"""
Slide 12: with_structured_output -- рекомендованный путь
Section 1: LangChain 1.0
Source: slides/section1-chains/slide-12.js

Сгенерировано автоматически из slide-NN.js / sectionN.pptx.
Паттерн провайдера скопирован из
bro-js/agents/teacher/assistant/src/angry_teacher/llm.py.

Запуск:
  cd langchain-evolution-deck
  cp .env.example .env   # заполни OPENAI_API_KEY
  python examples/slide-012/script.py
"""
import os
from dotenv import load_dotenv
from langchain_openai import ChatOpenAI

load_dotenv()

llm = ChatOpenAI(
    # model="openrouter/free",
    # model="qwen/qwen3.5-35b-a3b",
    model="openai/gpt-oss-20b",
    # model="nvidia/nemotron-3-nano",
    # model="qwen/qwen3.5-9b",
    base_url="https://llm.brojs.ru/v1",
    # base_url="https://api.minimax.io/v1",
    # base_url="http://0.0.0.0:8090/v1",
    # base_url="https://openrouter.ai/api/v1",
    # api_key=os.getenv("MINIMAX_API_KEY"),
    api_key=(os.getenv("OPENAI_API_KEY") or "").strip() or None,
    temperature=0.7,
    stream_usage=True,
)


# === Code from slide 12 ===
from pydantic import BaseModel, Field
from langchain.chat_models import init_chat_model

class Weather(BaseModel):
    city: str = Field(description="City name")
    temperature_c: float = Field(description="Temperature in Celsius")
    conditions: str = Field(description="Weather summary")

# Один вызов -- и модель возвращает типизированный Pydantic-объект
model = init_chat_model("openai:gpt-4.1-mini")
structured = model.with_structured_output(Weather)

result: Weather = structured.invoke("Weather in Paris?")
print(result.city, result.temperature_c, result.conditions)

# method="json_mode" -- если провайдер не поддерживает tool calling
# structured_json = model.with_structured_output(Weather, method="json_mode")