@clduab11/gemini-flow/src/core/model-orchestrator.ts

Revolutionary AI agent swarm coordination platform with Google Services integration, multimedia processing, and production-ready monitoring. Features 8 Google AI services, quantum computing capabilities, and enterprise-grade security.

/**
 * Multi-Model Orchestration Engine
 *
 * Intelligent routing between Google AI models with <100ms overhead
 * Supports Gemini 2.0 Flash, DeepMind 2.5, and Vertex AI models
 */

import { GoogleGenerativeAI, GenerativeModel } from "@google/generative-ai";
import { Logger } from "../utils/logger.js";
import { PerformanceMonitor } from "./performance-monitor.js";
import { AuthenticationManager } from "./auth-manager.js";
import { ModelRouter } from "./model-router.js";
import { CacheManager } from "./cache-manager.js";
import { EventEmitter } from "events";
import { safeImport } from "../utils/feature-detection.js";

export interface ModelConfig {
  name: string;
  endpoint?: string;
  apiKey?: string;
  projectId?: string;
  location?: string;
  tier: "free" | "pro" | "enterprise";
  capabilities: string[];
  latencyTarget: number; // ms
  costPerToken: number;
  maxTokens: number;
}

export interface RoutingContext {
  task: string;
  userTier: "free" | "pro" | "enterprise";
  priority: "low" | "medium" | "high" | "critical";
  latencyRequirement: number; // ms
  tokenBudget?: number;
  capabilities?: string[];
  previousModel?: string;
  retryCount?: number;
}

export interface ModelResponse {
  modelUsed: string;
  content: string;
  latency: number;
  tokenUsage: {
    input: number;
    output: number;
    total: number;
  };
  cost: number;
  cached: boolean;
  metadata: any;
}

export class ModelOrchestrator extends EventEmitter {
  private models: Map<string, ModelConfig> = new Map();
  private clients: Map<string, any> = new Map();
  private router: ModelRouter;
  private auth: AuthenticationManager;
  private performance: PerformanceMonitor;
  private cache: CacheManager;
  private logger: Logger;

  // Performance tracking
  private metrics = {
    totalRequests: 0,
    routingTime: 0,
    modelSwitches: 0,
    cacheHits: 0,
    failovers: 0,
    tierUpgrades: 0,
  };

  constructor(config?: { cacheSize?: number; performanceThreshold?: number }) {
    super();
    this.logger = new Logger("ModelOrchestrator");
    this.performance = new PerformanceMonitor();
    this.cache = new CacheManager({ maxMemorySize: config?.cacheSize || 1000 });
    this.auth = new AuthenticationManager();
    this.router = new ModelRouter();

    this.initializeDefaultModels();
    this.setupPerformanceMonitoring();
  }

  /**
   * Initialize with default Google AI models
   */
  private initializeDefaultModels(): void {
    // Gemini 2.0 Flash - Fast and efficient
    this.addModel({
      name: "gemini-2.0-flash",
      tier: "free",
      capabilities: ["text", "code", "reasoning", "multimodal"],
      latencyTarget: 800,
      costPerToken: 0.000001,
      maxTokens: 1000000,
    });

    // Gemini 2.5 Flash - Enhanced performance and efficiency
    this.addModel({
      name: "gemini-2.5-flash",
      tier: "pro",
      capabilities: ["text", "code", "reasoning", "multimodal", "fast"],
      latencyTarget: 600,
      costPerToken: 0.0000006,
      maxTokens: 1000000,
    });

    // Gemini 2.0 Flash Thinking - Advanced reasoning
    this.addModel({
      name: "gemini-2.0-flash-thinking",
      tier: "pro",
      capabilities: ["text", "code", "advanced-reasoning", "multimodal"],
      latencyTarget: 1200,
      costPerToken: 0.000002,
      maxTokens: 1000000,
    });

