75601988c2
- Cover, TOC, 5 dividers, 3 recap slides - 5 sections (chains, langgraph, deepagents, openswe, ecosystem) - design-system.js with theme tokens + 9 helper functions - research/: timeline + sources + per-tech notes - final-compile.js + merge.js for rebuild pipeline - output/: langchain-evolution.pptx (2.3 MB) + langchain-evolution.pdf (1.1 MB) + 7 sample previews
54 lines
1.8 KiB
JavaScript
54 lines
1.8 KiB
JavaScript
// Slide 10: StrOutputParser -- the most common parser
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// Code slide: parse AIMessage.content down to plain str.
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const ds = require('./design-system');
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function createSlide(pres, theme) {
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const slide = pres.addSlide();
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ds.helpers.slideBase(slide, pres, theme);
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ds.helpers.addHeader(slide, pres, theme, {
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eyebrow: 'STAGE 1: CHAINS',
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section: 'Output parsers',
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title: 'StrOutputParser -- самый частый',
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sectionNumber: 1,
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});
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ds.helpers.addCodeBlock(slide, pres, theme, {
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x: 0.5, y: 1.5, w: 9.0, h: 3.25,
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language: 'python',
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filePath: 'examples/parser_str.py',
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code: [
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'from langchain_core.prompts import ChatPromptTemplate',
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'from langchain_core.output_parsers import StrOutputParser',
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'from langchain.chat_models import init_chat_model',
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'',
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'prompt = ChatPromptTemplate.from_messages([',
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' ("system", "Translate to French."),',
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' ("human", "{text}"),',
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'])',
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'model = init_chat_model("openai:gpt-4.1-mini")',
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'',
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'# Склеиваем через LCEL: prompt | model | parser',
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'chain = prompt | model | StrOutputParser()',
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'',
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'# Теперь на выходе str, а не AIMessage',
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'result = chain.invoke({"text": "Hello world"})',
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'print(type(result).__name__, "->", result)',
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'# >>> str -> "Bonjour le monde"',
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].join('\n'),
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});
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ds.helpers.addCallout(slide, pres, theme, {
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x: 0.5, y: 4.85, w: 9.0, h: 0.3,
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kind: 'info',
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text: 'StrOutputParser просто достает .content из AIMessage. Без него пришлось бы делать result.content вручную.',
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});
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ds.helpers.addSourceLine(slide, pres, theme, {
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source: 'python.langchain.com/docs/concepts/output_parsers/',
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});
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ds.helpers.addPageNumber(slide, pres, theme, 10);
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}
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module.exports = { createSlide }; |