AWS Bedrock Plugin for Genkit Go

A comprehensive AWS Bedrock plugin for Firebase Genkit Go that provides text generation, image generation, and embedding capabilities using AWS Bedrock foundation models via the Converse API.

Features

• Text Generation: Support for multiple foundation models via AWS Bedrock Converse API
• Image Generation: Support for image generation models like Amazon Titan Image Generator
• Embeddings: Support for text embedding models from Amazon Titan and Cohere
• Streaming: Full streaming support for real-time responses
• Tool Calling: Complete function calling capabilities with schema validation and type conversion
• Multimodal Support: Support for text + image inputs (vision models)
• Schema Management: Automatic conversion between Genkit and AWS Bedrock schemas
• Type Safety: Robust type conversion for tool parameters (handles AWS document.Number types)

Supported Models

Text Generation Models (with Tool Calling Support)

• Anthropic Claude 3/3.5/4: Haiku, Sonnet, Opus (all versions)
• Amazon Nova: Micro, Lite, Pro
• Meta Llama: 3.1/3.2/3.3 (8B, 70B, 405B)
• Mistral AI: Large, 7B models
• Amazon Titan: Text Express, Premier

Image Generation Models

• Amazon Titan Image Generator v1
• Amazon Titan Image Generator v2 (preview)

Embedding Models

• Amazon Titan Embeddings: Text v1/v2, Multimodal v1
• Cohere: Embed English/Multilingual v3

Multimodal Models (Text + Vision)

• All Claude 3/3.5/4 models
• Amazon Nova models

Installation

go get github.com/xavidop/genkit-aws-bedrock-go

Quick Start

Initialize the Plugin

package main

import (
	"context"
	"log"

	"github.com/firebase/genkit/go/ai"
	"github.com/firebase/genkit/go/genkit"
	bedrock "github.com/xavidop/genkit-aws-bedrock-go"
)

func main() {
	ctx := context.Background()
	bedrockPlugin := &bedrock.Bedrock{
		Region: "us-east-1",
	}

	// Initialize Genkit
	g := genkit.Init(ctx,
		genkit.WithPlugins(bedrockPlugin),
		genkit.WithDefaultModel("bedrock/anthropic.claude-3-haiku-20240307-v1:0"), // Set default model
	)

    bedrock.DefineCommonModels(bedrockPlugin, g) // Optional: Define common models for easy access

	log.Println("Starting basic Bedrock example...")

	// Example: Generate text (basic usage)
	response, err := genkit.Generate(ctx, g,
		ai.WithPrompt("What are the key benefits of using AWS Bedrock for AI applications?"),
	)
	if err != nil {
		log.Printf("Error generating text: %v", err)
	} else {
		log.Printf("Generated response: %s", response.Text())
	}

	log.Println("Basic Bedrock example completed")
}

Define Models and Generate Text

package main

import (
    "context"
    "log"
    
    "github.com/firebase/genkit/go/ai"
    "github.com/firebase/genkit/go/genkit"
    bedrock "github.com/xavidop/genkit-aws-bedrock-go"
)

func main() {
    ctx := context.Background()

    // Initialize Bedrock plugin
    bedrockPlugin := &bedrock.Bedrock{
        Region: "us-east-1", // Optional, defaults to AWS_REGION or us-east-1
    }
    
    // Initialize Genkit
    g := genkit.Init(ctx,
        genkit.WithPlugins(bedrockPlugin),
    )
    
    // Define a Claude 3 model
    claudeModel := bedrockPlugin.DefineModel(g, bedrock.ModelDefinition{
        Name: "anthropic.claude-3-haiku-20240307-v1:0",
        Type: "text",
    }, nil)
    
    // Generate text
    response, err := genkit.Generate(ctx, g,
        ai.WithModel(claudeModel),
        ai.WithMessages(ai.NewUserMessage(
            ai.NewTextPart("Hello! How are you?"),
        )),
    )
    
    if err != nil {
        log.Fatal(err)
    }
    
    log.Println(response.Text())
}

Configuration Options

The plugin supports various configuration options:

bedrockPlugin := &bedrock.Bedrock{
    Region:         "us-west-2",           // AWS region
    MaxRetries:     3,                     // Max retry attempts
    RequestTimeout: 30 * time.Second,     // Request timeout
    AWSConfig:      customAWSConfig,      // Custom AWS config (optional)
}

Available Configuration

Option	Type	Default	Description
Region	string	"us-east-1"	AWS region for Bedrock
MaxRetries	int	3	Maximum retry attempts
RequestTimeout	time.Duration	30s	Request timeout
AWSConfig	*aws.Config	nil	Custom AWS configuration

AWS Setup and Authentication

The plugin uses the standard AWS SDK v2 configuration methods:

Authentication Methods

1. Environment Variables:

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

2. AWS Credentials File (~/.aws/credentials):

   [default]
   aws_access_key_id = your-access-key
   aws_secret_access_key = your-secret-key
   region = us-east-1

3. IAM Roles (when running on AWS services like EC2, ECS, Lambda)

4. AWS SSO/CLI (aws configure sso)

Required IAM Permissions

Create an IAM policy with these permissions:

