> ## Documentation Index
> Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Google Vertex AI Embeddings

> Generate embeddings using Google Cloud Vertex AI

## Overview

Google Vertex AI provides enterprise-grade embedding models including textembedding-gecko and multimodal embeddings.

## Quick Start

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="vertex_ai/textembedding-gecko"
)
print(f"Dimensions: {len(result.embeddings[0])}")
```

## CLI Usage

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai embed "Hello world" --model vertex_ai/textembedding-gecko
```

## Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export GOOGLE_APPLICATION_CREDENTIALS="/path/to/service-account.json"
export VERTEXAI_PROJECT="your-project-id"
export VERTEXAI_LOCATION="us-central1"
```

## Available Models

| Model                                        | Dimensions | Use Case        |
| -------------------------------------------- | ---------- | --------------- |
| `vertex_ai/textembedding-gecko`              | 768        | General purpose |
| `vertex_ai/textembedding-gecko@003`          | 768        | Latest version  |
| `vertex_ai/textembedding-gecko-multilingual` | 768        | Multilingual    |
| `vertex_ai/text-embedding-004`               | 768        | Newest model    |
| `vertex_ai/text-multilingual-embedding-002`  | 768        | Multilingual v2 |

## With Project Configuration

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="vertex_ai/textembedding-gecko",
    vertex_project="my-project",
    vertex_location="us-central1"
)
```

## Batch Embeddings

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

texts = ["Document 1", "Document 2", "Document 3"]
result = embedding(
    input=texts,
    model="vertex_ai/textembedding-gecko"
)
print(f"Generated {len(result.embeddings)} embeddings")
```

## Multimodal Embeddings

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

# Image + text embeddings
result = embedding(
    input="A beautiful sunset",
    model="vertex_ai/multimodalembedding",
    image="base64_encoded_image_data"
)
```

## Related

* [Embedding Providers Overview](/docs/embeddings/index)
* [Gemini Embeddings](/docs/embeddings/providers/gemini)