refactor: 重构工具获取逻辑，移除过时的工具接口，优化上下文工具管理
This commit is contained in:
Wenjie Zhang
parent
9d1e2b92f5
commit
339cd0f3b2
@ -36,7 +36,67 @@
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<<< @/../src/agents/reporter/graph.py
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### 工具系统
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系统提供统一的工具获取函数 `get_tools_from_context(context)`，自动从上下文配置中组装工具列表：
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```python
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from src.agents.common.tools import get_tools_from_context
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async def get_graph(self, **kwargs):
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context = self.get_context()
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tools = await get_tools_from_context(context)
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# tools 已包含：基础工具、知识库工具、MCP 工具
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```
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该函数会自动处理三类工具的组装：
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1. **基础工具**: 从 `context.tools` 筛选的内置工具
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2. **知识库工具**: 根据 `context.knowledges` 自动生成检索工具
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3. **MCP 工具**: 根据 `context.mcps` 加载并过滤的 MCP 服务器工具
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### BaseContext 配置字段
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`BaseContext` 已内置以下常用配置字段，所有智能体可直接复用：
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| 字段 | 类型 | 说明 |
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|------|------|------|
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| `model` | str | 使用的 LLM 模型 |
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| `system_prompt` | str | 系统提示词 |
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| `tools` | list[str] | 启用的内置工具列表 |
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| `knowledges` | list[str] | 关联的知识库列表 |
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| `mcps` | list[str] | 启用的 MCP 服务器名称 |
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```python
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from src.agents.common import BaseContext
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@dataclass(kw_only=True)
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class MyAgentContext(BaseContext):
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# 继承所有 BaseContext 字段
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# 可在此添加智能体特有的额外配置
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custom_field: str = "默认值"
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```
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如需自定义工具选项（如 ReporterAgent 的 MySQL 工具），可覆盖 `tools` 字段的 `options` 元数据：
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```python
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from src.agents.common import BaseContext, gen_tool_info
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from src.agents.common.tools import get_buildin_tools
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from src.agents.common.toolkits.mysql import get_mysql_tools
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@dataclass(kw_only=True)
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class ReporterContext(BaseContext):
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tools: Annotated[list[dict], {"__template_metadata__": {"kind": "tools"}}] = field(
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default_factory=lambda: [t.name for t in get_mysql_tools()],
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metadata={
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"name": "工具",
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"options": lambda: gen_tool_info(get_buildin_tools() + get_mysql_tools()),
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"description": "包含内置工具和 MySQL 工具包。",
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},
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)
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def __post_init__(self):
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self.mcps = ["mcp-server-chart"] # 默认启用图表 MCP
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```
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智能体实例的生命周期交给管理器处理，会在自动发现时完成初始化并缓存单例，以便快速响应请求。在容器内热重载时，只要保存文件即可触发重新导入；需要强制刷新可调用 `agent_manager.get_agent(<id>, reload=True)`。
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@ -81,7 +141,7 @@ from src.agents.common.middlewares import inject_attachment_context
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async def get_graph(self):
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graph = create_agent(
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model=load_chat_model("..."),
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tools=get_tools(),
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tools=tools,
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middleware=[
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inject_attachment_context, # 添加附件中间件
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context_aware_prompt, # 其他中间件...
