """ + content = content.strip() + if dedupe: + existing = await recall(session, scope=scope, query=content, limit=1, min_score=0.0) + if existing and existing[0]["score"] > 0.92: + item = await session.get(MemoryItem, existing[0]["id"]) + if item: + item.importance = max(item.importance, importance) + item.use_count += 1 + await session.commit() + return item + + vec = await embed_text(content) + item = MemoryItem( + scope=scope, + kind=kind, + content=content, + meta=meta or {}, + importance=importance, + embedding=to_blob(vec), + ) + session.add(item) + await session.commit() + await session.refresh(item) + return item + + +async def recall( + session: AsyncSession, + *, + scope: str, + query: str, + limit: int = 5, + kind: str | None = None, + min_score: float = 0.05, +) -> list[dict[str, Any]]: + """按混合相关度取回记忆，并按重要性和使用次数做轻微加权。""" + stmt = select(MemoryItem).where(MemoryItem.scope == scope) + if kind: + stmt = stmt.where(MemoryItem.kind == kind) + rows = list((await session.execute(stmt)).scalars()) + if not rows: + return [] + + query_vec = await embed_text(query) + ranked = hybrid_rank( + query, + [r.content for r in rows], + [from_blob(r.embedding) for r in rows], + query_vec, + ) + + results: list[dict[str, Any]] = [] + now = datetime.now(timezone.utc) + for idx, score, parts in ranked: + row = rows[idx] + # 重要的、被用得多的记忆略微上浮 + boosted = score * (0.8 + 0.4 * row.importance) * (1 + min(row.use_count, 10) * 0.01) + if boosted < min_score: + continue + results.append( + { + "id": row.id, + "content": row.content, + "kind": row.kind, + "meta": row.meta, + "importance": row.importance, + "score": round(boosted, 4), + "signals": {k: round(v, 4) for k, v in parts.items()}, + "created_at": row.created_at.isoformat() if row.created_at else None, + } + ) + if len(results) >= limit: + break + + # 记一下被召回过，作为下次排序的信号 + for r in results: + item = await session.get(MemoryItem, r["id"]) + if item: + item.use_count += 1 + item.last_used_at = now + await session.commit() + return results + + +async def forget(session: AsyncSession, memory_id: str) -> bool: + item = await session.get(MemoryItem, memory_id) + if not item: + return False + await session.delete(item) + await session.commit() + return True + + +async def clear_scope(session: AsyncSession, scope: str) -> int: + result = await session.execute(delete(MemoryItem).where(MemoryItem.scope == scope)) + await session.commit() + return result.rowcount or 0 + + +async def list_memories( + session: AsyncSession, *, scope: str | None = None, limit: int = 200 +) -> list[MemoryItem]: + stmt = select(MemoryItem).order_by(MemoryItem.created_at.desc()).limit(limit) + if scope: + stmt = stmt.where(MemoryItem.scope == scope) + return list((await session.execute(stmt)).scalars()) + + +async def list_scopes(session: AsyncSession) -> list[dict[str, Any]]: + rows = await session.execute( + select(MemoryItem.scope, func.count(MemoryItem.id)).group_by(MemoryItem.scope) + ) + return [{"scope": scope, "count": count} for scope, count in rows] diff --git a/backend/app/providers/__init__.py b/backend/app/providers/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/providers/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/providers/catalog.py b/backend/app/providers/catalog.py new file mode 100644 index 00000000..e5ac99a0 --- /dev/null +++ b/backend/app/providers/catalog.py @@ -0,0 +1,208 @@ +from __future__ import annotations + +from typing import Any + +# -------------------------------------------------------------------------- +# 模型目录 +# +# pricing 单位是「美元 / 百万 token」，用于 run 的成本估算。 +# 新模型的价格会变，这份表只是开箱即用的默认值，用户可以在设置页改。 +# -------------------------------------------------------------------------- + +ANTHROPIC_MODELS: list[dict[str, Any]] = [ + {"id": "claude-opus-5", "label": "Claude Opus 5", "context": 1_000_000, + "pricing": {"input": 5.0, "output": 25.0}, "thinking": "adaptive"}, + {"id": "claude-fable-5-1", "label": "Claude Fable 5.1", "context": 1_000_000, + "pricing": {"input": 10.0, "output": 50.0}, "thinking": "always"}, + {"id": "claude-opus-4-8", "label": "Claude Opus 4.8", "context": 1_000_000, + "pricing": {"input": 5.0, "output": 25.0}, "thinking": "adaptive"}, + {"id": "claude-sonnet-5", "label": "Claude Sonnet 5", "context": 1_000_000, + "pricing": {"input": 2.0, "output": 10.0}, "thinking": "adaptive"}, + {"id": "claude-haiku-4-5", "label": "Claude Haiku 4.5", "context": 200_000, + "pricing": {"input": 1.0, "output": 5.0}, "thinking": "budget"}, +] + +OPENAI_MODELS: list[dict[str, Any]] = [ + {"id": "gpt-5.2", "label": "GPT-5.2", "context": 400_000, + "pricing": {"input": 1.25, "output": 10.0}}, + {"id": "gpt-5.2-mini", "label": "GPT-5.2 mini", "context": 400_000, + "pricing": {"input": 0.25, "output": 2.0}}, + {"id": "o4", "label": "o4", "context": 200_000, + "pricing": {"input": 3.0, "output": 12.0}}, +] + +# 常见的 OpenAI 兼容服务，一键添加用 +COMPATIBLE_PRESETS: list[dict[str, Any]] = [ + { + "key": "deepseek", + "label": "DeepSeek", + "base_url": "https://api.deepseek.com/v1", + "models": [ + {"id": "deepseek-chat", "label": "DeepSeek Chat", "context": 128_000, + "pricing": {"input": 0.27, "output": 1.1}}, + {"id": "deepseek-reasoner", "label": "DeepSeek Reasoner", "context": 128_000, + "pricing": {"input": 0.55, "output": 2.19}}, + ], + }, + { + "key": "moonshot", + "label": "Moonshot / Kimi", + "base_url": "https://api.moonshot.cn/v1", + "models": [ + {"id": "kimi-k2-turbo-preview", "label": "Kimi K2 Turbo", "context": 256_000, + "pricing": {"input": 0.6, "output": 2.5}}, + {"id": "moonshot-v1-128k", "label": "Moonshot v1 128k", "context": 128_000, + "pricing": {"input": 0.8, "output": 3.0}}, + ], + }, + { + "key": "dashscope", + "label": "阿里云百炼 / 通义千问", + "base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1", + "models": [ + {"id": "qwen-max", "label": "Qwen Max", "context": 32_000, + "pricing": {"input": 0.34, "output": 1.37}}, + {"id": "qwen-plus", "label": "Qwen Plus", "context": 131_000, + "pricing": {"input": 0.11, "output": 0.28}}, + ], + }, + { + "key": "zhipu", + "label": "智谱 GLM", + "base_url": "https://open.bigmodel.cn/api/paas/v4", + "models": [ + {"id": "glm-4.6", "label": "GLM-4.6", "context": 200_000, + "pricing": {"input": 0.6, "output": 2.2}}, + ], + }, + { + "key": "siliconflow", + "label": "硅基流动", + "base_url": "https://api.siliconflow.cn/v1", + "models": [ + {"id": "deepseek-ai/DeepSeek-V3", "label": "DeepSeek V3", "context": 128_000, + "pricing": {"input": 0.27, "output": 1.1}}, + ], + }, + { + "key": "openrouter", + "label": "OpenRouter", + "base_url": "https://openrouter.ai/api/v1", + "models": [ + {"id": "anthropic/claude-opus-5", "label": "Claude Opus 5", "context": 1_000_000, + "pricing": {"input": 5.0, "output": 25.0}}, + ], + }, + { + "key": "ollama", + "label": "Ollama（本地）", + "base_url": "http://localhost:11434/v1", + "api_key_optional": True, + "models": [ + {"id": "qwen3:8b", "label": "Qwen3 8B (本地)", "context": 32_000, + "pricing": {"input": 0.0, "output": 0.0}}, + {"id": "llama3.2", "label": "Llama 3.2 (本地)", "context": 128_000, + "pricing": {"input": 0.0, "output": 0.0}}, + ], + }, +] + +MOCK_MODELS: list[dict[str, Any]] = [ + {"id": "mock-fast", "label": "Mock（不花钱，用来试编排）", "context": 128_000, + "pricing": {"input": 0.0, "output": 0.0}}, +] + +PROVIDER_KINDS = [ + { + "kind": "anthropic", + "label": "Anthropic", + "base_url_hint": "留空走官方；填了就是自定义网关（读 ANTHROPIC_BASE_URL）", + "models": ANTHROPIC_MODELS, + "default_model": "claude-opus-5", + }, + { + "kind": "openai", + "label": "OpenAI", + "base_url_hint": "留空走官方", + "models": OPENAI_MODELS, + "default_model": "gpt-5.2", + }, + { + "kind": "openai_compatible", + "label": "OpenAI 兼容（DeepSeek / Kimi / 通义 / Ollama…）", + "base_url_hint": "必填，例如 https://api.deepseek.com/v1", + "models": [], + "presets": COMPATIBLE_PRESETS, + "default_model": "", + }, + { + "kind": "mock", + "label": "Mock（无需 API Key）", + "base_url_hint": "", + "models": MOCK_MODELS, + "default_model": "mock-fast", + }, +] + + +# -------------------------------------------------------------------------- +# 模型能力 +# -------------------------------------------------------------------------- + +# Claude 4.6 及以后的模型移除了采样参数，传 temperature/top_p 会直接 400。 +_NO_SAMPLING_PREFIXES = ( + "claude-fable-5", + "claude-mythos-5", + "claude-opus-5", + "claude-opus-4-8", + "claude-opus-4-7", + "claude-opus-4-6", + "claude-sonnet-5", + "claude-sonnet-4-6", +) + +# 支持 adaptive thinking（而不是老的 budget_tokens）的模型 +_ADAPTIVE_THINKING_PREFIXES = _NO_SAMPLING_PREFIXES + +# 支持 output_config.effort +_EFFORT_PREFIXES = _NO_SAMPLING_PREFIXES + ("claude-opus-4-5",) + + +def _norm(model: str) -> str: + """去掉 openrouter 之类的前缀，只看模型名本身。""" + return (model or "").split("/")[-1].strip() + + +def supports_sampling(model: str) -> bool: + """这个模型还能不能传 temperature / top_p。""" + return not _norm(model).startswith(_NO_SAMPLING_PREFIXES) + + +def supports_adaptive_thinking(model: str) -> bool: + return _norm(model).startswith(_ADAPTIVE_THINKING_PREFIXES) + + +def supports_effort(model: str) -> bool: + return _norm(model).startswith(_EFFORT_PREFIXES) + + +def find_pricing(model: str) -> dict[str, float] | None: + target = _norm(model) + pools = [ANTHROPIC_MODELS, OPENAI_MODELS, MOCK_MODELS] + pools += [p["models"] for p in COMPATIBLE_PRESETS] + for pool in pools: + for entry in pool: + if _norm(entry["id"]) == target: + return entry.get("pricing") + return None + + +def estimate_cost(model: str, input_tokens: int, output_tokens: int) -> float: + """按目录价估算一次调用的花费（美元）。""" + pricing = find_pricing(model) + if not pricing: + return 0.0 + return ( + input_tokens / 1_000_000 * pricing.get("input", 0.0) + + output_tokens / 1_000_000 * pricing.get("output", 0.0) + ) diff --git a/backend/app/providers/factory.py b/backend/app/providers/factory.py new file mode 100644 index 00000000..3e422bbf --- /dev/null +++ b/backend/app/providers/factory.py @@ -0,0 +1,182 @@ +from __future__ import annotations + +import os +from typing import Any + +from langchain_core.language_models.chat_models import BaseChatModel +from pydantic import BaseModel, Field +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.core.crypto import decrypt +from app.db.models import Provider +from app.providers import catalog +from app.providers.mock_model import MockChatModel + + +class ModelSpec(BaseModel): + """节点上对模型的请求。省略的字段会回退到 provider 默认值。""" + + provider: str | None = None # provider 的 name 或 id + model: str | None = None + temperature: float | None = None + max_tokens: int | None = Field(default=None) + thinking: str | None = None # off | adaptive + effort: str | None = None # low | medium | high | xhigh | max + timeout: int | None = None + extra: dict[str, Any] = Field(default_factory=dict) + + +class ProviderNotConfigured(RuntimeError): + pass + + +async def list_providers(session: AsyncSession) -> list[Provider]: + rows = await session.execute(select(Provider).order_by(Provider.created_at)) + return list(rows.scalars()) + + +async def resolve_provider( + session: AsyncSession, ref: str | None, model: str | None = None +) -> Provider | None: + """找出该用哪个 provider。 + + 顺序很关键：显式指定的 ref 最优先；其次按模型名反查 —— 节点上通常只选了 + 模型（比如 mock-fast），没选 provider，这时必须找到真正提供该模型的那一个， + 否则会拿着 A 家的模型名去调 B 家的接口。最后才回退到默认 provider。 + """ + providers = await list_providers(session) + enabled = [p for p in providers if p.enabled] + + if ref: + for p in providers: + if p.name == ref or p.id == ref: + return p + return None + + if model: + for p in enabled: + if any((m or {}).get("id") == model for m in (p.models or [])): + return p + for p in enabled: + if p.default_model == model: + return p + + real = [p for p in enabled if p.kind != "mock"] + return (real or enabled or [None])[0] + + +def _resolve_key(provider: Provider) -> str | None: + """明文 key。没配就退回环境变量 —— 本地实验时最省事。""" + key = decrypt(provider.api_key) + if key: + return key + env_names = { + "anthropic": ("ANTHROPIC_API_KEY", "ANTHROPIC_AUTH_TOKEN"), + "openai": ("OPENAI_API_KEY",), + }.get(provider.kind, ()) + for name in env_names: + if os.environ.get(name): + return os.environ[name] + return None + + +def build_chat_model(provider: Provider, spec: ModelSpec) -> BaseChatModel: + """把一条 provider 记录 + 节点配置变成可调用的 LangChain 模型。 + + 这里最要紧的是按模型能力裁剪参数：Claude 4.6 之后的模型收到 temperature + 会直接 400，所以不能无脑透传节点上的滑块值。 + """ + model = spec.model or provider.default_model or "" + if not model: + raise ProviderNotConfigured(f"provider {provider.name!r} 没有可用的模型") + + kwargs: dict[str, Any] = {"model": model} + if spec.max_tokens: + kwargs["max_tokens"] = spec.max_tokens + if spec.timeout: + kwargs["timeout"] = spec.timeout + + # 采样参数：能力允许才传 + if spec.temperature is not None and catalog.supports_sampling(model): + kwargs["temperature"] = spec.temperature + + if provider.kind == "mock": + return MockChatModel(model_name=model) + + api_key = _resolve_key(provider) + base_url = provider.base_url or None + extra = dict(provider.extra or {}) + + if provider.kind == "anthropic": + from langchain_anthropic import ChatAnthropic + + base_url = base_url or os.environ.get("ANTHROPIC_BASE_URL") or None + if not api_key: + raise ProviderNotConfigured(f"provider {provider.name!r} 缺少 API Key") + + bearer = extra.get("auth_style") == "bearer" + kwargs["api_key"] = "placeholder-replaced-below" if bearer else api_key + if base_url: + kwargs["base_url"] = base_url + if extra.get("headers"): + kwargs["default_headers"] = dict(extra["headers"]) + + # thinking 这类模型会先花 token 思考再输出正文；给太小的 max_tokens + # 会出现"思考完就没额度写答案"，返回一条空正文。 + kwargs.setdefault("max_tokens", 8192) + + model_kwargs: dict[str, Any] = {} + if catalog.supports_adaptive_thinking(model): + mode = spec.thinking or "summarized" + if mode in ("adaptive", "summarized"): + # summarized 会把思考摘要一起返回，画布上就能展示"它在想什么" + kwargs["thinking"] = { + "type": "adaptive", + "display": "summarized" if mode == "summarized" else "omitted", + } + elif mode == "off": + kwargs["thinking"] = {"type": "disabled"} + if spec.effort and catalog.supports_effort(model): + model_kwargs["output_config"] = {"effort": spec.effort} + if model_kwargs: + kwargs["model_kwargs"] = model_kwargs + + chat = ChatAnthropic(**kwargs) + if bearer: + # 网关只认 Authorization: Bearer，且多带一个 x-api-key 就会 401。 + # ChatAnthropic 的 default_headers 是 dict[str,str]，塞不进 SDK 的 Omit 哨兵， + # 所以直接换掉底层 client —— anthropic SDK 原生支持 auth_token， + # 且 api_key=None 时不会发出 x-api-key。 + import anthropic + + client_args = {"base_url": base_url, "api_key": None, "auth_token": api_key} + chat._client = anthropic.Anthropic(**client_args) + chat._async_client = anthropic.AsyncAnthropic(**client_args) + return chat + + # openai 与 openai 兼容走同一个类，区别只在 base_url + from langchain_openai import ChatOpenAI + + if provider.kind == "openai_compatible" and not base_url: + raise ProviderNotConfigured(f"provider {provider.name!r} 需要填 base_url") + # 很多本地服务（Ollama、vLLM）不校验 key，但 SDK 要求非空 + kwargs["api_key"] = api_key or "not-needed" + if base_url: + kwargs["base_url"] = base_url + if extra.get("headers"): + kwargs["default_headers"] = extra["headers"] + return ChatOpenAI(**kwargs) + + +async def get_chat_model(session: AsyncSession, spec: ModelSpec) -> tuple[BaseChatModel, str]: + """返回 (模型, 实际使用的模型 id)。找不到任何 provider 时回退到 mock， + 保证一张图在没配 key 的机器上也能跑完。""" + provider = await resolve_provider(session, spec.provider, spec.model) + if provider is None: + return MockChatModel(model_name="mock-fast"), "mock-fast" + try: + model = build_chat_model(provider, spec) + except ProviderNotConfigured: + raise + return model, (spec.model or provider.default_model or "") diff --git a/backend/app/providers/mock_model.py b/backend/app/providers/mock_model.py new file mode 100644 index 00000000..442db7e8 --- /dev/null +++ b/backend/app/providers/mock_model.py @@ -0,0 +1,203 @@ +from __future__ import annotations + +import asyncio +import hashlib +import json +import random +import re +from typing import Any, AsyncIterator, Iterator, Sequence + +from langchain_core.callbacks import AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun +from langchain_core.language_models.chat_models import BaseChatModel +from langchain_core.messages import AIMessage, AIMessageChunk, BaseMessage, ToolMessage +from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult +from langchain_core.tools import BaseTool +from pydantic import Field + + +def _text_of(message: BaseMessage) -> str: + content = message.content + if isinstance(content, str): + return content + if isinstance(content, list): + return "".join( + b.get("text", "") if isinstance(b, dict) else str(b) for b in content + ) + return str(content) + + +def _fake_value(schema: dict[str, Any], key: str = "") -> Any: + """按 JSON Schema 造一个形状正确的假值，让下游校验节点也能跑通。""" + typ = schema.get("type") + if "enum" in schema: + return schema["enum"][0] + if typ == "integer": + return 42 + if typ == "number": + return 3.14 + if typ == "boolean": + return True + if typ == "array": + item = schema.get("items") or {"type": "string"} + return [_fake_value(item, key)] + if typ == "object" or "properties" in schema: + props = schema.get("properties") or {} + return {k: _fake_value(v, k) for k, v in props.items()} + return f"[mock:{key or 'value'}]" + + +class MockChatModel(BaseChatModel): + """不联网的假模型。 + + 目的不是"能用"，而是让人在没有 API Key 的情况下就能把整条编排链路跑通： + 它会真的产出 tool_call、真的按 schema 返回结构化 JSON、真的流式吐字， + 所以画布上的高亮、工具节点、校验节点全都会被触发。 + """ + + model_name: str = "mock-fast" + latency: float = 0.012 # 每个 chunk 的间隔，模拟真实流式手感 + seed: int | None = None + tools: list[dict[str, Any]] = Field(default_factory=list) + response_format: dict[str, Any] | None = None + + @property + def _llm_type(self) -> str: + return "mock" + + def bind_tools(self, tools: Sequence[Any], **kwargs: Any) -> "MockChatModel": + normalized: list[dict[str, Any]] = [] + for tool in tools: + if isinstance(tool, BaseTool): + normalized.append( + { + "name": tool.name, + "description": tool.description, + "schema": tool.args_schema.model_json_schema() + if getattr(tool, "args_schema", None) + else {}, + } + ) + elif isinstance(tool, dict): + fn = tool.get("function", tool) + normalized.append( + { + "name": fn.get("name", "tool"), + "description": fn.get("description", ""), + "schema": fn.get("parameters") or fn.get("input_schema") or {}, + } + ) + return self.model_copy(update={"tools": normalized}) + + def with_structured_output(self, schema: Any, **kwargs: Any) -> Any: # type: ignore[override] + json_schema = schema + if hasattr(schema, "model_json_schema"): + json_schema = schema.model_json_schema() + bound = self.model_copy(update={"response_format": json_schema}) + if hasattr(schema, "model_validate"): + return bound | (lambda msg: schema.model_validate(json.loads(_text_of(msg)))) + return bound | (lambda msg: json.loads(_text_of(msg))) + + # ---------------- 内部 ---------------- + + def _decide(self, messages: list[BaseMessage]) -> AIMessage: + prompt = "\n".join(_text_of(m) for m in messages[-4:]) + rng = random.Random( + self.seed + if self.seed is not None + else int(hashlib.sha1(prompt.encode()).hexdigest()[:8], 16) + ) + + # 要结构化输出就按 schema 造数据 + if self.response_format: + payload = _fake_value(self.response_format) + return AIMessage(content=json.dumps(payload, ensure_ascii=False, indent=2)) + + # 绑了工具、而且这一轮还没有工具返回过 —— 那就调一次工具， + # 让 agent 循环和画布上的工具节点真的被走到 + already_called = any(isinstance(m, ToolMessage) for m in messages) + if self.tools and not already_called: + tool = rng.choice(self.tools) + args = _fake_value(tool.get("schema") or {"type": "object"}) + if not isinstance(args, dict): + args = {} + return AIMessage( + content=f"我需要先调用 `{tool['name']}` 来获取信息。", + tool_calls=[ + {"name": tool["name"], "args": args, "id": f"call_{rng.randrange(1 << 30):08x}"} + ], + ) + + last_user = next( + (_text_of(m) for m in reversed(messages) if m.type == "human"), "" + ) + topic = re.sub(r"\s+", " ", last_user).strip()[:60] or "这个问题" + body = ( + f"（Mock 回复）关于「{topic}」，我的结论分三点：\n\n" + f"1. 这是一个由 mock provider 生成的假响应，用来验证编排链路。\n" + f"2. 当前节点接收到 {len(messages)} 条消息，工具 {len(self.tools)} 个。\n" + f"3. 在设置页配一个真实 provider，把节点的模型换掉就能拿到真实结果。" + ) + return AIMessage(content=body) + + def _generate( + self, + messages: list[BaseMessage], + stop: list[str] | None = None, + run_manager: CallbackManagerForLLMRun | None = None, + **kwargs: Any, + ) -> ChatResult: + message = self._decide(messages) + message.usage_metadata = { + "input_tokens": sum(len(_text_of(m)) // 4 for m in messages), + "output_tokens": len(_text_of(message)) // 4, + "total_tokens": 0, + } + return ChatResult(generations=[ChatGeneration(message=message)]) + + async def _agenerate( + self, + messages: list[BaseMessage], + stop: list[str] | None = None, + run_manager: AsyncCallbackManagerForLLMRun | None = None, + **kwargs: Any, + ) -> ChatResult: + await asyncio.sleep(self.latency) + return self._generate(messages, stop, **kwargs) + + def _stream( + self, + messages: list[BaseMessage], + stop: list[str] | None = None, + run_manager: CallbackManagerForLLMRun | None = None, + **kwargs: Any, + ) -> Iterator[ChatGenerationChunk]: + message = self._decide(messages) + if message.tool_calls: + yield ChatGenerationChunk( + message=AIMessageChunk( + content=message.content, tool_calls=message.tool_calls + ) + ) + return + for piece in re.findall(r".{1,6}", _text_of(message), re.DOTALL): + yield ChatGenerationChunk(message=AIMessageChunk(content=piece)) + + async def _astream( + self, + messages: list[BaseMessage], + stop: list[str] | None = None, + run_manager: AsyncCallbackManagerForLLMRun | None = None, + **kwargs: Any, + ) -> AsyncIterator[ChatGenerationChunk]: + message = self._decide(messages) + if message.tool_calls: + await asyncio.sleep(self.latency) + yield ChatGenerationChunk( + message=AIMessageChunk( + content=message.content, tool_calls=message.tool_calls + ) + ) + return + for piece in re.findall(r".{1,6}", _text_of(message), re.DOTALL): + await asyncio.sleep(self.latency) + yield ChatGenerationChunk(message=AIMessageChunk(content=piece)) diff --git a/backend/app/sandbox/__init__.py b/backend/app/sandbox/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/sandbox/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/sandbox/base.py b/backend/app/sandbox/base.py new file mode 100644 index 00000000..1363e19f --- /dev/null +++ b/backend/app/sandbox/base.py @@ -0,0 +1,75 @@ +from __future__ import annotations + +import abc +from typing import Any + +from pydantic import BaseModel, Field + +from app.core.config import settings + + +class SandboxLimits(BaseModel): + timeout: int = Field(default_factory=lambda: settings.sandbox_timeout) + memory_mb: int = Field(default_factory=lambda: settings.sandbox_memory_mb) + cpus: float = Field(default_factory=lambda: settings.sandbox_cpus) + network: bool = Field(default_factory=lambda: settings.sandbox_network) + max_output: int = Field(default_factory=lambda: settings.sandbox_max_output) + + +class ExecResult(BaseModel): + ok: bool = True + exit_code: int = 0 + stdout: str = "" + stderr: str = "" + duration_ms: int = 0 + timed_out: bool = False + truncated: bool = False + backend: str = "" + files_written: list[str] = Field(default_factory=list) + error: str | None = None + + def summary(self) -> str: + if self.timed_out: + return f"执行超时（>{'?'}s）" + if self.error: + return self.error + return self.stdout or self.stderr or "(无输出)" + + +class Sandbox(abc.ABC): + """代码执行后端的统一接口。""" + + name: str = "base" + + @abc.abstractmethod + async def run( + self, + code: str, + *, + language: str = "python", + limits: SandboxLimits | None = None, + session_id: str | None = None, + env: dict[str, str] | None = None, + files: dict[str, str] | None = None, + ) -> ExecResult: + """执行一段代码。session_id 相同的调用共享同一个文件系统， + 这样 agent 可以先写文件、再在下一步读回来。""" + + async def health(self) -> dict[str, Any]: + return {"backend": self.name, "available": True} + + async def cleanup(self, session_id: str) -> None: + return None + + async def close(self) -> None: + return None + + +def truncate(text: str, limit: int) -> tuple[str, bool]: + if len(text) <= limit: + return text, False + half = limit // 2 + return ( + text[:half] + f"\n\n…（省略 {len(text) - limit} 字符）…\n\n" + text[-half:], + True, + ) diff --git a/backend/app/sandbox/docker_sandbox.py b/backend/app/sandbox/docker_sandbox.py new file mode 100644 index 00000000..6a076d97 --- /dev/null +++ b/backend/app/sandbox/docker_sandbox.py @@ -0,0 +1,314 @@ +from __future__ import annotations + +import asyncio +import base64 +import shlex +import time +import uuid +from typing import Any + +from app.core.config import settings +from app.sandbox.base import ExecResult, Sandbox, SandboxLimits, truncate + +_WORKDIR = "/workspace" + +_LANG_CMD = { + "python": ["python", "-u", "/workspace/.main.py"], + "bash": ["bash", "/workspace/.main.sh"], + "sh": ["sh", "/workspace/.main.sh"], + "node": ["node", "/workspace/.main.js"], + "javascript": ["node", "/workspace/.main.js"], +} +_LANG_FILE = { + "python": ".main.py", + "bash": ".main.sh", + "sh": ".main.sh", + "node": ".main.js", + "javascript": ".main.js", +} + + +# 单条 shell 命令里塞的 base64 分块大小。留足余量避开 ARG_MAX。 +_CHUNK = 48 * 1024 +_MAX_FILE_BYTES = 4 * 1024 * 1024 + + +def _write_commands(files: dict[str, str]) -> list[list[str]]: + """生成往容器里写文件的 shell 命令。 + + 这里不用 docker 的 put_archive：那个 API 会被 `read_only` 的容器直接拒掉 + （即便目标是可写的 tmpfs 挂载点）。改成 exec + base64 管道写入， + 就能同时保住只读根文件系统和往 /workspace 落文件这两件事。 + """ + commands: list[list[str]] = [] + for path, content in files.items(): + rel = path.lstrip("/") + if ".." in rel.split("/"): + raise ValueError(f"非法文件路径：{path}") + full = f"{_WORKDIR}/{rel}" + data = content.encode() + if len(data) > _MAX_FILE_BYTES: + raise ValueError(f"{path} 超过 {_MAX_FILE_BYTES // 1024 // 1024}MB 上限") + blob = base64.b64encode(data).decode() + quoted = shlex.quote(full) + commands.append(["sh", "-c", f"mkdir -p $(dirname {quoted})"]) + if not blob: + commands.append(["sh", "-c", f": > {quoted}"]) + continue + for i in range(0, len(blob), _CHUNK): + chunk = blob[i : i + _CHUNK] + redirect = ">" if i == 0 else ">>" + commands.append(["sh", "-c", f"printf %s {chunk} {redirect} {quoted}.b64"]) + commands.append(["sh", "-c", f"base64 -d < {quoted}.b64 > {quoted} && rm -f {quoted}.b64"]) + return commands + + +class DockerSandbox(Sandbox): + """容器隔离的代码执行。 + + 安全姿态：丢掉所有 capability、禁 setuid 提权、只读根文件系统 + 可写 tmpfs、 + 默认断网、非 root 用户、限制内存 / CPU / 进程数。 + 同一个 session_id 复用容器，这样多次执行之间文件是连续的。 + """ + + name = "docker" + + def __init__(self, image: str | None = None) -> None: + self.image = image or settings.sandbox_image + self._client: Any = None + self._sessions: dict[str, str] = {} # session_id -> container id + self._lock = asyncio.Lock() + self._image_ready = False + + # ---------------- 基础设施 ---------------- + + def _get_client(self) -> Any: + if self._client is None: + import docker + + self._client = docker.from_env() + return self._client + + async def health(self) -> dict[str, Any]: + try: + info = await asyncio.to_thread(lambda: self._get_client().version()) + has_image = await asyncio.to_thread(self._has_image) + return { + "backend": self.name, + "available": True, + "docker_version": info.get("Version"), + "image": self.image, + "image_pulled": has_image, + "sessions": len(self._sessions), + } + except Exception as e: # noqa: BLE001 + return {"backend": self.name, "available": False, "error": str(e)} + + def _has_image(self) -> bool: + import docker.errors + + try: + self._get_client().images.get(self.image) + return True + except docker.errors.ImageNotFound: + return False + except Exception: # noqa: BLE001 + return False + + async def ensure_image(self) -> None: + """首次执行时拉镜像。拉取可能要几十秒，所以单独暴露出来让 API 可以预热。""" + if self._image_ready: + return + if await asyncio.to_thread(self._has_image): + self._image_ready = True + return + await asyncio.to_thread(lambda: self._get_client().images.pull(self.image)) + self._image_ready = True + + def _create_container(self, limits: SandboxLimits) -> Any: + client = self._get_client() + return client.containers.run( + self.image, + command=["sleep", str(max(limits.timeout * 20, 600))], + detach=True, + working_dir=_WORKDIR, + network_disabled=not limits.network, + mem_limit=f"{limits.memory_mb}m", + memswap_limit=f"{limits.memory_mb}m", # 禁止用 swap 绕过内存限制 + nano_cpus=int(limits.cpus * 1e9), + pids_limit=128, + cap_drop=["ALL"], + security_opt=["no-new-privileges"], + read_only=True, + # mode=1777：tmpfs 默认属 root、755，非 root 的 nobody 会写不进去 + tmpfs={ + _WORKDIR: "rw,exec,size=128m,mode=1777", + "/tmp": "rw,exec,size=64m,mode=1777", + }, + user="nobody", + environment={"HOME": "/tmp", "PYTHONDONTWRITEBYTECODE": "1"}, + labels={"agentlab": "sandbox"}, + auto_remove=False, + ) + + async def _get_container(self, session_id: str | None, limits: SandboxLimits) -> Any: + client = self._get_client() + async with self._lock: + if session_id and session_id in self._sessions: + try: + container = await asyncio.to_thread( + client.containers.get, self._sessions[session_id] + ) + if container.status == "running": + return container + except Exception: # noqa: BLE001 - 容器没了就重建 + self._sessions.pop(session_id, None) + container = await asyncio.to_thread(self._create_container, limits) + if session_id: + self._sessions[session_id] = container.id + return container + + # ---------------- 执行 ---------------- + + async def run( + self, + code: str, + *, + language: str = "python", + limits: SandboxLimits | None = None, + session_id: str | None = None, + env: dict[str, str] | None = None, + files: dict[str, str] | None = None, + ) -> ExecResult: + limits = limits or SandboxLimits() + language = (language or "python").lower() + if language not in _LANG_CMD: + return ExecResult( + ok=False, backend=self.name, error=f"不支持的语言：{language}" + ) + + started = time.perf_counter() + ephemeral = session_id is None + sid = session_id or f"tmp-{uuid.uuid4().hex[:8]}" + container = None + try: + await self.ensure_image() + container = await self._get_container(sid, limits) + + payload = dict(files or {}) + payload[_LANG_FILE[language]] = code + + def _stage() -> tuple[int, bytes]: + for cmd in _write_commands(payload): + res = container.exec_run(cmd, workdir=_WORKDIR, user="nobody") + if res.exit_code != 0: + return res.exit_code, res.output or b"" + return 0, b"" + + stage_code, stage_out = await asyncio.to_thread(_stage) + if stage_code != 0: + return ExecResult( + ok=False, + backend=self.name, + exit_code=stage_code, + error=f"写入代码失败：{stage_out.decode(errors='replace')[:500]}", + duration_ms=int((time.perf_counter() - started) * 1000), + ) + + cmd = _LANG_CMD[language] + exec_env = {"HOME": "/tmp", **(env or {})} + + def _exec() -> tuple[int, bytes, bytes]: + res = container.exec_run( + cmd, + workdir=_WORKDIR, + environment=exec_env, + demux=True, + user="nobody", + ) + out, err = res.output if isinstance(res.output, tuple) else (res.output, b"") + return res.exit_code, out or b"", err or b"" + + try: + exit_code, out, err = await asyncio.wait_for( + asyncio.to_thread(_exec), timeout=limits.timeout + ) + except asyncio.TimeoutError: + # 超时就把容器整个干掉，防止里面还有进程在烧 CPU + await self._kill(sid) + return ExecResult( + ok=False, + backend=self.name, + timed_out=True, + exit_code=124, + duration_ms=int((time.perf_counter() - started) * 1000), + error=f"执行超过 {limits.timeout}s 被终止", + ) + + stdout, t1 = truncate(out.decode(errors="replace"), limits.max_output) + stderr, t2 = truncate(err.decode(errors="replace"), limits.max_output) + return ExecResult( + ok=exit_code == 0, + exit_code=exit_code, + stdout=stdout, + stderr=stderr, + truncated=t1 or t2, + backend=self.name, + duration_ms=int((time.perf_counter() - started) * 1000), + ) + except Exception as e: # noqa: BLE001 + return ExecResult( + ok=False, + backend=self.name, + error=f"{type(e).__name__}: {e}", + duration_ms=int((time.perf_counter() - started) * 1000), + ) + finally: + if ephemeral: + await self._kill(sid) + + async def _kill(self, session_id: str) -> None: + cid = self._sessions.pop(session_id, None) + if not cid: + return + client = self._get_client() + + def _remove() -> None: + try: + client.containers.get(cid).remove(force=True) + except Exception: # noqa: BLE001 + pass + + await asyncio.to_thread(_remove) + + async def cleanup(self, session_id: str) -> None: + await self._kill(session_id) + + async def close(self) -> None: + for sid in list(self._sessions): + await self._kill(sid) + + async def reap_orphans(self) -> int: + """清掉上次进程崩溃留下的沙箱容器。""" + + def _reap() -> int: + client = self._get_client() + containers = client.containers.list( + all=True, filters={"label": "agentlab=sandbox"} + ) + live = set(self._sessions.values()) + count = 0 + for c in containers: + if c.id in live: + continue + try: + c.remove(force=True) + count += 1 + except Exception: # noqa: BLE001 + pass + return count + + try: + return await asyncio.to_thread(_reap) + except Exception: # noqa: BLE001 + return 0 diff --git a/backend/app/sandbox/local_sandbox.py b/backend/app/sandbox/local_sandbox.py new file mode 100644 index 00000000..94d22131 --- /dev/null +++ b/backend/app/sandbox/local_sandbox.py @@ -0,0 +1,160 @@ +from __future__ import annotations + +import asyncio +import os +import resource +import shutil +import sys +import time +from pathlib import Path + +from app.core.config import settings +from app.sandbox.base import ExecResult, Sandbox, SandboxLimits, truncate + +_LANG = { + "python": ("main.py", [sys.executable, "-u", "main.py"]), + "bash": ("main.sh", ["bash", "main.sh"]), + "sh": ("main.sh", ["sh", "main.sh"]), + "node": ("main.js", ["node", "main.js"]), + "javascript": ("main.js", ["node", "main.js"]), +} + + +class LocalSandbox(Sandbox): + """子进程执行，Docker 不可用时的降级方案。 + + 有 rlimit（CPU / 内存 / 文件大小 / 进程数）和独立工作目录，但**没有内核级隔离**： + 代码仍然跑在宿主机上，能读到你的文件系统。只适合本地自己玩， + 别拿它跑不信任的代码 —— 需要真隔离就把 Docker 起起来。 + """ + + name = "local" + + def __init__(self) -> None: + self._root = settings.workspace_dir / "local" + self._root.mkdir(parents=True, exist_ok=True) + + async def health(self) -> dict[str, object]: + return { + "backend": self.name, + "available": True, + "isolated": False, + "warning": "本地子进程执行，没有内核级隔离，不要跑不信任的代码", + "root": str(self._root), + } + + def _session_dir(self, session_id: str) -> Path: + safe = "".join(c for c in session_id if c.isalnum() or c in "-_")[:64] or "default" + path = self._root / safe + path.mkdir(parents=True, exist_ok=True) + return path + + @staticmethod + def _preexec(limits: SandboxLimits): # pragma: no cover - 只在子进程里跑 + def _apply() -> None: + resource.setrlimit(resource.RLIMIT_CPU, (limits.timeout, limits.timeout + 2)) + nbytes = limits.memory_mb * 1024 * 1024 + for res_name in ("RLIMIT_AS", "RLIMIT_DATA"): + res_id = getattr(resource, res_name, None) + if res_id is not None: + try: + resource.setrlimit(res_id, (nbytes, nbytes)) + except (ValueError, OSError): + pass + resource.setrlimit(resource.RLIMIT_FSIZE, (64 * 1024 * 1024,) * 2) + resource.setrlimit(resource.RLIMIT_NPROC, (64, 64)) + resource.setrlimit(resource.RLIMIT_CORE, (0, 0)) + os.setsid() # 独立进程组，超时时能整组 kill + + return _apply + + async def run( + self, + code: str, + *, + language: str = "python", + limits: SandboxLimits | None = None, + session_id: str | None = None, + env: dict[str, str] | None = None, + files: dict[str, str] | None = None, + ) -> ExecResult: + limits = limits or SandboxLimits() + language = (language or "python").lower() + if language not in _LANG: + return ExecResult(ok=False, backend=self.name, error=f"不支持的语言：{language}") + + filename, argv = _LANG[language] + if shutil.which(argv[0]) is None and argv[0] != sys.executable: + return ExecResult( + ok=False, backend=self.name, error=f"宿主机上找不到 {argv[0]}" + ) + + workdir = self._session_dir(session_id or f"tmp-{int(time.time()*1000)}") + (workdir / filename).write_text(code) + for rel, content in (files or {}).items(): + target = (workdir / rel).resolve() + if not str(target).startswith(str(workdir.resolve())): + return ExecResult(ok=False, backend=self.name, error=f"非法文件路径：{rel}") + target.parent.mkdir(parents=True, exist_ok=True) + target.write_text(content) + + # 只透传最小环境变量，避免把宿主机的 API key 泄漏给被执行的代码 + child_env = { + "PATH": os.environ.get("PATH", "/usr/bin:/bin"), + "HOME": str(workdir), + "LANG": "en_US.UTF-8", + "PYTHONDONTWRITEBYTECODE": "1", + **(env or {}), + } + + started = time.perf_counter() + try: + proc = await asyncio.create_subprocess_exec( + *argv, + cwd=str(workdir), + env=child_env, + stdout=asyncio.subprocess.PIPE, + stderr=asyncio.subprocess.PIPE, + preexec_fn=self._preexec(limits), + ) + except Exception as e: # noqa: BLE001 + return ExecResult(ok=False, backend=self.name, error=f"{type(e).__name__}: {e}") + + try: + out, err = await asyncio.wait_for(proc.communicate(), timeout=limits.timeout) + except asyncio.TimeoutError: + try: + os.killpg(os.getpgid(proc.pid), 9) + except (ProcessLookupError, PermissionError): + proc.kill() + await proc.wait() + return ExecResult( + ok=False, + backend=self.name, + timed_out=True, + exit_code=124, + duration_ms=int((time.perf_counter() - started) * 1000), + error=f"执行超过 {limits.timeout}s 被终止", + ) + + stdout, t1 = truncate(out.decode(errors="replace"), limits.max_output) + stderr, t2 = truncate(err.decode(errors="replace"), limits.max_output) + written = [ + p.name + for p in workdir.iterdir() + if p.is_file() and p.name != filename + ][:50] + return ExecResult( + ok=proc.returncode == 0, + exit_code=proc.returncode or 0, + stdout=stdout, + stderr=stderr, + truncated=t1 or t2, + backend=self.name, + duration_ms=int((time.perf_counter() - started) * 1000), + files_written=written, + ) + + async def cleanup(self, session_id: str) -> None: + path = self._session_dir(session_id) + await asyncio.to_thread(shutil.rmtree, path, True) diff --git a/backend/app/sandbox/manager.py b/backend/app/sandbox/manager.py new file mode 100644 index 00000000..0f2bc7b4 --- /dev/null +++ b/backend/app/sandbox/manager.py @@ -0,0 +1,101 @@ +from __future__ import annotations + +import asyncio +from typing import Any + +from app.core.config import settings +from app.sandbox.base import ExecResult, Sandbox, SandboxLimits +from app.sandbox.docker_sandbox import DockerSandbox +from app.sandbox.local_sandbox import LocalSandbox + + +class DisabledSandbox(Sandbox): + name = "off" + + async def run(self, code: str, **kwargs: Any) -> ExecResult: # type: ignore[override] + return ExecResult( + ok=False, + backend=self.name, + error="沙箱已在设置中关闭（AGENTLAB_SANDBOX_BACKEND=off）", + ) + + async def health(self) -> dict[str, Any]: + return {"backend": self.name, "available": False, "reason": "已关闭"} + + +class SandboxManager: + """按配置挑一个可用后端。auto = 有 Docker 用 Docker，没有就降级到本地子进程。""" + + def __init__(self) -> None: + self._backend: Sandbox | None = None + self._lock = asyncio.Lock() + self._resolved_from: str = "" + + async def get(self) -> Sandbox: + if self._backend is not None: + return self._backend + async with self._lock: + if self._backend is not None: + return self._backend + self._backend = await self._resolve() + return self._backend + + async def _resolve(self) -> Sandbox: + choice = (settings.sandbox_backend or "auto").lower() + if choice == "off": + self._resolved_from = "配置关闭" + return DisabledSandbox() + if choice == "local": + self._resolved_from = "配置指定" + return LocalSandbox() + if choice == "docker": + self._resolved_from = "配置指定" + return DockerSandbox() + + docker = DockerSandbox() + health = await docker.health() + if health.get("available"): + self._resolved_from = "自动探测到 Docker" + await docker.reap_orphans() + return docker + self._resolved_from = f"Docker 不可用（{health.get('error', '未知原因')}），降级到本地" + return LocalSandbox() + + async def run( + self, + code: str, + *, + language: str = "python", + limits: SandboxLimits | None = None, + session_id: str | None = None, + env: dict[str, str] | None = None, + files: dict[str, str] | None = None, + ) -> ExecResult: + backend = await self.get() + return await backend.run( + code, + language=language, + limits=limits, + session_id=session_id, + env=env, + files=files, + ) + + async def health(self) -> dict[str, Any]: + backend = await self.get() + info = await backend.health() + info["selected_because"] = self._resolved_from + info["configured"] = settings.sandbox_backend + info["defaults"] = SandboxLimits().model_dump() + return info + + async def cleanup(self, session_id: str) -> None: + backend = await self.get() + await backend.cleanup(session_id) + + async def close(self) -> None: + if self._backend: + await self._backend.close() + + +sandbox_manager = SandboxManager() diff --git a/backend/app/seed.py b/backend/app/seed.py new file mode 100644 index 00000000..6c2193bb --- /dev/null +++ b/backend/app/seed.py @@ -0,0 +1,334 @@ +from __future__ import annotations + +import logging +import os +from typing import Any + +from sqlalchemy import func, select + +from app.core.crypto import encrypt +from app.db.base import SessionLocal +from app.db.models import Provider, Skill, Workflow, WorkflowVersion +from app.engine.schema import GraphSpec +from app.providers import catalog + +logger = logging.getLogger(__name__) + + +def _n(node_id: str, node_type: str, label: str, **config: Any) -> dict[str, Any]: + return { + "id": node_id, + "type": node_type, + "position": {"x": 0, "y": 0}, + "data": {"label": label, "config": config}, + } + + +def _e(source: str, target: str, handle: str | None = None) -> dict[str, Any]: + edge: dict[str, Any] = {"source": source, "target": target} + if handle: + edge["sourceHandle"] = handle + return edge + + +# -------------------------------------------------------------------------- +# 内置模板：打开就有得玩，也是各种节点用法的活文档 +# -------------------------------------------------------------------------- + +TEMPLATES: list[dict[str, Any]] = [ + { + "name": "① 最小问答", + "description": "一个模型节点跑通全流程。