417 lines
14 KiB
Python
417 lines
14 KiB
Python
# -*- coding: utf-8 -*-
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"""
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预定航班（范式）
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"""
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import asyncio
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import datetime
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from typing import AsyncGenerator
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from logfire_api.variables import ValueDoesNotEqual
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from pydantic import BaseModel, Field, field_validator
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from pydantic_ai import (
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Agent,
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ApprovalRequired,
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DeferredToolRequests,
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DeferredToolResults,
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ModelMessage,
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ModelRetry,
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ModelSettings,
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RunContext,
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RunUsage,
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UsageLimits,
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)
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from pydantic_ai.models.openai import OpenAIChatModel
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from pydantic_ai.providers.openai import OpenAIProvider
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from sqlalchemy.sql.dml import ReturningDelete
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DEEPSEEK_V4_FLASH_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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MODEL_SETTINGS = ModelSettings(
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temperature=0, extra_body={"thinking": {"type": "disabled"}} # 温度控制
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) # 禁用思考模式
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class Deps(BaseModel):
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"""
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依赖类
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"""
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date: datetime.date = Field(..., description="日期")
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origin_airport_code: str = Field(..., description="出发机场代码")
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destination_airport_code: str = Field(..., description="到达机场代码")
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source_material: str = Field(..., description="来源资料")
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class Flight(BaseModel):
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"""
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航班类
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"""
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number: str = Field(..., description="航班号")
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date: datetime.date = Field(..., description="日期")
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origin_airport_code: str = Field(..., description="出发机场代码")
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destination_airport_code: str = Field(..., description="到达机场代码")
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airfare: int = Field(..., description="机票价格")
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@field_validator("origin_airport_code", "destination_airport_code")
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@classmethod
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def validate_airport_code(cls, airport_code: str) -> str:
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"""校验机场代码"""
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if (
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not airport_code.isalpha()
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or not airport_code.isupper()
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or len(airport_code) != 3
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):
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raise ValueError(f"机场代码须为英文、大写、3位字符串")
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return airport_code
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class FlightNoFound(BaseModel):
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"""
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未查询到航班类
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"""
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system_prompt = """你的任务是根据用户提供的日期、出发机场和到达机场选择航班。
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1. 使用 extract_flights 从来源资料提取所有航班信息；
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2. 使用 select_flight 从所有航班信息中选择匹配航班；
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"""
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# 主智能体
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agent = Agent[Deps, Flight | FlightNoFound](
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model=DEEPSEEK_V4_FLASH_MODEL,
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model_settings=MODEL_SETTINGS,
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deps_type=Deps,
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output_type=Flight | FlightNoFound,
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system_prompt=system_prompt,
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)
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# 航班信息提取智能体
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extraction_agent = Agent(
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model=DEEPSEEK_V4_FLASH_MODEL,
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model_settings=MODEL_SETTINGS,
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output_type=list[Flight],
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system_prompt=(
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"你的任务是根据给定的来源资料提取出所有航班信息，包括航班号、日期、出发机场代码、到达机场代码和机票价格。",
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"其中，出发机场代码和到达机场代码须为英文、大写、3位字符串，例如 San Francisco International Airport (SFO) 的机场代码为 SFO。",
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"若某航班信息不全则跳过该航班，若没有任何航班信息则返回空列表。",
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"禁止编造。",
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),
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) # 通过提示词约束输出
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# 航班选择智能体
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selection_agent = Agent(
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model=DEEPSEEK_V4_FLASH_MODEL,
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model_settings=MODEL_SETTINGS,
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output_type=Flight | FlightNoFound,
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system_prompt="你的任务是从所有航班信息中选择匹配用户提供的日期、出发机场和到达机场的航班：若有多个航班则选择最便宜的航班，选择完成需用户确认是否预定；若没有则返回 FlightNoFound。",
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)
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"""
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如何提高提取准确率
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1. 系统提示词规则约束
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2. 降低模型温度
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3. 输出类型约定和输出模型校验
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4. 业务规则约束（例如，输出不可为空列表）
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"""
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@agent.tool
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async def extract_flights(ctx: RunContext[Deps]) -> list[Flight]:
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"""
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提取所有航班信息
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"""
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result = await extraction_agent.run(
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ctx.deps.source_material,
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usage_limits=UsageLimits(request_limit=3),
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usage=ctx.usage,
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)
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if len(output := result.output) != 8:
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raise ModelRetry("提取到的所有航班信息应为 8 条")
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return output
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@agent.tool
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async def select_flight(ctx: RunContext[Deps]) -> Flight | FlightNoFound:
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"""
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选择航班
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"""
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result = await selection_agent.run(ctx.deps.source_material, usage=ctx.usage)