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@ -19,7 +19,6 @@ from server.utils.auth_middleware import get_db, get_required_user
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from src import executor
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from src import config as conf
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from src.agents import agent_manager
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from src.agents.common.tools import gen_tool_info, get_buildin_tools
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from src.models import select_model
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from src.plugins.guard import content_guard
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from src.services.doc_converter import (
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@ -728,26 +727,6 @@ async def update_chat_models(model_provider: str, model_names: list[str], curren
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return {"models": conf.model_names[model_provider].models}
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@chat.get("/tools")
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async def get_tools(agent_id: str, current_user: User = Depends(get_required_user)):
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"""获取所有可用工具（需要登录）"""
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logger.error("[DEPRECATED] 该接口已被弃用，将在未来版本中移除")
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# 获取Agent实例和配置类
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if not (agent := agent_manager.get_agent(agent_id)):
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raise HTTPException(status_code=404, detail=f"智能体 {agent_id} 不存在")
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if hasattr(agent, "get_tools") and callable(agent.get_tools):
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if asyncio.iscoroutinefunction(agent.get_tools):
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tools = await agent.get_tools()
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else:
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tools = agent.get_tools()
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else:
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tools = get_buildin_tools()
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tools_info = gen_tool_info(tools)
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return {"tools": {tool["id"]: tool for tool in tools_info}}
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@chat.post("/agent/{agent_id}/resume")
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async def resume_agent_chat(
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agent_id: str,
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@ -54,7 +54,7 @@ class AgentManager(metaclass=SingletonMeta):
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# 遍历所有子目录
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for item in agents_dir.iterdir():
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logger.info(f"尝试导入模块：{item}")
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# logger.info(f"尝试导入模块：{item}")
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# 跳过非目录、common 目录、__pycache__ 等
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if not item.is_dir() or item.name.startswith("_") or item.name == "common":
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continue
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@ -1,42 +0,0 @@
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from dataclasses import dataclass, field
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from typing import Annotated
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from src.agents.common import BaseContext, gen_tool_info
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from src.knowledge import knowledge_base
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from src.services.mcp_service import get_mcp_server_names
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from .tools import get_tools
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@dataclass(kw_only=True)
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class Context(BaseContext):
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tools: Annotated[list[dict], {"__template_metadata__": {"kind": "tools"}}] = field(
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default_factory=list,
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metadata={
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"name": "工具",
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"options": lambda: gen_tool_info(get_tools()), # 这里的选择是所有的工具
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"description": "内置的部分工具，包含 common 工具和本智能体的特有工具（不含 MCP）。",
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},
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)
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knowledges: list[str] = field(
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default_factory=list,
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metadata={
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"name": "知识库",
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"options": lambda: [k["name"] for k in knowledge_base.get_retrievers().values()],
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"description": "知识库列表，可以在左侧知识库页面中创建知识库。",
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"type": "list", # Explicitly mark as list type for frontend if needed
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},
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)
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mcps: list[str] = field(
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default_factory=list,
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metadata={
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"name": "MCP服务器",
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"options": lambda: get_mcp_server_names(),
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"description": (
