> ## 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.

# AWS Bedrock Embeddings

> Generate embeddings using Amazon Bedrock

## Overview

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

## Quick Start

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
praisonai embed "Hello world" --model bedrock/amazon.titan-embed-text-v1
```

## Setup

```bash theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
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

```python theme={"theme":{"light":"vitesse-light","dark":"vitesse-dark"}}
from praisonaiagents import embedding

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

## Related

* [Embedding Providers Overview](/docs/embeddings/index)
* [Vertex AI Embeddings](/docs/embeddings/providers/vertex-ai)