stacks-ai

# Stacks AI Comprehensive AI/LLM integration with 4 provider drivers, image generation, vision, RAG, MCP support, and personalization. ## Key Paths - Core package: `storage/framework/core/ai/src/` - Configuration: `config/ai.ts` ## Source Files ``` ai/src/ ├── drivers/ │ ├── anthropic.ts # Claude driver │ ├── openai.ts # GPT + DALL-E + Whisper + TTS │ ├── ollama.ts # Local LLM driver │ └── bedrock.ts # AWS Bedrock utilities ├── image.ts # Image generation & vision ├── search.ts # RAG, embeddings, vector index ├── mcp.ts # Model Context Protocol client ├── personalization.ts # Sentiment, classification, recommendations ├── buddy.ts # AI coding assistant ├── claude-agent.ts # Claude CLI agent (local & EC2) ├── claude-agent-sdk.ts # Claude Agent SDK driver └── text.ts # Bedrock text summarization ``` ## Anthropic Driver ```typescript import { anthropic } from '@stacksjs/ai' anthropic.configure({ apiKey: '...', model: 'claude-sonnet-4-20250514', maxTokens: 4096 }) const result = await anthropic.chat([{ role: 'user', content: 'Hello' }]) const stream = await anthropic.streamChat(messages, options) const response = await anthropic.prompt('Summarize this text...') const tokens = anthropic.estimateTokens(text) ``` ## OpenAI Driver ```typescript import { openai } from '@stacksjs/ai' openai.configure({ apiKey: '...', model: 'gpt-4o', embeddingModel: 'text-embedding-3-small' }) const result = await openai.chat(messages, { temperature: 0.7 }) const stream = await openai.streamChat(messages) const embeddings = await openai.embed('text to embed') const image = await openai.generateImage('a sunset over mountains') const transcription = await openai.transcribe(audioFile) const speech = await openai.textToSpeech('Hello world') ``` ## Provider-Neutral Client Use the config-driven client for application features that can run against Anthropic, OpenAI, or Ollama. Configuration inspection is safe to return from a status endpoint because it never includes credentials. ```typescript import { createAIClient, getAIProviderConfiguration } from '@stacksjs/ai' import aiConfig from './config/ai' const configuration = getAIProviderConfiguration(aiConfig) // { provider: 'openai', model: 'gpt-4o-mini', configured: true, source: 'environment' } if (configuration.configured) { const client = createAIClient(aiConfig) const result = await client.generate([{ role: 'user', content: 'Draft a launch plan.' }]) } ``` ## Ollama Driver (Local LLMs) ```typescript import { ollama } from '@stacksjs/ai' ollama.configure({ host: 'http://localhost:11434', model: 'llama3' }) const result = await ollama.chat(messages) const stream = await ollama.streamChat(messages) const text = await ollama.generate('Write a poem') const embeddings = await ollama.embed('text') const models = await ollama.listModels() await ollama.pullModel('llama3', (progress) => console.log(progress)) await ollama.deleteModel('old-model') const info = await ollama.showModel('llama3') const running = await ollama.isRunning() ``` ## Image Generation ```typescript import { generateImage, editImage, createImageVariation, analyzeImage, analyzeImages } from '@stacksjs/ai' // Generate (DALL-E 3) const result = await generateImage('a cat in space', { model: 'dall-e-3', size: '1024x1024', quality: 'hd', style: 'vivid', n: 1 }) // Edit (DALL-E 2) await editImage(imageInput, 'add a hat', { mask: maskInput }) // Variations await createImageVariation(imageInput, { n: 3 }) // Vision (GPT-4 Vision / Claude) const analysis = await analyzeImage({ url: 'https://...' }, 'What is in this image?') const multiAnalysis = await analyzeImages([img1, img2], 'Compare these') ``` Image inputs: `{ url: string }`, `{ base64: string }`, `{ file: string }` (auto-converted) ## RAG & Vector Search ```typescript import { createEmbedding, rag, VectorIndex, chunkText, indexText } from '@stacksjs/ai' // Create embeddings const embedding = await createEmbedding('text to embed') // Vector similarity cosineSimilarity(vecA, vecB) dotProduct(vecA, vecB) euclideanDistance(vecA, vecB) // Text chunking const chunks = chunkText(longText, { chunkSize: 500, overlap: 50 }) // Build index const index = await indexText(text, { chunkSize: 500 }) // RAG query const answer = await rag('What is X?', index, { model: 'claude-sonnet-4-20250514', maxTokens: 1000 }) // VectorIndex class const idx = new VectorIndex({ dimensions: 1536 }) await idx.add([{ id: '1', content: 'Hello', metadata: {} }]) const results = await idx.search('greeting', 5) const results2 = await idx.searchByVector(queryEmbedding, 5) idx.remove('1') idx.clear() idx.size // number of documents idx.ids // all document IDs ``` ## MCP (Model Context Protocol) ```typescript import { MCPClient, MCPManager, connectStdio, connectHTTP } from '@stacksjs/ai' // Single server const client = new MCPClient({ name: 'my-server', transport: 'stdio', command: 'npx', args: ['my-mcp-server'] }) await client.connect() const tools = await client.listTools() const resources = await client.listResources() const prompts = await client.listPrompts() const result = await client.callTool('tool-name', { arg: 'value' }) const resource = await client.readResource('resource://path') const prompt = await client.getPrompt('prompt-name', { arg: 'value' }) const anthropicTools = client.toAnthropicTools() const openaiTools = client.toOpenAITools() // Multiple servers const manager = new MCPManager() manager.addServer({ name: 'server1', transport: 'stdio', command: '...' }) manager.addServer({ name: 'server2', transport: 'streamable-http', url: '...' }) const allTools = await manager.getAllTools() await manager.callTool('server1/tool-name', args) await manager.disconnectAll() // Convenience functions const client = await connectStdio('name', 'command', ['args']) const client = await connectHTTP('name', 'https://server.com', headers) ``` Transport types: `'stdio'` | `'sse'` | `'streamable-http'` ## Personalization ```typescript import { analyzeSentiment, classifyText, summarize, recommend, createProfile } from '@stacksjs/ai' const sentiment = await analyzeSentiment('I love this product!') // { sentiment: 'positive', score: 0.95, confidence: 0.98, aspects: [...] } const classification = await classifyText('Fix the login bug', ['bug', 'feature', 'question']) // { label: 'bug', confidence: 0.92, allLabels: [...] } const summary = await summarize(longText, { maxLength: 100, style: 'bullet' }) const profile = createProfile('user-123', ['tech', 'gaming']) recordInteraction(profile, { type: 'view', itemId: 'article-1', weight: 1.0 }) const recs = await recommend(profile, contentItems, { limit: 10 }) const interests = await extractUserInterests(profile, items) ``` ## Buddy AI Assistant ```typescript import { processCommand, buddyProcessStreaming, buddyStreamSimple, getAvailableDrivers } from '@stacksjs/ai' const drivers = getAvailableDrivers() // ['anthropic', 'openai', 'ollama'] const context = await getRepoContext('/path/to/repo') await processCommand('Add error handling to auth.ts', 'anthropic') // Streaming for await (const chunk of buddyProcessStreaming('Refactor this function', 'openai', history)) { process.stdout.write(chunk) } ``` ## Compact Project Context Use the native context contract instead of sending dependency internals, lockfiles, or an arbitrary repository dump to a coding model: ```bash buddy ai:context buddy ai:context --json buddy ai:context --json --output storage/framework/runtime/ai-context.json buddy ai:context --max-chars 4000 --model claude-sonnet-4 ``` Programmatic callers use `buildProjectContext(projectRoot, options)`. Existing `getRepoContext(projectRoot)` calls return its compact text payload. The contract is deterministic, reports a heuristic token estimate, and describes canonical Model-View-Action roles, application overrides, instruction files, scripts, dependency names, and representative application files. It excludes `node_modules`, build output, caches, lockfiles, environment files, credentials, private keys, and secret files by default. The character budget applies to the prompt payload. JSON adds a versioned metadata envelope for tools. Token estimates are planning heuristics, not provider billing counts or evidence that model output is correct. ## Claude Agent ```typescript import { createClaudeLocalAgent, createClaudeEC2Agent } from '@stacksjs/ai' const agent = createClaudeLocalAgent({ cwd: '/project' }) for await (const chunk of agent.processCommandStreaming('Fix the tests')) { process.stdout.write(chunk) } // Remote EC2 agent const remoteAgent = createClaudeEC2Agent({ ec2Host: '...', ec2User: '...', ec2Key: '...' }) ``` ## Claude Agent SDK ```typescript import { createClaudeAgentSDKDriver } from '@stacksjs/ai' const driver = createClaudeAgentSDKDriver({ maxTurns: 10, cwd: '/project', permissionMode: 'auto', allowedTools: ['Read', 'Write', 'Edit', 'Bash'], customSystemPrompt: 'You are a Stacks expert' }) for await (const chunk of driver.processStreaming('Build a user registration flow')) { process.stdout.write(chunk) } const sessionId = driver.getLastSessionId() await driver.resumeSession(sessionId, 'Add validation') ``` ## config/ai.ts ```typescript { default: 'meta.llama2-70b-chat-v1', models: ['meta.llama2-70b-chat-v1', ...], // AWS Bedrock models deploy: true } ``` ## Gotchas - API keys should be in `.env` — `ANTHROPIC_API_KEY`, `OPENAI_API_KEY` - Ollama requires a local server running on port 11434 - Image generation uses OpenAI DALL-E by default - Vision supports both GPT-4V and Claude models - VectorIndex is in-memory — not persisted between restarts - RAG combines chunking + embedding + vector search + LLM generation - MCP supports stdio (subprocess), SSE, and HTTP transports - The buddy assistant has git integration (commit, push, apply changes) - Claude Agent SDK wraps the Claude Code CLI for agentic workflows - Bedrock utilities are for AWS-hosted model invocation - Sentiment/classification use AI models — they're not rule-based