@clduab11/gemini-flow/docs/api/google-services/getting-started.md

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.

# Getting Started with Google Services Integration

## Quick Start Guide

Get up and running with Gemini-Flow's Google Services integration in under 10 minutes.

### Prerequisites

- Node.js 18.0.0 or higher
- npm 8.0.0 or higher
- Google account with access to desired services
- Basic understanding of TypeScript/JavaScript

### Installation

```bash
# Install Gemini-Flow with Google Services support
npm install @clduab11/gemini-flow

# Optional: Install specific Google service dependencies
npm install googleapis google-auth-library @google-cloud/vertexai
```

### Quick Setup

#### 1. Google AI API Key (Fastest Start - 2 minutes)

```bash
# Get your API key from https://aistudio.google.com/app/apikey
export GEMINI_API_KEY="AIzaSyD-your-api-key-here"

# Test the integration
npx gemini-flow ai generate "Hello, world!" --model gemini-1.5-flash
```

#### 2. Initialize Project

```typescript
import { GeminiFlow } from '@clduab11/gemini-flow';

const geminiFlow = new GeminiFlow({
  apiKey: process.env.GEMINI_API_KEY
});

// Your first AI generation
const result = await geminiFlow.ai.generate({
  model: 'gemini-1.5-flash',
  prompt: 'Explain quantum computing in simple terms'
});

console.log(result.content);
```

## Service-Specific Getting Started

### Google AI & Gemini Models

#### Setup
```typescript
import { GoogleAI } from '@clduab11/gemini-flow/ai';

const ai = new GoogleAI({
  apiKey: process.env.GEMINI_API_KEY
});
```

#### Basic Usage
```typescript
// Text generation
const response = await ai.generate({
  model: 'gemini-1.5-flash',
  prompt: 'Write a product description for eco-friendly water bottles',
  temperature: 0.7,
  maxTokens: 200
});

console.log(response.content);
```

#### Streaming Response
```typescript
const stream = ai.generateStream({
  model: 'gemini-1.5-pro',
  prompt: 'Tell me a story about space exploration'
});

for await (const chunk of stream) {
  process.stdout.write(chunk.content);
}
```

#### Multimodal Input
```typescript
import fs from 'fs';

const imageData = fs.readFileSync('image.jpg');

const response = await ai.generate({
  model: 'gemini-1.5-pro',
  prompt: 'Describe what you see in this image',
  media: [{
    type: 'image',
    data: imageData.toString('base64'),
    mimeType: 'image/jpeg'
  }]
});
```

#### Function Calling
```typescript
const tools = [{
  name: 'get_weather',
  description: 'Get current weather for a location',
  parameters: {
    type: 'object',
    properties: {
      location: { type: 'string' },
      unit: { type: 'string', enum: ['celsius', 'fahrenheit'] }
    },
    required: ['location']
  }
}];

const response = await ai.generate({
  model: 'gemini-1.5-pro',
  prompt: 'What\'s the weather like in Tokyo?',
  tools,
  toolChoice: 'auto'
});

// Handle tool calls
if (response.toolCalls) {
  for (const call of response.toolCalls) {
    if (call.name === 'get_weather') {
      const weatherData = await getWeatherData(call.arguments.location);
      console.log(`Weather in ${call.arguments.location}:`, weatherData);
    }
  }
}
```

### Google Workspace Integration

#### Setup OAuth 2.0
```typescript
import { GoogleWorkspace } from '@clduab11/gemini-flow/workspace';

const workspace = new GoogleWorkspace({
  clientId: 'your-client-id.apps.googleusercontent.com',
  clientSecret: 'your-client-secret',
  redirectUri: 'http://localhost:3000/callback'
});

// Initialize OAuth flow
const authUrl = workspace.getAuthUrl();
console.log('Visit this URL to authorize:', authUrl);

// After user authorization, exchange code for tokens
const tokens = await workspace.getTokens(authorizationCode);
await workspace.initialize(tokens);
```

#### Google Drive Operations
```typescript
// Search for files
const files = await workspace.drive.search('type:document modified > 2025-01-01');

console.log(`Found ${files.length} documents:`);
files.forEach(file => {
  console.log(`- ${file.name} (${file.lastModified})`);
});

// Create a new document
const doc = await workspace.docs.create({
  title: 'Meeting Notes - Q1 Planning',
  content: `
# Q1 Planning Meeting

## Agenda
1. Review previous quarter
2. Set Q1 objectives
3. Resource allocation

## Action Items
- [ ] Finalize budget proposal
- [ ] Schedule team meetings
  `
});

console.log(`Document created: ${doc.url}`);
```

