> 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>


---

# Agent Instructions
This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. Learn more at gitbook.com.

## Querying This Documentation
If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter, and the optional `goal` query parameter:

```
GET https://epsilla-inc.gitbook.io/epsilladb/epsilla-vector-database/integrations/openai.md?ask=<question>&goal=<endgoal>
```

`ask` is the immediate question: it should be specific, self-contained, and written in natural language.
`goal` is optional and describes the broader end goal you are ultimately trying to accomplish on behalf of the user. GitBook uses it to tailor the answer towards what is most useful for that goal.

The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.