LangChain Error Handling Best Practices: Timeout, Rate Limits, and Recovery
Building Robust AI: LangChain Error Handling Best Practices: Timeout, Rate Limits, and Recovery
Imagine you’re building a super smart helper using LangChain. This helper talks to powerful AI models over the internet. Sometimes, these conversations don’t go perfectly, like a phone call dropping or a busy signal. This is where good error handling comes in.
You need to make sure your AI helper stays reliable and friendly, even when things get a bit bumpy. We will explore the best ways to manage common issues like connection timeouts and hitting service rate limits. This guide covers essential langchain timeout rate limit error handling strategies.
1. Understanding Timeouts in LangChain
What exactly is a timeout? Think of it like waiting for an answer to a question. If you ask a question and don’t get a reply after a certain amount of time, you might assume no one is listening. In the world of computers, a timeout means your program stopped waiting for a response because it took too long.
Timeouts can happen for many reasons. The AI service might be busy, your internet connection could be slow, or there might be an issue on the server’s end. If you don’t handle these, your entire application might freeze or crash. That’s why langchain timeout rate limit error handling is so important.
1.1. Timeout Configuration for LLMs
When you use an AI model (like OpenAI’s ChatGPT) with LangChain, you can tell it how long to wait for a response. This is called timeout configuration. Setting a reasonable timeout helps your application move on if the AI is taking too long. You don’t want your app to be stuck waiting forever.
You can usually set a request_timeout directly when you set up your LLM. This tells LangChain to give up if the AI doesn’t reply within that time. This is a crucial first step for graceful timeout handling. It prevents your program from hanging indefinitely.
Let’s look at an example of how you can configure a timeout for an OpenAI LLM in LangChain. This snippet shows you exactly where to add this important setting. It’s a simple change that makes a big difference in reliability.
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from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage, AIMessage
# Create an LLM instance with a 15-second timeout
# If the AI takes longer than 15 seconds, it will stop waiting
try:
llm_with_timeout = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0.7,
request_timeout=15, # Set the timeout to 15 seconds
api_key="YOUR_OPENAI_API_KEY"
)
# Now, try to use this LLM
print("Sending a request with a 15-second timeout...")
response = llm_with_timeout.invoke([HumanMessage(content="Tell me a short story about a brave knight and a dragon.")])
print("Response received:", response.content[:100], "...")
except Exception as e:
print(f"An error occurred: {e}")
if "Timeout" in str(e):
print("The request timed out!")
else:
print("Some other error happened.")
In this code, you told the ChatOpenAI model to wait only 15 seconds. If the AI doesn’t answer in time, your program will get an error instead of getting stuck. This makes your application more resilient by allowing you to handle the timeout specifically. Good request timeout handling is about planning for delays.
1.2. Timeout for Chains and Agents
LangChain often uses “Chains” or “Agents” which are like a series of steps your AI helper follows. A Chain might ask one AI model a question, then use that answer to ask another question. Each step in these chains can potentially hit a timeout. You need to consider how request timeout handling works in these more complex setups.
Sometimes, the individual LLM calls within a chain will respect the timeout you set. However, a chain itself might take longer if it involves multiple slow steps. For example, if a chain makes many API calls, the total time could exceed expectations even if each individual call is within its limit. You can learn more about building complex chains in our detailed guide on [Understanding LangChain Chains and Agents](/blog/understanding-langchain-chains-and-agents).
LangChain does not always have a single “timeout” setting for an entire Chain or Agent directly. Instead, you primarily manage timeouts at the individual LLM call level, as shown above. You might need to wrap your entire chain execution in a custom timeout mechanism, like using Python’s signal module or a library like tenacity (which we’ll discuss soon) for the entire block of code. This ensures graceful timeout handling for your whole workflow.
For example, if you have a chain that relies on an external tool or a slow LLM, its overall execution time can vary greatly. You can still apply the LLM-level timeouts, but also think about how to exit gracefully if the entire process takes too long. This might involve setting a hard limit on the function that runs your chain.
1.3. Practical Timeout Handling Example
Let’s imagine you have a LangChain expression language (LCEL) chain. You want to make sure the entire process finishes within a certain time. While LCEL chains might not have a direct request_timeout parameter for the whole chain, you can implement external request timeout handling. You can use a wrapper function or even integrate asyncio with asyncio.wait_for if you are using asynchronous calls.