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"MCP服务器列表，建议使用支持 SSE 的 MCP 服务器，"
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"如果需要使用 uvx 或 npx 运行的服务器，也请在项目外部启动 MCP 服务器，并在项目中配置 MCP 服务器。"
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),
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},
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)
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@ -5,46 +5,16 @@ from src.agents.common import BaseAgent, load_chat_model
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from src.agents.common.middlewares import (
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inject_attachment_context,
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)
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from src.agents.common.tools import get_kb_based_tools
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from src.services.mcp_service import get_enabled_mcp_tools
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from .context import Context
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from .tools import get_tools
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from src.agents.common.tools import get_tools_from_context
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class ChatbotAgent(BaseAgent):
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name = "智能体助手"
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description = "基础的对话机器人，可以回答问题，默认不使用任何工具，可在配置中启用需要的工具。"
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description = "基础的对话机器人，可以回答问题，可在配置中启用需要的工具。"
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capabilities = ["file_upload"] # 支持文件上传功能
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.context_schema = Context
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async def get_tools(self, tools: list[str] = None, mcps=None, knowledges=None):
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# 1. 基础工具 (从 context.tools 中筛选)
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all_basic_tools = get_tools()
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selected_tools = []
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if tools:
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# 创建工具映射表
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tools_map = {t.name: t for t in all_basic_tools}
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for tool_name in tools:
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if tool_name in tools_map:
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selected_tools.append(tools_map[tool_name])
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# 2. 知识库工具
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if knowledges:
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kb_tools = get_kb_based_tools(db_names=knowledges)
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selected_tools.extend(kb_tools)
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# 3. MCP 工具（使用统一入口，自动过滤 disabled_tools）
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if mcps:
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for server_name in mcps:
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mcp_tools = await get_enabled_mcp_tools(server_name)
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selected_tools.extend(mcp_tools)
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return selected_tools
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async def get_graph(self, **kwargs):
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"""构建图"""
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@ -57,7 +27,7 @@ class ChatbotAgent(BaseAgent):
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# 使用 create_agent 创建智能体
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graph = create_agent(
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model=load_chat_model(context.model), # 使用 context 中的模型配置
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tools=await self.get_tools(context.tools, context.mcps, context.knowledges),
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tools=await get_tools_from_context(context),
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system_prompt=context.system_prompt,
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middleware=[
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inject_attachment_context, # 附件上下文注入
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@ -1,56 +0,0 @@
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import os
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import uuid
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from typing import Any
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import requests
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from langchain.tools import tool
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from src.agents.common import get_buildin_tools
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from src.agents.common.subagents import calc_agent_tool
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from src.storage.minio import aupload_file_to_minio
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from src.utils import logger
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@tool
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async def text_to_img_demo(text: str) -> str:
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"""【测试用】使用模型生成图片， 会返回图片的URL"""
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url = "https://api.siliconflow.cn/v1/images/generations"
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payload = {
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"model": "Qwen/Qwen-Image",
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"prompt": text,
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}
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headers = {"Authorization": f"Bearer {os.getenv('SILICONFLOW_API_KEY')}", "Content-Type": "application/json"}
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try:
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response = requests.post(url, json=payload, headers=headers)
