399 lines
14 KiB
Python
399 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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"""
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智能体模块
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"""
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# 列举导入模块
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from pathlib import Path
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import time
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from typing import AsyncGenerator, List, Optional, Dict, Literal
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from uuid import uuid4
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from pydantic_ai.messages import ModelMessage
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from pydantic_ai import Agent as PydanticAIAgent, ModelMessage
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from pydantic_ai.capabilities import AgentCapability
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from asyncio import Task, create_task, sleep, Queue, QueueEmpty, Task
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from pydantic import Field
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from pydantic import BaseModel
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from pydantic_ai.messages import (
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ModelMessagesTypeAdapter,
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ModelResponse,
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FinalResultEvent,
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ModelMessageEvent,
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PartStartEvent,
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PartDeltaEvent,
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PartEndEvent,
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TextPart,
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ToolSearchCallPart,
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NativeToolSearchCallPart,
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LoadCapabilityCallPart,
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ThinkingPart,
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ToolCallPart,
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NativeToolCallPart,
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TextPartDelta,
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ThinkingPartDelta,
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ModelResponseStreamEvent,
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ToolReturnPart,
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AgentRunResultEvent,
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NativeToolReturnPart,
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)
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.output import OutputSpec
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from pydantic_ai.providers.openai import OpenAIProvider
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from .sqlite import SQLite
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class AgentMemory(SQLite):
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"""
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智能体记忆体，支持：
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create：新增对话消息
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read：查询会话历史消息
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"""
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def __init__(self):
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"""
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初始化
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"""
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# 构建数据库路径
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super().__init__(database=Path(__file__).parent.resolve() / "agent_memory.db")
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try:
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with self:
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self.execute(
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sql="""
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CREATE TABLE IF NOT EXISTS new_messages
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(
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--唯一标识
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id TEXT PRIMARY KEY,
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--会话唯一标识
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session_id TEXT NOT NULL,
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--新对话消息
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new_messages TEXT NOT NULL,
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--时间戳（毫秒）
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timestamp INTEGER NOT NULL
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)
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"""
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)
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except Exception as exception:
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raise RuntimeError(f"初始化数据库发生异常：{str(exception)}") from exception
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def create_new_messages(
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self, session_id: str, new_messages: List[ModelMessage]
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) -> bool:
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"""
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新增新对话消息
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:param session_id: 会话唯一标识
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:param new_messages: 新对话消息
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:return: 新增是否成功
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"""
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try:
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with self:
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return self.execute(
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sql="""
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INSERT INTO new_messages (id, session_id, new_messages, timestamp) VALUES (?, ?, ?, ?)
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""",
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parameters=(
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uuid4().hex.lower(),
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session_id,
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ModelMessagesTypeAdapter.dump_json(new_messages),
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int(time.time() * 1000),
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),
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)
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except Exception as exception:
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raise RuntimeError(f"新增对话消息发生异常：{str(exception)}") from exception
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def get_message_history(self, session_id: str) -> List[ModelMessage]:
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"""
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获取指定会话的消息历史
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:param session_id: 会话唯一标识
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:return: 消息历史
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"""
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try:
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with self:
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result = self.query_all(
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sql="""
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SELECT new_messages
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FROM new_messages
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WHERE session_id = ?