先从这个开始改。", + "tags": ["入门"], + "nodes": [ + _n("start", "input", "提问", fields=[{"name": "question", "required": True, + "description": "你想问什么"}]), + _n("answer", "llm", "回答", system="你是一个耐心、准确的助手。", + prompt="{{ input.question }}", assign_to="answer"), + _n("done", "output", "成果", fields=[{"name": "回答", "value": "{{ vars.answer }}"}]), + ], + "edges": [_e("start", "answer"), _e("answer", "done")], + }, + { + "name": "② 研究助手（Agent + 工具）", + "description": "Agent 自主决定搜什么、抓哪篇，再汇总成带出处的简报。", + "tags": ["agent", "工具"], + "nodes": [ + _n("start", "input", "研究主题", fields=[{"name": "topic", "required": True}]), + _n("research", "agent", "调研 Agent", + system="你是严谨的研究员。先搜索再抓取原文，只写有依据的内容。", + prompt="围绕这个主题做调研：{{ input.topic }}", + tools=["web_search", "web_fetch", "current_time"], + max_steps=8, approval="never", assign_to="findings"), + _n("brief", "llm", "写成简报", + system="你是一位把复杂信息讲清楚的编辑。", + prompt="把下面的调研结果整理成中文简报，分「结论 / 关键发现 / 待确认」三段，" + "每条结论后面标注来源链接：\n\n{{ vars.findings }}", + assign_to="brief"), + _n("done", "output", "简报", fields=[ + {"name": "简报", "value": "{{ vars.brief }}"}, + {"name": "调研过程", "value": "{{ nodes.research.tool_calls | json }}"}]), + ], + "edges": [_e("start", "research"), _e("research", "brief"), _e("brief", "done")], + }, + { + "name": "③ 带人工审批的发布流", + "description": "起草 → 人工把关 → 通过就发布、驳回就改写。演示中断与断点恢复。", + "tags": ["人工介入", "分支"], + "nodes": [ + _n("start", "input", "主题", fields=[{"name": "topic", "required": True}]), + _n("draft", "llm", "起草", system="你是品牌文案。", + prompt="为「{{ input.topic }}」写一条 100 字以内的对外公告。", + assign_to="draft"), + _n("review", "human", "人工把关", mode="approve", + title="这条公告可以发吗？", + message="草稿：\n\n{{ vars.draft }}"), + _n("publish", "transform", "标记发布", mode="template", + template="✅ 已批准发布\n\n{{ vars.draft }}", assign_to="final"), + _n("rewrite", "llm", "按意见改写", + prompt="下面这条公告被驳回了，理由是「{{ nodes.review.note }}」。请据此改写：\n\n{{ vars.draft }}", + assign_to="final"), + _n("done", "output", "成果", fields=[ + {"name": "结果", "value": "{{ vars.final }}"}, + {"name": "是否通过", "value": "{{ nodes.review.approved }}"}]), + ], + "edges": [ + _e("start", "draft"), _e("draft", "review"), + _e("review", "publish", "approved"), _e("review", "rewrite", "rejected"), + _e("publish", "done"), _e("rewrite", "done"), + ], + }, + { + "name": "④ 知识库问答（RAG）", + "description": "先检索再回答，检索不到就明说。需要先在知识库页上传文档。", + "tags": ["RAG", "知识库"], + "nodes": [ + _n("start", "input", "提问", fields=[{"name": "question", "required": True}, + {"name": "collection", "default": "default"}]), + _n("search", "retrieve", "检索知识库", query="{{ input.question }}", + collection="{{ input.collection }}", limit=5, alpha=0.5, assign_to="context"), + _n("gate", "branch", "检索到东西了吗", mode="expression", + cases=[{"key": "hit", "condition": "nodes.search.count > 0", "label": "有命中"}]), + _n("answer", "llm", "基于资料回答", + system="只根据提供的资料回答，每句话后面用 [编号] 标注依据。资料不足就直接说不确定。", + prompt="资料：\n{{ vars.context }}\n\n问题：{{ input.question }}", + assign_to="answer"), + _n("miss", "transform", "没有资料", mode="template", + template="知识库里没有找到与「{{ input.question }}」相关的内容，请先上传相关文档。", + assign_to="answer"), + _n("done", "output", "答案", fields=[ + {"name": "答案", "value": "{{ vars.answer }}"}, + {"name": "引用片段", "value": "{{ nodes.search.hits | json }}"}]), + ], + "edges": [ + _e("start", "search"), _e("search", "gate"), + _e("gate", "answer", "hit"), _e("gate", "miss", "default"), + _e("answer", "done"), _e("miss", "done"), + ], + }, + { + "name": "⑤ 写代码跑数据（沙箱 + 校验）", + "description": "模型写 Python，沙箱里真跑，输出还要过 JSON Schema 校验，不合格自动返工。", + "tags": ["沙箱", "结构化"], + "nodes": [ + _n("start", "input", "分析需求", fields=[ + {"name": "task", "required": True, "default": "统计 1 到 100 里所有质数的个数与总和"}]), + _n("write", "llm", "写代码", + system="你是数据工程师。只输出可直接运行的 Python 代码，不要解释、不要 markdown 围栏。" + "结果必须用 print(json.dumps(...)) 输出一个 JSON 对象。", + prompt="任务：{{ input.task }}", assign_to="code"), + _n("run", "code", "沙箱执行", language="python", code="{{ vars.code }}", + timeout=30, network=False, fail_fast=False, assign_to="result"), + _n("check", "validate", "校验结果", source="{{ nodes.run.stdout }}", + max_retries=2, repair_with_llm=True, fail_fast=False, + schema={"type": "object", "minProperties": 1}), + _n("done", "output", "成果", fields=[ + {"name": "结果", "value": "{{ nodes.check.data | json }}"}, + {"name": "代码", "value": "{{ vars.code }}"}, + {"name": "校验通过", "value": "{{ nodes.check.valid }}"}]), + ], + "edges": [_e("start", "write"), _e("write", "run"), _e("run", "check"), _e("check", "done")], + }, + { + "name": "⑥ 多 Agent 协作", + "description": "调度者按进展分派任务给研究员、批评家和写手，直到目标达成。", + "tags": ["多agent"], + "nodes": [ + _n("start", "input", "目标", fields=[{"name": "goal", "required": True}]), + _n("team", "supervisor", "协作团队", goal="{{ input.goal }}", max_rounds=6, + agents=[ + {"name": "researcher", "description": "查资料、找事实", + "system": "你是研究员，负责收集事实和数据，给出来源。", + "tools": ["web_search", "web_fetch"], "max_steps": 4}, + {"name": "critic", "description": "挑毛病、找漏洞", + "system": "你是批评家。指出前面工作里的事实错误、逻辑漏洞和遗漏，要具体。", + "tools": [], "max_steps": 2}, + {"name": "writer", "description": "整合成最终文稿", + "system": "你是写手，把团队成果整合成结构清晰的中文文稿。", + "tools": [], "max_steps": 2}, + ], assign_to="result"), + _n("done", "output", "成果", fields=[ + {"name": "结果", "value": "{{ vars.result }}"}, + {"name": "协作记录", "value": "{{ nodes.team.transcript | json }}"}]), + ], + "edges": [_e("start", "team"), _e("team", "done")], + }, + { + "name": "⑦ 批量处理（循环）", + "description": "对列表逐项处理再汇总。演示 loop 节点的 body / done 两个出口怎么接。", + "tags": ["循环"], + "nodes": [ + _n("start", "input", "条目列表", fields=[ + {"name": "items", "required": True, + "default": ["降低获客成本", "提升续费率", "缩短上线周期"], + "description": "JSON 数组或每行一条"}]), + _n("each", "loop", "逐条循环", mode="foreach", items="{{ input.items }}", + item_var="item", max_iterations=10), + _n("handle", "llm", "处理单条", + prompt="针对「{{ vars.item }}」给出一条可执行的建议，不超过 40 字。", + assign_to="one"), + _n("collect", "transform", "累积结果", mode="expression", + expression="(vars.collected or '') + '· ' + str(vars.one) + '\\n'", + assign_to="collected"), + _n("done", "output", "汇总", fields=[ + {"name": "逐条建议", "value": "{{ vars.collected }}"}, + {"name": "处理条数", "value": "{{ nodes.each.iteration }}"}]), + ], + "edges": [ + _e("start", "each"), + _e("each", "handle", "body"), _e("handle", "collect"), _e("collect", "each"), + _e("each", "done", "done"), + ], + }, +] + + +SKILLS: list[dict[str, Any]] = [ + { + "name": "结构化分析", + "description": "把开放问题拆成结论先行的分析", + "instructions": ( + "分析任何问题时遵循：\n" + "1. 先给结论，再给理由，不要铺垫\n" + "2. 区分「事实」「推断」「假设」，并明确标注\n" + "3. 给出至少一个反面视角\n" + "4. 指出当前信息不足以判断的部分，不要硬答" + ), + "tags": ["通用"], + }, + { + "name": "批判性审阅", + "description": "挑毛病而不是夸奖", + "instructions": ( + "审阅内容时：\n" + "1. 只指出问题，不要复述优点\n" + "2. 每条问题写清楚「问题是什么 / 为什么是问题 / 怎么改」\n" + "3. 按严重程度排序，最致命的放最前面\n" + "4. 没有问题就直说没有，不要为凑数硬挑" + ), + "tags": ["审阅"], + }, + { + "name": "中文写作", + "description": "干净、不翻译腔的中文", + "instructions": ( + "写中文时：\n" + "1. 不用「进行」「作出」这类空动词，直接用实义动词\n" + "2. 少用被动句和长定语从句\n" + "3. 术语首次出现时用括号给出英文原文\n" + "4. 能用短句就不写长句" + ), + "tags": ["写作"], + }, +] + + +async def seed_defaults() -> None: + """首次启动时灌入默认 provider、模板工作流和 Skill。 + + 已经存在的不会被覆盖 —— 这个函数每次启动都会跑，必须是幂等的。 + """ + async with SessionLocal() as session: + # --- provider --- + count = (await session.execute(select(func.count(Provider.id)))).scalar() or 0 + if count == 0: + session.add( + Provider( + name="Mock（无需 Key）", + kind="mock", + models=catalog.MOCK_MODELS, + default_model="mock-fast", + enabled=True, + ) + ) + # 环境里已有 Anthropic 凭据就自动配好，省得再手填一遍 + token = os.environ.get("ANTHROPIC_API_KEY") or os.environ.get("ANTHROPIC_AUTH_TOKEN") + if token: + base_url = os.environ.get("ANTHROPIC_BASE_URL") + is_gateway = bool(base_url) and not os.environ.get("ANTHROPIC_API_KEY") + session.add( + Provider( + name="Anthropic" + ("（网关）" if is_gateway else ""), + kind="anthropic", + base_url=base_url, + api_key=encrypt(token), + models=catalog.ANTHROPIC_MODELS, + default_model="claude-opus-5", + enabled=True, + # 中转网关通常认 Authorization: Bearer 而不是 x-api-key + extra={"auth_style": "bearer"} if is_gateway else {}, + ) + ) + logger.info("已从环境变量自动创建 Anthropic provider") + if os.environ.get("OPENAI_API_KEY"): + session.add( + Provider( + name="OpenAI", + kind="openai", + api_key=encrypt(os.environ["OPENAI_API_KEY"]), + models=catalog.OPENAI_MODELS, + default_model="gpt-5.2", + enabled=True, + ) + ) + await session.commit() + + # --- Skill --- + existing_skills = { + s for (s,) in await session.execute(select(Skill.name)) + } + for skill in SKILLS: + if skill["name"] not in existing_skills: + session.add(Skill(**skill)) + await session.commit() + + # --- 模板工作流 --- + from app.api.copilot import auto_layout + + existing = {n for (n,) in await session.execute(select(Workflow.name))} + added = 0 + for template in TEMPLATES: + if template["name"] in existing: + continue + spec = auto_layout( + GraphSpec.model_validate( + {"nodes": template["nodes"], "edges": template["edges"]} + ) + ) + graph = spec.model_dump(mode="json") + workflow = Workflow( + name=template["name"], + description=template["description"], + graph=graph, + tags=template["tags"], + is_template=True, + ) + session.add(workflow) + await session.flush() + session.add( + WorkflowVersion(workflow_id=workflow.id, version=1, graph=graph, note="内置模板") + ) + added += 1 + if added: + await session.commit() + logger.info("已导入 %d 个内置模板", added) diff --git a/backend/app/tools/__init__.py b/backend/app/tools/__init__.py new file mode 100644 index 00000000..8b137891 --- /dev/null +++ b/backend/app/tools/__init__.py @@ -0,0 +1 @@ + diff --git a/backend/app/tools/builtin/__init__.py b/backend/app/tools/builtin/__init__.py new file mode 100644 index 00000000..5f988361 --- /dev/null +++ b/backend/app/tools/builtin/__init__.py @@ -0,0 +1,5 @@ +"""导入即注册。新增内置工具时在这里加一行。""" + +from app.tools.builtin import code, data, files, knowledge, web # noqa: F401 + +__all__ = ["code", "data", "files", "knowledge", "web"] diff --git a/backend/app/tools/builtin/code.py b/backend/app/tools/builtin/code.py new file mode 100644 index 00000000..a72318fe --- /dev/null +++ b/backend/app/tools/builtin/code.py @@ -0,0 +1,83 @@ +from __future__ import annotations + +from typing import Any, Literal + +from pydantic import BaseModel, Field + +from app.sandbox.base import SandboxLimits +from app.sandbox.manager import sandbox_manager +from app.tools.registry import ToolContext, register + + +class PythonExecArgs(BaseModel): + code: str = Field(description="要执行的 Python 代码。用 print() 输出结果。") + timeout: int = Field(default=30, ge=1, le=300, description="超时秒数") + + +@register( + name="python_exec", + category="沙箱", + description=( + "在隔离沙箱里执行 Python 代码并返回 stdout/stderr。" + "默认断网、只读根文件系统，/workspace 可写且在同一次运行内持久。" + ), + args_schema=PythonExecArgs, + dangerous=True, +) +async def python_exec(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = PythonExecArgs(**kwargs) + result = await sandbox_manager.run( + args.code, + language="python", + limits=SandboxLimits(timeout=args.timeout), + session_id=ctx.sandbox_session, + ) + return result.model_dump(exclude={"files_written"} if not result.files_written else set()) + + +class ShellExecArgs(BaseModel): + command: str = Field(description="要执行的 shell 命令") + timeout: int = Field(default=30, ge=1, le=300) + + +@register( + name="shell_exec", + category="沙箱", + description="在隔离沙箱里执行 shell 命令。