Controlling API Costs and Usage: A Comprehensive Guide to Rate Limiting in LangChain Applications with UpstashRatelimitHandler
Controlling API Costs and Usage: A Comprehensive Guide to Rate Limiting in LangChain Applications with UpstashRatelimitHandler
In today’s landscape of AI application development, managing API costs and usage has become a critical concern. Large Language Models (LLMs) like those from OpenAI can quickly become expensive when deployed in production environments with high traffic. Implementing effective rate limiting strategies is essential to control costs, maintain service availability, and ensure fair usage across your user base.
LangChain, a popular framework for building LLM applications, provides a powerful solution through its UpstashRatelimitHandler. This article will explore how to effectively implement rate limiting in your LangChain applications to control both request frequency and token usage.
Understanding Rate Limiting in LLM Applications
Before diving into implementation details, it’s important to understand why rate limiting matters for LLM applications:
- Cost Control: LLM API calls can become expensive at scale
- Service Reliability: Preventing overuse helps maintain application stability
- Fair Usage: Ensuring resources are distributed equitably among users
- API Quota Management: Staying within provider limits to avoid service interruptions
Introducing UpstashRatelimitHandler
LangChain’s UpstashRatelimitHandler provides a flexible way to implement rate limiting based on either the number of requests or the number of tokens consumed. It leverages Upstash Redis to track and enforce these limits.
The handler can be particularly useful when you need to:
- Limit requests per user or IP address
- Control token usage for specific models
- Implement tiered access based on user roles
Getting Started with UpstashRatelimitHandler
To use the UpstashRatelimitHandler, you’ll first need to install the necessary dependencies:
pip install langchain upstash-redis upstash-ratelimit
Now, let’s look at a basic implementation:
from langchain_community.callbacks.upstash_ratelimit_callback import UpstashRatelimitHandler
from upstash_ratelimit import Ratelimit
from upstash_redis import Redis
# Initialize Upstash Redis
redis = Redis(url="YOUR_UPSTASH_REDIS_URL", token="YOUR_UPSTASH_REDIS_TOKEN")
# Create rate limit configurations
request_limit = Ratelimit(
redis=redis,
max_requests=10, # Maximum 10 requests
window=60, # Per minute (60 seconds)
prefix="user_requests"
)
token_limit = Ratelimit(
redis=redis,
max_requests=5000, # Maximum 5000 tokens
window=3600, # Per hour (3600 seconds)
prefix="user_tokens"
)
# Create the handler with a user identifier
handler = UpstashRatelimitHandler(
identifier="user_123", # Could be user ID or IP address
request_ratelimit=request_limit,
token_ratelimit=token_limit,
include_output_tokens=True # Count both input and output tokens
)
Key Concepts in UpstashRatelimitHandler
The UpstashRatelimitHandler requires a few important parameters:
- identifier: A unique identifier for the entity being rate-limited (user ID, IP address, etc.)
- request_ratelimit (optional): A Ratelimit object to limit the number of requests
- token_ratelimit (optional): A Ratelimit object to limit the number of tokens
- include_output_tokens (optional): Whether to count output tokens when rate limiting (defaults to False)
You must provide at least one of request_ratelimit or token_ratelimit when initializing the handler.