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ORDER BY timestamp ASC
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""",
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parameters=(session_id,),
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)
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message_history = []
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for row in result:
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message_history.extend(
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ModelMessagesTypeAdapter.validate_json(row["new_messages"])
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)
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return message_history
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except Exception as exception:
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raise RuntimeError(
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f"查询会话历史消息发生异常：{str(exception)}"
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) from exception
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class Buffer(BaseModel):
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"""
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模型消息事件缓冲模型
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"""
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part_type: Literal["thinking", "text"] = Field(..., description="片段类型")
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content_delta: str = Field(default="", description="片段内容增量")
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task: Optional[Task] = Field(default=None, description="延迟刷新异步协程任务")
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class EventsDebouncer:
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"""
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模型消息事件防抖器
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使用 Pydantic-AI 中 run_stream_events 方法时需就其返回的模型消息事件处理，延迟刷新片段类型为思考或文本的片段增量事件以实现防抖
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在 Pydantic-AI 中模型流式输出包含若干类型片段，例如思考片段、文本片段等。每类型片段包含若干模型消息事件，例如片段开始事件、片段增量事件、片段结束事件等
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"""
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def __init__(self, delay: float = 0.2):
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"""
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初始化
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:param delay: 延迟时长（单位为秒），停顿超该时长则刷新
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"""
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self.delay = delay
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# 模型消息事件缓冲字典（数据类型为字典，键为片段索引，值为缓冲模型）
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self.buffers: Dict[int, Buffer] = {}
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# 待刷新异步队列
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self.pending_flush_queue = Queue()
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async def handle_event(
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self, event: ModelMessageEvent
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) -> AsyncGenerator[ModelMessageEvent]:
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"""
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处理模型消息事件
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:param event: 模型消息事件
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:yield: AsyncGenerator
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"""
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# 返回待刷新异步队列中模型消息事件
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while True:
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try:
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yield self.pending_flush_queue.get_nowait()
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except QueueEmpty:
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break
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# 若为思考或文本的片段开始事件则先缓存片段再返回，其它事件直接返回
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if isinstance(event, PartStartEvent):
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index, part = event.index, event.part # 片段索引、片段
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match part:
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case ThinkingPart():
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part_type = "thinking"
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case TextPart():
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part_type = "text"
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case _:
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yield event
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return
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# 新增片段缓存
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self.buffers[index] = Buffer(
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part_type=part_type,
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)
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yield event
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return
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"""
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原理：
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收到思考或文本片段增量事件时取消未完成的延迟刷新任务、追加增量并重设；若模型持续输出则先缓存，停顿超过阈值再返回，实现防抖
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"""
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if isinstance(event, PartDeltaEvent):
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index, delta = event.index, event.delta
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buffer = self.buffers.get(index)
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if buffer and isinstance(delta, (ThinkingPartDelta, TextPartDelta)):
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# 追加增量
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buffer.content_delta += delta.content_delta or ""
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# 取消上一轮未完成的延迟刷新任务
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self._cancel_task(index=index)
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# 创建延迟刷新任务
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buffer.task = create_task(coro=self._delay_flush(index=index))
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else:
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yield event
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return
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# 若为其它事件则先批量刷新片段增量事件再返回该事件
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async for event_ in self._batch_flush():
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yield event_
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# 若为片段结束事件则删除该片段缓存
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if isinstance(event, PartEndEvent):
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self.buffers.pop(event.index, None)
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yield event
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def _cancel_task(self, index: int) -> None:
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"""
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取消延迟刷新异步协程任务
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:param index: 片段索引
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:return: None
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"""
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buffer = self.buffers.get(index)
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if not buffer or not buffer.task:
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return
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if not buffer.task.done():
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buffer.task.cancel()
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buffer.task = None
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async def _delay_flush(self, index: int) -> None:
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"""
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延迟刷新
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:param index: 片段索引
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:return: None
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"""
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await sleep(delay=self.delay)
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# 刷新片段增量事件
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event = await self._flush(index=index)
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if event:
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await self.pending_flush_queue.put(item=event)
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async def _flush(self, index: int) -> Optional[PartDeltaEvent]:
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"""
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刷新片段增量事件
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:param index: 片段索引
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:return: Optional[PartDeltaEvent]
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"""
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buffer = self.buffers.get(index)
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if not buffer or not buffer.content_delta:
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return
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# 构建片段增量事件中增量部分
