feat(chatbot): 添加MCP工具支持并优化工具管理逻辑
实现MCP服务器工具的动态加载和管理功能 重构工具获取逻辑以支持MCP工具和本地工具的混合使用 优化前端工具调用结果显示样式 移除未使用的工具相关代码 添加工具序列化转换工具函数
This commit is contained in:
Wenjie Zhang
parent
546cd3c46e
commit
63da8d9733
@ -185,8 +185,8 @@ async def get_tools(agent_id: str, current_user: User = Depends(get_required_use
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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, "agent_tools"):
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tools = agent.agent_tools
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if hasattr(agent, "get_tools"):
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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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@ -52,3 +52,14 @@ def log_operation(db: Session, user_id: int, operation: str, details: str = None
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def get_user_dict(user: User, include_password: bool = False) -> dict:
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"""获取用户字典表示"""
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return user.to_dict(include_password)
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def convert_serializable(obj):
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"""将对象转换为可序列化的格式"""
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if isinstance(obj, list | tuple):
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return [convert_serializable(item) for item in obj]
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if isinstance(obj, dict):
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return {k: convert_serializable(v) for k, v in obj.items()}
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if hasattr(obj, '__dict__'):
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return convert_serializable(vars(obj))
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return obj
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@ -3,6 +3,7 @@ from dataclasses import dataclass, field
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from src.agents.common.context import BaseContext
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from src.agents.common.tools import gen_tool_info
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from src.agents.common.mcp import MCP_SERVERS
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from .tools import get_tools
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@ -26,3 +27,12 @@ class Context(BaseContext):
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"description": "工具列表"
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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": list(MCP_SERVERS.keys()),
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"description": "MCP服务器列表"
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},
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)
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@ -17,6 +17,8 @@ from src.utils import logger
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from src.agents.common.utils import get_cur_time_with_utc
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from src.agents.common.base import BaseAgent
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from src.agents.common.models import load_chat_model
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from src.agents.common.mcp import get_mcp_tools
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from .state import State
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from .context import Context
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@ -34,38 +36,44 @@ class ChatbotAgent(BaseAgent):
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self.context_schema = Context
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self.workdir = Path(sys_config.save_dir) / "agents" / self.module_name
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self.workdir.mkdir(parents=True, exist_ok=True)
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self.agent_tools = get_tools()
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self.agent_tools = None
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def _get_tools(self, tools: list[str]):
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def get_tools(self):
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return get_tools()
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async def _get_invoke_tools(self, selected_tools: list[str], selected_mcps: list[str]):
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"""根据配置获取工具。
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默认不使用任何工具。
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如果配置为列表，则使用列表中的工具。
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"""
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self.agent_tools = get_tools()
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if tools is None or not isinstance(tools, list) or len(tools) == 0:
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# 默认不使用任何工具
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logger.info("未配置工具或配置为空，不使用任何工具")
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return []
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else:
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enabled_tools = []
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self.agent_tools = self.agent_tools or self.get_tools()
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if selected_tools and isinstance(selected_tools, list) and len(selected_tools) > 0:
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# 使用配置中指定的工具
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tools = [tool for tool in self.agent_tools if tool.name in tools]
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logger.info(f"使用工具: {[tool.name for tool in tools]}")
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return tools
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enabled_tools = [tool for tool in self.agent_tools if tool.name in selected_tools]
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if selected_mcps and isinstance(selected_mcps, list) and len(selected_mcps) > 0:
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for mcp in selected_mcps:
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enabled_tools.extend(await get_mcp_tools(mcp))
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return enabled_tools
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async def llm_call(self, state: State, runtime: Runtime[Context] = None) -> dict[str, Any]:
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"""调用 llm 模型 - 异步版本以支持异步工具"""
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system_prompt = f"{runtime.context.system_prompt}. Current time is {get_cur_time_with_utc()}"
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model = load_chat_model(runtime.context.model)
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# 这里要根据配置动态获取工具
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if tools := self._get_tools(runtime.context.tools):
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model = model.bind_tools(tools)
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available_tools = await self._get_invoke_tools(runtime.context.tools, runtime.context.mcps)
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logger.info(f"LLM binded ({len(available_tools)}) available_tools: {[tool.name for tool in available_tools]}")
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if available_tools:
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model = model.bind_tools(available_tools)
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# 使用异步调用
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response = cast(
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AIMessage,
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await model.ainvoke(
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[{"role": "system", "content": system_prompt}, *state.messages]
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[{"role": "system", "content": runtime.context.system_prompt}, *state.messages]
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),
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)
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return {"messages": [response]}
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@ -80,7 +88,7 @@ class ChatbotAgent(BaseAgent):
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and executes the requested tool calls from the last message.
