Vibe Coding Documentation MCP (MUSE)

import Anthropic from '@anthropic-ai/sdk'; import { logger } from './logger.js'; const aiLogger = logger.child({ tool: 'ai' }); let anthropicClient = null; /** * Initialize the Anthropic client */ export function initializeAI() { const apiKey = process.env.ANTHROPIC_API_KEY; if (!apiKey) { aiLogger.warn('ANTHROPIC_API_KEY not set. AI features will be disabled.'); return false; } try { anthropicClient = new Anthropic({ apiKey }); aiLogger.info('Anthropic client initialized successfully'); return true; } catch (error) { aiLogger.error('Failed to initialize Anthropic client', error); return false; } } /** * Check if AI features are available */ export function isAIAvailable() { return anthropicClient !== null; } /** * Get the Anthropic client instance */ export function getAnthropicClient() { return anthropicClient; } /** * Use Claude AI to analyze conversation and extract design decisions */ export async function analyzeWithAI(conversationLog, options = {}) { if (!anthropicClient) { throw new Error('Anthropic client not initialized. Set ANTHROPIC_API_KEY environment variable.'); } const { projectContext, language = 'en', maxDecisions = 10 } = options; const systemPrompt = language === 'ko' ? `당신은 소프트웨어 설계 결정을 분석하는 전문가입니다. 대화 로그를 분석하여 설계 결정을 추출하고 구조화된 JSON으로 반환하세요. 반드시 다음 JSON 형식으로만 응답하세요: { "decisions": [ { "title": "결정 제목", "description": "상세 설명", "rationale": "결정 이유", "alternatives": ["고려된 대안들"], "category": "architecture|implementation|library|pattern|other", "importance": "high|medium|low", "tradeoffs": ["트레이드오프"], "relatedTopics": ["관련 주제"] } ], "summary": "전체 요약", "insights": ["핵심 인사이트"], "recommendations": ["추천 사항"] }` : `You are an expert at analyzing software design decisions. Analyze the conversation log and extract design decisions in structured JSON format. Respond ONLY with JSON in this exact format: { "decisions": [ { "title": "Decision title", "description": "Detailed description", "rationale": "Reasoning behind the decision", "alternatives": ["Considered alternatives"], "category": "architecture|implementation|library|pattern|other", "importance": "high|medium|low", "tradeoffs": ["Trade-offs"], "relatedTopics": ["Related topics"] } ], "summary": "Overall summary", "insights": ["Key insights"], "recommendations": ["Recommendations"] }`; const userPrompt = projectContext ? `Project Context: ${projectContext}\n\nConversation Log:\n${conversationLog}\n\nExtract up to ${maxDecisions} design decisions.` : `Conversation Log:\n${conversationLog}\n\nExtract up to ${maxDecisions} design decisions.`; try { aiLogger.info('Sending request to Claude API', { logLength: conversationLog.length, language, maxDecisions }); const response = await anthropicClient.messages.create({ model: 'claude-sonnet-4-20250514', max_tokens: 4096, system: systemPrompt, messages: [ { role: 'user', content: userPrompt } ] }); const content = response.content[0]; if (content.type !== 'text') { throw new Error('Unexpected response type from Claude API'); } // Parse JSON response const jsonMatch = content.text.match(/\{[\s\S]*\}/); if (!jsonMatch) { throw new Error('Failed to extract JSON from Claude response'); } const result = JSON.parse(jsonMatch[0]); aiLogger.info('AI analysis completed', { decisionsCount: result.decisions.length, hasInsights: result.insights?.length > 0 }); return result; } catch (error) { aiLogger.error('AI analysis failed', error); throw error; } } /** * Generate a summary using AI */ export async function generateAISummary(content, options) { if (!anthropicClient) { throw new Error('Anthropic client not initialized. Set ANTHROPIC_API_KEY environment variable.'); } const { type, language = 'en', maxLength = 500 } = options; const prompts = { code: language === 'ko' ? `다음 코드를 분석하고 ${maxLength}자 이내로 요약하세요. 주요 기능, 구조, 패턴을 설명하세요.` : `Analyze the following code and summarize in ${maxLength} characters or less. Explain key functionality, structure, and patterns.`, conversation: language === 'ko' ? `다음 대화를 분석하고 ${maxLength}자 이내로 요약하세요. 주요 결정사항과 논의 내용을 포함하세요.` : `Analyze the following conversation and summarize in ${maxLength} characters or less. Include key decisions and discussion points.`, document: language === 'ko' ? `다음 문서를 ${maxLength}자 이내로 요약하세요.` : `Summarize the following document in ${maxLength} characters or less.` }; try { const response = await anthropicClient.messages.create({ model: 'claude-sonnet-4-20250514', max_tokens: 1024, messages: [ { role: 'user', content: `${prompts[type]}\n\n${content}` } ] }); const textContent = response.content[0]; if (textContent.type !== 'text') { throw new Error('Unexpected response type'); } return textContent.text; } catch (error) { aiLogger.error('AI summary generation failed', error); throw error; } } /** * Analyze code and generate insights using AI */ export async function analyzeCodeWithAI(code, options = {}) { if (!anthropicClient) { throw new Error('Anthropic client not initialized. Set ANTHROPIC_API_KEY environment variable.'); } const { language = 'unknown', analysisType = 'all', outputLanguage = 'en' } = options; const systemPrompt = outputLanguage === 'ko' ? `당신은 코드 분석 전문가입니다. 코드를 분석하고 JSON 형식으로 결과를 반환하세요. JSON 형식: { "summary": "코드 요약", "issues": [{"type": "타입", "severity": "high|medium|low", "description": "설명", "line": 라인번호}], "suggestions": ["개선 제안"], "metrics": {"복잡도": "값", "라인수": 숫자} }` : `You are a code analysis expert. Analyze the code and return results in JSON format. JSON format: { "summary": "Code summary", "issues": [{"type": "type", "severity": "high|medium|low", "description": "description", "line": lineNumber}], "suggestions": ["improvement suggestions"], "metrics": {"complexity": "value", "lines": number} }`; try { const response = await anthropicClient.messages.create({ model: 'claude-sonnet-4-20250514', max_tokens: 2048, system: systemPrompt, messages: [ { role: 'user', content: `Language: ${language}\nAnalysis type: ${analysisType}\n\nCode:\n\`\`\`${language}\n${code}\n\`\`\`` } ] }); const textContent = response.content[0]; if (textContent.type !== 'text') { throw new Error('Unexpected response type'); } const jsonMatch = textContent.text.match(/\{[\s\S]*\}/); if (!jsonMatch) { throw new Error('Failed to extract JSON from response'); } return JSON.parse(jsonMatch[0]); } catch (error) { aiLogger.error('AI code analysis failed', error); throw error; } } //# sourceMappingURL=ai.js.map