Here’s an example showing how you might wrap an entire chain execution in a timeout mechanism, using asyncio for a simple demonstration of principle. This demonstrates how to implement a broader graceful timeout handling strategy. It ensures that even if individual components don’t time out, the overall operation does.
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import asyncio
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableSequence
from langchain_core.messages import HumanMessage
async def run_chain_with_timeout(chain, input_data, timeout_seconds):
"""
Runs a LangChain chain with an asyncio timeout.
"""
try:
# Wrap the chain's invoke method with asyncio.wait_for
result = await asyncio.wait_for(
chain.ainvoke(input_data),
timeout=timeout_seconds
)
return result
except asyncio.TimeoutError:
print(f"Error: The chain operation timed out after {timeout_seconds} seconds.")
return "Chain operation timed out."
except Exception as e:
print(f"An unexpected error occurred: {e}")
return f"Error: {e}"
# Setup a simple LangChain LCEL chain
llm = ChatOpenAI(model="gpt-3.5-turbo", temperature=0.7, api_key="YOUR_OPENAI_API_KEY")
prompt = ChatPromptTemplate.from_template("What is the capital of {country}?")
output_parser = StrOutputParser()
simple_chain = prompt | llm | output_parser
# Example usage:
async def main_timeout_example():
print("\n--- Testing Chain Timeout ---")
# Simulate a country that might take longer (or just test the timeout)
country_input = {"country": "France"}
# Test with a generous timeout (should succeed)
print("Attempting chain run with 30-second timeout...")
result_success = await run_chain_with_timeout(simple_chain, country_input, 30)
print("Result (success):", result_success)
# Test with a very short timeout (likely to timeout, for demonstration)
print("\nAttempting chain run with 1-second timeout...")
result_timeout = await run_chain_with_timeout(simple_chain, country_input, 1)
print("Result (timeout):", result_timeout)
if __name__ == "__main__":
# Ensure you replace "YOUR_OPENAI_API_KEY" with your actual key for testing
asyncio.run(main_timeout_example())
This asyncio wrapper helps ensure that your entire chain operation respects a total time limit. This is a very robust way to manage request timeout handling for complex workflows. It gracefully catches TimeoutError and lets your program respond without crashing.
2. Dealing with Rate Limits
Besides timeouts, another common challenge is “rate limits.” Imagine a popular ice cream shop. If everyone tries to buy ice cream at the exact same moment, the shop can get overwhelmed. To handle this, they might limit how many customers they serve per minute. AI services do something similar. They set a limit on how many requests you can send in a given time period to ensure fair use and prevent their servers from crashing. This is a key aspect of langchain timeout rate limit error handling.
If you send too many requests too quickly, the AI service will tell you to slow down. You’ll usually receive a special error message, often with an HTTP status code like 429 (Too Many Requests). Ignoring these messages can lead to your access being temporarily blocked. Understanding and respecting these limits is crucial for quota management.
2.1. Detecting Rate Limits
How do you know you’ve hit a rate limit? When an API call fails because of a rate limit, the error message often contains specific clues. For instance, you might see “Rate limit exceeded” or “Too Many Requests” in the error details. The HTTP status code 429 is a dead giveaway. Your langchain timeout rate limit error handling must be able to spot these.
Many AI libraries, including the underlying HTTP clients LangChain uses, will raise a specific error type for rate limits. For OpenAI, you might see an openai.RateLimitError. Your code should specifically look for these types of errors. This targeted rate limit detection allows you to apply the right recovery strategy. Knowing exactly what kind of error occurred helps your program react intelligently.
Sometimes, the API response might even include headers like Retry-After. This header tells you exactly how many seconds you should wait before trying again. It’s like the ice cream shop telling you to come back in 5 minutes. Incorporating this information into your rate limit backoff strategy is ideal. Always check the error details for clues.
2.2. Implementing Rate Limit Backoff
Once you’ve detected a rate limit, you shouldn’t just try again immediately. That’s like repeatedly banging on the ice cream shop door after they told you to wait. Instead, you need to implement a “backoff” strategy. This means waiting for a period of time before retrying your request. The most common and effective strategy is “exponential backoff.”