    // Gemini 2.5 Pro - Enhanced capabilities
    this.addModel({
      name: "gemini-2.5-pro",
      tier: "enterprise",
      capabilities: [
        "text",
        "code",
        "advanced-reasoning",
        "multimodal",
        "long-context",
      ],
      latencyTarget: 1000,
      costPerToken: 0.0000012,
      maxTokens: 2000000,
    });

    // Gemini 2.5 Deep Think - Ultra tier only (Coming Soon)
    this.addModel({
      name: "gemini-2.5-deep-think",
      tier: "enterprise", // Note: Actually Ultra tier, but using enterprise as closest
      capabilities: [
        "text",
        "code",
        "multi-agent",
        "deep-reasoning",
        "complex-problem-solving",
      ],
      latencyTarget: 5000, // Longer for deep reasoning
      costPerToken: 0.000005, // Premium pricing
      maxTokens: 2000000,
    });

    // Vertex AI Gemini Pro
    this.addModel({
      name: "gemini-pro-vertex",
      tier: "enterprise",
      capabilities: [
        "text",
        "code",
        "reasoning",
        "multimodal",
        "enterprise-security",
      ],
      latencyTarget: 1000,
      costPerToken: 0.000003,
      maxTokens: 1000000,
    });

    this.logger.info("Default models initialized", {
      modelCount: this.models.size,
    });
  }

  /**
   * Add a new model configuration
   */
  addModel(config: ModelConfig): void {
    this.models.set(config.name, config);
    this.initializeModelClient(config);

    this.logger.info("Model added", {
      name: config.name,
      tier: config.tier,
      capabilities: config.capabilities,
    });
  }

  /**
   * Initialize model client based on configuration
   */
  private async initializeModelClient(config: ModelConfig): Promise<void> {
    try {
      if (config.name.includes("vertex")) {
        // Vertex AI client with conditional import
        const googleAuth = await safeImport("google-auth-library");
        if (!googleAuth?.GoogleAuth) {
          throw new Error("Google Auth Library not available for Vertex AI");
        }

        const auth = new googleAuth.GoogleAuth({
          scopes: ["https://www.googleapis.com/auth/cloud-platform"],
        });

        const client = {
          type: "vertex",
          auth,
          projectId: config.projectId,
          location: config.location || "us-central1",
        };

        this.clients.set(config.name, client);
      } else {
        // Standard Gemini API client
        const genAI = new GoogleGenerativeAI(
          config.apiKey || process.env.GOOGLE_AI_API_KEY!,
        );
        const model = genAI.getGenerativeModel({ model: config.name });

        this.clients.set(config.name, {
          type: "gemini",
          client: genAI,
          model,
        });
      }

      this.logger.debug("Model client initialized", { name: config.name });
    } catch (error) {
      this.logger.error("Failed to initialize model client", {
        name: config.name,
        error,
      });
    }
  }

  /**
   * Main orchestration method - route request to optimal model
   */
  async orchestrate(
    prompt: string,
    context: RoutingContext,
  ): Promise<ModelResponse> {
    const startTime = performance.now();
    this.metrics.totalRequests++;

    // 1. Authenticate and determine user tier
    const userTier = await this.auth.determineUserTier();
    const contextWithTier = {
      ...context,
      userTier: ((userTier as any).tier || userTier) as
        | "free"
        | "pro"
        | "enterprise",
    };

    try {
      // 2. Route to optimal model with smart routing engine
      const routingStart = performance.now();
      const routingDecision = await this.router.selectOptimalModel(
        contextWithTier,
        this.models,
      );
      const routingTime = performance.now() - routingStart;

      this.metrics.routingTime += routingTime;

      // Target: <75ms routing overhead (improved from 100ms)
      if (routingTime > 75) {
        this.logger.warn("Smart routing overhead exceeded target", {
          routingTime,
          target: 75,
          decision: routingDecision,
        });
      }

      const selectedModel = routingDecision.modelName;