同样断网、非 root。", + args_schema=ShellExecArgs, + dangerous=True, +) +async def shell_exec(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = ShellExecArgs(**kwargs) + result = await sandbox_manager.run( + args.command, + language="bash", + limits=SandboxLimits(timeout=args.timeout), + session_id=ctx.sandbox_session, + ) + return result.model_dump() + + +class RunCodeArgs(BaseModel): + code: str + language: Literal["python", "bash", "node"] = "python" + timeout: int = Field(default=30, ge=1, le=300) + network: bool = Field(default=False, description="是否允许联网") + + +@register( + name="run_code", + category="沙箱", + description="在沙箱里执行任意支持语言的代码，可单独打开网络。", + args_schema=RunCodeArgs, + dangerous=True, +) +async def run_code(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = RunCodeArgs(**kwargs) + result = await sandbox_manager.run( + args.code, + language=args.language, + limits=SandboxLimits(timeout=args.timeout, network=args.network), + session_id=ctx.sandbox_session, + ) + return result.model_dump() diff --git a/backend/app/tools/builtin/data.py b/backend/app/tools/builtin/data.py new file mode 100644 index 00000000..1472fc70 --- /dev/null +++ b/backend/app/tools/builtin/data.py @@ -0,0 +1,78 @@ +from __future__ import annotations + +import json +from datetime import datetime, timezone +from typing import Any +from zoneinfo import ZoneInfo + +from pydantic import BaseModel, Field + +from app.engine.expressions import ExpressionError, eval_expression, resolve_path +from app.tools.registry import ToolContext, register + + +class CalculatorArgs(BaseModel): + expression: str = Field(description="数学表达式，例如 (13*7 + 2) / 3") + + +@register( + name="calculator", + category="数据", + description="计算一个数学表达式。支持 + - * / // % ** 和 min/max/abs/round/sum 等函数。", + args_schema=CalculatorArgs, +) +async def calculator(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = CalculatorArgs(**kwargs) + try: + value = eval_expression(args.expression, {}) + except ExpressionError as e: + return {"error": str(e)} + except ZeroDivisionError: + return {"error": "除以零"} + return {"expression": args.expression, "result": value} + + +class CurrentTimeArgs(BaseModel): + timezone_name: str = Field(default="Asia/Shanghai", description="IANA 时区名") + + +@register( + name="current_time", + category="数据", + description="获取当前日期时间。模型不知道此刻是什么时候，需要时间信息时用这个。", + args_schema=CurrentTimeArgs, +) +async def current_time(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = CurrentTimeArgs(**kwargs) + try: + tz = ZoneInfo(args.timezone_name) + except Exception: # noqa: BLE001 + tz = timezone.utc + now = datetime.now(tz) + return { + "iso": now.isoformat(), + "date": now.strftime("%Y-%m-%d"), + "time": now.strftime("%H:%M:%S"), + "weekday": ["周一", "周二", "周三", "周四", "周五", "周六", "周日"][now.weekday()], + "timezone": str(tz), + } + + +class JsonQueryArgs(BaseModel): + data: str = Field(description="JSON 字符串") + path: str = Field(description="点号路径，例如 results.0.title 或 items[2].name") + + +@register( + name="json_query", + category="数据", + description="从 JSON 里按路径取值，避免让模型自己在长 JSON 里数括号。", + args_schema=JsonQueryArgs, +) +async def json_query(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = JsonQueryArgs(**kwargs) + try: + parsed = json.loads(args.data) + except json.JSONDecodeError as e: + return {"error": f"JSON 解析失败：{e}"} + return {"path": args.path, "value": resolve_path({"root": parsed, **(parsed if isinstance(parsed, dict) else {})}, args.path)} diff --git a/backend/app/tools/builtin/files.py b/backend/app/tools/builtin/files.py new file mode 100644 index 00000000..6d2ba09b --- /dev/null +++ b/backend/app/tools/builtin/files.py @@ -0,0 +1,107 @@ +from __future__ import annotations + +from pathlib import Path +from typing import Any + +from pydantic import BaseModel, Field + +from app.core.config import settings +from app.tools.registry import ToolContext, register + +_MAX_READ = 200_000 + + +def _resolve(rel: str, ctx: ToolContext) -> Path: + """把相对路径锁死在这次运行自己的工作目录里。 + + 先 resolve 再比较，所以 `../../etc/passwd` 和软链接都逃不出去。 + """ + root = (settings.workspace_dir / (ctx.sandbox_session or "default")).resolve() + root.mkdir(parents=True, exist_ok=True) + target = (root / rel.lstrip("/")).resolve() + if target != root and root not in target.parents: + raise ValueError(f"路径越界：{rel}") + return target + + +class FileReadArgs(BaseModel): + path: str = Field(description="相对工作目录的文件路径") + + +@register( + name="file_read", + category="文件", + description="读取工作目录下的文件内容。", + args_schema=FileReadArgs, +) +async def file_read(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = FileReadArgs(**kwargs) + try: + target = _resolve(args.path, ctx) + except ValueError as e: + return {"error": str(e)} + if not target.exists(): + return {"error": f"文件不存在：{args.path}"} + if target.is_dir(): + return {"error": f"{args.path} 是目录，用 file_list"} + data = target.read_bytes()[:_MAX_READ] + return { + "path": args.path, + "size": target.stat().st_size, + "content": data.decode("utf-8", errors="replace"), + "truncated": target.stat().st_size > _MAX_READ, + } + + +class FileWriteArgs(BaseModel): + path: str = Field(description="相对工作目录的文件路径") + content: str = Field(description="要写入的内容") + append: bool = Field(default=False, description="追加而不是覆盖") + + +@register( + name="file_write", + category="文件", + description="写入文件到工作目录。父目录会自动创建。", + args_schema=FileWriteArgs, + dangerous=True, +) +async def file_write(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = FileWriteArgs(**kwargs) + try: + target = _resolve(args.path, ctx) + except ValueError as e: + return {"error": str(e)} + target.parent.mkdir(parents=True, exist_ok=True) + with target.open("a" if args.append else "w", encoding="utf-8") as f: + f.write(args.content) + return {"path": args.path, "bytes": target.stat().st_size, "ok": True} + + +class FileListArgs(BaseModel): + path: str = Field(default=".", description="要列出的目录") + + +@register( + name="file_list", + category="文件", + description="列出工作目录下的文件。", + args_schema=FileListArgs, +) +async def file_list(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = FileListArgs(**kwargs) + try: + target = _resolve(args.path, ctx) + except ValueError as e: + return {"error": str(e)} + if not target.exists(): + return {"path": args.path, "entries": []} + entries = [ + { + "name": p.name, + "type": "dir" if p.is_dir() else "file", + "size": p.stat().st_size if p.is_file() else None, + } + for p in sorted(target.iterdir())[:500] + ] + return {"path": args.path, "entries": entries} diff --git a/backend/app/tools/builtin/knowledge.py b/backend/app/tools/builtin/knowledge.py new file mode 100644 index 00000000..b0d517d0 --- /dev/null +++ b/backend/app/tools/builtin/knowledge.py @@ -0,0 +1,80 @@ +from __future__ import annotations + +from typing import Any + +from pydantic import BaseModel, Field + +from app.db.base import SessionLocal +from app.memory import kb, store +from app.tools.registry import ToolContext, register + + +class KbSearchArgs(BaseModel): + query: str = Field(description="检索问题") + limit: int = Field(default=5, ge=1, le=20) + collection: str | None = Field(default=None, description="限定知识库集合，留空则用节点配置的") + + +@register( + name="kb_search", + category="知识", + description="在知识库里做混合检索（向量 + 关键词），返回最相关的片段。", + args_schema=KbSearchArgs, +) +async def kb_search(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = KbSearchArgs(**kwargs) + async with SessionLocal() as session: + hits = await kb.search( + session, + collection=args.collection or ctx.collection, + query=args.query, + limit=args.limit, + ) + return {"query": args.query, "hits": hits, "count": len(hits)} + + +class MemoryRecallArgs(BaseModel): + query: str = Field(description="想回忆什么") + limit: int = Field(default=5, ge=1, le=20) + + +@register( + name="memory_recall", + category="知识", + description="从长期记忆里取回与当前问题相关的内容。", + args_schema=MemoryRecallArgs, +) +async def memory_recall(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = MemoryRecallArgs(**kwargs) + async with SessionLocal() as session: + hits = await store.recall( + session, scope=ctx.memory_scope, query=args.query, limit=args.limit + ) + return {"scope": ctx.memory_scope, "memories": hits, "count": len(hits)} + + +class MemoryWriteArgs(BaseModel): + content: str = Field(description="要记住的内容，一条一个事实，写完整句子") + kind: str = Field(default="fact", description="fact | preference | episode") + importance: float = Field(default=0.5, ge=0.0, le=1.0) + + +@register( + name="memory_write", + category="知识", + description="把一条值得长期记住的信息写入记忆。相似内容会自动去重。", + args_schema=MemoryWriteArgs, + dangerous=False, +) +async def memory_write(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = MemoryWriteArgs(**kwargs) + async with SessionLocal() as session: + item = await store.remember( + session, + scope=ctx.memory_scope, + content=args.content, + kind=args.kind, + importance=args.importance, + meta={"run_id": ctx.run_id, "node_id": ctx.node_id}, + ) + return {"id": item.id, "scope": item.scope, "saved": True} diff --git a/backend/app/tools/builtin/web.py b/backend/app/tools/builtin/web.py new file mode 100644 index 00000000..661a1958 --- /dev/null +++ b/backend/app/tools/builtin/web.py @@ -0,0 +1,179 @@ +from __future__ import annotations + +import json +import os +from typing import Any, Literal + +from pydantic import BaseModel, Field + +from app.tools.net import UnsafeUrlError, safe_request +from app.tools.registry import ToolContext, register + +# -------------------------------------------------------------------------- +# HTTP 请求 +# -------------------------------------------------------------------------- + + +class HttpRequestArgs(BaseModel): + url: str = Field(description="完整的 http/https URL") + method: Literal["GET", "POST", "PUT", "PATCH", "DELETE", "HEAD"] = "GET" + headers: dict[str, str] | None = Field(default=None, description="额外请求头") + params: dict[str, str] | None = Field(default=None, description="查询参数") + json_body: dict[str, Any] | None = Field(default=None, description="JSON 请求体") + + +@register( + name="http_request", + category="网络", + description="发起 HTTP 请求并返回状态码与响应体。内网地址会被拒绝。", + args_schema=HttpRequestArgs, + dangerous=True, +) +async def http_request(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = HttpRequestArgs(**kwargs) + try: + result = await safe_request( + args.method, + args.url, + headers=args.headers, + params=args.params, + json_body=args.json_body, + ) + except UnsafeUrlError as e: + return {"error": str(e)} + body = result["body"] + if isinstance(body, str) and len(body) > 20_000: + result["body"] = body[:20_000] + "\n…（已截断）" + return result + + +# -------------------------------------------------------------------------- +# 抓网页 +# -------------------------------------------------------------------------- + + +class WebFetchArgs(BaseModel): + url: str = Field(description="要抓取的网页地址") + max_chars: int = Field(default=8000, description="返回正文的最大字符数") + + +def _html_to_text(html: str) -> tuple[str, str]: + """抽正文。去掉脚本样式导航，保留标题和段落结构。""" + from bs4 import BeautifulSoup + + soup = BeautifulSoup(html, "html.parser") + for tag in soup(["script", "style", "noscript", "iframe", "svg", "nav", "footer", "header"]): + tag.decompose() + title = (soup.title.string or "").strip() if soup.title else "" + main = soup.find("article") or soup.find("main") or soup.body or soup + lines: list[str] = [] + for el in main.find_all(["h1", "h2", "h3", "h4", "li", "p", "pre", "blockquote"]): + text = " ".join(el.get_text(" ", strip=True).split()) + if not text: + continue + if el.name.startswith("h"): + lines.append(f"\n{'#' * int(el.name[1])} {text}") + elif el.name == "li": + lines.append(f"- {text}") + else: + lines.append(text) + body = "\n".join(lines) + if not body.strip(): + body = " ".join(main.get_text(" ", strip=True).split()) + return title, body + + +@register( + name="web_fetch", + category="网络", + description="抓取网页并转换成干净的正文文本，适合让模型阅读。", + args_schema=WebFetchArgs, +) +async def web_fetch(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = WebFetchArgs(**kwargs) + try: + result = await safe_request("GET", args.url, headers={"User-Agent": "AgentLab/0.1"}) + except UnsafeUrlError as e: + return {"error": str(e)} + if result["status"] >= 400: + return {"error": f"HTTP {result['status']}", "url": result["url"]} + title, text = _html_to_text(str(result["body"])) + truncated = len(text) > args.max_chars + return { + "url": result["url"], + "title": title, + "content": text[: args.max_chars] + ("\n…（已截断）" if truncated else ""), + "truncated": truncated, + } + + +# -------------------------------------------------------------------------- +# 搜索 +# -------------------------------------------------------------------------- + + +class WebSearchArgs(BaseModel): + query: str = Field(description="搜索关键词") + max_results: int = Field(default=5, ge=1, le=20) + + +async def _tavily(query: str, n: int, key: str) -> list[dict[str, str]]: + result = await safe_request( + "POST", + "https://api.tavily.com/search", + json_body={"api_key": key, "query": query, "max_results": n}, + ) + data = json.loads(str(result["body"])) + return [ + {"title": r.get("title", ""), "url": r.get("url", ""), "snippet": r.get("content", "")} + for r in data.get("results", []) + ] + + +async def _duckduckgo(query: str, n: int) -> list[dict[str, str]]: + """没有搜索 API key 时的兜底：解析 DuckDuckGo 的 lite 页面。""" + from bs4 import BeautifulSoup + + result = await safe_request( + "POST", + "https://lite.duckduckgo.com/lite/", + data=f"q={query}", + headers={ + "Content-Type": "application/x-www-form-urlencoded", + "User-Agent": "Mozilla/5.0 (compatible; AgentLab/0.1)", + }, + ) + soup = BeautifulSoup(str(result["body"]), "html.parser") + out: list[dict[str, str]] = [] + for row in soup.select("a.result-link"): + out.append( + { + "title": row.get_text(" ", strip=True), + "url": row.get("href", ""), + "snippet": "", + } + ) + if len(out) >= n: + break + return out + + +@register( + name="web_search", + category="网络", + description="搜索互联网。