Initial commit
This commit is contained in:
Zhongwei Li
267
templates/embeddings.ts
Normal file
267
templates/embeddings.ts
Normal file
@@ -0,0 +1,267 @@
|
||||
/**
|
||||
* OpenAI Embeddings API - Complete Examples
|
||||
*
|
||||
* This template demonstrates:
|
||||
* - Basic embeddings generation
|
||||
* - Custom dimensions for storage optimization
|
||||
* - Batch processing multiple texts
|
||||
* - RAG (Retrieval-Augmented Generation) pattern
|
||||
* - Cosine similarity for semantic search
|
||||
*/
|
||||
|
||||
import OpenAI from 'openai';
|
||||
|
||||
const openai = new OpenAI({
|
||||
apiKey: process.env.OPENAI_API_KEY,
|
||||
});
|
||||
|
||||
// =============================================================================
|
||||
// BASIC EMBEDDINGS
|
||||
// =============================================================================
|
||||
|
||||
async function basicEmbedding() {
|
||||
const embedding = await openai.embeddings.create({
|
||||
model: 'text-embedding-3-small',
|
||||
input: 'The food was delicious and the waiter was friendly.',
|
||||
});
|
||||
|
||||
console.log('Embedding dimensions:', embedding.data[0].embedding.length);
|
||||
console.log('First 5 values:', embedding.data[0].embedding.slice(0, 5));
|
||||
console.log('Token usage:', embedding.usage);
|
||||
|
||||
return embedding.data[0].embedding;
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// CUSTOM DIMENSIONS (Storage Optimization)
|
||||
// =============================================================================
|
||||
|
||||
async function customDimensions() {
|
||||
// Default: 1536 dimensions
|
||||
const fullEmbedding = await openai.embeddings.create({
|
||||
model: 'text-embedding-3-small',
|
||||
input: 'Sample text',
|
||||
});
|
||||
|
||||
console.log('Full dimensions:', fullEmbedding.data[0].embedding.length);
|
||||
|
||||
// Reduced: 256 dimensions (6x storage reduction)
|
||||
const reducedEmbedding = await openai.embeddings.create({
|
||||
model: 'text-embedding-3-small',
|
||||
input: 'Sample text',
|
||||
dimensions: 256,
|
||||
});
|
||||
|
||||
console.log('Reduced dimensions:', reducedEmbedding.data[0].embedding.length);
|
||||
|
||||
return reducedEmbedding.data[0].embedding;
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// BATCH PROCESSING
|
||||
// =============================================================================
|
||||
|
||||
async function batchEmbeddings() {
|
||||
const texts = [
|
||||
'First document about TypeScript',
|
||||
'Second document about Python',
|
||||
'Third document about JavaScript',
|
||||
];
|
||||
|
||||
const response = await openai.embeddings.create({
|
||||
model: 'text-embedding-3-small',
|
||||
input: texts,
|
||||
dimensions: 512, // Optional: reduce dimensions
|
||||
});
|
||||
|
||||
// Process results
|
||||
const embeddings = response.data.map((item, index) => ({
|
||||
text: texts[index],
|
||||
embedding: item.embedding,
|
||||
}));
|
||||
|
||||
console.log(`Generated ${embeddings.length} embeddings`);
|
||||
console.log('Total tokens used:', response.usage.total_tokens);
|
||||
|
||||
return embeddings;
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// COSINE SIMILARITY
|
||||
// =============================================================================
|
||||
|
||||
function cosineSimilarity(a: number[], b: number[]): number {
|
||||
const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0);
|
||||
const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0));
|
||||
const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0));
|
||||
return dotProduct / (magnitudeA * magnitudeB);
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// L2 NORMALIZATION
|
||||
// =============================================================================
|
||||
|
||||
function normalizeL2(vector: number[]): number[] {
|
||||
const magnitude = Math.sqrt(vector.reduce((sum, val) => sum + val * val, 0));
|
||||
return vector.map(val => val / magnitude);
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// SEMANTIC SEARCH
|
||||
// =============================================================================
|
||||
|
||||
interface Document {
|
||||
text: string;
|
||||
embedding: number[];
|
||||
}
|
||||
|
||||
async function semanticSearch(query: string, documents: Document[]) {
|
||||
// Embed the query
|
||||
const queryEmbedding = await openai.embeddings.create({
|
||||
model: 'text-embedding-3-small',
|
||||
input: query,
|
||||
});
|
||||
|
||||
const queryVector = queryEmbedding.data[0].embedding;
|
||||
|
||||
