embeddings

Embeddings - Complete API Reference

Configure embedding providers, manage vector storage, and perform semantic search.

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Chat Commands

View Config

/embeddings                                 Show current settings
/embeddings status                          Provider status
/embeddings stats                           Cache statistics

Configure Provider

/embeddings provider openai                 Use OpenAI embeddings
/embeddings provider voyage                 Use Voyage AI
/embeddings provider local                  Use local model
/embeddings model text-embedding-3-small    Set model

Cache Management

/embeddings cache stats                     View cache stats
/embeddings cache clear                     Clear cache
/embeddings cache size                      Total cache size

Testing

/embeddings test "sample text"              Generate test embedding
/embeddings similarity "text1" "text2"      Compare similarity

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TypeScript API Reference

Create Embeddings Service

import { createEmbeddingsService } from 'clodds/embeddings';

const embeddings = createEmbeddingsService({
  // Provider
  provider: 'openai',  // 'openai' | 'voyage' | 'local' | 'cohere'
  apiKey: process.env.OPENAI_API_KEY,

  // Model
  model: 'text-embedding-3-small',
  dimensions: 1536,

  // Caching
  cache: true,
  cacheBackend: 'sqlite',
  cachePath: './embeddings-cache.db',

  // Batching
  batchSize: 100,
  maxConcurrent: 5,
});

Generate Embeddings

// Single text
const embedding = await embeddings.embed('Hello world');
console.log(`Dimensions: ${embedding.length}`);

// Multiple texts (batched)
const vectors = await embeddings.embedBatch([
  'First document',
  'Second document',
  'Third document',
]);

Semantic Search

// Search against stored vectors
const results = await embeddings.search({
  query: 'trading strategies',
  collection: 'documents',
  limit: 10,
  threshold: 0.7,
});

for (const result of results) {
  console.log(`${result.text} (score: ${result.score})`);
}