      // 3. Check cache first
      const cacheKey = this.generateCacheKey(
        prompt,
        selectedModel,
        contextWithTier,
      );
      const cachedResponse = await this.cache.get(cacheKey);

      if (cachedResponse) {
        this.metrics.cacheHits++;
        return { ...cachedResponse, cached: true };
      }

      // 4. Execute request with selected model
      const response = await this.executeWithModel(
        selectedModel,
        prompt,
        contextWithTier,
      );

      // 5. Cache successful responses
      if (response && !response.content.includes("error")) {
        await this.cache.set(cacheKey, response, 3600); // 1 hour TTL
      }

      // 6. Update performance metrics
      const totalLatency = performance.now() - startTime;
      this.performance.recordMetric("orchestration_latency", totalLatency);
      this.performance.recordMetric("routing_overhead", routingTime);

      // 7. Record performance for smart routing
      this.router.recordPerformance(
        selectedModel,
        response.latency,
        true, // success
        response.cost,
        response.tokenUsage,
      );

      // 8. Emit events for monitoring
      this.emit("request_completed", {
        model: selectedModel,
        latency: totalLatency,
        routingTime,
        userTier: contextWithTier.userTier,
        cached: false,
        routingDecision,
      });

      return response;
    } catch (error) {
      this.logger.error("Orchestration failed", {
        error,
        context: contextWithTier,
      });

      // Attempt failover
      if (contextWithTier.retryCount < 2) {
        const retryContext = {
          ...contextWithTier,
          retryCount: (contextWithTier.retryCount || 0) + 1,
        };
        this.metrics.failovers++;
        return this.orchestrate(prompt, retryContext);
      }

      throw error;
    }
  }

  /**
   * Execute request with specific model
   */
  private async executeWithModel(
    modelName: string,
    prompt: string,
    context: RoutingContext,
  ): Promise<ModelResponse> {
    const startTime = performance.now();
    const modelConfig = this.models.get(modelName);
    const client = this.clients.get(modelName);

    if (!modelConfig || !client) {
      throw new Error(`Model not available: ${modelName}`);
    }

    try {
      let response: any;
      let usage: any = { input: 0, output: 0, total: 0 };

      if (client.type === "vertex") {
        response = await this.executeVertexRequest(client, prompt, context);
        usage = response.usage || usage;
      } else {
        response = await this.executeGeminiRequest(client, prompt, context);
        usage = {
          input: response.usageMetadata?.promptTokenCount || 0,
          output: response.usageMetadata?.candidatesTokenCount || 0,
          total: response.usageMetadata?.totalTokenCount || 0,
        };
      }

      const latency = performance.now() - startTime;
      const cost = usage.total * modelConfig.costPerToken;

      return {
        modelUsed: modelName,
        content: response.text ? response.text() : response.content,
        latency,
        tokenUsage: usage,
        cost,
        cached: false,
        metadata: {
          finishReason: response.finishReason,
          safety: response.safetyRatings,
          model: modelName,
          tier: modelConfig.tier,
        },
      };
    } catch (error) {
      this.logger.error("Model execution failed", {
        model: modelName,
        error: error.message,
      });
      throw error;
    }
  }

  /**
   * Execute Vertex AI request
   */
  private async executeVertexRequest(
    client: any,
    prompt: string,
    context: RoutingContext,
  ): Promise<any> {
    // TODO: Implement Vertex AI request execution
    // This would use the Vertex AI client to make requests
    throw new Error("Vertex AI integration not yet implemented");
  }

