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chroma guide intermediate embeddings models
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Original Documentation

Documentation Index#

Fetch the complete documentation index at: https://docs.trychroma.com/llms.txt Use this file to discover all available pages before exploring further.

export const Callout = ({title, children}) =>

{title && <p className="block mb-2"><strong>{title}</strong></p>}
{children}

;

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

Visit OpenAI Embeddings documentation for more information.

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_env_var="OPENAI_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:

```python
    import chromadb.utils.embedding_functions as embedding_functions
    openai_ef = embedding_functions.OpenAIEmbeddingFunction(
        api_key_env_var="OPENAI_API_KEY",
        api_base="YOUR_API_BASE_PATH",
        api_type="azure",
        api_version="YOUR_API_VERSION",
        model_name="text-embedding-3-small"
    )
    ```
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You can pass in an optional `model` argument, which lets you choose which OpenAI embeddings model to use. By default, Chroma uses `text-embedding-3-small`.

```typescript
    // npm install @chroma-core/openai

    import { OpenAIEmbeddingFunction } from "@chroma-core/openai";

    const embeddingFunction = new OpenAIEmbeddingFunction({
        apiKeyEnvVar: "OPENAI_API_KEY",
        modelName: "text-embedding-3-small",
        // Optional: specify API base (e.g. for Azure OpenAI)
        apiBase: "your-api-base"
    });

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

    // pass documents to query for .add and .query
    let collection = await client.createCollection({
        name: "name",
        embeddingFunction: embeddingFunction,
    });
    collection = await client.getCollection({
        name: "name",
        embeddingFunction: embeddingFunction,
    });
    ```
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Link last verified June 7, 2026. View original ↗
Source: Chroma Docs
Link last verified: 2026-03-04