---
description: Integrate BroxiAI with Amazon Web Services (AWS) ecosystem
---

# AWS Integration

Learn how to integrate BroxiAI workflows with AWS services for enhanced functionality, scalability, and data processing capabilities.

## AWS Integration Architecture

<figure><img src="https://2739525811-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGUcjvdv7MzVy7GYqImRT%2Fuploads%2FXNtS2jePo4KkXIOHPDSo%2FUntitled%20diagram%20_%20Mermaid%20Chart-2025-09-07-155226.png?alt=media&#x26;token=830431b0-2ff9-4554-b123-4ddf71da5d91" alt=""><figcaption></figcaption></figure>

## Overview

BroxiAI integrates seamlessly with AWS services to provide:

* Scalable cloud infrastructure
* Advanced data processing capabilities
* Enterprise-grade security and compliance
* Cost-effective resource management
* Global availability and performance

## Core AWS Services Integration

### Amazon Bedrock

Connect BroxiAI to AWS's fully managed foundation model service.

**Configuration**

```json
{
  "provider": "aws_bedrock",
  "region": "us-east-1",
  "access_key_id": "${AWS_ACCESS_KEY_ID}",
  "secret_access_key": "${AWS_SECRET_ACCESS_KEY}",
  "models": {
    "text": "anthropic.claude-3-sonnet-20240229-v1:0",
    "embeddings": "amazon.titan-embed-text-v1"
  }
}
```

**Supported Models**

* Anthropic Claude 3 (Haiku, Sonnet, Opus)
* Amazon Titan (Text, Embeddings)
* Meta Llama 2
* Cohere Command
* AI21 Labs Jurassic

### Amazon S3

Store and process documents, images, and other files.

**Use Cases**

* Document storage for RAG applications
* File upload/download workflows
* Batch processing of documents
* Data archival and backup

**Integration Example**

```python
# BroxiAI workflow component
{
  "component": "S3FileLoader",
  "config": {
    "bucket_name": "my-documents-bucket",
    "object_key": "documents/manual.pdf",
    "aws_access_key_id": "${AWS_ACCESS_KEY_ID}",
    "aws_secret_access_key": "${AWS_SECRET_ACCESS_KEY}",
    "region": "us-west-2"
  }
}
```

### Amazon RDS/Aurora

Connect to managed relational databases for data retrieval and storage.

**Configuration**

```json
{
  "database_type": "postgresql",
  "host": "mydb.cluster-xyz.us-east-1.rds.amazonaws.com",
  "port": 5432,
  "database": "production",
  "username": "${DB_USERNAME}",
  "password": "${DB_PASSWORD}",
  "ssl_mode": "require"
}
```

**SQL Agent Integration**

* Natural language to SQL conversion
* Database schema analysis
* Automated query generation
* Results formatting and analysis

### Amazon DynamoDB

Integrate with NoSQL database for session management and metadata storage.

**Use Cases**

* User session storage
* Conversation history
* Metadata caching
* Real-time analytics

**Configuration**

```json
{
  "table_name": "broxi_sessions",
  "region": "us-east-1",
  "access_key_id": "${AWS_ACCESS_KEY_ID}",
  "secret_access_key": "${AWS_SECRET_ACCESS_KEY}",
  "partition_key": "session_id",
  "sort_key": "timestamp"
}
```

### Amazon SES

Send automated emails from BroxiAI workflows.

**Email Component Setup**

```json
{
  "component": "SESEmailSender",
  "config": {
    "region": "us-east-1",
    "from_email": "noreply@yourcompany.com",
    "access_key_id": "${AWS_ACCESS_KEY_ID}",
    "secret_access_key": "${AWS_SECRET_ACCESS_KEY}"
  }
}
```

**Use Cases**

* Automated notifications
* Report generation and delivery
* Customer communication
* Alert systems

## Advanced AWS Integrations

### Amazon Kinesis

Process streaming data in real-time workflows.

**Data Streams**

```json
{
  "stream_name": "broxi-events",
  "region": "us-east-1",
  "shard_count": 2,
  "retention_period": 24
}
```

**Integration Benefits**

* Real-time data processing
* Event-driven workflows
* Scalable data ingestion
* Analytics and monitoring

### AWS Lambda

Trigger BroxiAI workflows from serverless functions.

**Lambda Function Example**

```python
import json
import requests

def lambda_handler(event, context):
    # Trigger BroxiAI workflow
    response = requests.post(
        "https://api.broxi.ai/v1/flows/your-flow-id/run",
        headers={
            "Authorization": f"Bearer {os.environ['BROXI_API_TOKEN']}",
            "Content-Type": "application/json"
        },
        json={
            "input": event["input_message"],
            "variables": event.get("variables", {})
        }
    )
    
    return {
        'statusCode': 200,
        'body': json.dumps(response.json())
    }
```

### Amazon SNS

Send notifications and alerts from workflows.

**Topic Configuration**

```json
{
  "topic_arn": "arn:aws:sns:us-east-1:123456789012:broxi-alerts",
  "region": "us-east-1",
  "access_key_id": "${AWS_ACCESS_KEY_ID}",
  "secret_access_key": "${AWS_SECRET_ACCESS_KEY}"
}
```

**Notification Types**

* SMS messages
* Email notifications
* Mobile push notifications
* HTTP/HTTPS endpoints

### Amazon CloudWatch

Monitor and log BroxiAI workflow performance.

