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/section5-ecosystem/slide-08.js

@@ -0,0 +1,58 @@
+// Slide 08: Evaluators + run_on_dataset -- off-line evaluation pipeline
+// Code slide: custom evaluator + run_on_dataset.
+
+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 5: ECOSYSTEM',
+    section: 'Evaluators',
+    title: 'run_on_dataset + evaluator-ы',
+    sectionNumber: 5,
+    titleSize: 24,
+  });
+
+  ds.helpers.addCodeBlock(slide, pres, theme, {
+    x: 0.5, y: 1.45, w: 9.0, h: 2.85,
+    language: 'python',
+    fontSize: 10,
+    code: [
+      'from langsmith import Client',
+      'from langsmith.schemas import Example, Run',
+      '',
+      'client = Client()',
+      '',
+      '# 1. Target -- что прогоняем (chain, graph, agent, callable)',
+      'def target(inputs: dict) -> dict:',
+      '    return {"answer": my_chain.invoke(inputs["question"])}',
+      '',
+      '# 2. Evaluator -- scoring function',
+      'def answer_match(run: Run, example: Example) -> dict:',
+      '    score = 1.0 if example.outputs["answer"] in run.outputs["answer"] else 0.0',
+      '    return {"key": "answer_match", "score": score}',
+      '',
+      '# 3. Прогон: target на датасете + scoring',
+      'client.run_on_dataset(',
+      '    dataset_name="rag-qa-eval-v1",',
+      '    llm_or_chain_factory=target,',
+      '    evaluators=[answer_match],',
+      ')',
+    ].join('\n'),
+  });
+
+  ds.helpers.addCallout(slide, pres, theme, {
+    x: 0.5, y: 4.5, w: 9.0, h: 0.55,
+    kind: 'success',
+    text: 'Готовые evaluator-ы: llm_as_judge, exact_match, embedding_distance, cot_qa.',
+  });
+
+  ds.helpers.addSourceLine(slide, pres, theme, {
+    source: 'docs.smith.langchain.com Evaluation > Evaluator types',
+  });
+  ds.helpers.addPageNumber(slide, pres, theme, 8);
+}
+
+module.exports = { createSlide };