"""
Slide 11: PydanticOutputParser -- типизированный выход
Section 1: LangChain 1.0
Source: slides/section1-chains/slide-11.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-011/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 11 ===
from pydantic import BaseModel, Field
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import PydanticOutputParser
from langchain.chat_models import init_chat_model

class MovieReview(BaseModel):
    title: str = Field(description="Movie title")
    rating: int = Field(description="Rating from 1 to 10")
    summary: str = Field(description="One-sentence summary")

parser = PydanticOutputParser(pydantic_object=MovieReview)

prompt = ChatPromptTemplate.from_messages([
    ("system", "Extract review fields.\n{format_instructions}"),
    ("human", "{review_text}"),
]).partial(format_instructions=parser.get_format_instructions())

chain = prompt | init_chat_model("openai:gpt-4.1-mini") | parser
review = chain.invoke({"review_text": "Inception was brilliant. 9/10."})
print(review.title, review.rating, review.summary)