122 lines
4.5 KiB
Python
122 lines
4.5 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 (
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ModelRetryMiddleware,
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TodoListMiddleware,
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)
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from yuxi.agents import BaseAgent, load_chat_model
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from yuxi.agents.backends import create_agent_composite_backend, resolve_sandbox_backend
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from yuxi.agents.middlewares import (
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RuntimeConfigMiddleware,
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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.mcp_service import get_tools_from_all_servers
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from yuxi.services.subagent_service import get_subagents_from_names
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from .prompt import PROMPT
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def _create_fs_backend(rt):
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"""创建文件存储后端（支持沙盒执行）
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从 runtime context 获取 user_id 和 thread_id，
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如果有则创建沙盒后端作为默认后端。
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"""
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context = getattr(rt, "context", None)
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thread_id = getattr(context, "thread_id", None) or ""
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sandbox_backend = resolve_sandbox_backend(thread_id)
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return create_agent_composite_backend(rt, sandbox_backend=sandbox_backend)
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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，可在配置中启用需要的工具。"
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capabilities = ["file_upload", "files", "todo"] # 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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"推荐几本好书",
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"如何提高工作效率？",
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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 _build_middlewares(self, context):
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"""构建中间件列表"""
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all_mcp_tools = (
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await get_tools_from_all_servers()
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) # 因为异步加载，无法放在 RuntimeConfigMiddleware 的 __init__ 中
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# subagents
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subagents = await get_subagents_from_names(context.subagents)
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subagents_middleware = SubAgentMiddleware(
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default_model=load_chat_model(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_fs_backend), # 文件系统后端
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PatchToolCallsMiddleware(),
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],
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)
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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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# all middlewares
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middlewares = [
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save_attachments_to_fs, # 附件注入提示词
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FilesystemMiddleware(backend=_create_fs_backend), # 文件系统后端
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KnowledgeBaseMiddleware(), # 知识库工具
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RuntimeConfigMiddleware(extra_tools=all_mcp_tools), # 运行时配置应用（模型/工具/MCP/提示词）
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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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ModelRetryMiddleware(), # 模型重试中间件
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TodoListMiddleware(),
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PatchToolCallsMiddleware(),
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]
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return middlewares
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async def get_graph(self, context=None, **kwargs):
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context = context or self.context_schema() # 获取上下文配置
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system_prompt = PROMPT.strip() + "\n\n" + (context.system_prompt or "")
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# 使用 create_agent 创建智能体
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# 注意：tools 参数由 RuntimeConfigMiddleware 在 wrap_model_call 中动态设置
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graph = create_agent(
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model=load_chat_model(fully_specified_name=context.model),
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system_prompt=system_prompt.strip(),
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middleware=await self._build_middlewares(context),
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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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