File size: 7,040 Bytes
e706de2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 |
/**
* Exercise 12 Solution: Composition and Pipelines
*/
import {HumanMessage, SystemMessage, LlamaCppLLM, Runnable} from '../../../../src/index.js';
// Part 1: PromptFormatter Runnable
class PromptFormatter extends Runnable {
constructor(systemPrompt = "You are a helpful assistant. Be concise.") {
super();
this.systemPrompt = systemPrompt;
}
async _call(input, config) {
return [
new SystemMessage(this.systemPrompt),
new HumanMessage(input)
];
}
}
// Part 2: ResponseParser Runnable
class ResponseParser extends Runnable {
async _call(input, config) {
// Extract content from AIMessage
if (input.content) {
return input.content.trim();
}
return String(input).trim();
}
}
// Part 3: AnswerValidator Runnable
class AnswerValidator extends Runnable {
constructor(minLength = 10) {
super();
this.minLength = minLength;
}
async _call(input, config) {
if (input.length < this.minLength) {
return `Error: Response too short (${input.length} chars, min ${this.minLength})`;
}
return input;
}
}
async function exercise4() {
console.log('=== Exercise 4: Composition and Pipelines ===\n');
const llm = new LlamaCppLLM({
modelPath: './models/Meta-Llama-3.1-8B-Instruct-Q5_K_S.gguf',
temperature: 0.7,
maxTokens: 100
});
try {
// Part 1: Test individual components
console.log('Part 1: Testing individual components');
const formatter = new PromptFormatter();
const parser = new ResponseParser();
const validator = new AnswerValidator();
console.log('Testing formatter:');
const formatted = await formatter.invoke("What is AI?");
console.log(formatted);
console.log();
console.log('Testing LLM + parser:');
const llmResponse = await llm.invoke(formatted);
const parsed = await parser.invoke(llmResponse);
console.log('Parsed:', parsed);
console.log();
console.log('Testing validator with short input:');
const shortResult = await validator.invoke("Hi");
console.log(shortResult);
console.log();
// Part 2: Build a complete pipeline
console.log('Part 2: Complete pipeline');
// Chain all components together
const pipeline = formatter
.pipe(llm)
.pipe(parser)
.pipe(validator);
console.log('Pipeline structure:', pipeline.toString());
const result1 = await pipeline.invoke("What is machine learning?");
console.log('Result:', result1);
console.log();
// Part 3: Reusable agent pipelines
console.log('Part 3: Reusable agent pipeline');
// Creative pipeline: high temperature, no validator
const creativeLLM = new LlamaCppLLM({
modelPath: './models/Meta-Llama-3.1-8B-Instruct-Q5_K_S.gguf',
temperature: 0.9,
maxTokens: 100
});
const creativeFormatter = new PromptFormatter(
"You are a creative writer. Use vivid imagery."
);
const creativePipeline = creativeFormatter
.pipe(creativeLLM)
.pipe(parser);
// Factual pipeline: low temperature, with validator
const factualLLM = new LlamaCppLLM({
modelPath: './models/Meta-Llama-3.1-8B-Instruct-Q5_K_S.gguf',
temperature: 0.1,
maxTokens: 100
});
const factualFormatter = new PromptFormatter(
"You are a factual encyclopedia. Be precise and accurate."
);
const factualPipeline = factualFormatter
.pipe(factualLLM)
.pipe(parser)
.pipe(validator);
console.log('Creative (temp=0.9):');
const creative = await creativePipeline.invoke("Describe a sunset");
console.log(creative);
console.log();
console.log('Factual (temp=0.1):');
const factual = await factualPipeline.invoke("What is the capital of France?");
console.log(factual);
console.log();
// Part 4: Batch processing with pipelines
console.log('Part 4: Batch processing with pipeline');
const questions = [
"What is Python?",
"What is JavaScript?",
"What is Rust?"
];
const answers = await pipeline.batch(questions);
questions.forEach((q, i) => {
console.log(`Q: ${q}`);
console.log(`A: ${answers[i]}`);
console.log();
});
// Cleanup additional LLMs
await creativeLLM.dispose();
await factualLLM.dispose();
} finally {
await llm.dispose();
}
console.log('\n✓ Exercise 4 complete!');
}
// Run the solution
exercise4().catch(console.error);
/**
* Key Takeaways:
*
* 1. Building Runnables:
* - Extend Runnable base class
* - Override _call() method
* - Each Runnable does one thing well
* - Configure via constructor parameters
*
* 2. Composition with .pipe():
* - a.pipe(b).pipe(c) chains operations
* - Output of one becomes input of next
* - Creates a new RunnableSequence
* - Result is itself a Runnable
*
* 3. Pipeline benefits:
* - Reusable across different inputs
* - Testable components in isolation
* - Easy to modify (swap components)
* - Clear data flow
*
* 4. Specialized pipelines:
* - Create different LLM instances with different configs
* - Mix and match components
* - Same interface, different behavior
*
* 5. Batch compatibility:
* - Pipelines work with batch() automatically
* - Each input goes through entire pipeline
* - No extra code needed
*
* 6. Real-world pattern:
* - This is exactly how LangChain works
* - Prompt -> Model -> Parser is common pattern
* - You now understand what frameworks do
*
* 7. Design principles:
* - Single Responsibility: Each Runnable does one thing
* - Composition over Inheritance: Build complex from simple
* - Open/Closed: Easy to extend, no need to modify
*
* Example advanced pipeline:
*
* ```javascript
* const agent = inputValidator
* .pipe(promptFormatter)
* .pipe(llm)
* .pipe(responseParser)
* .pipe(errorChecker)
* .pipe(outputFormatter);
*
* // Use it
* const result = await agent.invoke(userInput);
*
* // Test a component
* const parsed = await responseParser.invoke(testData);
*
* // Swap LLM
* const newAgent = inputValidator
* .pipe(promptFormatter)
* .pipe(differentLLM) // Just changed this!
* .pipe(responseParser)
* .pipe(errorChecker)
* .pipe(outputFormatter);
* ```
*/ |