配了 TAVILY_API_KEY 就走 Tavily，否则退回 DuckDuckGo。", + args_schema=WebSearchArgs, +) +async def web_search(ctx: ToolContext, **kwargs: Any) -> dict[str, Any]: + args = WebSearchArgs(**kwargs) + key = os.environ.get("TAVILY_API_KEY") + try: + if key: + results = await _tavily(args.query, args.max_results, key) + engine = "tavily" + else: + results = await _duckduckgo(args.query, args.max_results) + engine = "duckduckgo" + except Exception as e: # noqa: BLE001 - 搜索失败不该让整个 run 挂掉 + return {"error": f"搜索失败：{type(e).__name__}: {e}", "results": []} + return {"engine": engine, "query": args.query, "results": results} diff --git a/backend/app/tools/custom.py b/backend/app/tools/custom.py new file mode 100644 index 00000000..8cbc2795 --- /dev/null +++ b/backend/app/tools/custom.py @@ -0,0 +1,151 @@ +from __future__ import annotations + +import json +from typing import Any + +from langchain_core.tools import BaseTool, StructuredTool +from pydantic import BaseModel, Field, create_model +from sqlalchemy import select +from sqlalchemy.ext.asyncio import AsyncSession + +from app.db.models import CustomTool +from app.engine.expressions import render_deep, render_template +from app.sandbox.base import SandboxLimits +from app.sandbox.manager import sandbox_manager +from app.tools.net import UnsafeUrlError, safe_request +from app.tools.registry import ToolContext + +_TYPES = { + "string": str, + "number": float, + "integer": int, + "boolean": bool, + "array": list, + "object": dict, +} + + +def schema_to_model(name: str, schema: dict[str, Any]) -> type[BaseModel]: + """把 JSON Schema 转成 pydantic 模型，好让它能绑成 LangChain 工具。""" + props = (schema or {}).get("properties") or {} + required = set((schema or {}).get("required") or []) + fields: dict[str, Any] = {} + for key, spec in props.items(): + py_type = _TYPES.get(spec.get("type", "string"), str) + description = spec.get("description", "") + if key in required: + fields[key] = (py_type, Field(description=description)) + else: + fields[key] = (py_type | None, Field(default=spec.get("default"), description=description)) + if not fields: + fields["input"] = (str, Field(default="", description="工具输入")) + return create_model(f"{name}_Args", **fields) # type: ignore[call-overload] + + +async def _run_http(row: CustomTool, args: dict[str, Any]) -> Any: + """HTTP 模板工具：url / headers / body 里可以用 {{ 参数名 }} 插值。""" + config = row.config or {} + ctx = dict(args) + url = render_template(config.get("url", ""), ctx) + headers = render_deep(config.get("headers") or {}, ctx) + body = config.get("body") + json_body = None + if body: + rendered = render_deep(body, ctx) if isinstance(body, dict) else render_template(str(body), ctx) + json_body = rendered if isinstance(rendered, dict) else json.loads(rendered or "null") + try: + result = await safe_request( + config.get("method", "GET"), + url, + headers={k: str(v) for k, v in (headers or {}).items()}, + json_body=json_body, + timeout=config.get("timeout"), + ) + except UnsafeUrlError as e: + return {"error": str(e)} + except json.JSONDecodeError as e: + return {"error": f"请求体不是合法 JSON：{e}"} + + text = str(result.get("body", "")) + if config.get("parse_json"): + try: + return {"status": result["status"], "data": json.loads(text)} + except json.JSONDecodeError: + return {"status": result["status"], "body": text} + return {"status": result["status"], "body": text} + + +async def _run_python(row: CustomTool, args: dict[str, Any], ctx: ToolContext) -> Any: + """Python 工具：用户代码跑在沙箱里，参数通过 args 变量注入。""" + config = row.config or {} + code = config.get("code", "") + preamble = ( + "import json, sys\n" + f"args = json.loads({json.dumps(json.dumps(args, ensure_ascii=False))})\n" + ) + result = await sandbox_manager.run( + preamble + code, + language="python", + limits=SandboxLimits(timeout=int(config.get("timeout") or 30)), + session_id=ctx.sandbox_session, + ) + if not result.ok: + return {"error": result.error or result.stderr or "执行失败", "exit_code": result.exit_code} + out = result.stdout.strip() + try: + return json.loads(out) + except json.JSONDecodeError: + return out + + +async def build_custom_tools( + names: list[str], ctx: ToolContext, session: AsyncSession +) -> list[BaseTool]: + rows = list( + ( + await session.execute( + select(CustomTool).where( + CustomTool.name.in_(names), CustomTool.enabled.is_(True) + ) + ) + ).scalars() + ) + tools: list[BaseTool] = [] + for row in rows: + args_model = schema_to_model(row.name, row.parameters or {}) + + def _make(bound_row: CustomTool): + async def _run(**kwargs: Any) -> str: + if bound_row.kind == "python": + value = await _run_python(bound_row, kwargs, ctx) + else: + value = await _run_http(bound_row, kwargs) + if isinstance(value, str): + return value + return json.dumps(value, ensure_ascii=False, default=str, indent=2) + + return _run + + tools.append( + StructuredTool( + name=row.name, + description=row.description or f"自定义工具 {row.name}", + args_schema=args_model, + coroutine=_make(row), + func=None, + ) + ) + return tools + + +async def run_custom_tool( + session: AsyncSession, name: str, args: dict[str, Any], ctx: ToolContext +) -> Any: + row = ( + await session.execute(select(CustomTool).where(CustomTool.name == name)) + ).scalar_one_or_none() + if not row: + raise KeyError(f"找不到自定义工具 {name!r}") + if row.kind == "python": + return await _run_python(row, args, ctx) + return await _run_http(row, args) diff --git a/backend/app/tools/mcp_manager.py b/backend/app/tools/mcp_manager.py new file mode 100644 index 00000000..c2103d35 --- /dev/null +++ b/backend/app/tools/mcp_manager.py @@ -0,0 +1,133 @@ +from __future__ import annotations + +import asyncio +import logging +from typing import Any + +from langchain_core.tools import BaseTool +from sqlalchemy import select + +from app.db.base import SessionLocal +from app.db.models import McpServer + +logger = logging.getLogger(__name__) + + +def _server_config(row: McpServer) -> dict[str, Any]: + if row.transport == "http": + return {"transport": "streamable_http", "url": row.url or ""} + return { + "transport": "stdio", + "command": row.command or "", + "args": list(row.args or []), + "env": dict(row.env or {}), + } + + +class McpManager: + """管理外部 MCP server 的连接与工具缓存。 + + 工具在图里用 `mcp:/` 引用。连接是懒建立的， + 并且按 server 缓存，避免每个节点都去重新拉起一次 stdio 子进程。 + """ + + def __init__(self) -> None: + self._tools: dict[str, list[BaseTool]] = {} # server name -> tools + self._lock = asyncio.Lock() + + async def _load_servers(self) -> list[McpServer]: + async with SessionLocal() as session: + rows = await session.execute(select(McpServer).where(McpServer.enabled.is_(True))) + return list(rows.scalars()) + + async def _connect(self, rows: list[McpServer]) -> dict[str, list[BaseTool]]: + from langchain_mcp_adapters.client import MultiServerMCPClient + + out: dict[str, list[BaseTool]] = {} + for row in rows: + try: + client = MultiServerMCPClient({row.name: _server_config(row)}) + tools = await asyncio.wait_for(client.get_tools(), timeout=30) + out[row.name] = list(tools) + await self._mark(row.id, "ok", None, [t.name for t in tools]) + except Exception as e: # noqa: BLE001 - 一个 server 挂了不影响其他的 + logger.warning("MCP server %s 连接失败: %s", row.name, e) + out[row.name] = [] + await self._mark(row.id, "error", f"{type(e).__name__}: {e}", []) + return out + + async def _mark( + self, server_id: str, status: str, error: str | None, tool_names: list[str] + ) -> None: + async with SessionLocal() as session: + row = await session.get(McpServer, server_id) + if row: + row.status = status + row.last_error = error + row.tools_cache = tool_names + await session.commit() + + async def refresh(self) -> dict[str, list[str]]: + async with self._lock: + rows = await self._load_servers() + self._tools = await self._connect(rows) + return {name: [t.name for t in tools] for name, tools in self._tools.items()} + + async def ensure_loaded(self) -> None: + if not self._tools: + await self.refresh() + + async def list_tools(self) -> list[dict[str, Any]]: + await self.ensure_loaded() + out: list[dict[str, Any]] = [] + for server, tools in self._tools.items(): + for tool in tools: + out.append( + { + "id": f"mcp:{server}/{tool.name}", + "name": tool.name, + "server": server, + "description": tool.description or "", + "category": f"MCP · {server}", + "schema": tool.args_schema.model_json_schema() + if getattr(tool, "args_schema", None) + else {}, + } + ) + return out + + async def get_tools(self, refs: list[str]) -> list[BaseTool]: + """按 `mcp:/` 取工具；`mcp:/*` 表示该 server 全部工具。""" + await self.ensure_loaded() + wanted: list[BaseTool] = [] + for ref in refs: + body = ref[4:] if ref.startswith("mcp:") else ref + server, _, tool_name = body.partition("/") + tools = self._tools.get(server, []) + if tool_name in ("", "*"): + wanted.extend(tools) + continue + for tool in tools: + if tool.name == tool_name: + wanted.append(tool) + break + return wanted + + async def probe(self, row: McpServer) -> dict[str, Any]: + """连一次看看，用于设置页的"测试连接"。""" + from langchain_mcp_adapters.client import MultiServerMCPClient + + try: + client = MultiServerMCPClient({row.name: _server_config(row)}) + tools = await asyncio.wait_for(client.get_tools(), timeout=30) + return { + "ok": True, + "tools": [ + {"name": t.name, "description": t.description or ""} for t in tools + ], + } + except Exception as e: # noqa: BLE001 + return {"ok": False, "error": f"{type(e).__name__}: {e}"} + + +mcp_manager = McpManager() diff --git a/backend/app/tools/net.py b/backend/app/tools/net.py new file mode 100644 index 00000000..c04b26c4 --- /dev/null +++ b/backend/app/tools/net.py @@ -0,0 +1,115 @@ +from __future__ import annotations + +import ipaddress +import socket +from urllib.parse import urlparse + +import httpx + +from app.core.config import settings + + +class UnsafeUrlError(ValueError): + pass + + +_BLOCKED_PORTS = {22, 23, 25, 135, 139, 445, 3389} + + +def _is_private(ip: str) -> bool: + try: + addr = ipaddress.ip_address(ip) + except ValueError: + return True + return ( + addr.is_private + or addr.is_loopback + or addr.is_link_local # 169.254.169.254 —— 云厂商的元数据服务 + or addr.is_reserved + or addr.is_multicast + or addr.is_unspecified + ) + + +def assert_safe_url(url: str) -> str: + """挡住 SSRF。 + + agent 手里的 HTTP 工具就是一把面向内网的扫描枪，所以这里做三件事： + 只放行 http/https、解析域名后逐个 IP 检查是不是内网/回环/元数据地址、 + 再按配置里的域名白名单过一遍。 + """ + parsed = urlparse(url) + if parsed.scheme not in ("http", "https"): + raise UnsafeUrlError(f"只支持 http/https，收到 {parsed.scheme!r}") + host = parsed.hostname + if not host: + raise UnsafeUrlError("URL 里没有主机名") + if parsed.port in _BLOCKED_PORTS: + raise UnsafeUrlError(f"端口 {parsed.port} 不允许访问") + + allowlist = settings.http_tool_allowlist + if allowlist: + if not any(host == d or host.endswith("." + d) for d in allowlist): + raise UnsafeUrlError(f"{host} 不在白名单里（AGENTLAB_HTTP_TOOL_ALLOWLIST）") + + # 逐个解析结果都要查：一个域名可能同时解析到公网和内网地址 + try: + infos = socket.getaddrinfo(host, parsed.port or (443 if parsed.scheme == "https" else 80)) + except socket.gaierror as e: + raise UnsafeUrlError(f"域名解析失败：{host}") from e + for info in infos: + ip = info[4][0] + if _is_private(ip): + raise UnsafeUrlError(f"{host} 解析到内网地址 {ip}，已拦截") + return url + + +async def safe_request( + method: str, + url: str, + *, + headers: dict[str, str] | None = None, + params: dict[str, str] | None = None, + json_body: object | None = None, + data: str | None = None, + timeout: int | None = None, + max_bytes: int | None = None, +) -> dict[str, object]: + """发一个受控的 HTTP 请求：禁跟随跳转（跳转是绕过 SSRF 检查的常见手法）， + 逐跳重新校验，并对响应体截断。""" + timeout = timeout or settings.http_tool_timeout + max_bytes = max_bytes or settings.http_tool_max_bytes + current = assert_safe_url(url) + + async with httpx.AsyncClient( + timeout=timeout, follow_redirects=False, trust_env=False + ) as client: + for _ in range(5): + resp = await client.request( + method.upper(), + current, + headers=headers or {}, + params=params, + json=json_body, + content=data, + ) + if resp.status_code in (301, 302, 303, 307, 308) and "location" in resp.headers: + current = assert_safe_url( + str(httpx.URL(current).join(resp.headers["location"])) + ) + continue + break + + body = resp.content[:max_bytes] + truncated = len(resp.content) > max_bytes + try: + text = body.decode(resp.encoding or "utf-8", errors="replace") + except (LookupError, UnicodeDecodeError): + text = body.decode("utf-8", errors="replace") + return { + "status": resp.status_code, + "url": str(resp.url), + "headers": dict(resp.headers), + "body": text, + "truncated": truncated, + } diff --git a/backend/app/tools/registry.py b/backend/app/tools/registry.py new file mode 100644 index 00000000..e23582ea --- /dev/null +++ b/backend/app/tools/registry.py @@ -0,0 +1,158 @@ +from __future__ import annotations + +import inspect +import json +from dataclasses import dataclass, field +from typing import Any, Awaitable, Callable + +from langchain_core.tools import BaseTool, StructuredTool +from pydantic import BaseModel + +ToolFunc = Callable[..., Awaitable[Any]] + + +@dataclass +class ToolContext: + """一次调用里工具能用到的运行期依赖。""" + + run_id: str = "" + node_id: str = "" + sandbox_session: str = "default" + memory_scope: str = "default" + collection: str = "default" + emit: Callable[..., Awaitable[None]] | None = None + extra: dict[str, Any] = field(default_factory=dict) + + +@dataclass +class ToolSpec: + name: str + description: str + category: str + args_schema: type[BaseModel] + func: ToolFunc + # 需要人工确认才执行（写文件、跑代码、发请求这类有副作用的） + dangerous: bool = False + # 函数第一个参数是否接收 ToolContext + needs_context: bool = True + + def json_schema(self) -> dict[str, Any]: + return self.args_schema.model_json_schema() + + +_REGISTRY: dict[str, ToolSpec] = {} + + +def register( + name: str, + description: str, + category: str, + args_schema: type[BaseModel], + *, + dangerous: bool = False, + needs_context: bool = True, +) -> Callable[[ToolFunc], ToolFunc]: + def deco(func: ToolFunc) -> ToolFunc: + _REGISTRY[name] = ToolSpec( + name=name, + description=description, + category=category, + args_schema=args_schema, + func=func, + dangerous=dangerous, + needs_context=needs_context, + ) + return func + + return deco + + +def all_specs() -> dict[str, ToolSpec]: + import app.tools.builtin # noqa: F401 触发内置工具注册 + + return dict(_REGISTRY) + + +def get_spec(name: str) -> ToolSpec | None: + return all_specs().get(name) + + +def _stringify(value: Any) -> str: + """工具结果最终是喂给模型的，所以统一成字符串；结构化数据转紧凑 JSON。""" + if isinstance(value, str): + return value + if value is None: + return "" + return json.dumps(value, ensure_ascii=False, default=str, indent=2) + + +def build_tool(spec: ToolSpec, ctx: ToolContext) -> BaseTool: + """把注册表里的定义 + 运行期上下文，绑成一个 LangChain 工具。""" + + async def _run(**kwargs: Any) -> str: + if spec.needs_context: + result = await spec.func(ctx, **kwargs) + else: + result = await spec.func(**kwargs) + return _stringify(result) + + return StructuredTool( + name=spec.name, + description=spec.description, + args_schema=spec.args_schema, + coroutine=_run, + # 只给异步实现；同步路径直接报错，避免在事件循环里被阻塞调用 + func=None, + ) + + +async def build_tools( + names: list[str], ctx: ToolContext, *, session: Any = None +) -> list[BaseTool]: + """按名字列表组装工具。