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Effect": "Allow",
            "Action": [
                "bedrock:InvokeModel",
                "bedrock:InvokeModelWithResponseStream"
            ],
            "Resource": [
                "arn:aws:bedrock:*::foundation-model/*"
            ]
        }
    ]
}

Model Access

Some models require additional access requests:

1. Go to AWS Bedrock Console → Model Access
2. Request access for specific models (e.g., Claude, Llama)
3. Wait for approval (usually instant for most models)

Examples Directory

The repository includes comprehensive examples:

• examples/basic/ - Simple text generation
• examples/streaming/ - Real-time streaming responses
• examples/tool_calling/ - Function calling with multiple tools
• examples/image_generation/ - Image generation and file saving
• examples/embeddings/ - Text embeddings and similarity
• examples/multimodal/ - Vision models with image inputs
• examples/advanced_schemas/ - Complex tool schemas and validation

Running Examples

# Clone the repository
git clone https://github.com/xavidop/genkit-aws-bedrock-go
cd genkit-aws-bedrock

# Run basic example
cd examples/basic
go run main.go

# Run tool calling example
cd ../tool_calling  
go run main.go

# Run image generation example
cd ../image_generation
go run main.go

Features in Detail

🔧 Tool Calling

• Schema Validation: Automatic validation and type conversion
• Type Safety: Handles AWS document.Number to Go numeric types
• Complex Schemas: Support for nested objects, arrays, enums
• Error Handling: Robust error handling and fallbacks

🖼️ Image Support

• Input: Supports base64 data URLs and binary data
• Output: Returns images as base64 data URLs
• Formats: PNG, JPEG, WebP, GIF support
• Vision: Text + image inputs for multimodal models

📡 Streaming

• Real-time: Token-by-token streaming responses
• Efficient: Low-latency streaming with proper buffering
• Error Handling: Stream error handling and recovery

🎯 Type Conversion

• AWS Document Types: Handles document.Number, document.String
• Schema Mapping: JSON Schema to AWS Bedrock schema conversion
• Parameter Types: String to number/boolean conversion for tools

Performance and Best Practices

Configuration Tips

// For development/testing
config := map[string]interface{}{
    "temperature": 0.1,        // Low for consistent results
    "maxOutputTokens": 1000,   // Reasonable limit
}

// For creative tasks  
config := map[string]interface{}{
    "temperature": 0.8,        // Higher for creativity
    "topP": 0.9,              // Nucleus sampling
    "maxOutputTokens": 2000,   // Allow longer responses
}

// For tool calling
config := map[string]interface{}{
    "temperature": 0.1,        // Low for consistent tool usage
    "maxOutputTokens": 1000,   // Tools need space for responses
}

Error Handling

response, err := genkit.Generate(ctx, g, /* options */)
if err != nil {
    // Handle specific AWS errors
    if strings.Contains(err.Error(), "ValidationException") {
        log.Printf("Request validation error: %v", err)
    } else if strings.Contains(err.Error(), "ThrottlingException") {
        log.Printf("Rate limit exceeded: %v", err)
        // Implement retry logic
    } else {
        log.Printf("Generation error: %v", err)
    }
    return
}

Troubleshooting

Common Issues

1. "Access Denied" Errors

   • Verify IAM permissions include bedrock:InvokeModel
   • Check AWS credentials are configured correctly
   • Ensure you're using the correct AWS region

2. "Model Not Found" Errors

   • Request model access in AWS Bedrock Console
   • Verify model name spelling and version
   • Check model availability in your region

3. "Validation Exception" Errors

   • Check tool schema definitions
   • Verify parameter types match schema
   • Ensure required fields are provided

4. "Throttling Exception" Errors

   • Implement exponential backoff retry logic
   • Consider upgrading to higher rate limits
   • Distribute requests across time

Semantic Versioning & Automated Releases

This project follows Semantic Versioning and uses automated releases based on Conventional Commits.

How Releases Work

1. Automatic Version Bumping: Commit messages determine version increments

   • feat: → Minor version bump (new features)
   • fix: → Patch version bump (bug fixes)
   • BREAKING CHANGE: → Major version bump (breaking changes)

2. Automated Changelog: Release notes are automatically generated from commit messages

3. GitHub Releases: Tagged releases with compiled artifacts and documentation

For Contributors

When contributing, use conventional commit format:

# Feature (minor bump)
feat(models): add support for Claude 3.5 Sonnet

# Bug fix (patch bump)  
fix(streaming): resolve timeout issue with long responses

# Breaking change (major bump)
feat(api)!: change model configuration interface

BREAKING CHANGE: Model configuration now requires explicit region specification.

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Follow Conventional Commits format
4. Commit your changes (git commit -m 'feat: add amazing feature')
5. Push to the branch (git push origin feature/amazing-feature)
6. Open a Pull Request

License

Apache 2.0 - see LICENSE file for details.

Acknowledgments

• Firebase Genkit team for the excellent Go framework
• AWS Bedrock team for the comprehensive AI model platform
• The open source community for inspiration and feedback

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Built with ❤️ for the Firebase Genkit Go community