  /**
   * Execute Gemini API request
   */
  private async executeGeminiRequest(
    client: any,
    prompt: string,
    context: RoutingContext,
  ): Promise<any> {
    const generationConfig = {
      temperature: 0.7,
      topP: 0.9,
      topK: 40,
      maxOutputTokens: Math.min(context.tokenBudget || 4096, 8192),
    };

    const result = await client.model.generateContent({
      contents: [{ role: "user", parts: [{ text: prompt }] }],
      generationConfig,
    });

    return result.response;
  }

  /**
   * Generate cache key for request
   */
  private generateCacheKey(
    prompt: string,
    model: string,
    context: RoutingContext,
  ): string {
    const key = {
      prompt: prompt.substring(0, 200), // Truncate for key size
      model,
      userTier: context.userTier,
      priority: context.priority,
    };

    return Buffer.from(JSON.stringify(key)).toString("base64").substring(0, 50);
  }

  /**
   * Setup performance monitoring
   */
  private setupPerformanceMonitoring(): void {
    // Monitor routing performance every 10 requests
    this.on("request_completed", (data) => {
      if (this.metrics.totalRequests % 10 === 0) {
        this.analyzePerformance();
      }
    });

    // Auto-optimize every 100 requests
    this.on("request_completed", (data) => {
      if (this.metrics.totalRequests % 100 === 0) {
        this.optimizeRouting();
      }
    });
  }

  /**
   * Analyze current performance
   */
  private analyzePerformance(): void {
    const avgRoutingTime =
      this.metrics.routingTime / this.metrics.totalRequests;
    const cacheHitRate = this.metrics.cacheHits / this.metrics.totalRequests;

    this.logger.info("Performance analysis", {
      avgRoutingTime,
      cacheHitRate,
      totalRequests: this.metrics.totalRequests,
      failovers: this.metrics.failovers,
    });

    // Alert if performance degrades
    if (avgRoutingTime > 100) {
      this.emit("performance_warning", {
        metric: "routing_time",
        value: avgRoutingTime,
        threshold: 100,
      });
    }
  }

  /**
   * Optimize routing algorithms based on performance data
   */
  private optimizeRouting(): void {
    const performanceData = this.performance.getMetrics();
    this.router.optimizeBasedOnPerformance(performanceData);

    this.logger.info("Routing optimization completed", {
      requestsAnalyzed: this.metrics.totalRequests,
    });
  }

  /**
   * Get comprehensive metrics
   */
  getMetrics() {
    return {
      ...this.metrics,
      avgRoutingTime: this.metrics.routingTime / this.metrics.totalRequests,
      cacheHitRate: this.metrics.cacheHits / this.metrics.totalRequests,
      failoverRate: this.metrics.failovers / this.metrics.totalRequests,
      modelDistribution: this.router.getModelUsageStats(),
      performance: this.performance.getMetrics(),
    };
  }

  /**
   * Health check for all models
   */
  async healthCheck(): Promise<{ [model: string]: boolean }> {
    const health: { [model: string]: boolean } = {};

    for (const [modelName] of this.models) {
      try {
        // Skip Deep Think for health checks (Coming Soon)
        if (modelName === "gemini-2.5-deep-think") {
          health[modelName] = false; // Coming Soon - API not yet available
          continue;
        }

        await this.executeWithModel(modelName, "Health check", {
          task: "health_check",
          userTier: "free",
          priority: "low",
          latencyRequirement: 5000,
        });
        health[modelName] = true;
      } catch (error) {
        health[modelName] = false;
        this.logger.warn("Model health check failed", {
          model: modelName,
          error,
        });
      }
    }

    return health;
  }

  /**
   * Shutdown orchestrator and cleanup resources
   */
  shutdown(): void {
    this.logger.info("Shutting down ModelOrchestrator", {
      totalRequests: this.metrics.totalRequests,
      modelsCount: this.models.size,
    });

    // Clear intervals and listeners
    this.removeAllListeners();

    // Clear caches and connections
    this.cache?.clear?.();
    this.models.clear();
    this.clients.clear();

    // Reset metrics
    this.metrics = {
      totalRequests: 0,
      routingTime: 0,
      modelSwitches: 0,
      cacheHits: 0,
      failovers: 0,
      tierUpgrades: 0,
    };

    this.logger.info("ModelOrchestrator shutdown completed");
  }
}