#### Google Sheets Analysis
```typescript
// Analyze spreadsheet data
const analysis = await workspace.sheets.analyze({
  spreadsheetId: '1BxiMVs0XRA5nFMdKvBdBZjgmUUqptlbs74OgvE2upms',
  range: 'Sheet1!A1:Z1000',
  analysisType: 'statistical'
});

console.log('Data Analysis Results:');
console.log(`Total rows: ${analysis.rowCount}`);
console.log(`Summary: ${analysis.summary}`);
```

### Vertex AI Platform

#### Setup Service Account
```typescript
import { VertexAI } from '@clduab11/gemini-flow/vertex-ai';

const vertexAI = new VertexAI({
  projectId: 'your-gcp-project-id',
  location: 'us-central1',
  credentials: {
    // Service account JSON or use ADC
    type: 'service_account',
    project_id: 'your-project',
    private_key: '-----BEGIN PRIVATE KEY-----...',
    client_email: 'service-account@project.iam.gserviceaccount.com'
  }
});
```

#### Enterprise AI Operations
```typescript
// List available models
const models = await vertexAI.listModels();
console.log('Available models:', models.map(m => m.name));

// Make predictions
const prediction = await vertexAI.predict({
  model: 'text-bison@001',
  instances: [{
    prompt: 'Analyze the following customer feedback for sentiment...'
  }],
  parameters: {
    temperature: 0.2,
    maxOutputTokens: 256
  }
});

console.log('Prediction result:', prediction);
```

### Future Google Services (Beta Access)

#### Video Generation (Veo3)
```typescript
import { VideoGeneration } from '@clduab11/gemini-flow/multimedia';

const video = new VideoGeneration({
  projectId: 'your-project-id',
  credentials: serviceAccountCredentials
});

// Generate video
const task = await video.generate({
  prompt: 'A peaceful lake at sunset with mountains in the background',
  duration: 10, // seconds
  resolution: '1080p',
  style: 'cinematic'
});

// Monitor generation progress
console.log(`Generation started: ${task.taskId}`);

const result = await video.waitForCompletion(task.taskId);
console.log(`Video ready: ${result.videoUrl}`);
```

#### Image Generation (Imagen 4)
```typescript
import { ImageGeneration } from '@clduab11/gemini-flow/multimedia';

const images = new ImageGeneration({
  projectId: 'your-project-id',
  credentials: serviceAccountCredentials
});

const result = await images.generate({
  prompt: 'A modern office space with natural lighting and plants',
  style: 'photorealistic',
  aspectRatio: '16:9',
  quality: 'high',
  samples: 4
});

result.images.forEach((image, index) => {
  console.log(`Generated image ${index + 1}: ${image.url}`);
});
```

## Common Integration Patterns

### 1. Content Creation Pipeline

```typescript
class ContentCreationPipeline {
  constructor(
    private ai: GoogleAI,
    private workspace: GoogleWorkspace
  ) {}

  async createBlogPost(topic: string): Promise<string> {
    // 1. Generate outline
    const outline = await this.ai.generate({
      model: 'gemini-1.5-pro',
      prompt: `Create a detailed outline for a blog post about: ${topic}`,
      temperature: 0.7
    });

    // 2. Generate full content
    const content = await this.ai.generate({
      model: 'gemini-1.5-pro',
      prompt: `Write a complete blog post based on this outline: ${outline.content}`,
      temperature: 0.6,
      maxTokens: 2000
    });

    // 3. Create Google Doc
    const doc = await this.workspace.docs.create({
      title: `Blog Post: ${topic}`,
      content: content.content
    });

    // 4. Generate and add images
    const imagePrompt = `Create an illustration for a blog post about ${topic}`;
    const image = await this.generateImage(imagePrompt);
    
    if (image) {
      await this.workspace.docs.addImage(doc.id, image.url, { position: 'top' });
    }

    return doc.url;
  }

  private async generateImage(prompt: string): Promise<{ url: string } | null> {
    try {
      // This would use Imagen 4 when available
      const result = await this.images.generate({ prompt });
      return result.images[0];
    } catch (error) {
      console.warn('Image generation failed:', error.message);
      return null;
    }
  }
}
```

