feat: 使用 create_agent 接口替换老版本的 create_react_agent
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Wenjie Zhang
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a8491c5beb
@ -11,16 +11,13 @@
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## Next
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- [ ] 修改现有的智能体Demo，并尽量将默认助手的特性兼容到 LangGraph 的 [`create_agent`](https://docs.langchain.com/oss/python/langchain/agents) 中
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- [x] 修改现有的智能体Demo，并尽量将默认助手的特性兼容到 LangGraph 的 [`create_agent`](https://docs.langchain.com/oss/python/langchain/agents) 中
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- [ ] 基于 create_agent 创建 SQL Viewer 智能体 <Badge type="info" text="0.3.5" />
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- [ ] 优化 MCP 逻辑，支持 common + special 创建方式 <Badge type="info" text="0.3.5" />
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- [ ] 添加对于上传文件的支持
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- [ ] 统一图谱数据结构，优化可视化方式 [#298](https://github.com/xerrors/Yuxi-Know/issues/298) <Badge type="info" text="0.4" />
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- [ ] 集成智能体评估，首先使用命令行来实现，然后考虑放在 UI 里面展示
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- [ ] 开发与生产环境隔离，构建生产镜像 <Badge type="info" text="0.4" />
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- [x] 支持 MinerU 2.5 的解析方法 <Badge type="info" text="0.3.5" />
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- [x] 文件管理：（1）文件选择的时候会跨数据库；（2）文件校验会算上失败的文件；
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- [x] Tasker 中获取历史任务的时候，仅获取 top100 个 task。
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## Later
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@ -37,3 +34,6 @@
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- [x] 优化对文档信息的检索展示（检索结果页、详情页）
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- [x] 当前 ReAct 智能体有消息顺序错乱的 bug，且不会默认调用工具
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- [x] 优化全局配置的管理模型，优化配置管理
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- [x] 支持 MinerU 2.5 的解析方法 <Badge type="info" text="0.3.5" />
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- [x] 文件管理：（1）文件选择的时候会跨数据库；（2）文件校验会算上失败的文件；
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- [x] Tasker 中获取历史任务的时候，仅获取 top100 个 task。
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@ -10,11 +10,6 @@ from .tools import get_tools
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@dataclass(kw_only=True)
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class Context(BaseContext):
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model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
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default="siliconflow/Qwen/Qwen3-235B-A22B-Instruct-2507",
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metadata={"name": "智能体模型", "options": [], "description": "智能体的驱动模型"},
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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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@ -1,12 +1,10 @@
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"""Define the configurable parameters for the agent."""
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from __future__ import annotations
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import os
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import uuid
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from dataclasses import MISSING, dataclass, field, fields
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from pathlib import Path
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from typing import get_args, get_origin
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from typing import Annotated, get_args, get_origin
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import yaml
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@ -46,8 +44,13 @@ class BaseContext:
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metadata={"name": "系统提示词", "description": "用来描述智能体的角色和行为"},
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)
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model: Annotated[str, {"__template_metadata__": {"kind": "llm"}}] = field(
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default="siliconflow/Qwen/Qwen3-235B-A22B-Instruct-2507",
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metadata={"name": "智能体模型", "options": [], "description": "智能体的驱动模型"},
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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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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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# 从文件加载配置
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@ -1,12 +1,10 @@
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from pathlib import Path
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from langchain.messages import AnyMessage, SystemMessage
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from langgraph.prebuilt import create_react_agent
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from langgraph.runtime import get_runtime
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from langchain.agents import create_agent
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from langchain.agents.middleware import ModelRequest, ModelResponse, dynamic_prompt, wrap_model_call
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from src import config as sys_config
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from src.agents.common.base import BaseAgent
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from src.agents.common.context import BaseContext
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from src.agents.common.models import load_chat_model
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from src.agents.common.tools import get_buildin_tools
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from src.utils import logger
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@ -14,14 +12,24 @@ from src.utils import logger
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model = load_chat_model("siliconflow/Qwen/Qwen3-235B-A22B-Instruct-2507")
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def prompt(state) -> list[AnyMessage]:
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runtime = get_runtime(BaseContext)
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system_msg = SystemMessage(content=runtime.context.system_prompt)
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return [system_msg] + state["messages"]
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@dynamic_prompt
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def context_aware_prompt(request: ModelRequest) -> str:
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runtime = request.runtime
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return runtime.context.system_prompt
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@wrap_model_call
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async def context_based_model(request: ModelRequest, handler) -> ModelResponse:
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# 从 runtime context 读取配置
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model_spec = request.runtime.context.model
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model = load_chat_model(model_spec)
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request = request.override(model=model)
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return await handler(request)
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class ReActAgent(BaseAgent):
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name = "ReAct (all tools)"
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name = "智能体 Demo"
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description = "A react agent that can answer questions and help with tasks."
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def __init__(self, **kwargs):
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@ -30,15 +38,21 @@ class ReActAgent(BaseAgent):
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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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def get_tools(self):
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return get_buildin_tools()
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async def get_graph(self, **kwargs):
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if self.graph:
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return self.graph
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available_tools = get_buildin_tools()
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self.checkpointer = await self._get_checkpointer()
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# 创建 ReActAgent
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graph = create_react_agent(model, tools=available_tools, prompt=prompt, checkpointer=self.checkpointer)
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graph = create_agent(
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model=model,
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tools=self.get_tools(),
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middleware=[context_aware_prompt, context_based_model],
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checkpointer=await self._get_checkpointer(),
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)
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self.graph = graph
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logger.info("ReActAgent 使用内存 checkpointer 构建成功")
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return graph
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@ -46,21 +46,21 @@ class KnowledgeBaseManager:
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logger.info("KnowledgeBaseManager initialized")
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# 在后台运行数据一致性检测（不阻塞初始化）
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try:
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# 尝试获取当前事件循环，如果没有则创建新的
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try:
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loop = asyncio.get_event_loop()
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if loop.is_running():
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# 如果已经在事件循环中，创建任务
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asyncio.create_task(self.detect_data_inconsistencies())
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else:
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# 如果事件循环未运行，直接运行
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loop.run_until_complete(self.detect_data_inconsistencies())
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except RuntimeError:
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# 没有事件循环，创建一个来运行检测
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asyncio.run(self.detect_data_inconsistencies())
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except Exception as e:
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logger.warning(f"初始化时运行数据一致性检测失败: {e}")
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# try:
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# # 尝试获取当前事件循环，如果没有则创建新的
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# try:
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# loop = asyncio.get_event_loop()
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# if loop.is_running():
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# # 如果已经在事件循环中，创建任务
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# asyncio.create_task(self.detect_data_inconsistencies())
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# else:
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# # 如果事件循环未运行，直接运行
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# loop.run_until_complete(self.detect_data_inconsistencies())
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# except RuntimeError:
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# # 没有事件循环，创建一个来运行检测
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# asyncio.run(self.detect_data_inconsistencies())
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# except Exception as e:
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# logger.warning(f"初始化时运行数据一致性检测失败: {e}")
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def _load_global_metadata(self):
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"""加载全局元数据"""
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@ -18,7 +18,7 @@
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<!-- 侧边栏内容 -->
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<div class="sidebar-content">
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<div class="agent-info" v-if="selectedAgent">
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<div class="agent-basic-info">
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<div class="agent-basic-info" @click="console.log(configurableItems)">
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<p class="agent-description">{{ selectedAgent.description }}</p>
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</div>
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