支持三种来源：内置、MCP（mcp:/）、自定义。""" + specs = all_specs() + tools: list[BaseTool] = [] + mcp_names: list[str] = [] + custom_names: list[str] = [] + + for name in names: + if name.startswith("mcp:"): + mcp_names.append(name) + elif name in specs: + tools.append(build_tool(specs[name], ctx)) + else: + custom_names.append(name) + + if mcp_names: + from app.tools.mcp_manager import mcp_manager + + tools.extend(await mcp_manager.get_tools(mcp_names)) + + if custom_names and session is not None: + from app.tools.custom import build_custom_tools + + tools.extend(await build_custom_tools(custom_names, ctx, session)) + + return tools + + +async def call_tool(name: str, args: dict[str, Any], ctx: ToolContext, session: Any = None) -> Any: + """直接调用一个工具（工具节点、以及设置页的"试一下"按钮用）。""" + spec = get_spec(name) + if spec: + validated = spec.args_schema(**args) + payload = validated.model_dump() + if spec.needs_context: + return await spec.func(ctx, **payload) + return await spec.func(**payload) + + tools = await build_tools([name], ctx, session=session) + if not tools: + raise KeyError(f"找不到工具 {name!r}") + tool = tools[0] + if tool.coroutine: + return await tool.coroutine(**args) + result = tool.invoke(args) + if inspect.isawaitable(result): + return await result + return result diff --git a/backend/pyproject.toml b/backend/pyproject.toml new file mode 100644 index 00000000..fd48a9f1 --- /dev/null +++ b/backend/pyproject.toml @@ -0,0 +1,35 @@ +[project] +name = "agentlab-backend" +version = "0.1.0" +description = "AgentLab — 可视化 Agent 编排实验台后端" +requires-python = ">=3.12" +dependencies = [ + "fastapi>=0.115", + "uvicorn[standard]>=0.32", + "pydantic>=2.9", + "pydantic-settings>=2.6", + "sqlalchemy[asyncio]>=2.0.36", + "aiosqlite>=0.20", + "greenlet>=3.1", + "httpx>=0.28", + "python-multipart>=0.0.17", + "langgraph", + "langgraph-checkpoint-sqlite", + "langchain-core", + "langchain-anthropic", + "langchain-openai", + "langchain-mcp-adapters", + "mcp", + "docker>=7.1", + "numpy>=2.0", + "jsonschema>=4.23", + 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'@typescript/typescript-linux-ppc64@7.0.2': + optional: true + + '@typescript/typescript-linux-riscv64@7.0.2': + optional: true + + '@typescript/typescript-linux-s390x@7.0.2': + optional: true + + '@typescript/typescript-linux-x64@7.0.2': + optional: true + + '@typescript/typescript-netbsd-arm64@7.0.2': + optional: true + + '@typescript/typescript-netbsd-x64@7.0.2': + optional: true + + '@typescript/typescript-openbsd-arm64@7.0.2': + optional: true + + '@typescript/typescript-openbsd-x64@7.0.2': + optional: true + + '@typescript/typescript-sunos-x64@7.0.2': + optional: true + + '@typescript/typescript-win32-arm64@7.0.2': + optional: true + + '@typescript/typescript-win32-x64@7.0.2': + optional: true + + '@vitejs/plugin-react@6.1.1(vite@8.3.0(jiti@2.7.0))': + dependencies: + '@rolldown/pluginutils': 1.0.1 + vite: 8.3.0(jiti@2.7.0) + + '@xyflow/react@12.11.6(@types/react-dom@19.3.0(@types/react@19.3.0))(@types/react@19.3.0)(react-dom@19.3.0(react@19.3.0))(react@19.3.0)': + dependencies: + '@xyflow/system': 0.0.82 + classcat: 5.0.5 + react: 19.3.0 + react-dom: 19.3.0(react@19.3.0) + zustand: 4.5.7(@types/react@19.3.0)(react@19.3.0) + optionalDependencies: + '@types/react': 19.3.0 + '@types/react-dom': 19.3.0(@types/react@19.3.0) + transitivePeerDependencies: + - immer + + '@xyflow/system@0.0.82': + dependencies: + '@types/d3-drag': 3.0.7 + '@types/d3-interpolate': 3.0.4 + '@types/d3-selection': 3.0.11 + '@types/d3-transition': 3.0.9 + '@types/d3-zoom': 3.0.8 + d3-drag: 3.0.0 + d3-interpolate: 3.0.1 + d3-selection: 3.0.0 + d3-zoom: 3.0.0 + + classcat@5.0.5: {} + + clsx@2.1.1: {} + + cookie@1.1.1: {} + + csstype@3.2.3: {} + + d3-color@3.1.0: {} + + d3-dispatch@3.0.1: {} + + d3-drag@3.0.0: + dependencies: + d3-dispatch: 3.0.1 + d3-selection: 3.0.0 + + d3-ease@3.0.1: {} + + d3-interpolate@3.0.1: + dependencies: + d3-color: 3.1.0 + + d3-selection@3.0.0: {} + + d3-timer@3.0.1: {} + + d3-transition@3.0.1(d3-selection@3.0.0): + dependencies: + d3-color: 3.1.0 + d3-dispatch: 3.0.1 + d3-ease: 3.0.1 + d3-interpolate: 3.0.1 + d3-selection: 3.0.0 + d3-timer: 3.0.1 + + d3-zoom@3.0.0: + dependencies: + d3-dispatch: 3.0.1 + d3-drag: 3.0.0 + d3-interpolate: 3.0.1 + d3-selection: 3.0.0 + d3-transition: 3.0.1(d3-selection@3.0.0) + + detect-libc@2.1.2: {} + + enhanced-resolve@5.25.0: + dependencies: + graceful-fs: 4.2.11 + tapable: 2.3.3 + + fdir@6.5.0(picomatch@4.0.7): + optionalDependencies: + picomatch: 4.0.7 + + fsevents@2.3.3: + optional: true + + graceful-fs@4.2.11: {} + + jiti@2.7.0: {} + + lightningcss-android-arm64@1.32.0: + optional: true + + lightningcss-android-arm64@1.33.0: + optional: true + + lightningcss-darwin-arm64@1.32.0: + optional: true + + lightningcss-darwin-arm64@1.33.0: + optional: true + + lightningcss-darwin-x64@1.32.0: + optional: true + + lightningcss-darwin-x64@1.33.0: + optional: true + + lightningcss-freebsd-x64@1.32.0: + optional: true + + lightningcss-freebsd-x64@1.33.0: + optional: true + + lightningcss-linux-arm-gnueabihf@1.32.0: + optional: true + + lightningcss-linux-arm-gnueabihf@1.33.0: + optional: true + + lightningcss-linux-arm64-gnu@1.32.0: + optional: true + + lightningcss-linux-arm64-gnu@1.33.0: + optional: true + + lightningcss-linux-arm64-musl@1.32.0: + optional: true + + lightningcss-linux-arm64-musl@1.33.0: + optional: true + + lightningcss-linux-x64-gnu@1.32.0: + optional: true + + lightningcss-linux-x64-gnu@1.33.0: + optional: true + + lightningcss-linux-x64-musl@1.32.0: + optional: true + + lightningcss-linux-x64-musl@1.33.0: + optional: true + + lightningcss-win32-arm64-msvc@1.32.0: + optional: true + + lightningcss-win32-arm64-msvc@1.33.0: + optional: true + + lightningcss-win32-x64-msvc@1.32.0: + optional: true + + lightningcss-win32-x64-msvc@1.33.0: + optional: true + + lightningcss@1.32.0: + dependencies: + detect-libc: 2.1.2 + optionalDependencies: + lightningcss-android-arm64: 1.32.0 + lightningcss-darwin-arm64: 1.32.0 + lightningcss-darwin-x64: 1.32.0 + lightningcss-freebsd-x64: 1.32.0 + lightningcss-linux-arm-gnueabihf: 1.32.0 + lightningcss-linux-arm64-gnu: 1.32.0 + lightningcss-linux-arm64-musl: 1.32.0 + lightningcss-linux-x64-gnu: 1.32.0 + lightningcss-linux-x64-musl: 1.32.0 + lightningcss-win32-arm64-msvc: 1.32.0 + lightningcss-win32-x64-msvc: 1.32.0 + + lightningcss@1.33.0: + dependencies: + detect-libc: 2.1.2 + optionalDependencies: + lightningcss-android-arm64: 1.33.0 + lightningcss-darwin-arm64: 1.33.0 + lightningcss-darwin-x64: 1.33.0 + lightningcss-freebsd-x64: 1.33.0 + lightningcss-linux-arm-gnueabihf: 1.33.0 + lightningcss-linux-arm64-gnu: 1.33.0 + lightningcss-linux-arm64-musl: 1.33.0 + lightningcss-linux-x64-gnu: 1.33.0 + lightningcss-linux-x64-musl: 1.33.0 + lightningcss-win32-arm64-msvc: 1.33.0 + lightningcss-win32-x64-msvc: 1.33.0 + + lucide-react@1.45.0(react@19.3.0): + dependencies: + react: 19.3.0 + + magic-string@0.30.21: + dependencies: + '@jridgewell/sourcemap-codec': 1.6.0 + + nanoid@3.3.19: {} + + picocolors@1.1.1: {} + + picomatch@4.0.7: {} + + playwright-core@1.63.0: {} + + postcss@8.5.28: + dependencies: + nanoid: 3.3.19 + picocolors: 1.1.1 + source-map-js: 1.2.1 + + react-dom@19.3.0(react@19.3.0): + dependencies: + react: 19.3.0 + scheduler: 0.28.0 + + react-router-dom@7.18.3(react-dom@19.3.0(react@19.3.0))(react@19.3.0): + dependencies: + react: 19.3.0 + react-dom: 19.3.0(react@19.3.0) + react-router: 7.18.3(react-dom@19.3.0(react@19.3.0))(react@19.3.0) + + react-router@7.18.3(react-dom@19.3.0(react@19.3.0))(react@19.3.0): + dependencies: + cookie: 1.1.1 + react: 19.3.0 + set-cookie-parser: 2.7.2 + optionalDependencies: + react-dom: 19.3.0(react@19.3.0) + + react@19.3.0: {} + + rolldown@1.2.8: + dependencies: + '@oxc-project/types': 0.149.0 + '@rolldown/pluginutils': 1.0.1 + optionalDependencies: + '@rolldown/binding-android-arm-eabi': 1.2.8 + '@rolldown/binding-android-arm64': 1.2.8 + '@rolldown/binding-darwin-arm64': 1.2.8 + '@rolldown/binding-darwin-x64': 1.2.8 + '@rolldown/binding-freebsd-x64': 1.2.8 + '@rolldown/binding-linux-arm-gnueabihf': 1.2.8 + '@rolldown/binding-linux-arm64-gnu': 1.2.8 + '@rolldown/binding-linux-arm64-musl': 1.2.8 + '@rolldown/binding-linux-ppc64-gnu': 1.2.8 + '@rolldown/binding-linux-s390x-gnu': 1.2.8 + '@rolldown/binding-linux-x64-gnu': 1.2.8 + '@rolldown/binding-linux-x64-musl': 1.2.8 + '@rolldown/binding-openharmony-arm64': 1.2.8 + '@rolldown/binding-win32-arm64-msvc': 1.2.8 + '@rolldown/binding-win32-x64-msvc': 1.2.8 + + scheduler@0.28.0: {} + + set-cookie-parser@2.7.2: {} + + source-map-js@1.2.1: {} + + tailwindcss@4.3.3: {} + + tapable@2.3.3: {} + + tinyglobby@0.2.17: + dependencies: + fdir: 6.5.0(picomatch@4.0.7) + picomatch: 4.0.7 + + typescript@7.0.2: + optionalDependencies: + '@typescript/typescript-aix-ppc64': 7.0.2 + '@typescript/typescript-darwin-arm64': 7.0.2 + '@typescript/typescript-darwin-x64': 7.0.2 + '@typescript/typescript-freebsd-arm64': 7.0.2 + '@typescript/typescript-freebsd-x64': 7.0.2 + '@typescript/typescript-linux-arm': 7.0.2 + '@typescript/typescript-linux-arm64': 7.0.2 + '@typescript/typescript-linux-loong64': 7.0.2 + '@typescript/typescript-linux-mips64el': 7.0.2 + '@typescript/typescript-linux-ppc64': 7.0.2 + '@typescript/typescript-linux-riscv64': 7.0.2 + '@typescript/typescript-linux-s390x': 7.0.2 + '@typescript/typescript-linux-x64': 7.0.2 + '@typescript/typescript-netbsd-arm64': 7.0.2 + '@typescript/typescript-netbsd-x64': 7.0.2 + '@typescript/typescript-openbsd-arm64': 7.0.2 + '@typescript/typescript-openbsd-x64': 7.0.2 + '@typescript/typescript-sunos-x64': 7.0.2 + '@typescript/typescript-win32-arm64': 7.0.2 + '@typescript/typescript-win32-x64': 7.0.2 + + use-sync-external-store@1.7.0(react@19.3.0): + dependencies: + react: 19.3.0 + + vite@8.3.0(jiti@2.7.0): + dependencies: + lightningcss: 1.33.0 + picomatch: 4.0.7 + postcss: 8.5.28 + rolldown: 1.2.8 + tinyglobby: 0.2.17 + optionalDependencies: + fsevents: 2.3.3 + jiti: 2.7.0 + + zustand@4.5.7(@types/react@19.3.0)(react@19.3.0): + dependencies: + use-sync-external-store: 1.7.0(react@19.3.0) + optionalDependencies: + '@types/react': 19.3.0 + react: 19.3.0 + + zustand@5.0.15(@types/react@19.3.0)(react@19.3.0)(use-sync-external-store@1.7.0(react@19.3.0)): + optionalDependencies: + '@types/react': 19.3.0 + react: 19.3.0 + use-sync-external-store: 1.7.0(react@19.3.0) diff --git a/frontend/src/App.tsx b/frontend/src/App.tsx new file mode 100644 index 00000000..52962535 --- /dev/null +++ b/frontend/src/App.tsx @@ -0,0 +1,86 @@ +import { useEffect } from 'react' +import { NavLink, Navigate, Route, Routes } from 'react-router-dom' +import { BookOpen, FlaskConical, History, Settings, Wrench } from 'lucide-react' +import clsx from 'clsx' +import { api } from './api/client' +import { useCatalog } from './store/catalog' +import { StudioPage } from './pages/StudioPage' +import { RunsPage } from './pages/RunsPage' +import { ToolsPage } from './pages/ToolsPage' +import { KnowledgePage } from './pages/KnowledgePage' +import { SettingsPage, applyTheme } from './pages/SettingsPage' +import { Spinner } from './components/ui' + +const NAV = [ + { to: '/studio', label: '编排', icon: FlaskConical }, + { to: '/runs', label: '运行', icon: History }, + { to: '/tools', label: '工具', icon: Wrench }, + { to: '/knowledge', label: '知识', icon: BookOpen }, + { to: '/settings', label: '设置', icon: Settings }, +] + +export default function App() { + const { loaded, refresh, refreshApprovals, approvals } = useCatalog() + + useEffect(() => { + void refresh() + void api.settings.get().then((s) => applyTheme(s?.ui?.theme ?? 'system')).catch(() => {}) + }, [refresh]) + + // 待审批是全局待办：别的页面也要能看到有东西等着人处理 + useEffect(() => { + const timer = setInterval(() => void refreshApprovals(), 4000) + return () => clearInterval(timer) + }, [refreshApprovals]) + + const pending = approvals.filter((a) => a.status === 'pending').length + + if (!loaded) { + return ( +