// Calculate similarity scores
|
||||
const results = documents.map(doc => ({
|
||||
text: doc.text,
|
||||
similarity: cosineSimilarity(queryVector, doc.embedding),
|
||||
}));
|
||||
|
||||
// Sort by similarity (highest first)
|
||||
results.sort((a, b) => b.similarity - a.similarity);
|
||||
|
||||
return results;
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// RAG (Retrieval-Augmented Generation)
|
||||
// =============================================================================
|
||||
|
||||
async function ragExample() {
|
||||
// 1. Create knowledge base
|
||||
const knowledgeBase = [
|
||||
'TypeScript is a superset of JavaScript that adds static typing.',
|
||||
'Python is a high-level programming language known for readability.',
|
||||
'React is a JavaScript library for building user interfaces.',
|
||||
'Node.js is a JavaScript runtime built on Chrome\'s V8 engine.',
|
||||
];
|
||||
|
||||
// 2. Generate embeddings for knowledge base
|
||||
const embeddingsResponse = await openai.embeddings.create({
|
||||
model: 'text-embedding-3-small',
|
||||
input: knowledgeBase,
|
||||
});
|
||||
|
||||
const documents: Document[] = knowledgeBase.map((text, index) => ({
|
||||
text,
|
||||
embedding: embeddingsResponse.data[index].embedding,
|
||||
}));
|
||||
|
||||
// 3. User query
|
||||
const userQuery = 'What is TypeScript?';
|
||||
|
||||
// 4. Find relevant documents
|
||||
const searchResults = await semanticSearch(userQuery, documents);
|
||||
const topResults = searchResults.slice(0, 2); // Top 2 most relevant
|
||||
|
||||
console.log('Most relevant documents:');
|
||||
topResults.forEach(result => {
|
||||
console.log(`- [${result.similarity.toFixed(3)}] ${result.text}`);
|
||||
});
|
||||
|
||||
// 5. Generate answer using retrieved context
|
||||
const context = topResults.map(r => r.text).join('\n\n');
|
||||
|
||||
const completion = await openai.chat.completions.create({
|
||||
model: 'gpt-5',
|
||||
messages: [
|
||||
{
|
||||
role: 'system',
|
||||
content: `Answer the question using the following context:\n\n${context}`,
|
||||
},
|
||||
{
|
||||
role: 'user',
|
||||
content: userQuery,
|
||||
},
|
||||
],
|
||||
});
|
||||
|
||||
console.log('\nAnswer:', completion.choices[0].message.content);
|
||||
|
||||
return completion.choices[0].message.content;
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// DIMENSION REDUCTION (Post-Generation)
|
||||
// =============================================================================
|
||||
|
||||
async function manualDimensionReduction() {
|
||||
// Get full embedding
|
||||
const response = await openai.embeddings.create({
|
||||
model: 'text-embedding-3-small',
|
||||
input: 'Testing 123',
|
||||
});
|
||||
|
||||
const fullEmbedding = response.data[0].embedding;
|
||||
console.log('Full dimensions:', fullEmbedding.length);
|
||||
|
||||
// Truncate to 256 dimensions
|
||||
const truncated = fullEmbedding.slice(0, 256);
|
||||
console.log('Truncated dimensions:', truncated.length);
|
||||
|
||||
// Normalize (recommended after truncation)
|
||||
const normalized = normalizeL2(truncated);
|
||||
|
||||
return normalized;
|
||||
}
|
||||
|
||||
// =============================================================================
|
||||
// MAIN EXECUTION
|
||||
// =============================================================================
|
||||
|
||||
async function main() {
|
||||
console.log('=== OpenAI Embeddings Examples ===\n');
|
||||
|
||||
// Example 1: Basic embedding
|
||||
console.log('1. Basic Embedding:');
|
||||
await basicEmbedding();
|
||||
console.log();
|
||||
|
||||
// Example 2: Custom dimensions
|
||||
console.log('2. Custom Dimensions:');
|
||||
await customDimensions();
|
||||
console.log();
|
||||
|
||||
// Example 3: Batch processing
|
||||
console.log('3. Batch Processing:');
|
||||
await batchEmbeddings();
|
||||
console.log();
|
||||
|
||||
// Example 4: RAG pattern
|
||||
console.log('4. RAG (Retrieval-Augmented Generation):');
|
||||
await ragExample();
|
||||
console.log();
|
||||
|
||||
// Example 5: Manual dimension reduction
|
||||
console.log('5. Manual Dimension Reduction:');
|
||||
await manualDimensionReduction();
|
||||
console.log();
|
||||
}
|
||||
|
||||
// Run if executed directly
|
||||
if (require.main === module) {
|
||||
main().catch(console.error);
|
||||
}
|
||||
|
||||
export {
|
||||
basicEmbedding,
|
||||
customDimensions,
|
||||
batchEmbeddings,
|
||||
semanticSearch,
|
||||
ragExample,
|
||||
cosineSimilarity,
|
||||
normalizeL2,
|
||||
};
|
||||
Reference in New Issue
Block a user