@agenite/bedrock
AWS Bedrock provider for Agenite, enabling seamless integration with Amazon's foundation models through the Bedrock runtime.
Features
- 🚀 Easy Integration - Simple setup with AWS Bedrock
- 🔄 Streaming Support - Real-time streaming responses
- 🛠️ Tool Integration - Native support for function calling
- 🎯 Model Selection - Support for multiple Bedrock models
- 🔐 AWS Authentication - Automatic credential management
- 🌐 Region Support - Configurable AWS regions
Installation
npm install @agenite/bedrock
Quick Start
import { BedrockProvider } from '@agenite/bedrock';
// Initialize the provider
const provider = new BedrockProvider({
model: 'anthropic.claude-3-5-haiku-20241022-v1:0',
region: 'us-west-2',
});
// Generate a simple response
const result = await provider.generate(
'What are the main features of Llama 2?'
);
console.log(result);
// Use streaming for real-time responses
const generator = provider.stream('Tell me about AWS Bedrock.');
for await (const chunk of generator) {
if (chunk.type === 'text') {
process.stdout.write(chunk.text);
}
}
Configuration
Provider Options
interface BedrockProviderOptions {
model: string; // Bedrock model ID
region: string; // AWS region
temperature?: number; // Temperature for response generation
maxTokens?: number; // Maximum tokens in response
topP?: number; // Top P sampling parameter
}
Available Models
- Anthropic Claude Models:
anthropic.claude-3-5-haiku-20241022-v1:0anthropic.claude-3-sonnet-20240229-v1:0anthropic.claude-instant-v1
- Amazon Titan Models:
amazon.titan-text-express-v1amazon.titan-text-lite-v1
Advanced Usage
Usage with Claude 3.7 thinking
const provider = new BedrockProvider({
model: `us.anthropic.claude-3-7-sonnet-20250219-v1:0`,
region: 'us-east-2',
converseCommandConfig: {
additionalModelRequestFields: {
reasoning_config: {
type: 'enabled',
budget_tokens: 1024,
},
},
inferenceConfig: {
temperature: 1,
},
},
});
Tool Integration
import { BedrockProvider } from '@agenite/bedrock';
import type { ToolDefinition } from '@agenite/llm';
// Define a calculator tool
const calculatorTool: ToolDefinition = {
name: 'calculator',
description: 'Perform basic arithmetic operations',
inputSchema: {
type: 'object',
properties: {
operation: {
type: 'string',
enum: ['add', 'subtract', 'multiply', 'divide'],
},
a: { type: 'number' },
b: { type: 'number' },
},
required: ['operation', 'a', 'b'],
},
};
// Initialize provider with tool support
const provider = new BedrockProvider({
model: 'anthropic.claude-3-5-haiku-20241022-v1:0',
region: 'us-west-2',
});
// Use tool in conversation
const messages = [
{
role: 'user',
content: [{ type: 'text', text: 'What is 123 multiplied by 456?' }],
},
];
const generator = provider.iterate(messages, {
tools: [calculatorTool],
stream: true,
systemPrompt:
'You are a helpful AI assistant with access to a calculator tool.',
});
// Process streaming response with tool usage
for await (const chunk of generator) {
if (chunk.type === 'text') {
process.stdout.write(chunk.text);
} else if (chunk.type === 'toolUse') {
console.log('Tool Use:', chunk);
}
}
Conversation Management
// Maintain conversation history
let messages = [];
const result = await provider.generate(messages, {
systemPrompt: 'You are a helpful AI assistant.',
});
messages.push(
{ role: 'user', content: [{ type: 'text', text: 'Hello!' }] },
result
);
API Reference
BedrockProvider Class
class BedrockProvider implements LLMProvider {
constructor(options: BedrockProviderOptions);
generate(
messages: string | BaseMessage[],
options?: GenerateOptions
): Promise<BaseMessage>;
stream(
messages: string | BaseMessage[],
options?: StreamOptions
): AsyncGenerator<StreamChunk>;
iterate(
messages: string | BaseMessage[],
options?: StreamOptions
): AsyncGenerator<StreamChunk>;
}
Message Types
interface BaseMessage {
role: 'user' | 'assistant' | 'system';
content: ContentBlock[];
}
type ContentBlock = TextBlock | ToolUseBlock | ToolResultBlock;
Examples
Check out the examples directory for more:
basic-chat.ts - Simple chat interactiontool-usage.ts - Advanced tool integration example
AWS Setup
- Install AWS CLI and configure credentials:
aws configure
-
Ensure your AWS account has access to Bedrock and the required models.
-
Set up IAM permissions for Bedrock access:
{
"Version": "2012-10-17",
"Statement": [
{
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel",
"bedrock:InvokeModelWithResponseStream"
],
"Resource": "*"
}
]
}
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
License
MIT
