petya
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
62 lines
2.3 KiB
JavaScript
62 lines
2.3 KiB
JavaScript
// Slide 18: Retrievers + Vector Stores -- FAISS / Chroma / PGVector
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// Code slide: index documents, embed, retrieve top-k, plug into a chain.
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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: 'Retrievers',
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title: 'Vector store -> retriever -> RAG-цепочка',
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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/retriever_rag.py',
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code: [
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'from langchain_openai import OpenAIEmbeddings',
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'from langchain_community.vectorstores import FAISS',
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'from langchain_core.runnables import RunnablePassthrough',
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'',
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'# 1. Эмбеддинги и индекс',
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'embeddings = OpenAIEmbeddings(model="text-embedding-3-small")',
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'docs = ["Cats are mammals.", "Python is a programming language.",',
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' "LangChain helps build LLM apps."]',
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'vectorstore = FAISS.from_texts(docs, embedding=embeddings)',
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'',
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'# 2. .as_retriever() превращает store в Runnable',
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'retriever = vectorstore.as_retriever(search_kwargs={"k": 2})',
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'',
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'# 3. RAG-цепочка через LCEL: context + question -> answer',
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'from langchain_core.prompts import ChatPromptTemplate',
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'from langchain.chat_models import init_chat_model',
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'',
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'prompt = ChatPromptTemplate.from_template(',
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' "Answer based on context.\\nContext: {context}\\nQ: {question}"',
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')',
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'model = init_chat_model("openai:gpt-4.1-mini")',
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'rag = (',
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' {"context": retriever, "question": RunnablePassthrough()}',
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' | prompt | model | StrOutputParser()',
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')',
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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: 'Альтернативные сторы: Chroma (легковесный), PGVector (production Postgres), Pinecone, Weaviate, Qdrant.',
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});
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ds.helpers.addSourceLine(slide, pres, theme, {
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source: 'python.langchain.com/docs/concepts/retrievers/',
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});
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ds.helpers.addPageNumber(slide, pres, theme, 18);
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}
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module.exports = { createSlide }; |