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Overview

Amazon Bedrock provides access to embedding models from Amazon Titan, Cohere, and other providers through AWS infrastructure.

Quick Start

from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="bedrock/amazon.titan-embed-text-v1"
)
print(f"Dimensions: {len(result.embeddings[0])}")

CLI Usage

praisonai embed "Hello world" --model bedrock/amazon.titan-embed-text-v1

Setup

export AWS_ACCESS_KEY_ID="your-access-key"
export AWS_SECRET_ACCESS_KEY="your-secret-key"
export AWS_REGION_NAME="us-east-1"

Available Models

ModelDimensionsProvider
bedrock/amazon.titan-embed-text-v11536Amazon
bedrock/amazon.titan-embed-text-v2:01024Amazon
bedrock/amazon.nova-2-multimodal-embeddings-v1:01024Amazon
bedrock/cohere.embed-english-v31024Cohere
bedrock/cohere.embed-multilingual-v31024Cohere
bedrock/cohere.embed-v4:01024Cohere

With AWS Profile

from praisonaiagents import embedding
import os

os.environ["AWS_PROFILE"] = "my-profile"

result = embedding(
    input="Hello world",
    model="bedrock/amazon.titan-embed-text-v1"
)

Batch Embeddings

from praisonaiagents import embedding

texts = ["Document 1", "Document 2", "Document 3"]
result = embedding(
    input=texts,
    model="bedrock/amazon.titan-embed-text-v1"
)
print(f"Generated {len(result.embeddings)} embeddings")

Cross-Region Usage

from praisonaiagents import embedding

result = embedding(
    input="Hello world",
    model="bedrock/amazon.titan-embed-text-v1",
    aws_region_name="eu-west-1"
)