Sentence Transformer

Chroma provides a convenient wrapper around the Sentence Transformers library. This embedding function runs locally and uses pre-trained models from Hugging Face.

Python
TypeScript

This embedding function relies on the sentence_transformers python package, which you can install with pip install sentence_transformers.

from chromadb.utils.embedding_functions import SentenceTransformerEmbeddingFunction

sentence_transformer_ef = SentenceTransformerEmbeddingFunction(
    model_name="all-MiniLM-L6-v2",
    device="cpu",
    normalize_embeddings=False
)

texts = ["Hello, world!", "How are you?"]
embeddings = sentence_transformer_ef(texts)

You can pass in optional arguments:

model_name: The name of the Sentence Transformer model to use (default: “all-MiniLM-L6-v2”)
device: Device used for computation, “cpu” or “cuda” (default: “cpu”)
normalize_embeddings: Whether to normalize returned vectors (default: False)

For a full list of available models, visit Sentence Transformers models on Hugging Face or SBERT documentation.

// npm install @chroma-core/sentence-transformer

import { SentenceTransformersEmbeddingFunction } from "@chroma-core/sentence-transformer";

const sentenceTransformerEF = new SentenceTransformersEmbeddingFunction({
    modelName: "all-MiniLM-L6-v2",
    device: "cpu",
    normalizeEmbeddings: false,
});

const texts = ["Hello, world!", "How are you?"];
const embeddings = await sentenceTransformerEF.generate(texts);

Sentence Transformers are great for semantic search tasks. Popular models include all-MiniLM-L6-v2 (fast and efficient) and all-mpnet-base-v2 (higher quality). Visit SBERT documentation for more model recommendations.

Dense Embedding Models

Sparse Embedding Models

Frameworks

Dense Embedding Models

Sparse Embedding Models

Frameworks

Documentation Index