Twinmind Rate Limits

TwinMind Rate Limits

Overview

Handle TwinMind rate limits gracefully with exponential backoff and request optimization.

Prerequisites

• TwinMind API access (Pro/Enterprise)
• Understanding of async/await patterns
• Familiarity with rate limiting concepts

Instructions

Step 1: Understand Rate Limit Tiers

Key Limits:

• Transcription: Based on audio duration ($0.23/hour with Ear-3)
• AI Operations: Token-based (2M context for Pro)
• Summarization: 10/minute (Free), 30/minute (Pro)
• Memory Search: 60/minute (Free), 300/minute (Pro)

Step 2: Implement Exponential Backoff with Jitter

// src/twinmind/rate-limit.ts
interface RateLimitConfig {
  maxRetries: number;
  baseDelayMs: number;
  maxDelayMs: number;
  jitterMs: number;
}

const defaultConfig: RateLimitConfig = {
  maxRetries: 5,
  baseDelayMs: 1000,
  maxDelayMs: 60000, // Max 1 minute
  jitterMs: 500,
};

export async function withRateLimit<T>(
  operation: () => Promise<T>,
  config: Partial<RateLimitConfig> = {}
): Promise<T> {
  const { maxRetries, baseDelayMs, maxDelayMs, jitterMs } = {
    ...defaultConfig,
    ...config,
  };

  for (let attempt = 0; attempt <= maxRetries; attempt++) {
    try {
      return await operation();
    } catch (error: any) {
      if (attempt === maxRetries) throw error;

      const status = error.response?.status;
      if (status !== 429 && status !== 503) throw error; // Only retry on rate limits

      // Check Retry-After header
      const retryAfter = error.response?.headers?.['retry-after'];
      let delay: number;

      if (retryAfter) {
        delay = parseInt(retryAfter) * 1000;
      } else {
        // Exponential backoff with jitter
        const exponential = baseDelayMs * Math.pow(2, attempt);
        const jitter = Math.random() * jitterMs;
        delay = Math.min(exponential + jitter, maxDelayMs);
      }

      console.log(`Rate limited (attempt ${attempt + 1}). Waiting ${delay}ms...`);
      await new Promise(r => setTimeout(r, delay));
    }
  }

  throw new Error('Max retries exceeded');
}

Step 3: Implement Request Queue

// src/twinmind/queue.ts
import PQueue from 'p-queue';

interface QueueConfig {
  concurrency: number;
  intervalMs: number;
  intervalCap: number;
}

const tierConfigs: Record<string, QueueConfig> = {
  free: { concurrency: 1, intervalMs: 60000, intervalCap: 30 },
  pro: { concurrency: 3, intervalMs: 60000, intervalCap: 60 },
  enterprise: { concurrency: 10, intervalMs: 60000, intervalCap: 300 },
};

export class TwinMindQueue {
  private queue: PQueue;
  private tier: string;

  constructor(tier: 'free' | 'pro' | 'enterprise' = 'pro') {
    const config = tierConfigs[tier];
    this.tier = tier;
    this.queue = new PQueue({
      concurrency: config.concurrency,
      interval: config.intervalMs,
      intervalCap: config.intervalCap,
    });
  }

  async add<T>(operation: () => Promise<T>, priority?: number): Promise<T> {
    return this.queue.add(operation, { priority }) as Promise<T>;
  }

  get pending(): number {
    return this.queue.pending;
  }

  get size(): number {
    return this.queue.size;
  }

  pause(): void {
    this.queue.pause();
  }

  resume(): void {
    this.queue.start();
  }

  clear(): void {
    this.queue.clear();
  }
}

// Singleton instance
let queueInstance: TwinMindQueue | null = null;

export function getQueue(tier?: 'free' | 'pro' | 'enterprise'): TwinMindQueue {
  if (!queueInstance) {
    queueInstance = new TwinMindQueue(tier);
  }
  return queueInstance;
}

Step 4: Monitor Rate Limit Headers

// src/twinmind/rate-monitor.ts
export interface RateLimitStatus {
  limit: number;
  remaining: number;
  reset: Date;
  percentUsed: number;
}

export class RateLimitMonitor {
  private limits = new Map<string, RateLimitStatus>();

  updateFromResponse(endpoint: string, headers: Headers): void {
    const limit = parseInt(headers.get('X-RateLimit-Limit') || '60');
    const remaining = parseInt(headers.get('X-RateLimit-Remaining') || '60');
    const resetTimestamp = headers.get('X-RateLimit-Reset');
    const reset = resetTimestamp
      ? new Date(parseInt(resetTimestamp) * 1000)
      : new Date(Date.now() + 60000);

    this.limits.set(endpoint, {
      limit,
      remaining,
      reset,
      percentUsed: ((limit - remaining) / limit) * 100,
    });
  }

  getStatus(endpoint: string): RateLimitStatus | undefined {
    return this.limits.get(endpoint);
  }

  shouldThrottle(endpoint: string, threshold = 10): boolean {
    const status = this.limits.get(endpoint);
    if (!status) return false;