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"""
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# Get available tools based on configuration
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available_tools = get_tools()
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available_tools = await self._get_invoke_tools(runtime.context.tools, runtime.context.mcps)
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# Create a ToolNode with the available tools
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tool_node = ToolNode(available_tools)
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@ -92,11 +100,8 @@ class ChatbotAgent(BaseAgent):
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async def get_graph(self, **kwargs):
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"""构建图"""
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if self.graph:
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return self.graph
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runnable_tools = get_tools()
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logger.debug(f"build graph `{self.id}` with {len(runnable_tools)} tools")
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# if self.graph:
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# return self.graph
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builder = StateGraph(State, context_schema=self.context_schema)
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builder.add_node("chatbot", self.llm_call)
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@ -30,5 +30,5 @@ def get_tools() -> dict[str, 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(calculator)
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return tools
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@ -36,7 +36,6 @@ class BaseAgent:
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"name": self.name if hasattr(self, "name") else "Unknown",
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"description": self.description if hasattr(self, "description") else "Unknown",
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"configurable_items": self.context_schema.get_configurable_items(),
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"all_tools": self.all_tools if hasattr(self, "all_tools") else [],
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"has_checkpointer": await self.check_checkpointer(),
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}
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@ -51,9 +50,8 @@ class BaseAgent:
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async def stream_messages(self, messages: list[str], input_context = None, **kwargs):
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graph = await self.get_graph()
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logger.debug(f"stream_messages: {input_context}")
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context = self.context_schema.from_file(module_name=self.module_name, input_context=input_context)
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logger.debug(f"stream_messages: {context}")
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# TODO 的 Checkpointer 似乎还没有适配最新的 Context API
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async for msg, metadata in graph.astream({"messages": messages}, stream_mode="messages", context=context, config={"configurable": input_context}):
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yield msg, metadata
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100
src/agents/common/mcp.py
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100
src/agents/common/mcp.py
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@ -0,0 +1,100 @@
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"""MCP Client setup and management for LangGraph ReAct Agent."""
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import traceback
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from typing import Any, cast
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from collections.abc import Callable
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from langchain_mcp_adapters.tools import load_mcp_tools
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from langchain_mcp_adapters.client import ( # type: ignore[import-untyped]
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MultiServerMCPClient,
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)
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from src.utils import logger
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# Global MCP client and tools cache
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_mcp_client: MultiServerMCPClient | None = None
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_mcp_tools_cache: dict[str, list[Callable[..., Any]]] = {}
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# MCP Server configurations
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MCP_SERVERS = {
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"deepwiki": {
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"url": "https://mcp.deepwiki.com/mcp",
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"transport": "streamable_http",
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},
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# Add more MCP servers here as needed
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"sequentialthinking": {
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"url": "https://remote.mcpservers.org/sequentialthinking/mcp",
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"transport": "streamable_http",
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},
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"time": {
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"command": "uvx",
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"args": ["mcp-server-time"],
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"transport": "stdio",
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}
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}
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async def get_mcp_client(
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server_configs: dict[str, Any] | None = None,
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) -> MultiServerMCPClient | None:
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"""Get or initialize the global MCP client with given server configurations."""
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global _mcp_client
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if _mcp_client is None:
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configs = server_configs or MCP_SERVERS
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try:
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_mcp_client = MultiServerMCPClient(configs) # pyright: ignore[reportArgumentType]
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logger.info(f"Initialized MCP client with servers: {list(configs.keys())}")
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except Exception as e:
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logger.error("Failed to initialize MCP client: %s", e)
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return None
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return _mcp_client
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async def get_mcp_tools(server_name: str) -> list[Callable[..., Any]]:
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"""Get MCP tools for a specific server, initializing client if needed."""
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global _mcp_tools_cache
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# Return cached tools if available
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if server_name in _mcp_tools_cache:
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return _mcp_tools_cache[server_name]
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try:
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client = await get_mcp_client({server_name: MCP_SERVERS[server_name]})
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if client is None:
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_mcp_tools_cache[server_name] = []
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return []
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# Get all tools and filter by server (if tools have server metadata)
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all_tools = await client.get_tools()
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tools = cast(list[Callable[..., Any]], all_tools)
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_mcp_tools_cache[server_name] = tools
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logger.info(f"Loaded {len(tools)} tools from MCP server '{server_name}'")
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return tools
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except Exception as e:
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logger.warning(f"Failed to load tools from MCP server '{server_name}': %s\n{traceback.format_exc()}", e)
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_mcp_tools_cache[server_name] = []
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return []
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async def get_all_mcp_tools() -> list[Callable[..., Any]]:
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"""Get all tools from all configured MCP servers."""
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all_tools = []
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for server_name in MCP_SERVERS.keys():
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tools = await get_mcp_tools(server_name)
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all_tools.extend(tools)
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return all_tools
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def add_mcp_server(name: str, config: dict[str, Any]) -> None:
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"""Add a new MCP server configuration."""
