211 lines
7.7 KiB
Python
211 lines
7.7 KiB
Python
from __future__ import annotations
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import os
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import yaml
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from pathlib import Path
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from typing import Type, Annotated, Optional, TypedDict
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from enum import Enum
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from abc import abstractmethod
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from dataclasses import dataclass, fields, field
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from langchain_core.runnables import RunnableConfig
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from langchain_core.messages import BaseMessage
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from langgraph.graph.state import CompiledStateGraph
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from langgraph.graph.message import add_messages
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from src.utils import logger
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class State(TypedDict):
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messages: Annotated[list[BaseMessage], add_messages]
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@dataclass(kw_only=True)
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class Configuration(dict):
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"""
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定义一个基础 Configuration 供 各类 graph 继承
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配置优先级:
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1. 运行时配置(RunnableConfig)：最高优先级，直接从函数参数传入
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2. 文件配置(config.private.yaml)：中等优先级，从文件加载
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3. 类默认配置：最低优先级，类中定义的默认值
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"""
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@classmethod
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def from_runnable_config(
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cls, config: Optional[RunnableConfig] = None, agent_name: str = None
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) -> Configuration:
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"""Create a Configuration instance from a RunnableConfig object.
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Args:
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config: RunnableConfig object with highest priority
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agent_name: Name of the agent to load config file for
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Returns:
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Configuration instance with merged config values
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"""
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# 获取类默认配置：创建一个实例获取所有默认值
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instance = cls()
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_fields = {f.name for f in fields(cls) if f.init}
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# 尝试加载文件配置(中等优先级)
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file_config = {}
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if agent_name:
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file_config = cls.from_file(agent_name)
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# 获取运行时配置(最高优先级)
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configurable = (config.get("configurable") or {}) if config else {}
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# 合并三级配置，注意优先级
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merged_config = {}
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for field in _fields:
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# 1. 默认使用类默认值
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if hasattr(instance, field):
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merged_config[field] = getattr(instance, field)
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# 2. 如果文件配置中有此字段，则覆盖
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if field in file_config:
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merged_config[field] = file_config[field]
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# 3. 如果运行时配置中有此字段，则覆盖
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if field in configurable:
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merged_config[field] = configurable[field]
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# 创建并返回配置实例
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# logger.debug(f"合并配置: {merged_config}")
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return cls(**merged_config)
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@classmethod
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def from_file(cls, agent_name: str) -> Configuration:
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"""从文件加载配置"""
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config_file_path = Path(f"src/agents/{agent_name}/config.private.yaml")
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file_config = {}
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if os.path.exists(config_file_path):
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try:
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with open(config_file_path, 'r', encoding='utf-8') as f:
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file_config = yaml.safe_load(f) or {}
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# logger.info(f"从文件加载智能体 {agent_name} 配置: {file_config}")
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except Exception as e:
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logger.error(f"加载智能体配置文件出错: {e}")
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return file_config
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@classmethod
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def save_to_file(cls, config: dict, agent_name: str) -> bool:
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"""Save configuration to a YAML file
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Args:
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config: Configuration dictionary to save
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agent_name: Name of the agent to save config for
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Returns:
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True if saving was successful, False otherwise
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"""
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try:
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config_file_path = Path(f"src/agents/{agent_name}/config.private.yaml")
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# 确保目录存在
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os.makedirs(os.path.dirname(config_file_path), exist_ok=True)
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with open(config_file_path, 'w', encoding='utf-8') as f:
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yaml.dump(config, f, indent=2, allow_unicode=True)
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# logger.info(f"智能体 {agent_name} 配置已保存到 {config_file_path}")
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return True
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except Exception as e:
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logger.error(f"保存智能体配置文件出错: {e}")
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return False
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@classmethod
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def to_dict(cls):
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# 创建一个实例来处理 default_factory
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instance = cls()
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confs = {}
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configurable_items = {}
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for f in fields(cls):
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if f.init and not f.metadata.get("hide", False):
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value = getattr(instance, f.name)
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if callable(value) and hasattr(value, "__call__"):
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confs[f.name] = value()
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else:
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confs[f.name] = value
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if f.metadata.get("configurable"):
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configurable_items[f.name] = {
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"type": f.type.__name__,
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"name": f.metadata.get("name", f.name),
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"options": f.metadata.get("options", []),
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"default": f.default,
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"description": f.metadata.get("description", ""),
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}
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confs["configurable_items"] = configurable_items
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return confs
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class BaseAgent():
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"""
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定义一个基础 Agent 供 各类 graph 继承
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"""
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name: str = field(default="base_agent")
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description: str = field(default="base_agent")
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config_schema: Configuration = Configuration
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requirements: list[str]
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def __init__(self, **kwargs):
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self.check_requirements()
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@classmethod
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def get_info(cls):
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return {
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"name": cls.name,
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"description": cls.description,
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"config_schema": cls.config_schema.to_dict(),
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"requirements": cls.requirements if hasattr(cls, "requirements") else [],
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"all_tools": cls.all_tools if hasattr(cls, "all_tools") else [],
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}
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def check_requirements(self):
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if not hasattr(self, "requirements") or not self.requirements:
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return
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for requirement in self.requirements:
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if requirement not in os.environ:
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raise ValueError(f"没有配置{requirement} 环境变量，请在 src/.env 文件中配置，并重新启动服务")
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async def stream_values(self, messages: list[str], config_schema: RunnableConfig = None, **kwargs):
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graph = await self.get_graph(config_schema=config_schema, **kwargs)
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logger.debug(f"stream_values: {config_schema}")
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for event in graph.astream({"messages": messages}, stream_mode="values", config=config_schema):
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yield event["messages"]
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async def stream_messages(self, messages: list[str], config_schema: RunnableConfig = None, **kwargs):
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graph = await self.get_graph(config_schema=config_schema, **kwargs)
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logger.debug(f"stream_messages: {config_schema}")
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async for msg, metadata in graph.astream({"messages": messages}, stream_mode="messages", config=config_schema):
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yield msg, metadata
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async def get_history(self, user_id, thread_id) -> list[dict]:
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"""获取历史消息"""
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# 获取LangGraph应用实例
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app = await self.get_graph()
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# 构建配置信息
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config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}
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# 获取状态
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state = await app.aget_state(config)
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result = []
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if state:
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messages = state.values.get('messages', [])
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for msg in messages:
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if hasattr(msg, 'model_dump'):
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msg_dict = msg.model_dump() # 转换成字典
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else:
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# 如果消息没有model_dump方法，尝试转成dict
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msg_dict = dict(msg) if hasattr(msg, '__dict__') else {"content": str(msg)}
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result.append(msg_dict)
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return result
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@abstractmethod
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def get_graph(self, **kwargs) -> CompiledStateGraph:
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pass |