> ## 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.

# Embeddings

> Get embeddings from Bedrock

Bedrock supports embedding text and images through Amazon Titan and Cohere models.
The Prisma AIRS AI Gateway provides a standardized interface for embedding multiple modalities.

# Bedrock Titan

## Embedding Text

```sh cURL theme={"system"}
curl --location 'https://aigw.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer PORTKEY_API_KEY' \
--data-raw '{
    "model": "@bedrock/amazon.titan-embed-text-v2:0",
    "input": "Hello this is a test",
    "normalize": false, // if you would like to disable normalization
    "dimensions": 1024, // embedding dimensions
    "encoding_format": "float" // embedding format
}'
```

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

gateway_client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://aigw.portkey.ai/v1",
)

embeddings = gateway_client.embeddings.create(
    model="@bedrock/amazon.titan-embed-text-v2:0",
    input="Hello this is a test",
    # normalize=False # if you would like to disable normalization
    # dimensions=1024, # embedding dimensions
    # encoding_format="float", # embedding format
)
```

```js OpenAI NodeJS theme={"system"}
import OpenAI from 'openai'; // We're using the v4 SDK

const gatewayClient = new OpenAI({
  apiKey: "PORTKEY_API_KEY", // defaults to process.env["OPENAI_API_KEY"],
  baseURL: "https://aigw.portkey.ai/v1"
});

const embedding = await gatewayClient.embeddings.create({
    model: "@bedrock/amazon.titan-embed-text-v2:0",
    input: "Hello this is a test",
    // normalize=False, // if you would like to disable normalization
    // dimensions=1024, // embedding dimensions
    // encoding_format="float", // embedding format
});

console.log(embedding);
```

## Embeddings Images

```sh cURL theme={"system"}
curl --location 'https://aigw.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer PORTKEY_API_KEY' \
--data-raw '{
"model": "@bedrock/amazon.titan-embed-image-v1",
"dimensions": 256,
"input": [
    {
        "text": "this is the caption of the image",
        "image": {
            "base64": "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
]
}'
```

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

gateway_client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://aigw.portkey.ai/v1",
)

embeddings = gateway_client.embeddings.create(
    model="@bedrock/amazon.titan-embed-image-v1",
    dimensions=256,
    input=[
    {
        "text": "this is the caption of the image",
        "image": {
            "base64": "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
    ]
)
```

```js OpenAI NodeJS theme={"system"}
import OpenAI from 'openai'; // We're using the v4 SDK

const gatewayClient = new OpenAI({
  apiKey: "PORTKEY_API_KEY", // defaults to process.env["OPENAI_API_KEY"],
  baseURL: "https://aigw.portkey.ai/v1"
});

const embedding = await gatewayClient.embeddings.create({
    model: "@bedrock/amazon.titan-embed-image-v1",
    dimensions: 256,
    input: [
    {
        text: "this is the caption of the image",
        image: {
            base64: "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
    ]
});

console.log(embedding);
```

# Cohere

## Embedding Text

```sh cURL theme={"system"}
curl --location 'https://aigw.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer PORTKEY_API_KEY' \
--data-raw '{
  "model": "@bedrock/cohere.embed-english-v3",
  "input": ["Hello this is a test", "skibidi"],
  "input_type": "classification"
}'
```

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

gateway_client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://aigw.portkey.ai/v1",
)

embeddings = gateway_client.embeddings.create(
    model="@bedrock/cohere.embed-english-v3",
    input=["Hello this is a test", "skibidi"],
    input_type="classification"
)
```

```js OpenAI NodeJS theme={"system"}
import OpenAI from 'openai'; // We're using the v4 SDK

const gatewayClient = new OpenAI({
  apiKey: "PORTKEY_API_KEY", // defaults to process.env["OPENAI_API_KEY"],
  baseURL: "https://aigw.portkey.ai/v1"
});

const embedding = await gatewayClient.embeddings.create({
    model: "@bedrock/cohere.embed-english-v3",
    input: ["Hello this is a test", "skibidi"],
    input_type: "classification"
});

console.log(embedding);
```

## Embeddings Images

```sh cURL theme={"system"}
curl --location 'https://aigw.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer PORTKEY_API_KEY' \
--data-raw '{
"model": "@bedrock/cohere.embed-english-v3",
"input_type": "image",
"dimensions": 256,
"input": [
    {
        "image": {
            "base64": "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
]
}'
```

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

gateway_client = OpenAI(
    api_key="PORTKEY_API_KEY",
    base_url="https://aigw.portkey.ai/v1",
)

embeddings = gateway_client.embeddings.create(
    model="@bedrock/cohere.embed-english-v3",
    input_type="image",
    dimensions=256,
    input=[
    {
        image: {
            base64: "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
]
)
```

```js OpenAI NodeJS theme={"system"}
import OpenAI from 'openai'; // We're using the v4 SDK

const gatewayClient = new OpenAI({
  apiKey: "PORTKEY_API_KEY", // defaults to process.env["OPENAI_API_KEY"],
  baseURL: "https://aigw.portkey.ai/v1"
});

const embedding = await gatewayClient.embeddings.create({
    model: "@bedrock/cohere.embed-english-v3",
    input_type: "image",
    dimensions: 256,
    input: [
    {
        image: {
            base64: "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
  ]
});

console.log(embedding);
```


## Related topics

- [Embeddings](https://docs.portkey.ai/docs/aigw/integrations/llms/openai/embeddings.md)
- [Create embedding](https://docs.portkey.ai/docs/aigw/api-reference/embeddings/create-embedding.md)
- [Guardrails for Embedding Requests](https://docs.portkey.ai/docs/aigw/product/guardrails/embedding-guardrails.md)
- [Langchain (JS/TS)](https://docs.portkey.ai/docs/aigw/integrations/libraries/langchain-js.md)


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