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AWS Bedrock integration for @aeye","maintainers":[{"name":"philip.diffenderfer","email":"pdiffenderfer@gmail.com"}],"readme":"# @aeye/aws - AWS Bedrock Provider\r\n\r\nAWS Bedrock integration for the [@aeye](https://github.com/ClickerMonkey/aeye) framework, providing seamless access to Claude, Llama, Mistral, Cohere, Stability AI, and Amazon Titan models.\r\n\r\n## Features\r\n\r\n- 🤖 **Multi-Model Support** - Access to all AWS Bedrock foundation models\r\n- 🔐 **Automatic Authentication** - Uses AWS SDK credential chain for seamless authentication\r\n- 🌊 **Streaming** - Full streaming support for real-time responses\r\n- 🎨 **Image Generation** - Stability AI models for image creation\r\n- 📊 **Embeddings** - Amazon Titan embeddings for semantic search\r\n- 🔧 **Type-Safe** - Full TypeScript support with type inference\r\n- ⚡ **Regional Flexibility** - Deploy across any AWS region with Bedrock\r\n\r\n## Supported Models\r\n\r\n### Chat Models\r\n- **Anthropic Claude** - Claude 3 Opus, Sonnet, Haiku, and Claude 3.5 Sonnet\r\n- **Meta Llama** - Llama 2, Llama 3, Llama 3.1, Llama 3.2\r\n- **Mistral AI** - Mistral 7B, Mixtral 8x7B, Mistral Large\r\n- **Cohere** - Command, Command Light, Command R, Command R+\r\n- **AI21 Labs** - Jurassic-2 models\r\n\r\n### Image Models\r\n- **Stability AI** - Stable Diffusion XL, Stable Diffusion 3\r\n\r\n### Embedding Models\r\n- **Amazon Titan** - Titan Text Embeddings v1 and v2\r\n\r\n## Installation\r\n\r\n```bash\r\nnpm install @aeye/aws @aeye/ai @aeye/core zod\r\n```\r\n\r\n## Prerequisites\r\n\r\n### AWS Credentials\r\n\r\nThe provider uses the AWS SDK v3 credential chain to automatically discover credentials:\r\n\r\n1. **Environment variables** - `AWS_ACCESS_KEY_ID`, `AWS_SECRET_ACCESS_KEY`, `AWS_SESSION_TOKEN`\r\n2. **Shared credentials file** - `~/.aws/credentials`\r\n3. **IAM roles** - When running on EC2, ECS, Lambda, etc.\r\n\r\n### AWS Bedrock Access\r\n\r\nEnsure you have:\r\n1. Access to AWS Bedrock in your target region\r\n2. Permissions to invoke foundation models\r\n3. Model access enabled in the AWS Bedrock console\r\n\r\n## Quick Start\r\n\r\n### Basic Chat Completion\r\n\r\n```typescript\r\nimport { AI } from '@aeye/ai';\r\nimport { AWSBedrockProvider } from '@aeye/aws';\r\n\r\n// Create provider (credentials picked up automatically)\r\nconst aws = new AWSBedrockProvider({\r\n  region: 'us-east-1',\r\n});\r\n\r\n// Create AI instance\r\nconst ai = AI.with()\r\n  .providers({ aws })\r\n  .create();\r\n\r\n// Chat with Claude\r\nconst response = await ai.chat.get([\r\n  { role: 'user', content: 'What is AWS Bedrock?' }\r\n], {\r\n  model: 'anthropic.claude-3-sonnet-20240229-v1:0'\r\n});\r\n\r\nconsole.log(response.content);\r\n```\r\n\r\n### Streaming Responses\r\n\r\n```typescript\r\n// Stream responses in real-time\r\nfor await (const chunk of ai.chat.stream([\r\n  { role: 'user', content: 'Write a poem about AI' }\r\n], {\r\n  model: 'anthropic.claude-3-haiku-20240307-v1:0'\r\n})) {\r\n  if (chunk.content) {\r\n    process.stdout.write(chunk.content);\r\n  }\r\n}\r\n```\r\n\r\n### Image Generation\r\n\r\n```typescript\r\nimport { AWSBedrockProvider } from '@aeye/aws';\r\n\r\nconst aws = new AWSBedrockProvider({\r\n  region: 'us-east-1',\r\n  defaultModels: {\r\n    imageGenerate: 'stability.stable-diffusion-xl-v1',\r\n  },\r\n});\r\n\r\nconst ai = AI.with()\r\n  .providers({ aws })\r\n  .create();\r\n\r\nconst result = await ai.image.generate({\r\n  prompt: 'A serene mountain landscape at sunset',\r\n  size: { width: 1024, height: 1024 },\r\n});\r\n\r\nconsole.log('Image URL:', result.images[0].url);\r\n```\r\n\r\n### Text Embeddings\r\n\r\n```typescript\r\nconst aws = new AWSBedrockProvider({\r\n  region: 'us-east-1',\r\n  defaultModels: {\r\n    embedding: 'amazon.titan-embed-text-v1',\r\n  },\r\n});\r\n\r\nconst ai = AI.with()\r\n  .providers({ aws })\r\n  .create();\r\n\r\nconst embeddings = await ai.embed.get({\r\n  input: 'AWS Bedrock is a fully managed service',\r\n  model: 'amazon.titan-embed-text-v1',\r\n});\r\n\r\nconsole.log('Embedding dimensions:', embeddings.embeddings[0].length);\r\n```\r\n\r\n## Configuration\r\n\r\n### Provider Configuration\r\n\r\n```typescript\r\nimport { AWSBedrockProvider, type AWSBedrockConfig } from '@aeye/aws';\r\n\r\nconst config: AWSBedrockConfig = {\r\n  // AWS region (defaults to AWS_REGION env var or 'us-east-1')\r\n  region: 'us-west-2',\r\n  \r\n  // Optional: Explicit credentials (if not using default chain)\r\n  credentials: {\r\n    accessKeyId: process.env.AWS_ACCESS_KEY_ID!,\r\n    secretAccessKey: process.env.AWS_SECRET_ACCESS_KEY!,\r\n    sessionToken: process.env.AWS_SESSION_TOKEN, // Optional\r\n  },\r\n  \r\n  // Optional: Default models for different capabilities\r\n  defaultModels: {\r\n    chat: 'anthropic.claude-3-sonnet-20240229-v1:0',\r\n    imageGenerate: 'stability.stable-diffusion-xl-v1',\r\n    embedding: 'amazon.titan-embed-text-v2:0',\r\n  },\r\n};\r\n\r\nconst provider = new AWSBedrockProvider(config);\r\n```\r\n\r\n### Using with Multiple Providers\r\n\r\n```typescript\r\nimport { AI } from '@aeye/ai';\r\nimport { AWSBedrockProvider } from '@aeye/aws';\r\nimport { OpenAIProvider } from '@aeye/openai';\r\n\r\nconst aws = new AWSBedrockProvider({ region: 'us-east-1' });\r\nconst openai = new OpenAIProvider({ apiKey: process.env.OPENAI_API_KEY! });\r\n\r\nconst ai = AI.with()\r\n  .providers({ aws, openai })\r\n  .create({\r\n    // Automatic model selection across all providers\r\n    defaultMetadata: {\r\n      required: ['chat', 'streaming'],\r\n      weights: {\r\n        cost: 0.5,\r\n        speed: 0.3,\r\n        quality: 0.2,\r\n      },\r\n    },\r\n  });\r\n\r\n// AI instance will automatically select the best model across AWS and OpenAI\r\nconst response = await ai.chat.get([\r\n  { role: 'user', content: 'Hello!' }\r\n]);\r\n```\r\n\r\n## Model IDs\r\n\r\n### Anthropic Claude Models\r\n\r\n```typescript\r\n// Claude 3.5\r\n'anthropic.claude-3-5-sonnet-20240620-v1:0'\r\n'anthropic.claude-3-5-sonnet-20241022-v2:0'\r\n\r\n// Claude 3\r\n'anthropic.claude-3-opus-20240229-v1:0'\r\n'anthropic.claude-3-sonnet-20240229-v1:0'\r\n'anthropic.claude-3-haiku-20240307-v1:0'\r\n\r\n// Claude 2.x\r\n'anthropic.claude-v2:1'\r\n'anthropic.claude-v2'\r\n'anthropic.claude-instant-v1'\r\n```\r\n\r\n### Meta Llama Models\r\n\r\n```typescript\r\n'meta.llama3-2-1b-instruct-v1:0'\r\n'meta.llama3-2-3b-instruct-v1:0'\r\n'meta.llama3-2-11b-instruct-v1:0'\r\n'meta.llama3-2-90b-instruct-v1:0'\r\n'meta.llama3-1-8b-instruct-v1:0'\r\n'meta.llama3-1-70b-instruct-v1:0'\r\n'meta.llama3-1-405b-instruct-v1:0'\r\n'meta.llama3-8b-instruct-v1:0'\r\n'meta.llama3-70b-instruct-v1:0'\r\n'meta.llama2-13b-chat-v1'\r\n'meta.llama2-70b-chat-v1'\r\n```\r\n\r\n### Mistral AI Models\r\n\r\n```typescript\r\n'mistral.mistral-7b-instruct-v0:2'\r\n'mistral.mixtral-8x7b-instruct-v0:1'\r\n'mistral.mistral-large-2402-v1:0'\r\n'mistral.mistral-large-2407-v1:0'\r\n```\r\n\r\n### Cohere Models\r\n\r\n```typescript\r\n'cohere.command-text-v14'\r\n'cohere.command-light-text-v14'\r\n'cohere.command-r-v1:0'\r\n'cohere.command-r-plus-v1:0'\r\n```\r\n\r\n### Stability AI Models\r\n\r\n```typescript\r\n'stability.stable-diffusion-xl-v1'\r\n'stability.sd3-large-v1:0'\r\n```\r\n\r\n### Amazon Titan Models\r\n\r\n```typescript\r\n// Embeddings\r\n'amazon.titan-embed-text-v1'\r\n'amazon.titan-embed-text-v2:0'\r\n\r\n// Image Generation\r\n'amazon.titan-image-generator-v1'\r\n'amazon.titan-image-generator-v2:0'\r\n```\r\n\r\n## Advanced Features\r\n\r\n### Vision with Claude 3\r\n\r\n```typescript\r\nconst response = await ai.chat.get([\r\n  {\r\n    role: 'user',\r\n    content: [\r\n      { type: 'text', content: 'What do you see in this image?' },\r\n      { type: 'image', content: 'https://example.com/image.jpg' },\r\n    ],\r\n  },\r\n], {\r\n  model: 'anthropic.claude-3-sonnet-20240229-v1:0',\r\n});\r\n```\r\n\r\n### Custom Request Parameters\r\n\r\n```typescript\r\nconst response = await ai.chat.get([\r\n  { role: 'user', content: 'Hello!' }\r\n], {\r\n  model: 'anthropic.claude-3-haiku-20240307-v1:0',\r\n  temperature: 0.7,\r\n  maxTokens: 2048,\r\n  topP: 0.9,\r\n  stop: ['Human:', 'Assistant:'],\r\n});\r\n```\r\n\r\n### Multi-Region Setup\r\n\r\n```typescript\r\n// Primary region\r\nconst awsEast = new AWSBedrockProvider({\r\n  region: 'us-east-1',\r\n});\r\n\r\n// Fallback region\r\nconst awsWest = new AWSBedrockProvider({\r\n  region: 'us-west-2',\r\n});\r\n\r\nconst ai = AI.with()\r\n  .providers({ awsEast, awsWest })\r\n  .create();\r\n```\r\n\r\n## Error Handling\r\n\r\n```typescript\r\nimport {\r\n  AWSError,\r\n  AWSAuthError,\r\n  AWSRateLimitError,\r\n  AWSQuotaError,\r\n  AWSContextWindowError,\r\n} from '@aeye/aws';\r\n\r\ntry {\r\n  const response = await ai.chat.get([\r\n    { role: 'user', content: 'Hello!' }\r\n  ]);\r\n} catch (error) {\r\n  if (error instanceof AWSAuthError) {\r\n    console.error('Authentication failed. Check your AWS credentials.');\r\n  } else if (error instanceof AWSRateLimitError) {\r\n    console.error('Rate limit exceeded. Retry after:', error.retryAfter);\r\n  } else if (error instanceof AWSQuotaError) {\r\n    console.error('AWS quota exceeded. Request quota increase.');\r\n  } else if (error instanceof AWSContextWindowError) {\r\n    console.error('Input too long for model context window.');\r\n  } else if (error instanceof AWSError) {\r\n    console.error('AWS Bedrock error:', error.message);\r\n  }\r\n}\r\n```\r\n\r\n## Health Check\r\n\r\n```typescript\r\nconst isHealthy = await aws.checkHealth();\r\nconsole.log('AWS Bedrock is accessible:', isHealthy);\r\n```\r\n\r\n## List Available Models\r\n\r\n```typescript\r\nconst models = await aws.listModels();\r\nconsole.log('Available models:', models.map(m => m.id));\r\n\r\n// Filter by capability\r\nconst chatModels = models.filter(m => m.capabilities.has('chat'));\r\nconst imageModels = models.filter(m => m.capabilities.has('image'));\r\nconst embeddingModels = models.filter(m => m.capabilities.has('embedding'));\r\n```\r\n\r\n## Best Practices\r\n\r\n1. **Use IAM Roles** - When running on AWS infrastructure (EC2, ECS, Lambda), use IAM roles instead of hardcoded credentials\r\n2. **Region Selection** - Choose regions close to your users for lower latency\r\n3. **Model Selection** - Use smaller models (Haiku, 7B) for faster, cheaper responses when appropriate\r\n4. **Streaming** - Enable streaming for better user experience with long responses\r\n5. **Error Handling** - Implement proper error handling and retry logic for production use\r\n6. **Cost Management** - Monitor usage through AWS Cost Explorer and set up billing alerts\r\n\r\n## Environment Variables\r\n\r\n```bash\r\n# AWS credentials (if not using IAM roles)\r\nAWS_ACCESS_KEY_ID=your_access_key\r\nAWS_SECRET_ACCESS_KEY=your_secret_key\r\nAWS_SESSION_TOKEN=your_session_token  # Optional\r\n\r\n# AWS region\r\nAWS_REGION=us-east-1\r\n```\r\n\r\n## Links\r\n\r\n- [AWS Bedrock Documentation](https://docs.aws.amazon.com/bedrock/)\r\n- [Model IDs Reference](https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html)\r\n- [@aeye Framework](https://github.com/ClickerMonkey/aeye)\r\n- [AWS SDK for JavaScript](https://docs.aws.amazon.com/sdk-for-javascript/v3/)\r\n\r\n## License\r\n\r\nGPL-3.0 - See [LICENSE](../../LICENSE) file for details.\r\n\r\n## Contributing\r\n\r\nIssues and pull requests are welcome! See the [main repository](https://github.com/ClickerMonkey/aeye) for contribution guidelines.\r\n","readmeFilename":"README.md"}