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if isinstance(result.output, Flight):
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if not ctx.tool_call_approved:
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raise ApprovalRequired(metadata={"reason": "protected"})
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return result.output
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@agent.output_validator
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async def validate_output(
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ctx: RunContext[Deps], output: Flight | FlightNoFound
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) -> Flight | FlightNoFound:
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"""
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输出校验
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"""
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# 不校验未查询到航班
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if isinstance(output, FlightNoFound):
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return output
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errors = []
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if output.date != ctx.deps.date:
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errors.append(f"航班日期应为 {ctx.deps.date}, 不是 {output.date}")
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if output.origin != ctx.deps.origin:
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errors.append(f"航班出发机场应为 {ctx.deps.origin}, 不是 {output.origin}")
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if output.destination != ctx.deps.destination:
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errors.append(
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f"航班到达机场应为 {ctx.deps.destination}, 不是 {output.destination}"
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)
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if errors:
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raise ModelRetry("\n".join(errors))
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return output
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source_material = """
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1. Flight SFO-AK123
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- Price: $350
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- Origin: San Francisco International Airport (SFO)
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- Destination: Ted Stevens Anchorage International Airport (ANC)
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- Date: January 10, 2025
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2. Flight SFO-AK456
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- Price: $370
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- Origin: San Francisco International Airport (SFO)
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- Destination: Fairbanks International Airport (FAI)
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- Date: January 10, 2025
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3. Flight SFO-AK789
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- Price: $400
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- Origin: San Francisco International Airport (SFO)
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- Destination: Juneau International Airport (JNU)
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- Date: January 20, 2025
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4. Flight NYC-LA101
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- Price: $250
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- Origin: San Francisco International Airport (SFO)
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- Destination: Ted Stevens Anchorage International Airport (ANC)
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- Date: January 10, 2025
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5. Flight CHI-MIA202
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- Price: $200
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- Origin: Chicago O'Hare International Airport (ORD)
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- Destination: Miami International Airport (MIA)
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- Date: January 12, 2025
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6. Flight BOS-SEA303
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- Price: $120
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- Origin: Boston Logan International Airport (BOS)
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- Destination: Ted Stevens Anchorage International Airport (ANC)
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- Date: January 12, 2025
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7. Flight DFW-DEN404
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- Price: $150
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- Origin: Dallas/Fort Worth International Airport (DFW)
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- Destination: Denver International Airport (DEN)
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- Date: January 10, 2025
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8. Flight ATL-HOU505
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- Price: $180
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- Origin: Hartsfield-Jackson Atlanta International Airport (ATL)
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- Destination: George Bush Intercontinental Airport (IAH)
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- Date: January 10, 2025
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"""
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async def run_stream_events(
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user_prompt: str,
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deps: Deps,
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message_history: list[ModelMessage],
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usage: RunUsage,
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deferred_tool_results: DeferredToolResults | None = None,
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) -> AsyncGenerator:
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async with agent.run_stream_events(
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user_prompt=user_prompt,
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deps=deps,
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message_history=message_history,
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usage=usage,
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deferred_tool_results=deferred_tool_results,
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) as events:
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async for event in events:
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yield event
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async def main():
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deps = Deps(
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source_material=source_material,
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date=datetime.date(2025, 1, 10),
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origin="SFO",
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destination="ANC",
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)
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# message_history 传空列表，不要传 None
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msg_history: list[ModelMessage] = []
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prompt = f"Find me a flight from {deps.origin} to {deps.destination} on {deps.date}"
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gen = run_stream_events(
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user_prompt=prompt,
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deps=deps,
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message_history=msg_history,
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usage=RunUsage(),
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)
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# 消费异步生成器
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async for ev in gen:
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print(ev)
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asyncio.run(main())
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"""
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if not isinstance(event, AgentRunResultEvent):
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yield event
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else:
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result = event.result
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# 更新任务使用量
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task.usage = usage_to_dict(result.usage)
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if isinstance(result.output, FlightDetail):
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content = "\n---\n已查询到航班，请回复 buy 购票 或 search 重新查询\n"
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# 更新任务节点为提取座位偏好
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task.node = "seat_preference_extraction"