With exponential backoff, you wait a little longer each time you retry. For example, if your first retry is after 1 second, the next might be after 2 seconds, then 4 seconds, then 8 seconds, and so on. This gives the AI service time to recover and reduces the chance of hitting the limit again immediately. This is a cornerstone of robust langchain timeout rate limit error handling. Using exponential backoff ensures graceful timeout handling by giving the service space.
A popular Python library called tenacity is perfect for implementing this. It makes it very easy to automatically retry your code with exponential backoff and handle specific error types. It’s an indispensable tool for rate limit backoff. Using tenacity allows you to declare how retries should behave, keeping your code clean.
2.3. Code Example: Rate Limit Backoff
Let’s see how tenacity can help you with rate limit backoff for your LangChain calls. This example simulates an openai.RateLimitError and shows how tenacity will automatically retry the function with increasing delays. This is a practical example of langchain timeout rate limit error handling. It helps you prevent getting blocked by the AI service.
First, make sure you have tenacity installed: pip install tenacity.
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import time
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
)
import openai
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
# --- Simulate an OpenAI RateLimitError ---
# In a real scenario, this would be raised by the OpenAI API client
class MockOpenAIRateLimitError(openai.RateLimitError):
def __init__(self, message, *args, **kwargs):
super().__init__(message, *args, **kwargs)
# Function to simulate an LLM call that sometimes hits a rate limit
call_count = 0
@retry(
retry=retry_if_exception_type(MockOpenAIRateLimitError), # Retry only on RateLimitError
wait=wait_exponential(multiplier=1, min=4, max=60), # Wait 4s, 8s, 16s... up to 60s
stop=stop_after_attempt(5), # Try a maximum of 5 times
reraise=True # Re-raise the exception if all retries fail
)
def chat_with_retry(llm_instance, prompt_content):
global call_count
call_count += 1
print(f"Attempt {call_count}: Calling LLM...")
if call_count < 3: # Simulate rate limit for the first two attempts
print(f" Simulating RateLimitError on attempt {call_count}")
raise MockOpenAIRateLimitError(
"You exceeded your current quota, please check your plan and billing details.",
json_body={"error": {"code": "rate_limit_exceeded"}}
)
else:
# On third attempt, succeed
print(f" Attempt {call_count}: LLM call succeeded!")
return llm_instance.invoke([HumanMessage(content=prompt_content)])
# --- Main part of the script ---
if __name__ == "__main__":
print("--- Testing Rate Limit Backoff ---")
my_llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0.7,
api_key="YOUR_OPENAI_API_KEY" # Replace with your actual key
)
try:
response_message = chat_with_retry(my_llm, "What is the largest ocean on Earth?")
print("\nFinal successful response:", response_message.content[:100], "...")
except MockOpenAIRateLimitError as e:
print(f"\nFailed after multiple retries due to RateLimitError: {e}")
except Exception as e:
print(f"\nAn unexpected error occurred: {e}")
In this example, the @retry decorator from tenacity handles all the logic. It catches MockOpenAIRateLimitError (which would be openai.RateLimitError in a real scenario). It then waits an exponentially increasing amount of time before retrying, up to a maximum of 5 attempts. This is smart rate limit backoff and essential for langchain timeout rate limit error handling. It helps you prevent getting stuck and ensures your application eventually succeeds.
3. Recovery Strategies and Best Practices
So, you’ve handled timeouts and rate limits, but what happens after an error? How does your application get back to normal operations? This is where good recovery strategies come in. It’s not enough to just catch an error; you need a plan to resume normal service. This is vital for recovery after limits. You want your system to be robust and self-healing.
Effective langchain timeout rate limit error handling includes planning for how to recover. This might mean trying again with smarter delays, stopping attempts for a while, or even having a backup plan. Your goal is to keep your AI helper running smoothly without constant manual intervention.
3.1. Retries with Jitter
We talked about exponential backoff, which is excellent. But what if many parts of your application, or many users, all hit a rate limit at the same time and then all retry with the exact same exponential backoff? They might all try again at the exact same next second, causing another big surge of requests. This is called the “thundering herd” problem.
