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

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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")
    provider="@PROVIDER",
)

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
)

​
Embeddings Images

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
    provider="@PROVIDER",
)

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

​
Cohere

​
Embedding Text

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
    provider="@PROVIDER",
)

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

​
Embeddings Images

from portkey_ai import Portkey

client = Portkey(
    api_key="YOUR_PORTKEY_API_KEY", # defaults to os.environ.get("PORTKEY_API_KEY")
    provider="@PROVIDER",
)

embeddings = client.embeddings.create(
    model="cohere.embed-english-v3",
    input_type="image",
    dimensions=256,
    input=[
    {
        "image": {
            "base64": "Data:image/webp;base64,UklGRkacAABXRUJQVlA4IDqcAACQggKdASqpAn8B....."
        }
    }
]
)
Last modified on January 28, 2026