250 lines
8.4 KiB
Python
250 lines
8.4 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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from sys import path
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from typing import List, Optional, cast
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from uuid import uuid4
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from pydantic_ai import Agent as BaseAgent, AgentRunResult
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from pydantic_ai.builtin_tools import AbstractBuiltinTool
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from pydantic_ai.capabilities import AgentCapability
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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 pydantic_ai.settings import ModelSettings
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from pydantic_core.core_schema import is_subclass_schema
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from starlette.applications import Starlette
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from starlette.routing import Mount, Route
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from typing_extensions import is_protocol
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from memory import Memory
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path.append(Path(__file__).resolve().parent.as_posix())
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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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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 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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# 生成会话唯一标识
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self.session_id = uuid4().hex.lower()
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# 创建智能体
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self.agent = self._create_agent(
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instructions=instructions,
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capabilities=capabilities,
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output_type=output_type,
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)
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a = self.agent.to_web()
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# 实例记忆体
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self.memory = Memory()
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def _create_agent(
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self,
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instructions: str,
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capabilities: Optional[List[AgentCapability]],
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output_type: OutputSpec,
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) -> BaseAgent:
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"""
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创建智能体
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:param instructions: 指令
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:param capabilities: 智能体能力列表
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:param output_type: 输出类型
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:return: Agent 实例
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"""
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agent = BaseAgent(
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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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return agent
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async def run(self, user_prompt: str | List[str]) -> AgentRunResult:
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"""
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异步运行
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:param user_prompt: 用户提示词
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:return: 智能体回复
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"""
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# 查询会话历史消息
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message_history = self.memory.read(session_id=self.session_id)
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result = await self.agent.run(
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user_prompt=user_prompt, message_history=message_history
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)
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# 记录会话历史消息
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self.memory.create(
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session_id=self.session_id,
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dialogue_message=result.new_messages(),
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)
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return result
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def create_web_application(
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self,
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builtin_tools: Optional[List[AbstractBuiltinTool]] = None,
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) -> Starlette:
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"""
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创建 Starlette 应用，为智能体提供交互式对话界面
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:param builtin_tools: 前端可选工具列表
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:return: Starlette 实例
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"""
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from starlette.requests import Request
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from starlette.responses import JSONResponse, HTMLResponse, Response
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def api(
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self,
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builtin_tools: Optional[List[AbstractBuiltinTool]] = None,
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) -> Starlette:
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"""
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创建 API 应用
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:param builtin_tools: 前端可选工具列表
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:return: Starlette 实例
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"""
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from pydantic_ai.models import Model
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from pydantic_ai.ui._web.api import ModelInfo
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from pydantic_ai.ui._web.api import (
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ConfigureFrontend,
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BuiltinToolInfo,
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ChatRequestExtra,
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validate_request_options,
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)
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from pydantic_ai.ui.vercel_ai import VercelAIAdapter
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from typing import TypeVar
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agent_model = cast(Model, self.agent.model)
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# 前端可选工具列表
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frontend_builtin_tools = [
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t
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for t in (builtin_tools or [])
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if t.unique_id
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not in {
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t.unique_id
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for t in self.agent._cap_builtin_tools
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if isinstance(t, AbstractBuiltinTool)
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}
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]
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async def options_chat(request: Request) -> Response:
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"""处理跨域预检请求"""
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return Response()
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async def configurations(request: Request) -> Response:
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"""处理前端模型与工具配置请求"""
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configurations = ConfigureFrontend(
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models=[
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ModelInfo(
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id=agent_model.model_id,
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name=agent_model.label,
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builtin_tools=[
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t.unique_id
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for t in frontend_builtin_tools
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if type(t) in agent_model.supported_builtin_tools()
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],
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)
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], # 前端仅可选择智能体配置的模型
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builtin_tools=[
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BuiltinToolInfo(id=t.unique_id, name=t.label)
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for t in frontend_builtin_tools
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],
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)
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return JSONResponse(content=configurations.model_dump(by_alias=True))
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async def chat(request: Request) -> Response:
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"""处理对话请求"""
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# 实例 Vercel AI 适配器
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adapter = await VercelAIAdapter[
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TypeVar("AgentDepsT"), TypeVar("OutputDataT")
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].from_request(request=request, agent=self.agent)
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# 解析请求中额外数据，包括前端选择的模型标识、工具标识和其它配置等
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extra_data = ChatRequestExtra.model_validate(
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adapter.run_input.__pydantic_extra__
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)
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if error := validate_request_options(
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extra_data=extra_data,
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model_ids={agent_model.model_id}, # 前端仅可选择智能体配置的模型
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builtin_tool_ids={t.unique_id for t in frontend_builtin_tools},
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):
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return JSONResponse(content={"error": error}, status_code=400)
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streaming_response = await VercelAIAdapter[
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TypeVar("AgentDepsT"), TypeVar("OutputDataT")
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].dispatch_request(
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request=request,
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agent=self.agent,
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builtin_tools=[
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t
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for t in frontend_builtin_tools
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if t.unique_id in extra_data.builtin_tools
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],
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)
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return streaming_response
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async def health(request: Request) -> Response:
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"""处理健康检查请求"""
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return JSONResponse(content={"ok": True})
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return Starlette(
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routes=[
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Route("/configure", configurations, methods=["GET"]),
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Route("/chat", options_chat, methods=["OPTIONS"]),
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Route("/chat", chat, methods=["POST"]),
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Route("/health", health, methods=["GET"]),
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]
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)
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async def ui(request: Request) -> Response:
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"""Serve the chat UI from filesystem cache or CDN."""
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content = await _get_ui_html(html_source)
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return HTMLResponse(
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content=content,
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headers={
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"Cache-Control": "public, max-age=3600",
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},
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)
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application = Starlette(
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routes=[
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Mount(
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"/api",
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app=api(self, builtin_tools=builtin_tools),
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)
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]
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)
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app.router.add_route("/", ui, methods=["GET"])
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app.router.add_route("/{id}", ui, methods=["GET"])
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exit()
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return application
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