Sentence Transformers Embedding Function for Chroma

This package provides a Sentence Transformers embedding provider for Chroma using transformers.js (@huggingface/transformers). It allows you to run Sentence Transformer models directly in Node.js without requiring a separate server.

Installation

npm install @chroma-core/sentence-transformer

Usage

import { ChromaClient } from 'chromadb';
import { SentenceTransformersEmbeddingFunction } from '@chroma-core/sentence-transformer';

// Initialize the embedder with the default model (all-MiniLM-L6-v2)
const embedder = new SentenceTransformersEmbeddingFunction();

// Or initialize with a custom model
const customEmbedder = new SentenceTransformersEmbeddingFunction({
  modelName: 'Xenova/all-mpnet-base-v2', // Higher quality model
  device: 'cpu', // 'cpu' or 'gpu' (default: 'cpu')
  normalizeEmbeddings: false, // Whether to normalize embeddings (default: false)
  kwargs: { quantized: true }, // Optional: additional arguments like quantized
});

// Create a new ChromaClient
const client = new ChromaClient({
  path: 'http://localhost:8000',
});

// Create a collection with the embedder
const collection = await client.createCollection({
  name: 'my-collection',
  embeddingFunction: embedder,
});

// Add documents
await collection.add({
  ids: ["1", "2", "3"],
  documents: ["Document 1", "Document 2", "Document 3"],
});

// Query documents
const results = await collection.query({
  queryTexts: ["Sample query"],
  nResults: 2,
});

Configuration Options

• modelName: The Sentence Transformer model to use (default: "all-MiniLM-L6-v2")
  • Short names (recommended): Use short names like "all-MiniLM-L6-v2" for cross-client compatibility with Python. These are automatically resolved to Xenova/all-MiniLM-L6-v2 for transformers.js.
  • Full names: You can also use full model identifiers like Xenova/all-MiniLM-L6-v2 or sentence-transformers/all-MiniLM-L6-v2 if you need to specify a particular variant.
  • Popular models: all-MiniLM-L6-v2 (default), all-mpnet-base-v2, bge-small-en-v1.5
• device: Device to run the model on - 'cpu' or 'gpu' (default: 'cpu')
• normalizeEmbeddings: Whether to normalize returned vectors (default: false)
• kwargs: Additional arguments to pass to the model (e.g., { quantized: true })

Supported Models

You can use any Sentence Transformer model that is compatible with transformers.js. Popular models include:

• Xenova/all-MiniLM-L6-v2 - Fast, lightweight model (384 dimensions)
• Xenova/all-mpnet-base-v2 - Higher quality model (768 dimensions)
• sentence-transformers/all-MiniLM-L6-v2 - Alternative model identifier
• sentence-transformers/all-mpnet-base-v2 - Alternative model identifier

Check the transformers.js documentation for more available models.

Features

• Local Execution: Run models directly in Node.js without external API calls
• Multiple Models: Support for various Sentence Transformer models
• GPU Support: Optional GPU acceleration when available
• No API Keys: No external API keys required
• Configurable Normalization: Control whether embeddings are normalized

Notes

• Models are downloaded and cached on first use
• You can pass quantized: true in kwargs for faster loading and reduced memory usage
• GPU support requires appropriate hardware and drivers
• Model loading may take some time on first use