Initial commit: LangChain evolution tutorial deck (132 slides)

- 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

slides/section1-chains/slide-18.js

@@ -0,0 +1,62 @@
+// Slide 18: Retrievers + Vector Stores -- FAISS / Chroma / PGVector
+// Code slide: index documents, embed, retrieve top-k, plug into a chain.
+
+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: 'Retrievers',
+    title: 'Vector store -> retriever -> RAG-цепочка',
+    sectionNumber: 1,
+  });
+
+  ds.helpers.addCodeBlock(slide, pres, theme, {
+    x: 0.5, y: 1.5, w: 9.0, h: 3.25,
+    language: 'python',
+    filePath: 'examples/retriever_rag.py',
+    code: [
+      'from langchain_openai import OpenAIEmbeddings',
+      'from langchain_community.vectorstores import FAISS',
+      'from langchain_core.runnables import RunnablePassthrough',
+      '',
+      '# 1. Эмбеддинги и индекс',
+      'embeddings = OpenAIEmbeddings(model="text-embedding-3-small")',
+      'docs = ["Cats are mammals.", "Python is a programming language.",',
+      '        "LangChain helps build LLM apps."]',
+      'vectorstore = FAISS.from_texts(docs, embedding=embeddings)',
+      '',
+      '# 2. .as_retriever() превращает store в Runnable',
+      'retriever = vectorstore.as_retriever(search_kwargs={"k": 2})',
+      '',
+      '# 3. RAG-цепочка через LCEL: context + question -> answer',
+      'from langchain_core.prompts import ChatPromptTemplate',
+      'from langchain.chat_models import init_chat_model',
+      '',
+      'prompt = ChatPromptTemplate.from_template(',
+      '    "Answer based on context.\\nContext: {context}\\nQ: {question}"',
+      ')',
+      'model = init_chat_model("openai:gpt-4.1-mini")',
+      'rag = (',
+      '    {"context": retriever, "question": RunnablePassthrough()}',
+      '    | prompt | model | StrOutputParser()',
+      ')',
+    ].join('\n'),
+  });
+
+  ds.helpers.addCallout(slide, pres, theme, {
+    x: 0.5, y: 4.85, w: 9.0, h: 0.3,
+    kind: 'info',
+    text: 'Альтернативные сторы: Chroma (легковесный), PGVector (production Postgres), Pinecone, Weaviate, Qdrant.',
+  });
+
+  ds.helpers.addSourceLine(slide, pres, theme, {
+    source: 'python.langchain.com/docs/concepts/retrievers/',
+  });
+  ds.helpers.addPageNumber(slide, pres, theme, 18);
+}
+
+module.exports = { createSlide };