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Update Gemini Model #401

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8 changes: 4 additions & 4 deletions docs/weaviate/model-providers/google/embeddings-multimodal.md
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Expand Up @@ -19,7 +19,7 @@ import TSCode from '!!raw-loader!../_includes/provider.vectorizer.ts';
Weaviate's integration with [Google Gemini API](https://ai.google.dev/?utm_source=weaviate&utm_medium=referral&utm_campaign=partnerships&utm_content=) and [Google Vertex AI](https://cloud.google.com/vertex-ai) APIs allows you to access their models' capabilities directly from Weaviate.

:::note Gemini API multimodal support
The `gemini-embedding-2-preview` model supports multimodal embeddings (text, images, and PDFs) and is available via both Vertex AI and Google AI Studio (Gemini API). The `multimodalembedding@001` model remains available for Vertex AI users only.
The `gemini-embedding-2` model supports multimodal embeddings (text, images, and PDFs) and is available via both Vertex AI and Google AI Studio (Gemini API). The `multimodalembedding@001` model remains available for Vertex AI users only.
:::

[Configure a Weaviate vector index](#configure-the-vectorizer) to use a Google embedding model, and Weaviate will generate embeddings for various operations using the specified model and your Google API key. This feature is called the *vectorizer*.
Expand Down Expand Up @@ -164,8 +164,8 @@ The following examples show how to configure Google-specific options.
- `location` (Required): e.g. `"us-central1"`
- `projectId` (Only required if using Vertex AI): e.g. `cloud-large-language-models`
- `apiEndpoint` (Optional): e.g. `us-central1-aiplatform.googleapis.com`
- `modelId` (Optional): e.g. `gemini-embedding-2-preview`, `multimodalembedding@001`
- `dimensions` (Optional): For `multimodalembedding@001`: `128`, `256`, `512`, or `1408` (default `1408`). For `gemini-embedding-2-preview`: `3072` (default).
- `modelId` (Optional): e.g. `gemini-embedding-2`, `multimodalembedding@001`
- `dimensions` (Optional): For `multimodalembedding@001`: `128`, `256`, `512`, or `1408` (default `1408`). For `gemini-embedding-2`: `3072` (default).

<Tabs className="code" groupId="languages">
<TabItem value="py" label="Python">
Expand Down Expand Up @@ -318,7 +318,7 @@ The query below returns the `n` most similar objects to the input image from the

### Available models

- `gemini-embedding-2-preview` (Vertex AI and Gemini API, added in 1.36.5) — supports text, images, and PDFs; `3072` dimensions
- `gemini-embedding-2` (Vertex AI and Gemini API, added in 1.36.5) — supports text, images, and PDFs; `3072` dimensions
- `multimodalembedding@001` (Vertex AI only) — supports text, images, and video; dimensions: `128`, `256`, `512`, `1408`

## Further resources
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