Squashed 'vendor/ruvector/' content from commit b64c2172
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ruv
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docs/research/dspy-ts-quick-start-guide.md
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docs/research/dspy-ts-quick-start-guide.md
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# DSPy.ts Quick Start Guide
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## Self-Learning AI with TypeScript
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**TL;DR:** DSPy.ts enables automatic prompt optimization achieving 1.5-3x performance improvements and 22-90x cost reduction through systematic programming instead of manual prompt engineering.
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---
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## 🚀 Quick Start (5 minutes)
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### Installation
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```bash
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# Primary recommendation: Ax framework
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npm install @ax-llm/ax
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# Alternative: DSPy.ts
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npm install dspy.ts
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# Alternative: TS-DSPy
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npm install @ts-dspy/core
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```
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### Basic Example
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```typescript
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import { ai, ax } from '@ax-llm/ax';
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// 1. Configure LLM
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const llm = ai({
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name: 'anthropic',
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apiKey: process.env.ANTHROPIC_API_KEY,
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model: 'claude-3.5-sonnet-20241022'
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});
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// 2. Define signature (not prompt!)
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const classifier = ax('review:string -> sentiment:class "positive, negative, neutral"');
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// 3. Use it
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const result = await classifier.forward(llm, {
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review: "This product is amazing!"
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});
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console.log(result.sentiment); // "positive"
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```
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---
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## 🎯 Framework Comparison
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| Feature | **Ax** ⭐ | DSPy.ts | TS-DSPy |
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|---------|----------|---------|---------|
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| **Production Ready** | ✅ Yes | ⚠️ Beta | ⚠️ Alpha |
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| **Type Safety** | ✅✅ Full | ✅ Full | ✅ Basic |
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| **LLM Support** | 15+ | 10+ | 5+ |
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| **Optimization** | GEPA, MiPRO | MIPROv2, Bootstrap | Basic |
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| **Observability** | OpenTelemetry | Basic | None |
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| **Documentation** | Excellent | Good | Limited |
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| **Recommendation** | **Best for production** | Good for learning | Experimental |
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**Winner:** Ax framework for production applications
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---
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## ⚡ 3-Minute Tutorial: Zero to Optimized
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### Step 1: Create Baseline Program
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```typescript
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import { ai, ax } from '@ax-llm/ax';
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import { BootstrapFewShot } from '@ax-llm/ax/optimizers';
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const llm = ai({
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name: 'openai',
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apiKey: process.env.OPENAI_API_KEY,
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model: 'gpt-4o-mini'
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});
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// Simple question answering
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const qa = ax('question:string -> answer:string');
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```
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### Step 2: Prepare Training Data
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```typescript
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const trainset = [
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{
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question: "What is the capital of France?",
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answer: "Paris"
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},
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{
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question: "What is 2+2?",
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answer: "4"
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},
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{
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question: "Who wrote Hamlet?",
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answer: "William Shakespeare"
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}
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// ... 20-50 examples recommended
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];
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```
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### Step 3: Optimize Automatically
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```typescript
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// Define success metric
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const metric = (example, prediction) => {
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return prediction.answer.toLowerCase().includes(example.answer.toLowerCase())
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? 1.0
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: 0.0;
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};
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// Optimize
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const optimizer = new BootstrapFewShot({ metric });
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const optimizedQA = await optimizer.compile(qa, trainset);
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// Now it's smarter!
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const result = await optimizedQA.forward(llm, {
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question: "What is the capital of Japan?"
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});
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```
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**Expected Results:**
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- Baseline accuracy: ~65%
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- Optimized accuracy: ~85%
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- Improvement: **+30%**
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---
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## 💡 Common Use Cases
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### 1. Sentiment Analysis
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```typescript
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const sentiment = ax('review:string -> sentiment:class "positive, negative, neutral", confidence:number');
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const result = await sentiment.forward(llm, {
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review: "The product arrived damaged but customer service was helpful."
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});
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// { sentiment: "neutral", confidence: 0.75 }
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```
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### 2. Entity Extraction
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```typescript
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const extractor = ax(`
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text:string
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->
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entities:{name:string, type:class "person, org, location"}[]
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`);
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const result = await extractor.forward(llm, {
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text: "Apple CEO Tim Cook announced new products in Cupertino."
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});
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// {
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// entities: [
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// {name: "Apple", type: "org"},
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// {name: "Tim Cook", type: "person"},
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// {name: "Cupertino", type: "location"}
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// ]
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// }
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```
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### 3. Question Answering with Context
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```typescript
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const contextQA = ax(`
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context:string,
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question:string
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->
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answer:string,
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confidence:number
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`);
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const result = await contextQA.forward(llm, {
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context: "The Eiffel Tower is 330 meters tall. It was built in 1889.",
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question: "How tall is the Eiffel Tower?"
