feat(langchain): LCEL Composition Support (M1.5)

packages/langchain-cascadeflow/examples/lcel-chains.ts

@@ -0,0 +1,168 @@
+/**
+ * LCEL (LangChain Expression Language) Chains Example
+ *
+ * Demonstrates how to use CascadeFlow with LCEL composition patterns.
+ * The cascade works seamlessly with pipes, sequences, and other LCEL constructs.
+ */
+
+import { ChatOpenAI } from '@langchain/openai';
+import { PromptTemplate } from '@langchain/core/prompts';
+import { StringOutputParser } from '@langchain/core/output_parsers';
+import { RunnableSequence, RunnablePassthrough } from '@langchain/core/runnables';
+import { withCascade } from '../src/index.js';
+
+async function main() {
+  console.log('=== CascadeFlow LCEL Chains Demo ===\n');
+
+  const drafter = new ChatOpenAI({ model: 'gpt-4o-mini', temperature: 0.7 });
+  const verifier = new ChatOpenAI({ model: 'gpt-4o', temperature: 0.7 });
+
+  const cascade = withCascade({
+    drafter,
+    verifier,
+    qualityThreshold: 0.7,
+  });
+
+  // Example 1: Simple Pipe Chain
+  console.log('--- Example 1: Simple Pipe Chain ---');
+
+  const parser = new StringOutputParser();
+  const simpleChain = cascade.pipe(parser);
+
+  const result1 = await simpleChain.invoke('What is the capital of France?');
+  console.log('Query: What is the capital of France?');
+  console.log(`Response: ${result1}`);
+
+  const metadata1 = cascade.getLastCascadeResult();
+  console.log(`Model used: ${metadata1?.modelUsed}`);
+  console.log('\n');
+
+  // Example 2: Prompt | Cascade | Parser
+  console.log('--- Example 2: Prompt → Cascade → Parser ---');
+
+  const prompt = PromptTemplate.fromTemplate(
+    'You are a helpful assistant. Answer this question: {question}'
+  );
+
+  const chain2 = prompt.pipe(cascade).pipe(parser);
+
+  const result2 = await chain2.invoke({
+    question: 'Explain how photosynthesis works in simple terms',
+  });
+
+  console.log('Query: Explain how photosynthesis works');
+  console.log(`Response: ${result2.substring(0, 150)}...`);
+
+  const metadata2 = cascade.getLastCascadeResult();
+  console.log(`Model used: ${metadata2?.modelUsed}`);
+  console.log(`Quality: ${metadata2?.drafterQuality?.toFixed(2)}`);
+  console.log('\n');
+
+  // Example 3: RunnableSequence
+  console.log('--- Example 3: RunnableSequence ---');
+
+  const sequenceChain = RunnableSequence.from([
+    PromptTemplate.fromTemplate('Topic: {topic}\n\nWrite a brief summary.'),
+    cascade,
+    new StringOutputParser(),
+  ]);
+
+  const result3 = await sequenceChain.invoke({ topic: 'Artificial Intelligence' });
+  console.log('Topic: Artificial Intelligence');
+  console.log(`Summary: ${result3.substring(0, 150)}...`);
+  console.log('\n');
+
+  // Example 4: Batch Processing
+  console.log('--- Example 4: Batch Processing ---');
+
+  const batchChain = cascade.pipe(parser);
+
+  const questions = [
+    'What is 2+2?',
+    'What is the speed of light?',
+    'Who wrote Romeo and Juliet?',
+  ];
+
+  const results4 = await batchChain.batch(questions);
+
+  console.log('Batch processing 3 questions:');
+  results4.forEach((result, idx) => {
+    console.log(`  ${idx + 1}. ${questions[idx]}`);
+    console.log(`     → ${result.substring(0, 60)}...`);
+  });
+  console.log('\n');
+
+  // Example 5: Complex Chain with RunnablePassthrough
+  console.log('--- Example 5: RunnablePassthrough.assign() ---');
+
+  const complexChain = RunnablePassthrough.assign({
+    answer: cascade.pipe(new StringOutputParser()),
+    context: () => 'Generated by CascadeFlow',
+  });
+
+  const result5 = await complexChain.invoke('What is machine learning?');
+
+  console.log('Query: What is machine learning?');
+  console.log(`Answer: ${result5.answer.substring(0, 100)}...`);
+  console.log(`Context: ${result5.context}`);
+  console.log('\n');
+
+  // Example 6: Streaming in LCEL Chain
+  console.log('--- Example 6: Streaming in LCEL Chain ---');
+
+  const streamChain = prompt.pipe(cascade).pipe(parser);
+
+  console.log('Query: What are the benefits of renewable energy?');
+  process.stdout.write('Response (streaming): ');
+
+  const stream = await streamChain.stream({
+    question: 'What are the benefits of renewable energy?',
+  });
+
+  for await (const chunk of stream) {
+    process.stdout.write(chunk);
+  }
+
+  console.log('\n');
+
+  const metadata6 = cascade.getLastCascadeResult();
+  console.log(`Model used: ${metadata6?.modelUsed}`);
+  console.log(`Pre-routed: ${metadata6?.preRouted}`);
+  console.log('\n');
+
+  // Example 7: Nested Chains
+  console.log('--- Example 7: Nested Chains ---');
+
+  const innerChain = cascade.pipe(new StringOutputParser());
+  const outerChain = RunnableSequence.from([
+    PromptTemplate.fromTemplate('Question: {text}'),
+    innerChain,
+  ]);
+
+  const result7 = await outerChain.invoke({ text: 'What is the meaning of life?' });
+
+  console.log('Query: What is the meaning of life?');
+  console.log(`Response: ${result7.substring(0, 150)}...`);
+  console.log('\n');
+
+  // Example 8: Method Chaining before Piping
+  console.log('--- Example 8: .bind() + Pipe ---');
+
+  const boundChain = cascade
+    .bind({ temperature: 0.3 }) // More deterministic
+    .pipe(new StringOutputParser());
+
+  const result8 = await boundChain.invoke('Count from 1 to 5');
+
+  console.log('Query: Count from 1 to 5 (temperature=0.3)');
+  console.log(`Response: ${result8}`);
+  console.log('\n');
+
+  console.log('=== LCEL Chains Demo Complete ===');
+  console.log('\n💡 CascadeFlow works seamlessly with LCEL');
+  console.log('💡 Use .pipe() to chain cascade with other runnables');
+  console.log('💡 Batch processing, streaming, and all LCEL patterns supported');
+  console.log('💡 Cascade metadata remains accessible through getLastCascadeResult()');
+}
+
+main().catch(console.error);