7d4722b62c
- 实现QA数据 - 优化现在创建知识库的体验 - 优化文件处理与加载方法 - 整体的格式检查与优化 - 优化 Embedding model 的加载逻辑，修复并行问题 - 优化整体的颜色布局 - 移除未使用的接口（前后端） - 优化知识库的 chunk 逻辑 - 添加新的 Embedding模型支持 - 修复新建知识库后，Agent无法reload的问题
370 lines
13 KiB
Python
370 lines
13 KiB
Python
from __future__ import annotations
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import os
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import yaml
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import uuid
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from pathlib import Path
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from typing import Annotated, TypedDict, Optional, Any
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from abc import abstractmethod
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from dataclasses import dataclass, fields, field
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from pydantic import BaseModel, 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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thread_id: str = field(
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default_factory=lambda: str(uuid.uuid4()),
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metadata={
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"name": "线程ID",
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"configurable": False,
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"description": "用来描述智能体的角色和行为"
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},
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)
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user_id: str = field(
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default_factory=lambda: str(uuid.uuid4()),
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metadata={
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"name": "用户ID",
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"configurable": False,
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"description": "用来描述智能体的角色和行为"
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},
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)
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@classmethod
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def from_runnable_config(
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cls, config: RunnableConfig | None = None, module_name: str | None = 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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module_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 module_name:
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file_config = cls.from_file(module_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 config_field in _fields:
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# 1. 默认使用类默认值
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if hasattr(instance, config_field):
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merged_config[config_field] = getattr(instance, config_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 config_field in file_config:
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merged_config[config_field] = file_config[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 config_field in configurable:
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merged_config[config_field] = configurable[config_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, module_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/{module_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, 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 {module_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, module_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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module_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/{module_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 {module_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", True):
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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 BaseModelConfiguration(BaseModel):
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thread_id: str = Field(
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default_factory=lambda: str(uuid.uuid4()),
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metadata={
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"title": "线程ID",
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"description": "用来描述智能体的角色和行为",
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"configurable": False,
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},
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)
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user_id: str = Field(
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default_factory=lambda: str(uuid.uuid4()),
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metadata={
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"title": "用户ID",
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"description": "用来描述智能体的角色和行为",
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"configurable": False,
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},
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)
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@classmethod
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def from_runnable_config(
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cls,
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config: RunnableConfig | None = None,
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module_name: str | None = None,
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) -> BaseModelConfiguration:
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"""
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从 RunnableConfig 和 YAML 文件中构建 Configuration 实例
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"""
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# 默认配置
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default_instance = cls()
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default_values = default_instance.dict()
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# 文件配置
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file_config = cls.from_file(module_name) if module_name else {}
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# 运行时配置（最高优先级）
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runtime_config = config.get("configurable") if config else {}
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merged_config = {
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**default_values,
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**file_config,
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**runtime_config,
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}
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return cls(**merged_config)
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@classmethod
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def from_file(cls, module_name: str) -> dict[str, Any]:
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"""
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从 YAML 文件加载配置
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"""
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config_file_path = Path(f"src/agents/{module_name}/config.private.yaml")
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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, encoding="utf-8") as f:
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return yaml.safe_load(f) or {}
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except Exception as e:
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logger.error(f"加载配置文件失败: {e}")
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return {}
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@classmethod
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def save_to_file(cls, config: dict, module_name: str) -> bool:
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"""
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保存配置到 YAML 文件
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"""
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try:
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config_file_path = Path(f"src/agents/{module_name}/config.private.yaml")
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os.makedirs(config_file_path.parent, 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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return True
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except Exception as e:
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logger.error(f"保存配置文件失败: {e}")
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return False
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@classmethod
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def to_dict(cls) -> dict[str, Any]:
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"""
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提取类字段的默认值与字段元数据，主要用于前端动态生成配置项。
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"""
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# 创建实例以获得 default_factory 值
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instance = cls()
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confs: dict[str, Any] = {}
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configurable_items: dict[str, Any] = {}
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for name, _field in cls.model_fields.items():
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value = getattr(instance, name)
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confs[name] = value
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# 安全地处理 Pydantic 字段元数据
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field_metadata = {}
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if hasattr(_field, 'json_schema_extra') and _field.json_schema_extra:
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if isinstance(_field.json_schema_extra, dict):
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field_metadata = _field.json_schema_extra['metadata']
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elif isinstance(_field.json_schema_extra, list | tuple):
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# 在 Pydantic v2 中，metadata 可能是列表，合并所有字典项
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for item in _field.json_schema_extra:
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if isinstance(item, dict):
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field_metadata.update(item['metadata'])
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# 检查字段是否应该可配置 - 支持不同的元数据格式
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if field_metadata.get("configurable", True):
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default_type = _field.annotation.__name__ if hasattr(_field.annotation, '__name__') else 'str'
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configurable_items[name] = {
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"type": field_metadata.get("type", default_type),
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"name": field_metadata.get("name") or name,
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"options": field_metadata.get("options") or [],
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"default": _field.default if _field.default is not None else None,
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"description": field_metadata.get("description") or "",
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"x_oap_ui_config": field_metadata.get("x_oap_ui_config", {}),
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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 = "base_agent"
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description = "base_agent"
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config_schema: Configuration = Configuration
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def __init__(self, **kwargs):
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pass
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@property
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def module_name(self) -> str:
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"""Get the module name of the agent class."""
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return self.__class__.__module__.split('.')[-2]
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@property
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def id(self) -> str:
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"""Get the agent's class name."""
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return self.__class__.__name__
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async def get_info(self):
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return {
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"id": self.id,
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"name": self.name if hasattr(self, "name") else "Unknown",
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"description": self.description if hasattr(self, "description") else "Unknown",
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"config_schema": self.config_schema.to_dict(),
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"all_tools": self.all_tools if hasattr(self, "all_tools") else [],
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"has_checkpointer": await self.check_checkpointer(),
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}
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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()
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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()
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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 check_checkpointer(self):
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app = await self.get_graph()
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if not hasattr(app, "checkpointer") or app.checkpointer is None:
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logger.warning(f"智能体 {self.name} 的 Graph 未配置 checkpointer，无法获取历史记录")
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return False
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return True
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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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try:
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app = await self.get_graph()
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if not await self.check_checkpointer():
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return []
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config = {"configurable": {"thread_id": thread_id, "user_id": user_id}}
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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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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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except Exception as e:
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logger.error(f"获取智能体 {self.name} 历史消息出错: {e}")
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return []
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@abstractmethod
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async def get_graph(self, **kwargs) -> CompiledStateGraph:
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"""
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获取并编译对话图实例。
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必须确保在编译时设置 checkpointer，否则将无法获取历史记录。
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例如: graph = workflow.compile(checkpointer=sqlite_checkpointer)
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"""
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pass
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