### 2. Data Analysis Workflow

```typescript
class DataAnalysisWorkflow {
  constructor(
    private ai: GoogleAI,
    private workspace: GoogleWorkspace
  ) {}

  async analyzeSpreadsheet(spreadsheetId: string): Promise<string> {
    // 1. Extract data from spreadsheet
    const data = await this.workspace.sheets.getValues({
      spreadsheetId,
      range: 'A1:Z1000'
    });

    // 2. Perform AI-powered analysis
    const analysis = await this.ai.generate({
      model: 'gemini-1.5-pro',
      prompt: `
        Analyze this spreadsheet data and provide insights:
        ${JSON.stringify(data.values.slice(0, 100))} // First 100 rows
        
        Please provide:
        1. Summary of the data
        2. Key trends and patterns
        3. Anomalies or outliers
        4. Recommendations
      `,
      temperature: 0.2
    });

    // 3. Create analysis report
    const report = await this.workspace.docs.create({
      title: `Data Analysis Report - ${new Date().toLocaleDateString()}`,
      content: `
# Data Analysis Report

## Source
Spreadsheet ID: ${spreadsheetId}
Analysis Date: ${new Date().toLocaleString()}

## Analysis Results
${analysis.content}

## Raw Data Summary
- Total Rows: ${data.values.length}
- Total Columns: ${data.values[0]?.length || 0}
- Data Range: ${data.range}
      `
    });

    return report.url;
  }
}
```

### 3. Customer Support Automation

```typescript
class CustomerSupportBot {
  constructor(
    private ai: GoogleAI,
    private workspace: GoogleWorkspace
  ) {}

  async handleSupportTicket(ticketData: SupportTicket): Promise<void> {
    // 1. Analyze customer query
    const analysis = await this.ai.generate({
      model: 'gemini-1.5-flash',
      prompt: `
        Analyze this customer support ticket:
        Subject: ${ticketData.subject}
        Message: ${ticketData.message}
        Customer: ${ticketData.customerEmail}
        
        Classify the issue type and urgency level.
        Suggest a response strategy.
      `,
      temperature: 0.3
    });

    // 2. Generate response draft
    const response = await this.ai.generate({
      model: 'gemini-1.5-pro',
      prompt: `
        Based on this analysis: ${analysis.content}
        
        Draft a professional, helpful response to the customer.
        Be empathetic and provide clear next steps.
      `,
      temperature: 0.4
    });

    // 3. Create ticket tracking document
    const ticketDoc = await this.workspace.docs.create({
      title: `Support Ticket - ${ticketData.id}`,
      content: `
# Support Ticket ${ticketData.id}

## Customer Information
- Email: ${ticketData.customerEmail}
- Date: ${new Date().toLocaleString()}

## Issue Details
**Subject:** ${ticketData.subject}

**Message:**
${ticketData.message}

## AI Analysis
${analysis.content}

## Suggested Response
${response.content}

## Status
- [x] Received
- [ ] Responded
- [ ] Resolved
- [ ] Closed
      `
    });

    // 4. Log ticket in tracking spreadsheet
    await this.workspace.sheets.appendRow({
      spreadsheetId: 'support-tickets-spreadsheet-id',
      range: 'Tickets!A:E',
      values: [[
        ticketData.id,
        ticketData.customerEmail,
        ticketData.subject,
        'Open',
        new Date().toISOString()
      ]]
    });
  }
}
```

## Error Handling Best Practices

### Graceful Degradation
```typescript
class RobustGoogleServiceClient {
  constructor(
    private ai: GoogleAI,
    private workspace: GoogleWorkspace
  ) {}

  async generateContentWithFallback(prompt: string): Promise<string> {
    const strategies = [
      // Primary: Gemini 1.5 Pro
      () => this.ai.generate({ model: 'gemini-1.5-pro', prompt }),
      
      // Fallback 1: Gemini 1.5 Flash
      () => this.ai.generate({ model: 'gemini-1.5-flash', prompt }),
      
      // Fallback 2: Simplified prompt
      () => this.ai.generate({
        model: 'gemini-1.5-flash',
        prompt: this.simplifyPrompt(prompt),
        maxTokens: 500
      })
    ];

    for (const strategy of strategies) {
      try {
        const result = await strategy();
        return result.content;
      } catch (error) {
        console.warn(`Strategy failed: ${error.message}`);
        continue;
      }
    }

    throw new Error('All generation strategies failed');
  }

  private simplifyPrompt(prompt: string): string {
    // Reduce complex prompts to basic versions
    return prompt.slice(0, 100) + '...';
  }
}
```