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match buffer.part_type:
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case "thinking":
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delta = ThinkingPartDelta(content_delta=buffer.content_delta)
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case "text":
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delta = TextPartDelta(content_delta=buffer.content_delta)
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buffer.content_delta = ""
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buffer.task = None
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return PartDeltaEvent(index=index, delta=delta)
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async def _batch_flush(self) -> AsyncGenerator[PartDeltaEvent, None]:
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"""
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批量刷新片段增量事件
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:yield: AsyncGenerator
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"""
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for index in list(self.buffers.keys()):
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event = await self._flush(index=index)
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if event:
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yield event
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class Agent:
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"""
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智能体，支持：
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1 实例智能体
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2 异步运行
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"""
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def __init__(
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self,
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session_id: str,
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instructions: str,
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output_type: OutputSpec = str,
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capabilities: Optional[List[AgentCapability]] = None,
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):
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"""
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初始化智能体
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:param session_id: 会话唯一标识
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:param instructions: 指令
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:param skills: 智能体技能列表，默认为不使用技能
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:param output_type: 输出类型
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:return: 智能体实例
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"""
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# 会话唯一标识
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self.session_id = session_id
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# 实例智能体的记忆体
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self.agent_memory = AgentMemory()
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# 实例智能体
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self.agent = PydanticAIAgent(
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model=OpenAIChatModel(
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model_name="deepseek-v4-flash",
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provider=OpenAIProvider(
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base_url="https://tokenhub.tencentmaas.com/v1",
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api_key="sk-D9Y1mCe8VlvNqLuSC4mAjqEwxJ2nW4C0h8a7EPn8kg9RLsHq",
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),
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),
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instructions=instructions,
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capabilities=capabilities,
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output_type=output_type,
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retries=1,
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)
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self.agent.to_web()
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async def stream_messages_events(
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self, user_prompt: str | List[str], delay: float = 0.2
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) -> AsyncGenerator[str, None]:
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"""
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流式输出消息事件
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:param user_prompt: 用户提示词（用户输入消息）
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:param delay: 延迟时长（单位为秒），停顿超该时长则刷新
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:yield: AsyncGenerator
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"""
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"""定义：一次会话（session）包含若干论对话（turn），每一轮对话由用户输入消息（message）和智能体输出消息组成"""
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# 获取指定会话的消息历史
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message_history = self.agent_memory.get_message_history(
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session_id=self.session_id
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)
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# 实例模型消息事件防抖器
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event_debouncer = EventsDebouncer(delay=delay)
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async with self.agent.run_stream_events(
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user_prompt=user_prompt,
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message_history=message_history,
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) as events:
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async for event in events:
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if isinstance(event, AgentRunResultEvent):
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evnet : AgentRunResultEvent = event
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new_messages = evnet.result
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# 处理模型消息事件
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async for event_ in event_debouncer.handle_event(event):
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match event_: # event_ 为处理后模型消息事件
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case PartStartEvent():
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part = event_.part
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match part:
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case ThinkingPart(content=content):
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yield f"00:{content}"
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case TextPart(content=content):
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yield f"01:{content}"
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case (
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NativeToolSearchCallPart()
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| NativeToolCallPart()
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| ToolSearchCallPart()
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| ToolCallPart()
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| LoadCapabilityCallPart()
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) as tool:
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yield f"02:使用技能：{tool.tool_name}"
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case (
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NativeToolReturnPart() | ToolReturnPart()
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) as tool_return:
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yield f"03:技能返回：{tool_return.content}"
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case _:
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yield f"99:未知片段类型{type(part)}"
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case PartDeltaEvent():
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delta = event_.delta
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match delta:
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case ThinkingPartDelta(content_delta=content_delta):
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yield f"00:{content_delta}"
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case TextPartDelta(content_delta=content_delta):
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yield f"01:{content_delta}"
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case _:
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yield f"99:未知片段类型{type(delta)}"
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# 片段结束事件无需处理
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case PartEndEvent() | AgentRunResultEvent():
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pass
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case _:
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yield f"99:未知事件类型{type(event_)}"
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self.agent_memory.create_new_messages(
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session_id=self.session_id,
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new_messages=new_messages,
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)
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