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response_json = response.json()
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except Exception as e:
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logger.error(f"Failed to generate image with: {e}")
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raise ValueError(f"Image generation failed: {e}")
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try:
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image_url = response_json["images"][0]["url"]
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except (KeyError, IndexError, TypeError) as e:
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logger.error(f"Failed to parse image URL from response: {e}, {response_json=}")
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raise ValueError(f"Image URL extraction failed: {e}")
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# 2. Upload to MinIO (Simplified)
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response = requests.get(image_url)
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file_data = response.content
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file_name = f"{uuid.uuid4()}.jpg"
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image_url = await aupload_file_to_minio(
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bucket_name="generated-images", file_name=file_name, data=file_data, file_extension="jpg"
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)
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logger.info(f"Image uploaded. URL: {image_url}")
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return image_url
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def get_tools() -> list[Any]:
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"""获取所有可运行的工具（给大模型使用）"""
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tools = get_buildin_tools()
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tools.append(text_to_img_demo)
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tools.append(calc_agent_tool)
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return tools
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@ -9,8 +9,12 @@ from typing import Annotated, get_args, get_origin
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import yaml
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from src import config as sys_config
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from src.knowledge import knowledge_base
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from src.services.mcp_service import get_mcp_server_names
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from src.utils import logger
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from .tools import gen_tool_info, get_buildin_tools
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@dataclass(kw_only=True)
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class BaseContext:
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@ -53,6 +57,37 @@ class BaseContext:
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},
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)
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tools: Annotated[list[dict], {"__template_metadata__": {"kind": "tools"}}] = field(
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default_factory=list,
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metadata={
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"name": "工具",
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"options": lambda: gen_tool_info(get_buildin_tools()),
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"description": "内置的工具。",
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},
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)
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knowledges: list[str] = field(
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default_factory=list,
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metadata={
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"name": "知识库",
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"options": lambda: [k["name"] for k in knowledge_base.get_retrievers().values()],
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"description": "知识库列表，可以在左侧知识库页面中创建知识库。",
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"type": "list", # Explicitly mark as list type for frontend if needed
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},
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)
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mcps: list[str] = field(
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default_factory=list,
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metadata={
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"name": "MCP服务器",
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"options": lambda: get_mcp_server_names(),
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"description": (
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"MCP服务器列表，建议使用支持 SSE 的 MCP 服务器，"
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"如果需要使用 uvx 或 npx 运行的服务器，也请在项目外部启动 MCP 服务器，并在项目中配置 MCP 服务器。"
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),
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},
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)
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@classmethod
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def from_file(cls, module_name: str, input_context: dict = None) -> "BaseContext":
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"""Load configuration from a YAML file. 用于持久化配置"""
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@ -8,7 +8,7 @@ from src.agents.common.tools import calculator
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calc_agent = create_agent(
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model=load_chat_model(config.default_model),
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tools=[calculator],
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system_prompt="你可以使用计算器工具，处理各种数学计算任务。",