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});
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// { answer: "330 meters", confidence: 0.95 }
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```
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### 4. Code Generation
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```typescript
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const coder = ax(`
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description:string,
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language:class "typescript, python, rust"
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->
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code:string,
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explanation:string
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`);
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const result = await coder.forward(llm, {
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description: "Function to calculate fibonacci numbers",
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language: "typescript"
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});
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```
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---
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## 🎓 Optimization Strategies
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### Strategy 1: Bootstrap Few-Shot (Default)
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**Best for:** 10-100 examples, quick optimization
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```typescript
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const optimizer = new BootstrapFewShot({
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metric: exactMatch,
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maxBootstrappedDemos: 4
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});
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const optimized = await optimizer.compile(program, trainset);
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```
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**Time:** 5-15 minutes
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**Improvement:** 15-30%
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**Cost:** $1-5
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### Strategy 2: MIPROv2 (Advanced)
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**Best for:** 100+ examples, maximum accuracy
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```typescript
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import { MIPROv2 } from '@ax-llm/ax/optimizers';
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const optimizer = new MIPROv2({
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metric: f1Score,
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numCandidates: 10,
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numTrials: 100
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});
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const optimized = await optimizer.compile(program, trainset);
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```
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**Time:** 1-3 hours
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**Improvement:** 30-50%
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**Cost:** $20-50
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### Strategy 3: GEPA (Cost-Optimized)
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**Best for:** Quality + cost optimization
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```typescript
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import { GEPA } from '@ax-llm/ax/optimizers';
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const optimizer = new GEPA({
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objectives: [
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{ metric: accuracy, weight: 0.7 },
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{ metric: costPerRequest, weight: 0.3 }
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]
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});
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const optimized = await optimizer.compile(program, trainset);
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```
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**Time:** 2-3 hours
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**Improvement:** 40-60% with 22-90x cost reduction
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**Cost:** $30-80 (pays for itself in production)
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---
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## 🔌 Multi-Model Integration
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### OpenAI (GPT-4)
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```typescript
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const llm = ai({
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name: 'openai',
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apiKey: process.env.OPENAI_API_KEY,
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model: 'gpt-4-turbo'
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});
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```
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### Anthropic (Claude)
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```typescript
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const llm = ai({
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name: 'anthropic',
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apiKey: process.env.ANTHROPIC_API_KEY,
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model: 'claude-3-5-sonnet-20241022'
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});
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```
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### Local (Ollama)
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```typescript
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const llm = ai({
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name: 'ollama',
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model: 'llama3.1:70b',
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config: {
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baseURL: 'http://localhost:11434'
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}
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});
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```
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### OpenRouter (Multi-Model with Failover)
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```typescript
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const llm = ai({
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name: 'openrouter',
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apiKey: process.env.OPENROUTER_API_KEY,
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model: 'anthropic/claude-3.5-sonnet',
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config: {
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extraHeaders: {
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'HTTP-Referer': 'https://your-app.com',
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'X-Fallback': JSON.stringify([
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'openai/gpt-4-turbo',
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'meta-llama/llama-3.1-70b-instruct'
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])
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}
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}
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});
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```
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---
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## 💰 Cost Optimization Patterns
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### Pattern 1: Model Cascade
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```typescript
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async function smartPredict(input) {
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// Try cheap model first
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const cheap = ai({ name: 'openai', model: 'gpt-4o-mini' });
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const result = await program.forward(cheap, input);
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// If confident, return
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if (result.confidence > 0.9) return result;
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// Otherwise, use expensive model
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const expensive = ai({ name: 'anthropic', model: 'claude-3.5-sonnet' });
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return program.forward(expensive, input);
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}
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```
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**Cost Reduction:** 60-80%
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### Pattern 2: Caching
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```typescript
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import Redis from 'ioredis';
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const redis = new Redis();
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async function cachedPredict(input) {
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const cacheKey = `llm:${hashInput(input)}`;
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const cached = await redis.get(cacheKey);
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if (cached) return JSON.parse(cached);
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const result = await program.forward(llm, input);
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await redis.setex(cacheKey, 86400, JSON.stringify(result));
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return result;
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}
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```
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**Cost Reduction:** 40-70%
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### Pattern 3: Batch Processing
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```typescript
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async function batchProcess(inputs, batchSize=10) {
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const results = [];
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for (let i = 0; i < inputs.length; i += batchSize) {
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const batch = inputs.slice(i, i + batchSize);
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const batchResults = await Promise.all(
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batch.map(input => program.forward(llm, input))
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);
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results.push(...batchResults);
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}
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return results;
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}
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```
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**Cost Reduction:** 20-40% (through rate optimization)
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---
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## 📊 Benchmarking
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### Simple Evaluation
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```typescript
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async function evaluate(program, testset, metric) {
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const scores = [];
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for (const example of testset) {
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const prediction = await program.forward(llm, example.input);
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const score = metric(example, prediction);
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scores.push(score);
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}
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const avgScore = scores.reduce((a, b) => a + b) / scores.length;
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return avgScore;
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}
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// Use it
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const accuracy = await evaluate(optimizedProgram, testset, exactMatch);
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console.log(`Accuracy: ${(accuracy * 100).toFixed(2)}%`);
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```
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### Compare Multiple Programs
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```typescript
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const programs = {
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baseline: baselineProgram,
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bootstrap: await new BootstrapFewShot(metric).compile(baselineProgram, trainset),
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mipro: await new MIPROv2(metric).compile(baselineProgram, trainset)
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};
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for (const [name, program] of Object.entries(programs)) {
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const score = await evaluate(program, testset, metric);
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console.log(`${name}: ${(score * 100).toFixed(2)}%`);
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}
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// Output:
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// baseline: 65.30%
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// bootstrap: 82.10%
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// mipro: 91.40%
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```
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---
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## 🚨 Common Pitfalls
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### ❌ DON'T: Write prompts manually
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```typescript
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// Bad - brittle and hard to optimize
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const prompt = `
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You are a sentiment analyzer. Given a review, classify it.
