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

# Voyage AI

> Use Voyage AI's embeddings and reranking models through Prisma AIRS AI Gateway.

## Quick Start

Get started with Voyage AI in under 2 minutes:

<CodeGroup>
  ```bash cURL icon="square-terminal" theme={"system"}
  # 1. Add @voyage provider in model catalog
  # 2. Use it:

  curl https://aigw.portkey.ai/v1/embeddings \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $PORTKEY_API_KEY" \
    -d '{
      "model": "@voyage/voyage-3",
      "input": "Name the tallest buildings in Hawaii"
    }'
  ```

  ```python OpenAI Py icon="python" theme={"system"}
  from openai import OpenAI

  # 1. Install: pip install openai
  # 2. Add @voyage provider in model catalog
  # 3. Use it:

  client = OpenAI(
      api_key="PORTKEY_API_KEY",  # AI Gateway API key
      base_url="https://aigw.portkey.ai/v1"
  )

  embedding = client.embeddings.create(
      model="@voyage/voyage-3",
      input="Name the tallest buildings in Hawaii"
  )

  print(embedding.data[0].embedding)
  ```

  ```js OpenAI JS icon="square-js" theme={"system"}
  import OpenAI from "openai"

  // 1. Install: npm install openai
  // 2. Add @voyage provider in model catalog
  // 3. Use it:

  const client = new OpenAI({
      apiKey: "PORTKEY_API_KEY",  // AI Gateway API key
      baseURL: "https://aigw.portkey.ai/v1"
  })

  const embedding = await client.embeddings.create({
      model: "@voyage/voyage-3",
      input: "Name the tallest buildings in Hawaii"
  })

  console.log(embedding.data[0].embedding)
  ```
</CodeGroup>

<Note>
  **Tip:** You can also send `x-portkey-provider: @voyage` as a header and use just `model="voyage-3"` in the request.
</Note>

## Add Provider in Model Catalog

Before making requests, add Voyage AI to your Model Catalog:

1. Go to [**Model Catalog → Add Provider**](https://stratacloudmanager.paloaltonetworks.com/)
2. Select **Voyage AI**
3. Enter your [Voyage API key](https://dash.voyageai.com/)
4. Name your provider (e.g., `voyage`)

<Card title="Complete Setup Guide" icon="book" href="/docs/aigw/product/model-catalog">
  See all setup options and detailed configuration instructions
</Card>

***

## Voyage AI Capabilities

### Embeddings

Generate high-quality embeddings:

### Reranking

Rerank documents for better search results:

***

## Supported Models

Voyage AI offers specialized embedding and reranking models:

| Model         | Type      | Description                            |
| ------------- | --------- | -------------------------------------- |
| voyage-3      | Embedding | Latest general-purpose embedding model |
| voyage-3-lite | Embedding | Faster, lighter version                |
| voyage-code-3 | Embedding | Optimized for code                     |
| rerank-2      | Reranking | High-quality reranking                 |
| rerank-2-lite | Reranking | Faster reranking                       |

Check [Voyage AI's documentation](https://docs.voyageai.com/) for the complete model list.

***

## Next Steps

<CardGroup cols={2}>
  <Card title="Gateway Configs" icon="sliders" href="/docs/aigw/product/ai-gateway">
    Add fallbacks, load balancing, and more
  </Card>

  <Card title="Observability" icon="chart-line" href="/docs/aigw/product/observability">
    Monitor and trace your Voyage requests
  </Card>

  <Card title="Caching" icon="database" href="/docs/aigw/product/ai-gateway/cache-simple-and-semantic">
    Cache embeddings for faster responses
  </Card>

  <Card title="Metadata" icon="tag" href="/docs/aigw/product/observability/metadata">
    Add custom metadata to requests
  </Card>
</CardGroup>


## Related topics

- [Overview](/docs/aigw/integrations/llms.md)
- [Supported Providers](/docs/aigw/api-reference/inference-api/supported-providers.md)
- [Enterprise Gateway](/docs/aigw/changelog/enterprise.md)
- [Langchain (JS/TS)](/docs/aigw/integrations/libraries/langchain-js.md)
- [Langchain (Python)](/docs/aigw/integrations/libraries/langchain-python.md)