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

# Google Gemini Embeddings

> Generate embeddings using Google Gemini API

## Overview

Google Gemini provides embedding models through the Gemini API with simple API key authentication.

## Quick Start

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

result = embedding(
    input="Hello world",
    model="gemini/text-embedding-004"
)
print(f"Dimensions: {len(result.embeddings[0])}")
```

## CLI Usage

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai embed "Hello world" --model gemini/text-embedding-004
```

## Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
export GEMINI_API_KEY="your-gemini-api-key"
# or
export GOOGLE_API_KEY="your-google-api-key"
```

## Available Models

| Model | Dimensions | Use Case |
| - | - | - |
| `gemini/text-embedding-004` | 768 | Latest, recommended |
| `gemini/embedding-001` | 768 | Legacy model |

## 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="gemini/text-embedding-004"
)
print(f"Generated {len(result.embeddings)} embeddings")
```

## Task Types

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

# For retrieval queries
result = embedding(
    input="What is machine learning?",
    model="gemini/text-embedding-004",
    task_type="RETRIEVAL_QUERY"
)

# For documents
result = embedding(
    input="Machine learning is...",
    model="gemini/text-embedding-004",
    task_type="RETRIEVAL_DOCUMENT"
)
```

## Related

* [Embedding Providers Overview](/docs/embeddings/index)
* [Vertex AI Embeddings](/docs/embeddings/providers/vertex-ai)


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