Sentence Transformer - Chroma

import { Callout } from '/snippets/callout.mdx';

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

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

python

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.

</Tab> <Tab title="TypeScript" icon="js">

typescript

// 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);

</Tab> </Tabs> <Callout> 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](https://www.sbert.net/docs/pretrained_models.html) for more model recommendations. </Callout>