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MCP_SERVERS[name] = config
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# Clear client to force reinitialization with new config
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clear_mcp_cache()
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def clear_mcp_cache() -> None:
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"""Clear the MCP client and tools cache (useful for testing)."""
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global _mcp_client, _mcp_tools_cache
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_mcp_client = None
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_mcp_tools_cache = {}
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@ -1,7 +1,7 @@
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import inspect
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import asyncio
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import traceback
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from typing import Annotated, Any
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from pydantic import BaseModel, Field
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from langchain_core.tools import StructuredTool, tool
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from langchain_tavily import TavilySearch
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@ -22,7 +22,7 @@ def query_knowledge_graph(query: Annotated[str, "The keyword to query knowledge
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logger.error(f"Knowledge graph query error: {e}, {traceback.format_exc()}")
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return f"知识图谱查询失败: {str(e)}"
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def get_static_tools() -> dict[str, Any]:
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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 = [
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query_knowledge_graph,
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@ -45,8 +45,7 @@ class KnowledgeRetrieverModel(BaseModel):
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def get_kb_based_tools() -> dict[str, Any]:
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def get_kb_based_tools() -> list:
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"""获取所有知识库基于的工具"""
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# 获取所有知识库
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kb_tools = []
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@ -80,7 +79,7 @@ def get_kb_based_tools() -> dict[str, Any]:
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# 构建工具描述
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description = (
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f"使用 {retrieve_info['name']} 知识库进行检索。\n"
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f"下面是这个知识库的描述：\n{retrieve_info['description'] or '没有描述。'}"
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f"下面是这个知识库的描述：\n{retrieve_info['description'] or '没有描述。'} "
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)
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# 使用工厂函数创建检索器包装函数，避免闭包问题
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@ -107,7 +106,7 @@ def get_kb_based_tools() -> dict[str, Any]:
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return kb_tools
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def get_buildin_tools() -> dict[str, Any]:
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def get_buildin_tools() -> list:
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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 = []
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@ -119,11 +118,10 @@ def get_buildin_tools() -> dict[str, Any]:
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except Exception as e:
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logger.error(f"Failed to get knowledge base retrievers: {e}")
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logger.info(f"Total tools available: {len(tools)}")
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return tools
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def gen_tool_info(tools) -> dict[str, dict[str, Any]]:
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def gen_tool_info(tools) -> list[dict[str, Any]]:
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"""获取所有工具的信息（用于前端展示）"""
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tools_info = []
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@ -137,29 +135,29 @@ def gen_tool_info(tools) -> dict[str, dict[str, Any]]:
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"name": metadata.get('name', tool_obj.name),
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"description": tool_obj.description,
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'metadata': metadata,
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"args": []
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"args": [],
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# "is_async": is_async # Include async information
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}
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schema = tool_obj.args_schema.schema()
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for arg_name, arg_info in schema['properties'].items():
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info["args"].append({
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"name": arg_name,
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"type": arg_info.get('type', ''),
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"description": arg_info.get('description', '')
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})
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if hasattr(tool_obj, 'args_schema') and tool_obj.args_schema:
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schema = tool_obj.args_schema.schema()
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for arg_name, arg_info in schema.get('properties', {}).items():
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info["args"].append({
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"name": arg_name,
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"type": arg_info.get('type', ''),
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"description": arg_info.get('description', '')
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})
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tools_info.append(info)
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# logger.debug(f"Successfully processed tool info for {tool_obj.name}")
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except Exception as e:
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logger.error(f"Failed to process tool {tool_obj.name}: {e}")
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logger.error(f"Failed to process tool {getattr(tool_obj, 'name', 'unknown')}: {e}\n{traceback.format_exc()}")
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continue
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except Exception as e:
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logger.error(f"Failed to get tools info: {e}")
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return {}
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logger.error(f"Failed to get tools info: {e}\n{traceback.format_exc()}")
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return []
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logger.info(f"Successfully extracted info for {len(tools_info)} tools")
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return tools_info
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@ -377,19 +377,10 @@ const toggleToolCall = (toolCallId) => {
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transition: all 0.3s ease;
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.tool-params {
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padding: 10px 16px;
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padding: 8px 12px;
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background-color: var(--gray-25);
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border-bottom: 1px solid var(--gray-150);
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.tool-params-header {
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// unused
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background-color: var(--gray-100);
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font-size: 13px;
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color: var(--gray-800);
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margin-bottom: 8px;
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font-weight: 500;
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}
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.tool-params-content {
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margin: 0;
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font-size: 13px;
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@ -27,9 +27,9 @@
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<!-- 默认的原始数据展示 -->
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<div v-else class="default-result">
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<div class="default-header">
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<!-- <div class="default-header">
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<h4><ToolOutlined /> {{ toolName }} 执行结果</h4>
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</div>
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</div> -->
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<div class="default-content">
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<pre>{{ formatData(parsedData) }}</pre>
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</div>
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