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else:
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content = (
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"\n---\n未查询到满足您需求的航班，流程结束！\n"
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)
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# 更新任务为空
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task = None
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# 返回任务节点结果事件
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yield TaskNodeResultEvent(
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task=task,
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content=content,
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)
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yield event
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return
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# 提取座位偏好
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case "seat_preference_extraction":
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if user_prompt == "buy":
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# 返回任务节点结果事件
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yield TaskNodeResultEvent(
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task=task,
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content="请和我说下您的座位偏好：\nA、F 座位是靠窗位；1 排、14 排、20 排腿部空间更大、更舒展，你更想要靠窗座位，宽敞大空间座位",
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)
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return
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elif user_prompt == "search":
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# 更新任务节点为航班查询
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task.node = "flight_search"
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else:
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async with seat_preference_extraction_agent.run_stream_events(
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user_prompt=user_prompt,
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message_history=message_history,
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usage=usage_to_object(dict(task.usage)),
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usage_limits=usage_limits_to_object(dict(task.usage_limits)),
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) as events:
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async for event in events:
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if not isinstance(event, AgentRunResultEvent):
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yield event
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else:
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result = event.result
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# 更新任务使用量
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task.usage = usage_to_dict(result.usage)
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# 更新任务为空
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task = None
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# 返回任务节点结果事件
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yield TaskNodeResultEvent(
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task=task,
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content="已帮您定好座位，流程结束！",
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)
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yield event
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return
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# 工具调用分片开始事件
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case ToolCallPart(
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tool_name=tool_name, tool_call_id=tool_call_id
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):
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# 构建消息实例
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message = Message(
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type=MessageType.TOOL_CALL,
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title=tool_name,
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content=",",
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is_running=True,
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)
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tool_call_ids.add(tool_call_id)
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# 添加至消息字典
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dialog.thoughts[index] = Thought(
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type="tool_call",
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content="正在生成调用参数",
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)
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# ========== 函数工具调用事件 ==========
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case FunctionToolCallEvent(tool_call_id=tool_call_id, part=part):
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# 获取分片索引
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index = tool_call_ids[tool_call_id]
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match dialog.thoughts[index].type:
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# 工具检索
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case "tool_search":
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dialog.thoughts[index].content = (
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f"正在检索 {part.args_as_json_str()}"
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)
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# 能力加载
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case "capability_load":
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dialog.thoughts[index].content = (
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f"正在加载能力 {part.tool_name}"
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)
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# 工具调用
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case "tool_call":
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dialog.thoughts[index].content = (
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f"正在调用工具 {part.tool_name}"
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)
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case _:
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continue
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# ========== 函数工具结果事件 ==========
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case FunctionToolResultEvent(
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tool_call_id=tool_call_id,
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content=content,
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):
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index = tool_call_ids[tool_call_id]
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match dialog.thoughts[index].type:
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# 工具检索
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case "tool_search":
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dialog.thoughts[index].content = (
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content if isinstance(content, str) else ""
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) # 暂仅考虑文本内容
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# 能力加载
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case "capability_load":
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dialog.thoughts[index].content = f"已加载 {content}"
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# 工具调用
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case "tool_call":
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dialog.thoughts[index].content = f"已调用 {content}"
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case _:
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continue
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def init_work_flow() -> WorkFlow:
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return WorkFlow(
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type=WorkFlowType.BOOK_FLIGHT,
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deps=Dependences(
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source_material=source_material,
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date=datetime.date(2025, 1, 10),
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origin="SFO",
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destination="ANC",
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),
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)
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from application.states.models import (
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WorkFlow,
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WorkFlowType,
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)
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from application.workshop.models import (
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DEEPSEEK_V4_FLASH_MODEL,
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MODEL_SETTINGS_DISABLED_THINKING,
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)
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"""
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