# Google Gemini integration

> Google Gemini integration - Leverage LLMs proposed by hyperscalers via the Custom Model Workshop.

This Markdown file sits beside the HTML page at the same path (with a `.md` suffix). It summarizes the topic and lists links for tools and LLM context.

Companion generated at `2026-05-06T18:17:09.574901+00:00` (UTC).

## Primary page

- [Google Gemini integration](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/gemini-google.html): Full documentation for this topic (HTML).

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Developer learning](https://docs.datarobot.com/en/docs/api/dev-learning/index.html): Linked from this page.
- [AI accelerators](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/index.html): Linked from this page.
- [Custom model development](https://docs.datarobot.com/en/docs/api/dev-learning/accelerators/custom-model-dev/index.html): Linked from this page.
- [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html#custom-model-workshop): Linked from this page.
- [credentials](https://docs.datarobot.com/en/docs/platform/acct-settings/stored-creds.html#credentials-management): Linked from this page.
- [DataRobot global models](https://docs.datarobot.com/en/docs/workbench/nxt-registry/nxt-model-directory/nxt-global-models.html#access-global-models-in-the-registry): Linked from this page.
- [Deploy](https://docs.datarobot.com/en/docs/classic-ui/mlops/monitor/generative-model-monitoring.html): Linked from this page.
- [Create custom metrics](https://docs.datarobot.com/en/docs/api/reference/sdk/custom-metrics.html#custom-metrics-tab): Linked from this page.
- [Deploy a Streamlit app](https://docs.datarobot.com/en/docs/classic-ui/app-builder/custom-apps/custom-apps-hosting.html#host-custom-applications-with-drapps): Linked from this page.

## Documentation content

[Access this AI accelerator on GitHub](https://github.com/datarobot-community/ai-accelerators/tree/main/generative_ai/gemini-app)

DataRobot allows you to leverage LLMs proposed by hyperscalers via the [Custom Model Workshop](https://docs.datarobot.com/en/docs/classic-ui/mlops/deployment/custom-models/custom-model-workshop/index.html#custom-model-workshop).

This AI Accelerator demonstrates how to implement a Streamlit application based on the Google Gemini LLM and host it on the DataRobot platform. The user of this AI Accelerator is expected to be familiar with the custom model deployment process and custom metrics creation in DataRobot as well as with Google Vertex AI.

This accelerator requires the service account for the Vertex AI project. The following steps outline the accelerator workflow.

1. Createcredentialswith a GCP service account (base64 encoded).
2. Optional. Deploy a guard model from theDataRobot global models.
3. Deploya text model (Gemini Pro).
4. Deploya multimodal model (Gemini Pro Vision).
5. Create custom metricsfor both deployments (text and multimodal).
6. Deploy a Streamlit appto DataRobot.