### Retry Logic
```typescript
class RetryableGoogleService {
  async executeWithRetry<T>(
    operation: () => Promise<T>,
    maxRetries = 3,
    baseDelay = 1000
  ): Promise<T> {
    let lastError: Error;

    for (let attempt = 1; attempt <= maxRetries; attempt++) {
      try {
        return await operation();
      } catch (error) {
        lastError = error;
        
        if (!this.isRetryable(error) || attempt === maxRetries) {
          throw error;
        }

        const delay = baseDelay * Math.pow(2, attempt - 1);
        console.log(`Attempt ${attempt} failed, retrying in ${delay}ms`);
        await this.sleep(delay);
      }
    }

    throw lastError!;
  }

  private isRetryable(error: any): boolean {
    const retryableCodes = [
      'INTERNAL_ERROR',
      'SERVICE_UNAVAILABLE', 
      'RESOURCE_EXHAUSTED',
      'DEADLINE_EXCEEDED'
    ];
    
    return retryableCodes.includes(error.code) || error.status >= 500;
  }

  private sleep(ms: number): Promise<void> {
    return new Promise(resolve => setTimeout(resolve, ms));
  }
}
```

## Performance Optimization

### Request Batching
```typescript
class BatchProcessor {
  private batches = new Map<string, any[]>();
  private timers = new Map<string, NodeJS.Timeout>();

  addToBatch(service: string, request: any): Promise<any> {
    return new Promise((resolve, reject) => {
      const batch = this.batches.get(service) || [];
      batch.push({ request, resolve, reject });
      this.batches.set(service, batch);

      this.scheduleExecution(service);
    });
  }

  private scheduleExecution(service: string): void {
    if (this.timers.has(service)) {
      return; // Already scheduled
    }

    const timer = setTimeout(() => {
      this.executeBatch(service);
      this.timers.delete(service);
    }, 100); // 100ms batch window

    this.timers.set(service, timer);
  }

  private async executeBatch(service: string): Promise<void> {
    const batch = this.batches.get(service) || [];
    if (batch.length === 0) return;

    this.batches.set(service, []);

    try {
      const results = await this.executeBatchRequest(service, batch);
      
      batch.forEach((item, index) => {
        item.resolve(results[index]);
      });
    } catch (error) {
      batch.forEach(item => {
        item.reject(error);
      });
    }
  }

  private async executeBatchRequest(service: string, batch: any[]): Promise<any[]> {
    // Service-specific batch implementation
    switch (service) {
      case 'sheets-read':
        return this.batchSheetsRead(batch);
      case 'drive-metadata':
        return this.batchDriveMetadata(batch);
      default:
        throw new Error(`Unsupported batch service: ${service}`);
    }
  }
}
```

### Caching Strategy
```typescript
import { LRUCache } from 'lru-cache';

class CachedGoogleService {
  private cache = new LRUCache<string, any>({
    max: 1000,
    ttl: 1000 * 60 * 15 // 15 minutes
  });

  async getCachedResult<T>(
    key: string,
    fetcher: () => Promise<T>,
    ttl?: number
  ): Promise<T> {
    const cached = this.cache.get(key);
    if (cached) {
      return cached;
    }

    const result = await fetcher();
    this.cache.set(key, result, { ttl });
    return result;
  }

  async generateWithCache(prompt: string, model: string): Promise<any> {
    const cacheKey = `ai:${model}:${this.hashPrompt(prompt)}`;
    
    return this.getCachedResult(
      cacheKey,
      () => this.ai.generate({ model, prompt }),
      1000 * 60 * 60 * 24 // 24 hours for AI generations
    );
  }

  private hashPrompt(prompt: string): string {
    return require('crypto')
      .createHash('md5')
      .update(prompt)
      .digest('hex');
  }
}
```

## Next Steps

1. **Explore Advanced Features**
   - Multi-agent orchestration
   - Complex workflow automation
   - Real-time collaboration features

2. **Production Deployment**
   - Set up monitoring and alerting
   - Implement proper error handling
   - Configure rate limiting and quotas

3. **Security Hardening**
   - Review authentication configuration
   - Implement proper access controls
   - Set up audit logging

4. **Scale Optimization**
   - Implement caching strategies
   - Set up load balancing
   - Configure auto-scaling

For more detailed examples and advanced use cases, see the [Code Examples](./code-examples/) section and [API Reference](./api-reference/).

## Support

- **Documentation**: [Complete API Documentation](https://docs.gemini-flow.dev)
- **GitHub**: [Issues and Discussions](https://github.com/clduab11/gemini-flow)
- **Community**: [Discord Server](https://discord.gg/gemini-flow)

## What's Next?

Continue to the [Code Examples](./code-examples/) section for more detailed implementation examples, or jump to [Authentication Setup](./authentication-guide.md) for production-ready authentication configuration.