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Review: ${review}
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Classification:`;
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const response = await llm.generate(prompt);
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```
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### ✅ DO: Use signatures
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```typescript
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// Good - optimizable and type-safe
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const classifier = ax('review:string -> sentiment:class "positive, negative, neutral"');
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const result = await classifier.forward(llm, { review });
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```
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### ❌ DON'T: Use too little training data
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```typescript
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// Bad - not enough examples
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const trainset = [
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{ input: "example1", output: "result1" },
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{ input: "example2", output: "result2" }
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];
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```
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### ✅ DO: Use 20-50+ examples
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```typescript
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// Good - sufficient for optimization
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const trainset = generateExamples(50); // 50+ examples
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```
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### ❌ DON'T: Optimize without metrics
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```typescript
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// Bad - can't measure improvement
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const optimizer = new BootstrapFewShot();
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const optimized = await optimizer.compile(program, trainset);
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```
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### ✅ DO: Define clear metrics
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```typescript
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// Good - measurable improvement
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const metric = (example, prediction) => {
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return prediction.answer === example.answer ? 1.0 : 0.0;
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};
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const optimizer = new BootstrapFewShot({ metric });
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```
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---
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## 🎯 Production Checklist
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- [ ] Use Ax framework (not experimental alternatives)
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- [ ] Configure error handling and retries
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- [ ] Implement caching layer
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- [ ] Add monitoring (OpenTelemetry)
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- [ ] Use environment variables for API keys
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- [ ] Implement model failover
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- [ ] Set rate limits
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- [ ] Add request timeout
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- [ ] Log predictions for analysis
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- [ ] Version your prompts/signatures
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- [ ] Test with production data
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- [ ] Monitor costs in production
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- [ ] Set up alerts for failures
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- [ ] Document your signatures
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---
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## 📚 Resources
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### Documentation
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- **Ax Framework:** https://axllm.dev/
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- **DSPy.ts:** https://github.com/ruvnet/dspy.ts
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- **Stanford DSPy:** https://dspy.ai/
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### Community
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- **Ax Discord:** Community support
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- **Twitter:** @dspy_ai
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- **GitHub Issues:** Report bugs, request features
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### Learning
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- **Ax Examples:** 70+ production examples
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- **DSPy.ts Examples:** Browser-based examples
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- **Tutorials:** See comprehensive research report
|
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---
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## 🚀 Next Steps
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1. **Install Ax framework** (5 min)
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2. **Try basic example** (10 min)
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3. **Prepare training data** (30 min)
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4. **Optimize with BootstrapFewShot** (15 min)
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5. **Evaluate improvement** (10 min)
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6. **Deploy to production** (1 hour)
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**Total Time to Production:** ~2 hours
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---
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## 💡 Pro Tips
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1. **Start Simple:** Begin with BootstrapFewShot before trying GEPA/MIPROv2
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2. **Use Claude for Reasoning:** Claude 3.5 Sonnet excels at complex logic
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3. **Use GPT-4 for Code:** Best for code generation tasks
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4. **Optimize Offline:** Don't optimize in production, deploy pre-optimized
|
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5. **Cache Aggressively:** 40-70% cost savings from caching
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6. **Monitor Everything:** Track costs, latency, and quality
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||||
7. **Version Prompts:** Keep track of what works
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8. **Test Thoroughly:** Use validation sets, not just training data
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|
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---
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|
||||
**Quick Start Guide Created By:** Research Agent
|
||||
**Last Updated:** 2025-11-22
|
||||
**For Full Details:** See comprehensive research report
|
||||
Reference in New Issue
Block a user