Using the Handler with LangChain
The UpstashRatelimitHandler should be created fresh for each invocation, rather than being passed to the chain during initialization. Here’s a practical example of how to use it with a LangChain LLM:
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage
# Create a new handler for this specific request
handler = UpstashRatelimitHandler(
identifier="user_123",
request_ratelimit=request_limit,
token_ratelimit=token_limit
)
# Initialize the LLM
llm = ChatOpenAI(temperature=0)
try:
# Execute with the handler
response = llm.invoke(
[HumanMessage(content="Tell me about rate limiting")],
callbacks=[handler]
)
print(response.content)
except Exception as e:
print(f"Rate limit exceeded: {e}")
Advanced Usage Patterns
Differentiating Between Users
A common requirement is to implement different rate limits for different user tiers:
def get_rate_limit_handler(user_id, user_tier):
# Define tier-specific limits
tier_limits = {
"free": {"requests": 10, "tokens": 5000},
"basic": {"requests": 50, "tokens": 20000},
"premium": {"requests": 200, "tokens": 100000}
}
limits = tier_limits.get(user_tier, tier_limits["free"])
# Create appropriate rate limiters
request_limit = Ratelimit(
redis=redis,
max_requests=limits["requests"],
window=3600,
prefix=f"{user_tier}_requests"
)
token_limit = Ratelimit(
redis=redis,
max_requests=limits["tokens"],
window=86400, # Daily limit
prefix=f"{user_tier}_tokens"
)
return UpstashRatelimitHandler(
identifier=user_id,
request_ratelimit=request_limit,
token_ratelimit=token_limit,
include_output_tokens=True
)
# Usage
handler = get_rate_limit_handler("user_123", "premium")
Resetting the Handler
The UpstashRatelimitHandler provides a reset method that allows you to create a new handler with the same configurations but a different identifier:
# Create initial handler
handler = UpstashRatelimitHandler(
identifier="user_123",
request_ratelimit=request_limit
)
# Reset for a different user
new_handler = handler.reset(identifier="user_456")
Token-Based Limiting with OpenAI Models
Token-based rate limiting works particularly well with OpenAI models since they provide token usage information in their responses. Here’s how to implement it:
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage
# Create token-focused rate limiter
token_limit = Ratelimit(
redis=redis,
max_requests=100000, # 100k tokens
window=86400, # Daily limit
prefix="openai_tokens"
)
handler = UpstashRatelimitHandler(
identifier="organization_xyz",
token_ratelimit=token_limit,
include_output_tokens=True # Count both prompt and completion tokens
)
llm = ChatOpenAI(temperature=0)
try:
response = llm.invoke(
[HumanMessage(content="Write a detailed explanation of quantum computing")],
callbacks=[handler]
)
print(response.content)
except Exception as e:
print(f"Token limit exceeded: {e}")
Implementation Considerations
When implementing rate limiting with UpstashRatelimitHandler, keep these considerations in mind:
-
Handler Lifecycle: Create a new handler for each invocation rather than reusing instances.
-
Error Handling: The handler will raise
UpstashRatelimitErrorwhen limits are exceeded, so implement appropriate error handling. -
Token Counting: Token-based limiting only works well with models that provide token usage information (like OpenAI models).
-
Storage Requirements: Upstash Redis is used to track rate limit state, so ensure your Redis instance has sufficient capacity.
-
Identifier Selection: Choose meaningful identifiers that align with your application’s user model.
Example: Web API with Rate Limiting
Here’s a more complete example using FastAPI to create a rate-limited API endpoint:
from fastapi import FastAPI, Depends, HTTPException, Request
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage
from langchain_community.callbacks.upstash_ratelimit_callback import UpstashRatelimitHandler
from upstash_ratelimit import Ratelimit
from upstash_redis import Redis
app = FastAPI()
# Initialize Redis and rate limiters
redis = Redis(url="YOUR_UPSTASH_REDIS_URL", token="YOUR_UPSTASH_REDIS_TOKEN")
def get_user_id(request: Request):
# In a real app, extract from auth token or session
return request.client.host
@app.post("/generate")
async def generate_text(prompt: str, request: Request):
user_id = get_user_id(request)
# Create fresh handler for this request
request_limit = Ratelimit(
redis=redis,
max_requests=5,
window=60,
prefix="api_requests"
)
token_limit = Ratelimit(
redis=redis,
max_requests=2000,
window=3600,
prefix="api_tokens"
)
handler = UpstashRatelimitHandler(
identifier=user_id,
request_ratelimit=request_limit,
token_ratelimit=token_limit
)
llm = ChatOpenAI(temperature=0.7)
try:
response = llm.invoke(
[HumanMessage(content=prompt)],
callbacks=[handler]
)
return {"generated_text": response.content}
except Exception as e:
raise HTTPException(status_code=429, detail=f"Rate limit exceeded: {str(e)}")
Conclusion
Effective rate limiting is a crucial component of any production LLM application. LangChain’s UpstashRatelimitHandler provides a flexible and powerful solution for controlling both request frequency and token usage.
By implementing proper rate limiting strategies, you can:
- Control API costs and prevent unexpected billing surprises
- Ensure fair resource allocation across your user base
- Maintain application stability and reliability
- Implement tiered access models for different user categories
The UpstashRatelimitHandler integrates seamlessly with LangChain’s callback system, making it easy to add rate limiting to existing applications with minimal code changes.
As LLM applications continue to grow in popularity and usage, implementing robust rate limiting will become increasingly important for managing costs and ensuring sustainable operation.
This post was originally written in my native language and then translated using an LLM. I apologize if there are any grammatical inconsistencies.