> For the complete documentation index, see [llms.txt](https://epsilla-inc.gitbook.io/epsilladb/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://epsilla-inc.gitbook.io/epsilladb/epsilla-vector-database/integrations/openai.md).

# OpenAI

On Epsilla Cloud, you can enable OpenAI integration by providing your OpenAI API key (we securely manage your keys using AWS KMS):

<figure><img src="/files/22sO5LjN1LhtPJ6fqL8N" alt=""><figcaption></figcaption></figure>

## Embeddings

Epsilla integrates with OpenAI with the following embedding models:

<table><thead><tr><th width="311">Name</th><th width="171">Dimensions</th><th>Support Dimension Reduction</th></tr></thead><tbody><tr><td><strong>openai/text-embedding-3-large</strong></td><td>3072</td><td>Yes</td></tr><tr><td><strong>openai/text-embedding-3-small</strong></td><td>1536</td><td>Yes</td></tr><tr><td><strong>openai/text-embedding-ada-002</strong></td><td>1536</td><td>No</td></tr></tbody></table>

For Epsilla open source vector db, you just need to add a header in the data ingestion and semantic search queries [like this](/epsilladb/epsilla-vector-database/advanced-topics/embeddings.md#openai-embedding).

Then you can start using the openai embedding model during vector table schema creation:

<figure><img src="/files/xSZiLEl1bnFXoDasc2mH" alt=""><figcaption></figcaption></figure>

For models that support dimension reduction, you can optionally provide a dimensions parameter to reduce the embedding response size:

<figure><img src="/files/vbLMn1XqVjbBKJRy9Wzq" alt=""><figcaption></figcaption></figure>