Bedrock supports embedding text and images through Amazon Titan and Cohere models. Portkey provides a standardized interface for embedding multiple modalities.

Bedrock Titan

Embedding Text

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
    virtual_key="VIRTUAL_KEY",
)

embeddings = client.embeddings.create(
    model="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
)

import { Portkey } from 'portkey-ai';

const portkey = new Portkey({
    apiKey: "YOUR_API_KEY",
    virtualKey: "YOUR_VIRTUAL_KEY"
});

const embedding = await portkey.embeddings.create({
    model: "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);

curl --location 'https://api.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: PORTKEY_API_KEY' \
--header 'x-portkey-virtual-key: PORTKEY_VIRTUAL_KEY' \
--data-raw '{
    "model": "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
}'

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

portkey_client = OpenAI(
    api_key='NOT_REQUIRED',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY"
    )
)

embeddings = portkey_client.embeddings.create(
    model="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
)

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const portkeyClient = new OpenAI({
  apiKey: 'NOT_REQUIRED', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "vertex-ai",
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "PORTKEY_VIRTUAL_KEY"
  })
});

const embedding = await portkeyClient.embeddings.create({
    model: "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

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
    virtual_key="VIRTUAL_KEY",
)

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

import { Portkey } from 'portkey-ai';

const portkey = new Portkey({
    apiKey: "YOUR_API_KEY",
    virtualKey: "YOUR_VIRTUAL_KEY"
});

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

console.log(embedding);

curl --location 'https://api.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: PORTKEY_API_KEY' \
--header 'x-portkey-virtual-key: PORTKEY_VIRTUAL_KEY' \
--data-raw '{
"model": "amazon.titan-embed-image-v1",
"dimensions": 256,
"input": [
    {
        "text": "this is the caption of the image",
        "image": {
            "base64": "UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
]
}'

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

portkey_client = OpenAI(
    api_key='NOT_REQUIRED',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY"
    )
)

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

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const portkeyClient = new OpenAI({
  apiKey: 'NOT_REQUIRED', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "PORTKEY_VIRTUAL_KEY"
  })
});

const embedding = await portkeyClient.embeddings.create({
    model: "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

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
    virtual_key="VIRTUAL_KEY",
)

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

import { Portkey } from 'portkey-ai';

const portkey = new Portkey({
    apiKey: "YOUR_API_KEY",
    virtualKey: "YOUR_VIRTUAL_KEY"
});

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

console.log(embedding);

curl --location 'https://api.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: PORTKEY_API_KEY' \
--header 'x-portkey-virtual-key: PORTKEY_VIRTUAL_KEY' \
--data-raw '{
  "model": "cohere.embed-english-v3",
  "input": ["Hello this is a test", "skibidi"],
  "input_type": "classification"
}'

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

portkey_client = OpenAI(
    api_key='NOT_REQUIRED',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY"
    )
)

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

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const portkeyClient = new OpenAI({
  apiKey: 'NOT_REQUIRED', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    provider: "vertex-ai",
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "PORTKEY_VIRTUAL_KEY"
  })
});

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

console.log(embedding);

Embeddings Images

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
    virtual_key="VIRTUAL_KEY",
)

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

import { Portkey } from 'portkey-ai';

const portkey = new Portkey({
    apiKey: "YOUR_API_KEY",
    virtualKey: "YOUR_VIRTUAL_KEY"
});

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

console.log(embedding);

curl --location 'https://api.portkey.ai/v1/embeddings' \
--header 'Content-Type: application/json' \
--header 'x-portkey-api-key: PORTKEY_API_KEY' \
--header 'x-portkey-virtual-key: PORTKEY_VIRTUAL_KEY' \
--data-raw '{
"model": "cohere.embed-english-v3",
"input_type": "image",
"dimensions": 256,
"input": [
    {
        "image": {
            "base64": "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
]
}'

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

portkey_client = OpenAI(
    api_key='NOT_REQUIRED',
    base_url=PORTKEY_GATEWAY_URL,
    default_headers=createHeaders(
        provider="openai",
        api_key="PORTKEY_API_KEY"
    )
)

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

import OpenAI from 'openai'; // We're using the v4 SDK
import { PORTKEY_GATEWAY_URL, createHeaders } from 'portkey-ai'

const portkeyClient = new OpenAI({
  apiKey: 'NOT_REQUIRED', // defaults to process.env["OPENAI_API_KEY"],
  baseURL: PORTKEY_GATEWAY_URL,
  defaultHeaders: createHeaders({
    apiKey: "PORTKEY_API_KEY", // defaults to process.env["PORTKEY_API_KEY"]
    virtualKey: "PORTKEY_VIRTUAL_KEY"
  })
});

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

console.log(embedding);

​Bedrock Titan

​Embedding Text

​Embeddings Images

​Cohere

​Embedding Text

​Embeddings Images

Bedrock Titan

Embedding Text

Embeddings Images

Cohere

Embedding Text

Embeddings Images