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Documentation Index

Fetch the complete documentation index at: https://docs.lemondata.cc/llms.txt

Use this file to discover all available pages before exploring further.

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Overview

LemonData implements rate limits to ensure fair usage and platform stability. Limits vary by account tier.

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Rate Limit Tiers

TierRequests/minDescription
User1,000Default tier for all accounts
Partner3,000For integration partners
VIP10,000High-volume users
Rate limits are subject to change. Contact [email protected] for custom limits.

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Rate Limit Response

When you exceed the rate limit, the API returns a 429 status code with a Retry-After header indicating how long to wait before retrying.

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Rate Limit Exceeded

When you exceed the limit, you’ll receive a 429 response: 
{
  "error": {
    "message": "Rate limit exceeded. Please retry later.",
    "type": "rate_limit_exceeded",
    "code": "rate_limit_exceeded"
  }
}
The response includes a Retry-After header: 
Retry-After: 60  # Seconds to wait before retrying

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Handling Rate Limits

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Exponential Backoff

Implement exponential backoff for automatic retries: 
import time
from openai import OpenAI, RateLimitError

client = OpenAI(
    api_key="sk-your-api-key",
    base_url="https://api.lemondata.cc/v1"
)

def make_request_with_backoff(messages, max_retries=5):
    for attempt in range(max_retries):
        try:
            return client.chat.completions.create(
                model="gpt-4o",
                messages=messages
            )
        except RateLimitError as e:
            if attempt == max_retries - 1:
                raise

            wait_time = 2 ** attempt  # 1, 2, 4, 8, 16 seconds
            print(f"Rate limited. Waiting {wait_time}s...")
            time.sleep(wait_time)

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Request Queuing

For high-volume applications, implement a request queue: 
import asyncio
from collections import deque

class RateLimitedClient:
    def __init__(self, requests_per_minute=60):
        self.rpm = requests_per_minute
        self.interval = 60 / requests_per_minute
        self.last_request = 0

    async def request(self, messages):
        # Wait if needed to respect rate limit
        now = asyncio.get_event_loop().time()
        wait_time = max(0, self.last_request + self.interval - now)
        if wait_time > 0:
            await asyncio.sleep(wait_time)

        self.last_request = asyncio.get_event_loop().time()
        return await self.client.chat.completions.create(
            model="gpt-4o",
            messages=messages
        )

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Batch Processing

For bulk operations, process in batches with delays: 
def process_batch(items, batch_size=50, delay=1):
    results = []
    for i in range(0, len(items), batch_size):
        batch = items[i:i + batch_size]
        for item in batch:
            result = client.chat.completions.create(
                model="gpt-4o",
                messages=[{"role": "user", "content": item}]
            )
            results.append(result)
        time.sleep(delay)  # Pause between batches
    return results

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Best Practices

Track rate limit headers to stay under limits proactively.
Cache responses for identical requests to reduce API calls.
Faster models (like gpt-5-mini) allow more throughput.
If you need higher limits, contact [email protected].

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Upgrading Your Tier

To request a tier upgrade:
  1. Log in to your Dashboard
  2. Go to Settings → Account
  3. Contact support with your use case
Or email [email protected] with:
  • Your account email
  • Expected request volume
  • Use case description