# -*- coding: utf-8 -*- """ Pydantic AI 聊天智能体和相关模块 """ # 列举导入模块 from enum import StrEnum from typing import AsyncGenerator, List, Optional, Union from uuid import uuid4 from numpy._core.numeric import str_ from pydantic_ai import Agent from pydantic_ai.capabilities import AgentCapability from pydantic_ai.messages import ( AgentStreamEvent, ModelMessage, PartStartEvent, ThinkingPart, ToolSearchCallPart, ThinkingPartDelta, ToolCallPart, LoadCapabilityCallPart, TextPartDelta, ToolCallPartDelta, FunctionToolResultEvent, PartDeltaEvent, TextPart, PartEndEvent, ) from pydantic_ai.models.openai import OpenAIChatModel from pydantic_ai.output import OutputSpec from pydantic_ai.providers.openai import OpenAIProvider from pydantic_ai.run import AgentRunResultEvent from pydantic import BaseModel, Field DEFAULT_INSTRUCTIONS: str = """ # 角色 专业友好AI助手，结构化解答各类问题。 # 输出硬性规则 1. 全文强制标准Markdown，禁止纯文本；不要额外说明排版格式，直接输出内容； 2. 层级使用 `#/##/###`，列表用 `-` 无序列表或数字有序列表； 3. 代码块用 ```语言名``` 包裹； 4. 重点内容标注 **粗体**/*斜体*； 5. 思考、工具日志仅输出文本，适配前端折叠面板，禁止输出HTML标签； 6. 内容分点拆分，排版整洁适配前端Markdown渲染。 # 行文要求 语言通俗，逻辑完整简洁，无多余废话。 """ class Event(BaseModel): """ 事件类 """ event_kind: str = Field(..., description="事件种类") content: Optional[str] = Field(default=None, description="事件内容") part_index: Optional[int] = Field(default=None, description="分片索引") part_kind: str = Field(..., description="分片种类") tool_name: Optional[str] = Field(default=None, description="工具名称") class AIAgent: """ 基于 Pydantic AI 封装的智能体 """ def __init__( self, chat_id: str, instructions: Optional[str] = None, output_type: OutputSpec = str, capabilities: Optional[List[AgentCapability]] = None, retries: int = 1, ): """ 初始化 :param chat_id: 聊天唯一标识 :param instructions: 指令 :param capabilities: 技能列表，默认为不使用技能 :param output_type: 输出类型 :param retries: 重试次数，默认为1次 :return: 智能体实例 """ # 聊天唯一标识 self.chat_id = chat_id # 一次聊天（chat）包含若干论对话（dialog），每轮对话包含用户提示词（user_prompt）和输出（output）。其中，输出包含若干分片（Part） # 本轮对话工具调用唯一标识与片段索引映射表 self.tool_call_ids: dict[str, int] = {} # 本轮对话新增消息列表 self.new_messages: List[ModelMessage] = [] # 若指令为空则使用默认指令 if not instructions: instructions = DEFAULT_INSTRUCTIONS # 初始化智能体 self.agent = Agent( model=OpenAIChatModel( model_name="deepseek-v4-flash", provider=OpenAIProvider( base_url="https://tokenhub.tencentmaas.com/v1", api_key="sk-D9Y1mCe8VlvNqLuSC4mAjqEwxJ2nW4C0h8a7EPn8kg9RLsHq", ), ), instructions=instructions, capabilities=capabilities, output_type=output_type, retries=retries, ) async def run( self, user_prompt: str | List[str], message_history: Optional[List[ModelMessage]] = None, ) -> AsyncGenerator[Event]: """ 运行 :param user_prompt: 用户提示词（用户提示词） :param flush_delay: 刷新延迟时长（单位为秒） :yield: AsyncGenerator[Event] """ async with self.agent.run_stream_events( user_prompt=user_prompt, message_history=message_history, ) as events: async for event in events: match event: # ========== 分片开始事件 ========== case PartStartEvent(event_kind=event_kind, index=index, part=part): match part: # 思考分片开始事件 case ThinkingPart(part_kind=part_kind, content=content): yield Event( event_kind=event_kind, content=content, part_index=index, part_kind=part_kind, ) # 检索工具分片开始事件 case ToolSearchCallPart( part_kind=part_kind, tool_call_id=tool_call_id, tool_name=tool_name ): # 记录工具调用唯一标识与片段索引映射 self.tool_call_ids[tool_call_id] = index yield Event( event_kind=event_kind, part_index=index, part_kind=part_kind, tool_name=tool_name, ) # 加载能力分片开始事件 case LoadCapabilityCallPart( part_kind=part_kind, tool_call_id=tool_call_id, tool_name=tool_name ): # 记录工具调用唯一标识与片段索引映射 self.tool_call_ids[tool_call_id] = index yield Event( event_kind=event_kind, part_index=index, part_kind=part_kind, tool_name=tool_name, ) # 调用工具分片开始事件 case ToolCallPart( part_kind=part_kind, tool_call_id=tool_call_id, tool_name=tool_name, ): # 记录工具调用唯一标识与片段索引映射 self.tool_call_ids[tool_call_id] = index yield Event( event_kind=event_kind, part_index=index, part_kind=part_kind, tool_name=tool_name, ) # 文本分片开始事件 case TextPart(part_kind=part_kind, content=content): yield Event( event_kind=event_kind, content=content, part_index=index, part_kind=part_kind, ) # ========== 分片增量事件（前端仅就文本片段实现打字机效果） ========== case PartDeltaEvent( event_kind=event_kind, index=index, delta=delta ): match delta: # 文本分片增量事件 case TextPartDelta( part_delta_kind=part_delta_kind, content_delta=content_delta, ): yield Event( event_kind=event_kind, content=content_delta, # 增量 part_index=index, part_kind=part_delta_kind, ) # ========== 分片结束事件 ========== case PartEndEvent(event_kind=event_kind, index=index, part=part): match part: # 思考分片结束事件 case ThinkingPart(part_kind=part_kind, content=content): yield Event( event_kind=event_kind, content=content, part_index=index, part_kind=part_kind, ) # 检索工具分片结束事件 case ToolSearchCallPart(part_kind=part_kind, tool_name=tool_name): yield Event( event_kind=event_kind, part_index=index, part_kind=part_kind, tool_name=tool_name, ) # 加载能力分片结束事件 case LoadCapabilityCallPart(part_kind=part_kind, tool_name=tool_name): yield Event( event_kind=event_kind, part_index=index, part_kind=part_kind, tool_name=tool_name, ) # 调用工具分片结束事件 case ToolCallPart(part_kind=part_kind, tool_name=tool_name): yield Event( event_kind=event_kind, part_index=index, part_kind=part_kind, tool_name=tool_name, ) # 文本分片结束事件 case TextPart(part_kind=part_kind): yield Event( event_kind=event_kind, part_index=index, part_kind=part_kind, ) # ========== 函数工具结果回调事件 ========== case FunctionToolResultEvent( event_kind=event_kind, content=content, part=part, ): yield Event( event_kind=event_kind, content=content, part_index=index, part_kind=part_kind, tool_name=tool_name, )