LangChain 1.2 入门（二十六）：ToolStrategy 三个参数与流式输出七种模式

Name: ContactInfo
Returning structured response: name='小明' email='shkstart@atguigu.com' phone='12345678912'

agent = create_agent(
    model=model,
    response_format=ToolStrategy(
        ContactInfo,
        tool_message_content="格式化输出成功！",
    ),
)

================================= Tool Message =================================
Name: ContactInfo
格式化输出成功！

agent = create_agent(
    model=model,
    response_format=ToolStrategy(
        Union[ContactInfo, EventDetails],
        tool_message_content="提取完成！",
        handle_errors=True,
        # handle_errors="请检查输入数据",
    ),
)

result = agent.invoke({"messages": [{
    "role": "user",
    "content": "请提取以下文本中内容：姓名：张三，电子邮箱：zhang3@atguigu.com，"
               "活动名称：公司年会，活动日期：2026-07-15",
}]})

from langchain.agents.structured_output import (
    ToolStrategy,
    StructuredOutputValidationError,
    MultipleStructuredOutputsError,
)

response_format=ToolStrategy(
    Union[ContactInfo, EventDetails],
    handle_errors=(MultipleStructuredOutputsError, StructuredOutputValidationError),
)

for chunk in agent.stream(
    {"messages": [{"role": "user", "content": "..."}]},
    stream_mode="messages",
):
    print(chunk)

for chunk in customer_service_agent.stream(
    {"messages": [{"role": "user", "content": "查询客户 ID 为 CUST123456 的个人信息和可用优惠"}]},
    stream_mode="updates",
):
    rprint(chunk)

from langgraph.config import get_stream_writer
import time

@tool
def generate_sales_report() -> str:
    """生成销售报告"""
    writer = get_stream_writer()
    writer({"type": "生成销售报告", "message": "开始生成销售报告"})
    for i in range(1, 4):
        time.sleep(0.5)
        writer({"type": "生成销售报告", "message": f"生成销售报告进度百分比：{i * 25}%"})
    writer({"type": "生成销售报告", "message": "报告生成完成"})
    return "销售报告：总收入 150 万元，同比增长 12%"

for chunk in reporting_agent.stream(
    {"messages": [{"role": "user", "content": "生成销售报告和库存报告"}]},
    stream_mode="custom",
):
    print(chunk)

{'type': '生成销售报告', 'message': '开始生成销售报告'}
--------------------------------------------------
{'type': '生成销售报告', 'message': '生成销售报告进度百分比：25%'}
--------------------------------------------------
...
{'type': '生成销售报告', 'message': '报告生成完成'}

for mode, chunk in agent.stream(inputs, stream_mode=["updates", "messages"]):
    print(mode, chunk)

import os
from dotenv import load_dotenv
from langchain.chat_models import init_chat_model
from langchain_core.tools import tool
import math
from datetime import datetime, timedelta

load_dotenv(override=True)
model = init_chat_model(
    model="gpt-5.4-mini",
    model_provider="openai",
    api_key=os.getenv("CLOSEAI_API_KEY"),
    base_url=os.getenv("CLOSEAI_BASE_URL"),
)

@tool
def get_weather(city: str) -> str:
    """获取指定城市的实时天气信息
    Args:
        city: 城市名称，如 "北京"、"上海"、"深圳" 等
    Returns:
        包含温度、天气状况、空气质量的详细信息
    """
    return f"{city}：多云，15-22℃，空气质量良"

from langchain.agents import create_agent

assistant = create_agent(
    model=model,
    tools=[get_weather, calculate, get_current_time, convert_currency, search_info],
    system_prompt="""你是一个多功能智能助手，可以查询天气、做数学计算、查询时间、换算货币、搜索信息。
需要精确数据时必须调用工具，不要凭记忆回答。""",
)

for chunk in assistant.stream(
    {"messages": [{"role": "user", "content": "北京天气怎么样？顺便算一下 128 * 37"}]},
    stream_mode="updates",
):
    rprint(chunk)