@reaatech/hybrid-rag-embedding
Status: Pre-1.0 — APIs may change in minor versions. Pin to a specific version in production.
Provider-agnostic embedding generation for hybrid RAG systems. Currently supports OpenAI embeddings with extension points for Vertex AI and local models.
Installation
terminal
npm install @reaatech/hybrid-rag-embedding
# or
pnpm add @reaatech/hybrid-rag-embeddingFeature Overview
- Provider abstraction — single interface across OpenAI, Vertex AI, and local models
- Batch processing — configurable batch size with automatic rate limiting
- Cost tracking — per-request cost calculation based on model pricing
- Dimension lookup — static method to get the embedding dimension for known models
- OpenAI integration — full
text-embedding-3-smallandtext-embedding-3-largesupport
Quick Start
typescript
import { EmbeddingService } from '@reaatech/hybrid-rag-embedding';
const embedder = new EmbeddingService({
provider: 'openai',
model: 'text-embedding-3-small',
apiKey: process.env.OPENAI_API_KEY,
batchSize: 100,
rateLimit: 3500, // requests per minute
});
// Single text
const { embedding, tokens, cost } = await embedder.embed('Hello world');
console.log(`Vector dimension: ${embedding.length}, Cost: $${cost}`);
// Batch
const texts = ['Document one...', 'Document two...', 'Document three...'];
const results = await embedder.embedBatch(texts);
const totalCost = results.reduce((sum, r) => sum + r.cost, 0);API Reference
EmbeddingService
Constructor
typescript
new EmbeddingService(config: EmbeddingConfig)EmbeddingConfig
| Property | Type | Default | Description |
|---|---|---|---|
provider | openai' | 'vertex' | 'local | (required) | Embedding provider |
model | string | (required) | Model name (e.g. text-embedding-3-small) |
apiKey | string | — | API key for cloud providers |
dimension | number | — | Embedding dimension (auto-detected for known models) |
batchSize | number | 100 | Max texts per batch request |
rateLimit | number | — | Requests per minute throttle |
Methods
| Method | Returns | Description |
|---|---|---|
embed(text) | Promise<EmbeddingResult> | Generate embedding for a single text |
embedBatch(texts) | Promise<EmbeddingResult[]> | Generate embeddings for multiple texts with batching and rate limiting |
Static Methods
| Method | Returns | Description |
|---|---|---|
getDimension(model) | number | Get the dimension for a known model (1536 for text-embedding-3-small, 3072 for text-embedding-3-large) |
EmbeddingResult
| Property | Type | Description |
|---|---|---|
embedding | number[] | The embedding vector |
tokens | number | Number of tokens consumed |
cost | number | Cost in USD |
Cost Calculation
Pricing is built-in for known models:
| Model | Cost per 1M tokens |
|---|---|
text-embedding-3-small | $0.02 |
text-embedding-3-large | $0.13 |
Provider Extensibility
The Vertex AI and local providers are extension points. To add a new provider:
typescript
class CustomEmbedder extends EmbeddingService {
private async embedCustom(text: string): Promise<EmbeddingResult> {
// Your implementation here
return { embedding: [/* ... */], tokens: 0, cost: 0 };
}
}Related Packages
- @reaatech/hybrid-rag — Core types
- @reaatech/hybrid-rag-qdrant — Qdrant vector DB (consumes embeddings)