c772e5ca3a
- 删除 runtime_config_middleware.py，将功能合并到: - prepare_agent_runtime_context(): 统一上下文准备入口 - resolve_configured_runtime_tools(): 运行时工具解析 - context.py: 规范化函数重命名 (_names → _keys)，重构 config 加载流程 - chatbot/deep_agent graph: 集成新上下文准备流程，移除 RuntimeConfigMiddleware 引用 - skills_middleware: 抽取 normalize_string_list 为共享工具函数 - subagent_service: get_subagents_from_names → get_subagents_from_slugs，支持 slug 查询 - 对应更新 repositories/services/routers 适配新接口
105 lines
3.8 KiB
Python
105 lines
3.8 KiB
Python
from deepagents.middleware.filesystem import FilesystemMiddleware
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from deepagents.middleware.patch_tool_calls import PatchToolCallsMiddleware
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from deepagents.middleware.subagents import SubAgentMiddleware
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from langchain.agents import create_agent
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from langchain.agents.middleware import ModelRetryMiddleware, TodoListMiddleware
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from yuxi.agents import BaseAgent, BaseState, load_chat_model
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from yuxi.agents.backends import create_agent_composite_backend
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from yuxi.agents.context import prepare_agent_runtime_context
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from yuxi.agents.middlewares import (
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SummaryOffloadMiddleware,
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save_attachments_to_fs,
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)
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from yuxi.agents.middlewares.knowledge_base_middleware import KnowledgeBaseMiddleware
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from yuxi.agents.middlewares.skills_middleware import SkillsMiddleware
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from yuxi.services.subagent_service import get_subagents_from_slugs
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from yuxi.services.tool_service import resolve_configured_runtime_tools
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from .prompt import TODO_MID_PROMPT, build_prompt_with_context
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async def _build_middlewares(context):
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"""构建中间件列表"""
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# summary middleware
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# 主 Agent 上下文优化：90k tokens 触发压缩（128k context window 的 70%）
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summary_middleware = SummaryOffloadMiddleware(
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model=load_chat_model(fully_specified_name=context.model),
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trigger=("tokens", getattr(context, "summary_threshold", 100) * 1024),
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trim_tokens_to_summarize=4000,
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summary_offload_threshold=500,
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max_retention_ratio=0.5,
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)
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# subagents
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subagents = await get_subagents_from_slugs(context.subagents)
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subagents_middleware = SubAgentMiddleware(
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default_model=load_chat_model(fully_specified_name=context.subagents_model),
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subagents=subagents,
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general_purpose_agent=True,
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default_middleware=[
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FilesystemMiddleware(backend=create_agent_composite_backend), # 文件系统后端
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PatchToolCallsMiddleware(),
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summary_middleware,
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],
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)
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# all middlewares
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middlewares = [
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FilesystemMiddleware(backend=create_agent_composite_backend), # 文件系统后端
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save_attachments_to_fs, # 附件注入提示词
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KnowledgeBaseMiddleware(), # 知识库工具
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SkillsMiddleware(), # Skills 中间件（提示词注入、依赖展开、动态激活）
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subagents_middleware,
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summary_middleware,
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TodoListMiddleware(system_prompt=TODO_MID_PROMPT), # 待办事项中间件
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PatchToolCallsMiddleware(),
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ModelRetryMiddleware(), # 模型重试中间件
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]
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return middlewares
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class ChatbotAgent(BaseAgent):
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name = "智能助手"
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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", "files"] # 支持文件上传功能
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metadata = {
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"examples": [
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"你好，请介绍一下你自己",
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"帮我写一封商务邮件",
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"解释一下什么是机器学习",
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"创建一个冒泡排序 python 并保存结果",
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]
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}
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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async def get_graph(self, context=None, **kwargs):
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context = await prepare_agent_runtime_context(
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context or self.context_schema(),
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context_schema=self.context_schema,
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)
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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(fully_specified_name=context.model),
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tools=await resolve_configured_runtime_tools(context),
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system_prompt=build_prompt_with_context(context),
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middleware=await _build_middlewares(context),
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state_schema=BaseState,
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checkpointer=await self._get_checkpointer(),
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)
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return graph
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def main():
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pass
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if __name__ == "__main__":
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main()
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# asyncio.run(main())
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