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system_prompt="你可以使用计算器工具，处理各种数学计算任务。最终仅返回计算结果，不需要任何额外的解释。",
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)
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@ -1,13 +1,18 @@
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import asyncio
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import os
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import traceback
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import uuid
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from typing import Annotated, Any
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import requests
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from langchain.tools import tool
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from langchain_core.tools import StructuredTool
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from langgraph.types import interrupt
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from pydantic import BaseModel, Field
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from src import config, graph_base, knowledge_base
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from src.services.mcp_service import get_enabled_mcp_tools
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from src.storage.minio import aupload_file_to_minio
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from src.utils import logger
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# Lazy initialization for TavilySearch (only when TAVILY_API_KEY is available)
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@ -45,6 +50,43 @@ def calculator(a: float, b: float, operation: str) -> float:
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raise
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@tool
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async def text_to_img_demo(text: str) -> str:
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"""【测试用】使用模型生成图片， 会返回图片的URL"""
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url = "https://api.siliconflow.cn/v1/images/generations"
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payload = {
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"model": "Qwen/Qwen-Image",
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"prompt": text,
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}
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headers = {"Authorization": f"Bearer {os.getenv('SILICONFLOW_API_KEY')}", "Content-Type": "application/json"}
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try:
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response = requests.post(url, json=payload, headers=headers)
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response_json = response.json()
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except Exception as e:
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logger.error(f"Failed to generate image with: {e}")
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raise ValueError(f"Image generation failed: {e}")
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try:
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image_url = response_json["images"][0]["url"]
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except (KeyError, IndexError, TypeError) as e:
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logger.error(f"Failed to parse image URL from response: {e}, {response_json=}")
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raise ValueError(f"Image URL extraction failed: {e}")
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# 2. Upload to MinIO (Simplified)
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response = requests.get(image_url)
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file_data = response.content
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file_name = f"{uuid.uuid4()}.jpg"
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image_url = await aupload_file_to_minio(
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bucket_name="generated-images", file_name=file_name, data=file_data, file_extension="jpg"
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)
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logger.info(f"Image uploaded. URL: {image_url}")
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return image_url
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@tool(name_or_callable="人工审批工具(Debug)", description="请求人工审批工具，用于在执行重要操作前获得人类确认。")
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def get_approved_user_goal(
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operation_description: str,
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@ -105,19 +147,6 @@ def query_knowledge_graph(query: Annotated[str, "The keyword to query knowledge
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return f"知识图谱查询失败: {str(e)}"
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def get_static_tools() -> list:
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"""注册静态工具"""
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static_tools = [query_knowledge_graph, get_approved_user_goal, calculator]
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# 检查是否启用网页搜索
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if config.enable_web_search:
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||||
tavily_search = get_tavily_search()
|
||||
if tavily_search:
|
||||
static_tools.append(tavily_search)
|
||||
|
||||
return static_tools
|
||||
|
||||
|
||||
class KnowledgeRetrieverModel(BaseModel):
|
||||
query_text: str = Field(
|
||||
description=(
|
||||
@ -257,23 +286,6 @@ def get_kb_based_tools(db_names: list[str] | None = None) -> list:
|
||||
return kb_tools
|
||||
|
||||
|
||||
def get_buildin_tools() -> list:
|
||||
"""获取所有可运行的工具（给大模型使用）"""
|
||||
tools = []
|
||||
|
||||
try:
|
||||
tools.extend(get_static_tools())
|
||||
|
||||
from src.agents.common.toolkits.mysql.tools import get_mysql_tools
|
||||
|
||||
tools.extend(get_mysql_tools())
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Failed to get knowledge base retrievers: {e}")