**Metrics Configuration**

```json
{
  "namespace": "BroxiAI/Workflows",
  "metrics": [
    "ExecutionTime",
    "SuccessRate", 
    "ErrorRate",
    "TokenUsage"
  ],
  "log_group": "/aws/broxi/workflows"
}
```

## Vector Database Integration

### Amazon OpenSearch Service

Use OpenSearch as a vector database for RAG applications.

**Configuration**

```json
{
  "endpoint": "https://search-mydomain.us-east-1.es.amazonaws.com",
  "region": "us-east-1",
  "index_name": "documents",
  "dimension": 1536,
  "similarity_metric": "cosine",
  "authentication": {
    "type": "aws_iam",
    "access_key_id": "${AWS_ACCESS_KEY_ID}",
    "secret_access_key": "${AWS_SECRET_ACCESS_KEY}"
  }
}
```

**Features**

* Vector similarity search
* Hybrid search (text + vector)
* Real-time indexing
* Scalable performance

### Amazon MemoryDB for Redis

Fast vector search with in-memory performance.

**Use Cases**

* Real-time recommendations
* Session-based search
* Caching layer for embeddings
* Low-latency applications

## Security and Compliance

### IAM Roles and Policies

Configure secure access to AWS resources.

**Minimal IAM Policy**

```json
{
  "Version": "2012-10-17",
  "Statement": [
    {
      "Effect": "Allow",
      "Action": [
        "bedrock:InvokeModel",
        "s3:GetObject",
        "s3:PutObject",
        "ses:SendEmail"
      ],
      "Resource": [
        "arn:aws:bedrock:*:*:model/*",
        "arn:aws:s3:::my-bucket/*",
        "arn:aws:ses:*:*:identity/*"
      ]
    }
  ]
}
```

### VPC Integration

Deploy BroxiAI integrations within your VPC for enhanced security.

**Network Configuration**

* Private subnets for database access
* NAT gateways for internet access
* Security groups for access control
* VPC endpoints for AWS services

### Encryption

**Data Protection**

* S3 bucket encryption (AES-256 or KMS)
* RDS encryption at rest
* SES encryption in transit
* Parameter Store for secrets

## Cost Optimization

### Resource Management

**Best Practices**

* Use appropriate instance sizes
* Implement auto-scaling policies
* Monitor and optimize usage
* Leverage spot instances where applicable

**Cost Monitoring**

* Set up billing alerts
* Use AWS Cost Explorer
* Tag resources for cost allocation
* Regular cost optimization reviews

### AWS Free Tier

Take advantage of AWS Free Tier services:

* Amazon Bedrock: Limited free usage
* Amazon S3: 5GB of storage
* Amazon DynamoDB: 25GB of storage
* AWS Lambda: 1M free requests per month

## Deployment Patterns

### Multi-Region Deployment

Deploy across multiple AWS regions for:

* High availability
* Disaster recovery
* Global performance
* Compliance requirements

**Architecture Example**

```yaml
Primary Region (us-east-1):
  - Production workloads
  - Primary databases
  - Main S3 buckets

Secondary Region (us-west-2):
  - Disaster recovery
  - Read replicas
  - Backup storage
```

### Auto Scaling

Configure auto-scaling for variable workloads:

* Application Load Balancer
* Auto Scaling Groups
* CloudWatch metrics
* Scaling policies

## Monitoring and Logging

### CloudWatch Integration

**Key Metrics**

* API response times
* Error rates
* Resource utilization
* Cost metrics

**Log Aggregation**

* Centralized logging
* Log retention policies
* Search and analysis
* Alerting rules

### AWS X-Ray

Distributed tracing for complex workflows:

* Request tracing
* Performance analysis
* Bottleneck identification
* Service map visualization

## Getting Started

### Prerequisites

1. AWS account with appropriate permissions
2. BroxiAI account and API access
3. AWS CLI configured
4. Basic understanding of AWS services

### Quick Start Guide

1. **Set up AWS credentials** in BroxiAI global variables
2. **Create S3 bucket** for document storage
3. **Configure Bedrock access** for AI models
4. **Build your first workflow** using AWS components
5. **Test and deploy** to production

### Example Workflow

Here's a complete workflow that processes documents from S3:

<figure><img src="https://2739525811-files.gitbook.io/~/files/v0/b/gitbook-x-prod.appspot.com/o/spaces%2FGUcjvdv7MzVy7GYqImRT%2Fuploads%2FWjJXdfyFtFS3CfpAtjyd%2FUntitled%20diagram%20_%20Mermaid%20Chart-2025-09-07-155306.png?alt=media&#x26;token=8c41fac4-3dd6-4d92-bb70-e91fb3b24bae" alt=""><figcaption></figcaption></figure>

## Support Resources

* [AWS Documentation](https://docs.aws.amazon.com/)
* [BroxiAI AWS Examples](https://github.com/GuidenAI/broxi_document/blob/main/examples/aws-integration.md)
* [Community Forums](https://community.broxi.ai/aws)
* [Technical Support](mailto:support@broxi.ai)

## Next Steps

* [Azure Integration](azure)
* [Google Cloud Platform](gcp)
* [Best Practices](../best-practices/overview)
* [Security Guide](../security/overview)
