// Slide 11: PydanticOutputParser -- structured output via Pydantic schema // Code slide: Pydantic model -> parser -> validated instance. const ds = require('./design-system'); function createSlide(pres, theme) { const slide = pres.addSlide(); ds.helpers.slideBase(slide, pres, theme); ds.helpers.addHeader(slide, pres, theme, { eyebrow: 'STAGE 1: CHAINS', section: 'Output parsers', title: 'PydanticOutputParser -- типизированный выход', sectionNumber: 1, }); ds.helpers.addCodeBlock(slide, pres, theme, { x: 0.5, y: 1.5, w: 9.0, h: 3.25, language: 'python', filePath: 'examples/parser_pydantic.py', code: [ '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)', ].join('\n'), }); ds.helpers.addCallout(slide, pres, theme, { x: 0.5, y: 4.85, w: 9.0, h: 0.3, kind: 'success', text: 'В v1.0 рекомендуется model.with_structured_output(Schema) -- он использует tool calling и точнее.', }); ds.helpers.addSourceLine(slide, pres, theme, { source: 'python.langchain.com/docs/how_to/pydantic_output_parser/', }); ds.helpers.addPageNumber(slide, pres, theme, 11); } module.exports = { createSlide };