|
||||
|
||||
return tools
|
||||
|
||||
|
||||
def gen_tool_info(tools) -> list[dict[str, Any]]:
|
||||
"""获取所有工具的信息（用于前端展示）"""
|
||||
tools_info = []
|
||||
@ -323,3 +335,53 @@ def gen_tool_info(tools) -> list[dict[str, Any]]:
|
||||
|
||||
logger.info(f"Successfully extracted info for {len(tools_info)} tools")
|
||||
return tools_info
|
||||
|
||||
|
||||
def get_buildin_tools() -> list:
|
||||
"""注册静态工具"""
|
||||
static_tools = [
|
||||
query_knowledge_graph,
|
||||
get_approved_user_goal,
|
||||
calculator,
|
||||
text_to_img_demo,
|
||||
]
|
||||
|
||||
# subagents 工具
|
||||
from .subagents import calc_agent_tool
|
||||
|
||||
static_tools.append(calc_agent_tool)
|
||||
|
||||
# 检查是否启用网页搜索（即是否配置了 API_KEY）
|
||||
if config.enable_web_search:
|
||||
tavily_search = get_tavily_search()
|
||||
if tavily_search:
|
||||
static_tools.append(tavily_search)
|
||||
|
||||
return static_tools
|
||||
|
||||
|
||||
async def get_tools_from_context(context, extra_tools=None) -> list:
|
||||
"""从上下文配置中获取工具列表"""
|
||||
# 1. 基础工具 (从 context.tools 中筛选)
|
||||
all_basic_tools = get_buildin_tools() + (extra_tools or [])
|
||||
selected_tools = []
|
||||
|
||||
if context.tools:
|
||||
# 创建工具映射表
|
||||
tools_map = {t.name: t for t in all_basic_tools}
|
||||
for tool_name in context.tools:
|
||||
if tool_name in tools_map:
|
||||
selected_tools.append(tools_map[tool_name])
|
||||
|
||||
# 2. 知识库工具
|
||||
if context.knowledges:
|
||||
kb_tools = get_kb_based_tools(db_names=context.knowledges)
|
||||
selected_tools.extend(kb_tools)
|
||||
|
||||
# 3. MCP 工具（使用统一入口，自动过滤 disabled_tools）
|
||||
if context.mcps:
|
||||
for server_name in context.mcps:
|
||||
mcp_tools = await get_enabled_mcp_tools(server_name)
|
||||
selected_tools.extend(mcp_tools)
|
||||
|
||||
return selected_tools
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
from langchain.agents import create_agent
|
||||
|
||||
from src import config
|
||||
from src.agents.common import BaseAgent, get_buildin_tools, load_chat_model
|
||||
from src.agents.common import BaseAgent, load_chat_model
|
||||
from src.agents.common.tools import get_tools_from_context
|
||||
|
||||
|
||||
class MiniAgent(BaseAgent):
|
||||
@ -11,9 +11,6 @@ class MiniAgent(BaseAgent):
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
def get_tools(self):
|
||||
return get_buildin_tools()
|
||||
|
||||
async def get_graph(self, **kwargs):
|
||||
if self.graph:
|
||||
return self.graph
|
||||
@ -22,9 +19,9 @@ class MiniAgent(BaseAgent):
|
||||
|
||||
# 创建 MiniAgent
|
||||
graph = create_agent(
|
||||
model=load_chat_model(config.default_model),
|
||||
model=load_chat_model(context.model),
|
||||
system_prompt=context.system_prompt,
|
||||
tools=self.get_tools(),
|
||||
tools=await get_tools_from_context(context),
|
||||
checkpointer=await self._get_checkpointer(),
|
||||
)
|
||||
|
||||
|
||||
@ -1,23 +1,41 @@
|
||||
from dataclasses import dataclass, field
|
||||
from typing import Annotated
|
||||
|
||||
from langchain.agents import create_agent
|
||||
|
||||
from src.agents.common import BaseAgent, load_chat_model
|
||||
from src.agents.common import BaseAgent, BaseContext, load_chat_model
|
||||
from src.agents.common.tools import gen_tool_info, get_buildin_tools
|
||||
from src.agents.common.toolkits.mysql import get_mysql_tools
|
||||
from src.services.mcp_service import get_mcp_tools
|
||||
from src.agents.common.tools import get_tools_from_context
|
||||
from src.utils import logger
|
||||
|
||||
|
||||
@dataclass(kw_only=True)
|
||||
class ReporterContext(BaseContext):
|
||||
|
||||
# 覆盖默认的工具列表，添加 MySQL 工具包
|
||||
tools: Annotated[list[dict], {"__template_metadata__": {"kind": "tools"}}] = field(
|
||||
default_factory=lambda: [t.name for t in get_mysql_tools()],
|
||||
metadata={
|
||||
"name": "工具",
|
||||
# 添加额外的 MySQL 工具包选项
|
||||
"options": lambda: gen_tool_info(get_buildin_tools() + get_mysql_tools()),
|
||||
"description": "包含内置的工具，以及用于数据库报表生成的 MySQL 工具包。",
|
||||
},
|
||||
)
|
||||
|
||||
def __post_init__(self):
|
||||
self.mcps = ["mcp-server-chart"] # 默认启用 Charts MCPs
|
||||
|
||||
|
||||
class SqlReporterAgent(BaseAgent):
|
||||
name = "数据库报表助手"
|
||||
description = "一个能够生成 SQL 查询报告的智能体助手。同时调用 Charts MCP 生成图表。"
|
||||
context_schema = ReporterContext
|
||||
|
||||
def __init__(self, **kwargs):
|
||||
super().__init__(**kwargs)
|
||||
|
||||
async def get_tools(self):
|
||||
mysql_tools = get_mysql_tools()
|
||||
chart_tools = await get_mcp_tools("mcp-server-chart")
|
||||
return mysql_tools + chart_tools
|
||||
|
||||
async def get_graph(self, **kwargs):
|
||||
if self.graph:
|
||||
return self.graph
|
||||
@ -28,7 +46,7 @@ class SqlReporterAgent(BaseAgent):
|
||||
graph = create_agent(
|
||||
model=load_chat_model(context.model), # 使用 context 中的模型配置
|
||||
system_prompt=context.system_prompt,
|
||||
tools=await self.get_tools(),
|
||||
tools=await get_tools_from_context(context, extra_tools=get_mysql_tools()),
|
||||
checkpointer=await self._get_checkpointer(),
|
||||
)
|
||||
|
||||
|
||||
@ -769,7 +769,7 @@ class EvaluationService:
|
||||
self, db_id: str, task_id: str, page: int = 1, page_size: int = 20, error_only: bool = False
|
||||
) -> dict[str, Any]:
|
||||
# Validate task_id format to prevent path traversal
|
||||
if not re.match(r'^eval_[a-f0-9]{8}$', task_id):
|
||||
if not re.match(r"^eval_[a-f0-9]{8}$", task_id):
|
||||
raise ValueError("Invalid task_id format")
|
||||
result_file_path = os.path.join(self._get_result_dir(db_id), f"{task_id}.json")
|
||||
if not os.path.exists(result_file_path):
|
||||
@ -838,7 +838,7 @@ class EvaluationService:
|
||||
|
||||
async def delete_evaluation_result_by_db(self, db_id: str, task_id: str) -> None:
|
||||
# Validate task_id format to prevent path traversal
|
||||
if not re.match(r'^eval_[a-f0-9]{8}$', task_id):
|
||||
if not re.match(r"^eval_[a-f0-9]{8}$", task_id):
|
||||
raise ValueError("Invalid task_id format")
|
||||
result_file_path = os.path.join(self._get_result_dir(db_id), f"{task_id}.json")
|
||||
if os.path.exists(result_file_path):
|
||||
|
||||
Loading…
Reference in New Issue
Block a user