    // Throttle if remaining < threshold AND reset hasn't happened
    return status.remaining < threshold && new Date() < status.reset;
  }

  getWaitTime(endpoint: string): number {
    const status = this.limits.get(endpoint);
    if (!status) return 0;

    const now = Date.now();
    const resetTime = status.reset.getTime();

    return Math.max(0, resetTime - now);
  }

  getAllStatuses(): Map<string, RateLimitStatus> {
    return new Map(this.limits);
  }
}

export const rateLimitMonitor = new RateLimitMonitor();

Step 5: Implement Adaptive Rate Limiting

// src/twinmind/adaptive-limiter.ts
export class AdaptiveRateLimiter {
  private successCount = 0;
  private failureCount = 0;
  private currentDelay = 0;
  private minDelay = 0;
  private maxDelay = 5000;
  private windowMs = 60000;
  private windowStart = Date.now();

  recordSuccess(): void {
    this.maybeResetWindow();
    this.successCount++;

    // Decrease delay on success (min 0)
    if (this.currentDelay > 0) {
      this.currentDelay = Math.max(0, this.currentDelay - 100);
    }
  }

  recordFailure(isRateLimit: boolean): void {
    this.maybeResetWindow();
    this.failureCount++;

    if (isRateLimit) {
      // Increase delay on rate limit
      this.currentDelay = Math.min(this.maxDelay, this.currentDelay + 500);
    }
  }

  private maybeResetWindow(): void {
    const now = Date.now();
    if (now - this.windowStart > this.windowMs) {
      this.successCount = 0;
      this.failureCount = 0;
      this.windowStart = now;
    }
  }

  getDelay(): number {
    return this.currentDelay;
  }

  getMetrics(): { success: number; failure: number; delay: number; ratio: number } {
    const total = this.successCount + this.failureCount;
    return {
      success: this.successCount,
      failure: this.failureCount,
      delay: this.currentDelay,
      ratio: total > 0 ? this.successCount / total : 1,
    };
  }

  async wait(): Promise<void> {
    if (this.currentDelay > 0) {
      await new Promise(r => setTimeout(r, this.currentDelay));
    }
  }
}

Step 6: Batch Requests for Efficiency

// src/twinmind/batch.ts
export interface BatchOptions {
  maxBatchSize: number;
  maxWaitMs: number;
}

export class TranscriptionBatcher {
  private pending: Array<{
    audioUrl: string;
    resolve: (value: any) => void;
    reject: (error: any) => void;
  }> = [];
  private timer: NodeJS.Timeout | null = null;
  private options: BatchOptions;

  constructor(options: Partial<BatchOptions> = {}) {
    this.options = {
      maxBatchSize: 5,
      maxWaitMs: 1000,
      ...options,
    };
  }

  async transcribe(audioUrl: string): Promise<any> {
    return new Promise((resolve, reject) => {
      this.pending.push({ audioUrl, resolve, reject });

      if (this.pending.length >= this.options.maxBatchSize) {
        this.flush();
      } else if (!this.timer) {
        this.timer = setTimeout(() => this.flush(), this.options.maxWaitMs);
      }
    });
  }

  private async flush(): Promise<void> {
    if (this.timer) {
      clearTimeout(this.timer);
      this.timer = null;
    }

    const batch = this.pending.splice(0, this.options.maxBatchSize);
    if (batch.length === 0) return;

    try {
      // Use batch API if available
      const results = await this.processBatch(batch.map(b => b.audioUrl));

      batch.forEach((item, index) => {
        item.resolve(results[index]);
      });
    } catch (error) {
      batch.forEach(item => item.reject(error));
    }
  }

  private async processBatch(audioUrls: string[]): Promise<any[]> {
    const client = getTwinMindClient();
    const response = await client.post('/transcribe/batch', {
      audio_urls: audioUrls,
      model: 'ear-3',
    });
    return response.data.transcripts;
  }
}

Output

• Reliable API calls with automatic retry
• Request queue with rate limit awareness
• Adaptive throttling based on response patterns
• Batch processing for efficiency
• Real-time rate limit monitoring

Error Handling

Rate Limit Best Practices

1. Always handle 429 responses - Never let rate limits crash your app
2. Use request queues - Don't burst requests
3. Monitor remaining quota - Throttle before hitting limits
4. Implement circuit breakers - Fail fast when API is overloaded
5. Cache responses - Avoid redundant requests
6. Batch when possible - Reduce total request count

Resources

• TwinMind Rate Limits
• p-queue Documentation
• Rate Limiting Patterns

Next Steps

For security configuration, see twinmind-security-basics.