> ## Documentation Index
> Fetch the complete documentation index at: https://docs.upsonic.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# AWS Bedrock Embeddings

> Using AWS Bedrock embedding models with Upsonic

## Overview

AWS Bedrock provides access to multiple embedding models including Amazon Titan, Cohere, and Marengo through a unified API. Offers enterprise-grade security, guardrails, and CloudWatch integration.

**Provider Class:** `BedrockEmbedding`

**Config Class:** `BedrockEmbeddingConfig`

## Dependencies

```bash theme={null}
uv pip install boto3
```

## Examples

```python theme={null}
from upsonic import Agent, Task, KnowledgeBase
from upsonic.embeddings import BedrockEmbedding, BedrockEmbeddingConfig
from upsonic.vectordb import ChromaProvider, ChromaConfig, ConnectionConfig, Mode

# Create embedding provider
embedding = BedrockEmbedding(BedrockEmbeddingConfig(
    model_name="amazon.titan-embed-text-v1",
    region_name="us-east-1"
))

# Setup KnowledgeBase
vectordb = ChromaProvider(ChromaConfig(
    collection_name="bedrock_docs",
    vector_size=1536,
    connection=ConnectionConfig(mode=Mode.IN_MEMORY)
))

kb = KnowledgeBase(
    sources=["document.txt"],
    embedding_provider=embedding,
    vectordb=vectordb
)

# Query with Agent
agent = Agent("anthropic/claude-sonnet-4-5")
task = Task("What is this document about?", context=[kb])
result = agent.do(task)
print(result)
```

## Parameters

| Parameter                       | Type          | Description                                    | Default                        | Source   |
| ------------------------------- | ------------- | ---------------------------------------------- | ------------------------------ | -------- |
| `model_name`                    | `str`         | Bedrock embedding model name                   | `"amazon.titan-embed-text-v1"` | Specific |
| `model_id`                      | `str \| None` | Full Bedrock model ID (overrides model\_name)  | `None`                         | Specific |
| `aws_access_key_id`             | `str \| None` | AWS access key ID                              | `None`                         | Specific |
| `aws_secret_access_key`         | `str \| None` | AWS secret access key                          | `None`                         | Specific |
| `aws_session_token`             | `str \| None` | AWS session token                              | `None`                         | Specific |
| `region_name`                   | `str`         | AWS region                                     | `"us-east-1"`                  | Specific |
| `profile_name`                  | `str \| None` | AWS profile name                               | `None`                         | Specific |
| `inference_profile`             | `str \| None` | Bedrock inference profile                      | `None`                         | Specific |
| `enable_guardrails`             | `bool`        | Enable Bedrock guardrails                      | `True`                         | Specific |
| `guardrail_id`                  | `str \| None` | Custom guardrail ID                            | `None`                         | Specific |
| `enable_model_caching`          | `bool`        | Enable model response caching                  | `True`                         | Specific |
| `prefer_provisioned_throughput` | `bool`        | Prefer provisioned throughput models           | `False`                        | Specific |
| `enable_cloudwatch_logging`     | `bool`        | Enable CloudWatch logging                      | `True`                         | Specific |
| `log_group_name`                | `str \| None` | CloudWatch log group name                      | `None`                         | Specific |
| `batch_size`                    | `int`         | Batch size for document embedding              | `100`                          | Base     |
| `max_retries`                   | `int`         | Maximum number of retries on failure           | `3`                            | Base     |
| `normalize_embeddings`          | `bool`        | Whether to normalize embeddings to unit length | `True`                         | Base     |