Documentation Index
Fetch the complete documentation index at: https://docs.praison.ai/llms.txt
Use this file to discover all available pages before exploring further.
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
| Model | Dimensions | Provider |
|---|
bedrock/amazon.titan-embed-text-v1 | 1536 | Amazon |
bedrock/amazon.titan-embed-text-v2:0 | 1024 | Amazon |
bedrock/amazon.nova-2-multimodal-embeddings-v1:0 | 1024 | Amazon |
bedrock/cohere.embed-english-v3 | 1024 | Cohere |
bedrock/cohere.embed-multilingual-v3 | 1024 | Cohere |
bedrock/cohere.embed-v4:0 | 1024 | Cohere |
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"
)
