import { DevlogManager } from '@codervisor/devlog-core';

// Initialize the manager
const devlog = new DevlogManager({
  workspaceRoot: '/path/to/your/project',
  // devlogDir: '/custom/path/.devlog' // optional custom directory
});

// Create a new devlog entry
const entry = await devlog.createDevlog({
  title: 'Implement user authentication',
  type: 'feature',
  description: 'Add JWT-based authentication system',
  priority: 'high',
  businessContext: 'Users need secure login to access protected feature',
  technicalContext: 'Using JWT tokens with refresh mechanism',
  acceptanceCriteria: [
    'Users can register with email/password',
    'Users can login and receive JWT token',
    'Protected routes require valid token',
  ],
});

// Update the devlog
await devlog.updateDevlog({
  id: entry.id,
  status: 'in-progress',
  progress: 'Completed user registration endpoint',
});

// Add a note
await devlog.addNote(entry.id, {
  category: 'progress',
  content: 'Fixed validation issues with email format',
});

// List all devlog
const allDevlogs = await devlog.listDevlogs();

// Filter devlog
const inProgressTasks = await devlog.listDevlogs({
  status: ['in-progress'],
  type: ['feature', 'bugfix'],
});

// Search devlog
const authDevlogs = await devlog.searchDevlogs('authentication');

// Get active context for AI assistants
const activeContext = await devlog.getActiveContext(5);

// Complete a devlog
await devlog.completeDevlog(entry.id, 'Authentication system implemented and tested');

import { createLLMServiceFromEnv, LLMService } from '@codervisor/devlog-core/server';

// Create service from environment variables
const llmService = createLLMServiceFromEnv();
await llmService.initialize();

// Simple completion
const response = await llmService.simpleCompletion(
  'Explain what a devlog is in software development.'
);

// Advanced completion with custom configuration
const completion = await llmService.generateCompletion({
  messages: [
    { role: 'system', content: 'You are a helpful assistant.' },
    { role: 'user', content: 'What are the benefits of TypeScript?' }
  ],
  model: {
    provider: 'openai',
    model: 'gpt-4',
    maxTokens: 500,
    temperature: 0.3,
  }
});

// Streaming completion
for await (const chunk of llmService.generateStreamingCompletion(request)) {
  if (chunk.content) {
    process.stdout.write(chunk.content);
  }
  if (chunk.isComplete) break;
}

// Single text embedding
const embedding = await llmService.simpleEmbed(
  'Text to convert to vector embedding'
);

// Multiple text embeddings
const embeddings = await llmService.simpleEmbedMany([
  'First text to embed',
  'Second text to embed',
  'Third text to embed'
]);

// Advanced embedding with custom model
const response = await llmService.embed({
  text: 'Advanced text for embedding',
  model: 'text-embedding-3-large',
  metadata: { source: 'documentation' }
});

// Calculate similarity between embeddings
function cosineSimilarity(vecA: number[], vecB: number[]): number {
  let dotProduct = 0;
  let normA = 0;
  let normB = 0;
  
  for (let i = 0; i < vecA.length; i++) {
    dotProduct += vecA[i] * vecB[i];
    normA += vecA[i] * vecA[i];
    normB += vecB[i] * vecB[i];
  }
  
  return dotProduct / (Math.sqrt(normA) * Math.sqrt(normB));
}

const similarity = cosineSimilarity(embeddings[0], embeddings[1]);

const service = new LLMService({
  apiKey: 'your-api-key',
  baseURL: 'https://api.openai.com/v1', // or Azure endpoint
  defaultModel: 'gpt-4',
  defaultEmbeddingModel: 'text-embedding-3-small',
  organization: 'your-org', // OpenAI only
  apiVersion: '2023-12-01-preview', // Azure only
  defaultModelConfig: {
    maxTokens: 1000,
    temperature: 0.7,
  }
});

new DevlogManager(options?: DevlogManagerOptions)