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Service for ObjectStack — implements IAIService with LLM adapter layer, conversation management, tool registry, and REST/SSE routes","maintainers":[{"name":"steedos-zhuangjianguo","email":"zhuangjianguo@steedos.com"}],"readme":"# @objectstack/service-ai\n\nAI Service for ObjectStack — implements `IAIService` with LLM adapter layer, conversation management, tool registry, and REST/SSE routes.\n\n## Features\n\n- **Multi-Provider LLM Support**: Supports OpenAI, Anthropic, Google, and custom gateway providers via Vercel AI SDK\n- **Conversation Management**: Track and manage AI conversations with full history\n- **Tool Registry**: Register and execute tools that AI agents can call\n- **Streaming Support**: Real-time streaming responses via Server-Sent Events (SSE)\n- **REST API**: Auto-generated endpoints for AI operations\n- **Type-Safe**: Full TypeScript support with type inference\n\n## Installation\n\n```bash\npnpm add @objectstack/service-ai\n```\n\n### Peer Dependencies\n\nInstall the LLM provider(s) you need:\n\n```bash\n# OpenAI\npnpm add @ai-sdk/openai\n\n# Anthropic (Claude)\npnpm add @ai-sdk/anthropic\n\n# Google (Gemini)\npnpm add @ai-sdk/google\n\n# Custom Gateway\npnpm add @ai-sdk/gateway\n```\n\nAll peer dependencies are optional — install only what you need.\n\n## Basic Usage\n\n```typescript\nimport { defineStack } from '@objectstack/spec';\nimport { ServiceAI } from '@objectstack/service-ai';\nimport { openai } from '@ai-sdk/openai';\n\nconst stack = defineStack({\n  services: [\n    ServiceAI.configure({\n      models: {\n        default: openai('gpt-4'),\n        fast: openai('gpt-3.5-turbo'),\n      },\n    }),\n  ],\n});\n```\n\n## Configuration\n\n```typescript\ninterface AIServiceConfig {\n  /** Map of model IDs to AI SDK model instances */\n  models: Record<string, LanguageModel>;\n\n  /** Default model to use when not specified */\n  defaultModel?: string;\n\n  /** Maximum conversation history length */\n  maxHistoryLength?: number;\n\n  /** Enable streaming responses (default: true) */\n  enableStreaming?: boolean;\n}\n```\n\n## Service API\n\nThe `IAIService` interface provides:\n\n### Conversation Management\n\n```typescript\n// Get AI service from kernel\nconst ai = kernel.getService<IAIService>('ai');\n\n// Create a new conversation\nconst conversation = await ai.createConversation({\n  model: 'default',\n  systemPrompt: 'You are a helpful assistant.',\n});\n\n// Send a message\nconst response = await ai.sendMessage({\n  conversationId: conversation.id,\n  message: 'What is ObjectStack?',\n});\n\n// Stream a message\nconst stream = await ai.streamMessage({\n  conversationId: conversation.id,\n  message: 'Explain in detail...',\n});\n\nfor await (const chunk of stream) {\n  process.stdout.write(chunk.text);\n}\n```\n\n### Tool Registry\n\n```typescript\n// Register a tool\nai.registerTool({\n  name: 'get_weather',\n  description: 'Get current weather for a location',\n  parameters: {\n    type: 'object',\n    properties: {\n      location: { type: 'string', description: 'City name' },\n    },\n    required: ['location'],\n  },\n  execute: async ({ location }) => {\n    // Your tool implementation\n    return { temperature: 72, condition: 'sunny' };\n  },\n});\n\n// Tools are automatically available to AI agents\nconst response = await ai.sendMessage({\n  conversationId: conversation.id,\n  message: 'What is the weather in San Francisco?',\n});\n```\n\n## REST API Endpoints\n\nWhen used with `@objectstack/rest`, the following endpoints are auto-generated:\n\n```\nPOST   /api/v1/ai/conversations          # Create conversation\nGET    /api/v1/ai/conversations/:id      # Get conversation\nPOST   /api/v1/ai/conversations/:id/messages  # Send message\nGET    /api/v1/ai/conversations/:id/stream    # Stream response (SSE)\nGET    /api/v1/ai/tools                  # List available tools\nPOST   /api/v1/ai/tools/:name/execute    # Execute a tool\n```\n\n## Multi-Model Configuration\n\n```typescript\nimport { openai } from '@ai-sdk/openai';\nimport { anthropic } from '@ai-sdk/anthropic';\nimport { google } from '@ai-sdk/google';\n\nconst stack = defineStack({\n  services: [\n    ServiceAI.configure({\n      models: {\n        // Fast model for simple tasks\n        fast: openai('gpt-3.5-turbo'),\n\n        // Default model for general use\n        default: openai('gpt-4'),\n\n        // Advanced reasoning\n        reasoning: openai('gpt-4-turbo'),\n\n        // Anthropic Claude\n        claude: anthropic('claude-3-opus-20240229'),\n\n        // Google Gemini\n        gemini: google('gemini-pro'),\n      },\n      defaultModel: 'default',\n    }),\n  ],\n});\n```\n\n## Advanced Features\n\n### Custom System Prompts\n\n```typescript\nconst conversation = await ai.createConversation({\n  model: 'default',\n  systemPrompt: `You are an expert in ObjectStack.\n    Answer questions about the framework accurately and concisely.\n    Always provide code examples when relevant.`,\n});\n```\n\n### Conversation History\n\n```typescript\n// Get full conversation history\nconst history = await ai.getConversationHistory(conversationId);\n\n// Clear conversation history\nawait ai.clearConversation(conversationId);\n\n// Delete conversation\nawait ai.deleteConversation(conversationId);\n```\n\n### Error Handling\n\n```typescript\ntry {\n  const response = await ai.sendMessage({\n    conversationId: conversation.id,\n    message: 'Hello',\n  });\n} catch (error) {\n  if (error.code === 'RATE_LIMIT_EXCEEDED') {\n    // Handle rate limiting\n  } else if (error.code === 'MODEL_NOT_FOUND') {\n    // Handle missing model\n  }\n}\n```\n\n## Integration with ObjectStack Agents\n\nThe AI service integrates with the `@objectstack/spec/ai` agent protocol:\n\n```typescript\nimport { defineAgent } from '@objectstack/spec';\n\nconst myAgent = defineAgent({\n  name: 'code_assistant',\n  model: 'default',\n  systemPrompt: 'You help write ObjectStack code.',\n  tools: ['create_object', 'create_field', 'create_view'],\n});\n\n// Agent automatically uses the AI service\nconst result = await myAgent.execute({\n  input: 'Create a contact object with name and email fields',\n});\n```\n\n## Architecture\n\nThe service follows a layered architecture:\n\n1. **Adapter Layer**: Abstracts different LLM providers (OpenAI, Anthropic, Google)\n2. **Conversation Manager**: Handles conversation state and history\n3. **Tool Registry**: Manages tool registration and execution\n4. **REST Routes**: Auto-generated HTTP endpoints\n5. **SSE Streaming**: Real-time streaming support\n\n## Contract Implementation\n\nImplements `IAIService` from `@objectstack/spec/contracts`:\n\n```typescript\ninterface IAIService {\n  createConversation(options: ConversationOptions): Promise<Conversation>;\n  sendMessage(options: MessageOptions): Promise<Message>;\n  streamMessage(options: MessageOptions): AsyncIterable<MessageChunk>;\n  registerTool(tool: AITool): void;\n  getConversationHistory(conversationId: string): Promise<Message[]>;\n  deleteConversation(conversationId: string): Promise<void>;\n}\n```\n\n## Performance Considerations\n\n- **Streaming**: Use streaming for long responses to improve perceived performance\n- **Model Selection**: Choose appropriate models based on task complexity\n- **Conversation History**: Limit history length to control token usage\n- **Caching**: Provider-level caching is handled automatically\n\n## Best Practices\n\n1. **Model Selection**: Use fast models for simple tasks, advanced models for complex reasoning\n2. **System Prompts**: Provide clear, specific instructions in system prompts\n3. **Tool Design**: Keep tools focused and well-documented\n4. **Error Handling**: Always handle rate limits and API errors gracefully\n5. **Streaming**: Use streaming for better UX on long-running queries\n\n## License\n\nApache-2.0. See [LICENSING.md](../../../LICENSING.md).\n\n## See Also\n\n- [Vercel AI SDK Documentation](https://sdk.vercel.ai/docs)\n- [@objectstack/spec/ai Protocol](../../spec/src/ai/)\n- [AI Agent Guide](/content/docs/guides/ai/)\n","readmeFilename":"README.md"}