OpenAI

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Python

JavaScript

Chroma provides a convenient wrapper around OpenAI's embedding API. This embedding function runs remotely on OpenAI's servers, and requires an API key. You can get an API key by signing up for an account at OpenAI.

The following OpenAI Embedding Models are supported:

• text-embedding-ada-002
• text-embedding-3-small
• text-embedding-3-large

More Info

Visit OpenAI Embeddings documentation for more information.

Python

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

You can pass in an optional model_name argument, which lets you choose which OpenAI embeddings model to use. By default, Chroma uses text-embedding-ada-002.

import chromadb.utils.embedding_functions as embedding_functions
openai_ef = embedding_functions.OpenAIEmbeddingFunction(
                api_key="YOUR_API_KEY",
                model_name="text-embedding-3-small"
            )

To use the OpenAI embedding models on other platforms such as Azure, you can use the api_base and api_type parameters:

import chromadb.utils.embedding_functions as embedding_functions
openai_ef = embedding_functions.OpenAIEmbeddingFunction(
                api_key="YOUR_API_KEY",
                api_base="YOUR_API_BASE_PATH",
                api_type="azure",
                api_version="YOUR_API_VERSION",
                model_name="text-embedding-3-small"
            )

JavaScript

You can pass in an optional model argument, which lets you choose which OpenAI embeddings model to use. By default, Chroma uses text-embedding-ada-002.

const {OpenAIEmbeddingFunction} = require('chromadb');
const embedder = new OpenAIEmbeddingFunction({
    openai_api_key: "apiKey", 
    model: "text-embedding-3-small"
})

// use directly 
const embeddings = embedder.generate(["document1","document2"])

// pass documents to query for .add and .query
const collection = await client.createCollection({
    name: "name", 
    embeddingFunction: embedder
})
const collection = await client.getCollection({
    name: "name", 
    embeddingFunction: embedder
})