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
127 lines
5.0 KiB
Python
127 lines
5.0 KiB
Python
import os
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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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TodoListMiddleware,
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ToolCallLimitMiddleware,
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)
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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.agents.toolkits.buildin.tools import _create_tavily_search
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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 yuxi.utils import logger
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from yuxi.utils.datetime_utils import shanghai_now
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from .prompt import DEEP_PROMPT
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class DeepAgent(BaseAgent):
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name = "深度分析"
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description = "具备规划、深度分析和子智能体协作能力的智能体，可以处理复杂的多步骤任务"
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capabilities = ["file_upload", "files"] # 支持文件上传功能
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metadata = {"examples": ["调研一下多模态 GraphRAG 的相关论文"]}
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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self.graph = None
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self.checkpointer = None
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async def get_tools(self):
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"""返回 Deep Agent 的专用工具"""
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tools = []
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if os.getenv("TAVILY_API_KEY"):
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tavily = _create_tavily_search()
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if tavily:
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tools.append(tavily)
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if not tools:
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logger.warning("No search tools configured, DeepAgent will work without web search")
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return tools
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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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current_date = f"当前日期：{shanghai_now().strftime('%Y-%m-%d')}"
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system_prompt = f"{current_date}\n\n{DEEP_PROMPT.strip()}\n\n{context.system_prompt or ''}"
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model = load_chat_model(context.model)
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sub_model = load_chat_model(context.subagents_model)
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search_tools = await self.get_tools()
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# 从数据库加载 subagent specs（工具名称已解析）
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user_subagents = await get_subagents_from_slugs(context.subagents)
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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=model,
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trigger=("tokens", 90000),
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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_middleware = SubAgentMiddleware(
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default_model=sub_model,
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default_tools=search_tools,
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subagents=user_subagents,
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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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# 子 Agent 搜索工具限制：tavily_search 最多 8 次
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ToolCallLimitMiddleware(
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tool_name="tavily_search",
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run_limit=8,
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exit_behavior="continue",
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),
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],
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general_purpose_agent=True,
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)
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# 使用 create_deep_agent 创建深度智能体
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graph = create_agent(
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model=model,
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tools=await resolve_configured_runtime_tools(context),
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system_prompt=system_prompt,
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middleware=[
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FilesystemMiddleware(backend=create_agent_composite_backend), # 文件系统后端
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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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save_attachments_to_fs, # 附件注入提示词
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TodoListMiddleware(system_prompt="任务结束前，应该检查维护的待办事项列表是否结束。"),
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PatchToolCallsMiddleware(),
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KnowledgeBaseMiddleware(), # 知识库工具
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subagents_middleware,
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summary_middleware,
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# 工具调用限制：tavily_search 总调用最多 20 次
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ToolCallLimitMiddleware(
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tool_name="tavily_search",
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thread_limit=20,
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exit_behavior="continue",
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),
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# 总工具调用轮次限制：防止单次运行无限循环
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ToolCallLimitMiddleware(
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run_limit=50,
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exit_behavior="end",
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),
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],
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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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