+ 正在连接后端… +
+ ) + } + + return ( +
+ + +
+ + } /> + } /> + } /> + } /> + } /> + } /> + +
+
+ ) +} diff --git a/frontend/src/api/client.ts b/frontend/src/api/client.ts new file mode 100644 index 00000000..345c030e --- /dev/null +++ b/frontend/src/api/client.ts @@ -0,0 +1,228 @@ +import type { + Approval, GraphSpec, KbDocument, MemoryItem, Provider, Run, RunEvent, + Skill, ToolInfo, ValidationIssue, Workflow, +} from '../types' + +const BASE = '/api' + +export class ApiError extends Error { + constructor(public status: number, message: string) { + super(message) + } +} + +async function request(path: string, init?: RequestInit): Promise { + const res = await fetch(BASE + path, { + ...init, + headers: { + ...(init?.body instanceof FormData ? {} : { 'Content-Type': 'application/json' }), + ...init?.headers, + }, + }) + if (!res.ok) { + let detail = `${res.status} ${res.statusText}` + try { + const body = await res.json() + // FastAPI 的校验错误是数组，直接 String() 会变成 [object Object] + detail = typeof body.detail === 'string' ? body.detail : JSON.stringify(body.detail ?? body) + } catch { + /* 响应体不是 JSON，保留状态行 */ + } + throw new ApiError(res.status, detail) + } + if (res.status === 204) return undefined as T + return res.json() +} + +const get = (p: string) => request(p) +const post = (p: string, body?: unknown) => + request(p, { method: 'POST', body: JSON.stringify(body ?? {}) }) +const patch = (p: string, body: unknown) => + request(p, { method: 'PATCH', body: JSON.stringify(body) }) +const put = (p: string, body: unknown) => + request(p, { method: 'PUT', body: JSON.stringify(body) }) +const del = (p: string) => request(p, { method: 'DELETE' }) + +export const api = { + health: () => get<{ status: string }>('/health'), + system: () => get('/system'), + + // ---- 工作流 ---- + workflows: { + list: () => get('/workflows'), + get: (id: string) => get(`/workflows/${id}`), + create: (body: { name: string; description?: string; graph?: GraphSpec; tags?: string[] }) => + post('/workflows', body), + update: (id: string, body: Partial & { note?: string }) => + patch(`/workflows/${id}`, body), + remove: (id: string) => del(`/workflows/${id}`), + duplicate: (id: string) => post(`/workflows/${id}/duplicate`), + versions: (id: string) => get(`/workflows/${id}/versions`), + restore: (id: string, v: number) => post(`/workflows/${id}/versions/${v}/restore`), + validate: (graph: GraphSpec) => + post<{ ok: boolean; issues: ValidationIssue[] }>('/workflows/validate', { graph }), + }, + + // ---- 运行 ---- + runs: { + list: (params?: { workflow_id?: string; status?: string; limit?: number }) => { + const q = new URLSearchParams( + Object.entries(params ?? {}).filter(([, v]) => v != null) as [string, string][], + ) + return get(`/runs${q.toString() ? `?${q}` : ''}`) + }, + get: (id: string) => get(`/runs/${id}`), + start: (body: { + workflow_id?: string; graph?: GraphSpec; input?: Record + memory_scope?: string; collection?: string + }) => post('/runs', body), + cancel: (id: string) => post<{ ok: boolean }>(`/runs/${id}/cancel`), + resume: (id: string, response: any) => post(`/runs/${id}/resume`, { response }), + events: (id: string, after = 0) => get(`/runs/${id}/events?after=${after}`), + state: (id: string) => get(`/runs/${id}/state`), + history: (id: string) => get(`/runs/${id}/history`), + remove: (id: string) => del(`/runs/${id}`), + }, + + approvals: { + list: (status = 'pending') => get(`/approvals?status=${status}`), + decide: (id: string, body: { approved: boolean; note?: string; value?: any; args?: any }) => + post(`/approvals/${id}/decide`, body), + }, + + // ---- 设置 ---- + providers: { + list: () => get('/providers'), + catalog: () => get('/providers/catalog'), + create: (body: any) => post('/providers', body), + update: (id: string, body: any) => patch(`/providers/${id}`, body), + remove: (id: string) => del(`/providers/${id}`), + test: (id: string, body: { model?: string; prompt?: string }) => + post(`/providers/${id}/test`, body), + }, + settings: { + get: () => get>('/settings'), + put: (values: Record) => put>('/settings', { values }), + }, + + // ---- 工具 ---- + tools: { + list: () => get('/tools'), + run: (name: string, args: Record) => + post(`/tools/${name}/run`, { args }), + }, + customTools: { + list: () => get('/custom-tools'), + create: (body: any) => post('/custom-tools', body), + update: (id: string, body: any) => patch(`/custom-tools/${id}`, body), + remove: (id: string) => del(`/custom-tools/${id}`), + test: (id: string, args: Record) => post(`/custom-tools/${id}/test`, { args }), + }, + mcp: { + list: () => get('/mcp/servers'), + create: (body: any) => post('/mcp/servers', body), + update: (id: string, body: any) => patch(`/mcp/servers/${id}`, body), + remove: (id: string) => del(`/mcp/servers/${id}`), + probe: (id: string) => post(`/mcp/servers/${id}/probe`), + refresh: () => post('/mcp/refresh'), + }, + + // ---- 沙箱 ---- + sandbox: { + health: () => get('/sandbox/health'), + exec: (body: { code: string; language?: string; timeout?: number; network?: boolean; memory_mb?: number }) => + post('/sandbox/exec', body), + }, + + // ---- 记忆 / 知识库 / Skill ---- + memory: { + scopes: () => get<{ scope: string; count: number }[]>('/memory/scopes'), + list: (scope?: string) => get(`/memory${scope ? `?scope=${scope}` : ''}`), + add: (body: { content: string; scope?: string; kind?: string; importance?: number }) => + post('/memory', body), + search: (q: string, scope = 'default') => + get(`/memory/search?q=${encodeURIComponent(q)}&scope=${scope}`), + remove: (id: string) => del(`/memory/${id}`), + clearScope: (scope: string) => del(`/memory/scope/${scope}`), + }, + kb: { + collections: () => get<{ collection: string; documents: number; chunks: number }[]>('/kb/collections'), + documents: (collection?: string) => + get(`/kb/documents${collection ? `?collection=${collection}` : ''}`), + ingest: (body: { collection?: string; title?: string; content: string; source?: string }) => + post('/kb/documents', body), + upload: (file: File, collection = 'default') => { + const form = new FormData() + form.append('file', file) + return request(`/kb/upload?collection=${collection}`, { method: 'POST', body: form }) + }, + search: (q: string, collection?: string, alpha = 0.5) => + get(`/kb/search?q=${encodeURIComponent(q)}&alpha=${alpha}${collection ? `&collection=${collection}` : ''}`), + remove: (id: string) => del(`/kb/documents/${id}`), + }, + skills: { + list: () => get('/skills'), + create: (body: Partial) => post('/skills', body), + update: (id: string, body: Partial) => patch(`/skills/${id}`, body), + remove: (id: string) => del(`/skills/${id}`), + }, + + // ---- Copilot ---- + copilot: { + generate: (body: { instruction: string; base_graph?: GraphSpec | null }) => + post<{ graph: GraphSpec; explanation: string; issues: ValidationIssue[] }>( + '/copilot/generate', body), + explain: (graph: GraphSpec) => post<{ explanation: string }>('/copilot/explain', { graph }), + layout: (graph: GraphSpec) => post('/copilot/layout', { graph }), + }, +} + +/** + * 订阅一次运行的事件流。 + * + * 先补历史再接实时由后端保证，这里只负责断线重连 —— 重连时带上已收到的最大 + * seq，所以不会重复也不会丢事件。 + */ +export function streamRun( + runId: string, + onEvent: (event: RunEvent) => void, + onClose?: () => void, +): () => void { + let socket: WebSocket | null = null + let closed = false + let lastSeq = 0 + let retry = 0 + + const connect = () => { + if (closed) return + const proto = location.protocol === 'https:' ? 'wss' : 'ws' + socket = new WebSocket(`${proto}://${location.host}/api/runs/${runId}/stream?after=${lastSeq}`) + + socket.onmessage = (ev) => { + const event = JSON.parse(ev.data) as RunEvent & { type: string } + if (event.type === 'stream.end') { + closed = true + socket?.close() + onClose?.() + return + } + if (event.seq) lastSeq = Math.max(lastSeq, event.seq) + onEvent(event) + } + socket.onclose = () => { + if (closed) return + // 指数退避重连，最多等 5 秒 + retry += 1 + setTimeout(connect, Math.min(300 * 2 ** retry, 5000)) + } + socket.onopen = () => { + retry = 0 + } + } + connect() + + return () => { + closed = true + socket?.close() + } +} diff --git a/frontend/src/canvas/FlowCanvas.tsx b/frontend/src/canvas/FlowCanvas.tsx new file mode 100644 index 00000000..0ec362df --- /dev/null +++ b/frontend/src/canvas/FlowCanvas.tsx @@ -0,0 +1,92 @@ +import { useCallback, useMemo, useRef } from 'react' +import { + Background, BackgroundVariant, Controls, MiniMap, ReactFlow, ReactFlowProvider, + useReactFlow, type Edge, +} from '@xyflow/react' +import { NodeCard } from './NodeCard' +import { useStudio } from '../store/studio' +import type { NodeType } from '../types' + +const nodeTypes = { card: NodeCard } + +const NODE_COLOR: Record = { + input: '#3fb950', output: '#a371f7', llm: '#4f8cff', agent: '#6366f1', + supervisor: '#db61a2', tool: '#f0883e', code: '#2dd4bf', branch: '#d29922', + loop: '#ec4899', human: '#f85149', validate: '#84cc16', memory: '#14b8a6', + retrieve: '#0ea5e9', transform: '#8b949e', subgraph: '#bc8cff', +} + +function CanvasInner() { + const wrapper = useRef(null) + const { screenToFlowPosition } = useReactFlow() + const nodes = useStudio((s) => s.nodes) + const edges = useStudio((s) => s.edges) + const activeEdges = useStudio((s) => s.activeEdges) + const { onNodesChange, onEdgesChange, onConnect, addNode, select } = useStudio() + + // 正在流动的边加动画，让"数据走到哪了"看得见 + const decorated = useMemo( + () => + edges.map((e) => + activeEdges.includes(e.id) + ? { ...e, className: 'edge-active', animated: true } + : { ...e, className: undefined, animated: false }, + ), + [edges, activeEdges], + ) + + const onDrop = useCallback( + (event: React.DragEvent) => { + event.preventDefault() + const type = event.dataTransfer.getData('application/agentlab-node') as NodeType + if (!type) return + addNode(type, screenToFlowPosition({ x: event.clientX, y: event.clientY })) + }, + [addNode, screenToFlowPosition], + ) + + return ( +
+ select(node.id)} + onPaneClick={() => select(null)} + onDrop={onDrop} + onDragOver={(e) => { + e.preventDefault() + e.dataTransfer.dropEffect = 'move' + }} + fitView + fitViewOptions={{ padding: 0.12, maxZoom: 1.1, minZoom: 0.5 }} + minZoom={0.2} + maxZoom={2} + defaultEdgeOptions={{ type: 'smoothstep' }} + proOptions={{ hideAttribution: true }} + deleteKeyCode={['Backspace', 'Delete']} + > + + + NODE_COLOR[(n.data as any)?.nodeType as string] ?? '#666'} + style={{ width: 140, height: 96 }} + /> + +
+ ) +} + +export function FlowCanvas() { + return ( + + + + ) +} diff --git a/frontend/src/canvas/Inspector.tsx b/frontend/src/canvas/Inspector.tsx new file mode 100644 index 00000000..66d1ab18 --- /dev/null +++ b/frontend/src/canvas/Inspector.tsx @@ -0,0 +1,447 @@ +import { useMemo, useState } from 'react' +import { Copy, Plus, Settings2, Trash2, X } from 'lucide-react' +import clsx from 'clsx' +import { NODE_DEFS, type FieldDef } from './nodeDefs' +import { useStudio } from '../store/studio' +import { useCatalog, modelOptions } from '../store/catalog' +import { JsonInput } from '../components/ui' + +/** 属性面板。所有字段都由 nodeDefs 的声明驱动渲染，加节点类型不用改这里。 */ +export function Inspector() { + const selectedId = useStudio((s) => s.selectedId) + const node = useStudio((s) => s.nodes.find((n) => n.id === s.selectedId)) + const allIssues = useStudio((s) => s.issues) + const issues = useMemo( + () => allIssues.filter((i) => i.node_id === selectedId), + [allIssues, selectedId], + ) + const runtime = useStudio((s) => (selectedId ? s.runtime[selectedId] : undefined)) + const { updateNode, removeNode, duplicateNode, select } = useStudio() + const [showAdvanced, setShowAdvanced] = useState(false) + + if (!node) { + return ( +
+ +
选中一个节点来编辑它
+
+ ) + } + + const def = NODE_DEFS[node.data.nodeType] + const config = node.data.config ?? {} + const setConfig = (key: string, value: any) => + updateNode(node.id, { config: { ...config, [key]: value } }) + + const visible = def.fields.filter((f) => !f.when || f.when(config)) + const basic = visible.filter((f) => !f.advanced) + const advanced = visible.filter((f) => f.advanced) + + return ( +
+
+ + {def.label} + + + +
+ +
+
{def.description}
+ + {!!issues.length && ( +
+ {issues.map((issue, i) => ( +
+ {issue.message} +
+ ))} +
+ )} + +
+ + updateNode(node.id, { label: e.target.value })} + placeholder={def.label} + /> +
+ ID: {node.id} · 下游用 {`{{ nodes.${node.id}.text }}`} 引用 +
+
+ + {basic.map((field) => ( + setConfig(field.key, v)} /> + ))} + + {!!advanced.length && ( +
+ + {showAdvanced && + advanced.map((field) => ( + setConfig(field.key, v)} /> + ))} +
+ )} + + {runtime?.preview != null && ( +
+
上次运行输出
+ 
+              {typeof runtime.preview === 'string'
+                ? runtime.preview
+                : JSON.stringify(runtime.preview, null, 2)}
+
+
+ )} +
+
+ ) +} + +// ------------------------------------------------------------------------- + +function Field({ field, value, onChange }: { + field: FieldDef; value: any; onChange: (v: any) => void +}) { + return ( +
+ {field.type !== 'switch' && } + + {field.help && field.type !== 'switch' && ( +
{field.help}
+ )} +
+ ) +} + +function FieldInput({ field, value, onChange }: { + field: FieldDef; value: any; onChange: (v: any) => void +}) { + const catalog = useCatalog() + + switch (field.type) { + case 'text': + return ( + onChange(e.target.value)} /> + ) + + case 'number': + return ( + onChange(e.target.value === '' ? undefined : Number(e.target.value))} + /> + ) + + case 'textarea': + case 'prompt': + return ( +