/**
 * Complete Workflow Example - All Features Combined
 *
 * This comprehensive example demonstrates using multiple features together:
 * - Knowledge base (RAG)
 * - Memory (conversation context)
 * - Tools (local functions)
 * - Structured outputs (Zod)
 * - RAI guardrails (safety)
 * - Streaming
 */

import { z } from 'zod';
import { v4 as uuidv4 } from 'uuid';
import { Studio } from '../src';

// Define response schema for structured outputs
const AnalysisSchema = z.object({
  summary: z.string(),
  keyPoints: z.array(z.string()),
  sentiment: z.enum(['positive', 'negative', 'neutral']),
  confidence: z.number().min(0).max(1)
});

type Analysis = z.infer<typeof AnalysisSchema>;

// Local tools
function searchDatabase(query: string): string {
  // Simulate database search
  return `Found 5 results for "${query}": [result1, result2, result3, result4, result5]`;
}

function calculateMetrics(data: string): string {
  // Simulate metric calculation
  return `Metrics calculated: 42 items processed, 98.5% success rate`;
}

async function main() {
  const studio = new Studio({
    apiKey: process.env.LYZR_API_KEY
  });

  try {
    console.log('🚀 Building complete enterprise workflow\n');

    // Step 1: Create Knowledge Base
    console.log('Step 1: Setting up Knowledge Base');
    const kb = await studio.createKnowledgeBase({
      name: 'company-knowledge',
      vectorStore: 'qdrant',
      description: 'Company documents and FAQs'
    });
    console.log(`✓ Knowledge base created\n`);

    // Add content
    await kb.addText(
      'Our company processes 1M transactions daily with 99.99% uptime SLA.',
      'company-facts'
    );

    // Step 2: Create Context
    console.log('Step 2: Setting up Global Context');
    const context = await studio.createContext(
      'business-rules',
      'Always prioritize customer satisfaction. Follow GDPR compliance. Report issues within 2 hours.'
    );
    console.log(`✓ Context created\n`);

    // Step 3: Create RAI Policy
    console.log('Step 3: Setting up Safety Policy');
    const policy = await studio.createRAIPolicy({
      name: 'Enterprise Safety',
      description: 'Production-grade safety policy',
      toxicityCheck: { enabled: true, threshold: 0.3 },
      piiDetection: {
        EMAIL: 'redact',
        PHONE_NUMBER: 'redact'
      }
    });
    console.log(`✓ Safety policy created\n`);

    // Step 4: Create Agent with All Features
    console.log('Step 4: Creating Advanced Agent');
    const agent = await studio.createAgent({
      name: 'Enterprise Assistant',
      provider: 'gpt-4o',
      role: 'Enterprise business assistant',
      goal: 'Provide data-driven insights with safety',
      instructions: `
        You are an enterprise assistant that:
        1. Uses the knowledge base for company information
        2. Follows business rules and compliance
        3. Calls available tools when needed
        4. Provides structured analysis
        5. Maintains conversation context
      `,
      temperature: 0.7,
      memory: 50,           // Remember last 50 messages
      responseModel: AnalysisSchema,  // Structured outputs
      raiPolicy: policy     // Safety guardrails
    });

    console.log(`✓ Agent created\n`);

    // Step 5: Add Tools
    console.log('Step 5: Registering Tools');
    await agent.addTool(searchDatabase);
    await agent.addTool(calculateMetrics);
    console.log(`✓ ${agent.listTools().length} tools registered\n`);

    // Step 6: Add Context
    console.log('Step 6: Adding Context');
    await agent.addContext(context);
    console.log(`✓ Context added\n`);

    // Step 7: Run Multi-turn Conversation
    console.log('Step 7: Running Multi-turn Conversation\n');
    const sessionId = uuidv4();

    const prompts = [
      'Hi, I need help analyzing our transaction data',
      'Can you search the database for high-value transactions?',
      'What metrics should we track?'
    ];

    for (const prompt of prompts) {
      console.log(`User: ${prompt}`);

      const response = await agent.run(prompt, {
        sessionId,
        knowledgeBases: [kb]
      });

      // Response is typed as Analysis due to responseModel
      const analysis = response as unknown as Analysis;

      if (typeof analysis === 'object' && analysis !== null && 'summary' in analysis) {
        console.log(`Assistant: ${(analysis as Analysis).summary}`);
        console.log(`Sentiment: ${(analysis as Analysis).sentiment}`);
      } else {
        console.log(`Assistant: ${(response as any).response}`);
      }
      console.log();
    }

    // Step 8: Streaming Example
    console.log('Step 8: Streaming Response\n');
    console.log('User: Tell me about our operational excellence');
    console.log('Assistant (streaming): ');

    let charCount = 0;
    for await (const chunk of await agent.run(
      'Give a detailed report about our operational performance',
      { sessionId, stream: true }
    )) {
      process.stdout.write(chunk.content);
      charCount += chunk.content.length;
    }

    console.log(`\n(${charCount} characters)\n`);

    // Step 9: Summary and Cleanup
    console.log('Step 9: Cleanup\n');
    await agent.delete();
    await kb.delete();
    await context.delete();
    await policy.delete();
    console.log('✓ All resources cleaned up\n');

    console.log('✨ Complete workflow example finished successfully!');
  } catch (error) {
    console.error('Error:', error);
    process.exit(1);
  }
}

main();