To avoid this, you can add “jitter” to your backoff strategy. Jitter means adding a small, random amount of time to each retry delay. So instead of waiting exactly 2 seconds, you might wait between 1.8 and 2.2 seconds. This small randomness helps spread out the retries, reducing the chance of hitting the limit again. Tenacity can easily add jitter to its exponential backoff. This makes your graceful timeout handling even smarter.
This simple addition can significantly improve recovery after limits in busy systems. It’s a small change with a big impact on system stability. It helps smooth out the load on the AI service.
3.2. Circuit Breakers
Imagine a power circuit in your house. If too much electricity flows through it, the circuit breaker “trips” and cuts the power to prevent damage. A “circuit breaker” in software works similarly. If your LangChain calls to an AI service repeatedly fail (maybe due to persistent timeouts or rate limits), a software circuit breaker will “trip.”
When tripped, the circuit breaker stops all further requests to that service for a set period. Instead of trying again and again, it immediately returns an error or a fallback response. After some time, it might cautiously try one request to see if the service has recovered. This pattern prevents your application from hammering an already struggling service, which is crucial for langchain timeout rate limit error handling. It gives the external service time to heal.
This is especially useful if an AI service is experiencing a widespread outage. You don’t want your application endlessly retrying against a service that is completely down. Implementing a circuit breaker is an advanced but very effective strategy for recovery after limits. Libraries like pybreaker can help you add this functionality.
3.3. Fallback Mechanisms
What if, despite all your retries and backoffs, the AI service remains unavailable or keeps hitting limits? This is where fallback mechanisms become invaluable. A fallback is a plan B, a way to provide some level of functionality even when the primary service isn’t working. This is a critical part of quota management. You always need a backup.
Possible fallback mechanisms include:
- Using a simpler, local LLM: If your main cloud LLM is down, maybe you can switch to a smaller, open-source model running on your own servers for basic tasks.
- Providing cached responses: If you’ve asked the same question before, maybe you can provide a stored answer instead of hitting the live AI. This is also a great
cost optimization strategy. - Displaying a helpful message: Inform the user that the service is temporarily unavailable and suggest they try again later.
- Switching to a different AI provider: If you have multiple API keys for different providers (e.g., OpenAI and Anthropic), you could try the second one if the first fails. You can learn more about managing multiple LLM providers
[here](/blog/choosing-your-llm-provider-strategy).
Implementing fallbacks ensures recovery after limits by keeping your application functional, even if in a degraded mode. It improves user experience by avoiding complete failures. It’s all about keeping your AI helper useful.
3.4. Logging and Monitoring
You can’t fix what you don’t know is broken. Robust langchain timeout rate limit error handling relies heavily on good logging and monitoring. When a timeout or rate limit occurs, your application should log detailed information about it. What was the request? What error was received? When did it happen?
Monitoring tools can then collect these logs and alert you if errors start happening too often. This proactive approach allows you to identify problems quickly, sometimes even before users notice them. This helps immensely with recovery after limits because you can respond to issues faster. Detailed logs are like the “black box” of your AI application, providing clues when things go wrong.
Set up alerts for specific error types or error rates. If your application starts getting 10% rate limit errors, that’s a sign you might need to adjust your quota management or scale up your AI service plan. Good logging is the first step to understanding and preventing future issues.
4. Advanced Rate Limiting Concepts
While you’re mostly concerned with reacting to rate limits, understanding how they are implemented can give you a deeper insight. Knowing these concepts helps you better anticipate and manage your quota management. It helps you predict when you might hit limits.
4.1. Token Bucket Algorithm
One common way services implement rate limits is using the “token bucket algorithm.” Imagine a bucket that holds “tokens.” These tokens represent permission to make a request. The bucket has a maximum capacity. Tokens are added to the bucket at a steady rate. Every time you make a request, you need to grab a token from the bucket.
If the bucket is empty, you can’t make a request, and you hit a rate limit. If you send requests slowly, the bucket will always have tokens. If you send many requests quickly, you might empty the bucket and have to wait for it to refill. This is an effective way to control both average request rate and bursts of requests. Understanding this helps you with your own rate limit backoff strategies.
4.2. Sliding Window Rate Limits
Another sophisticated way to implement rate limits is using a “sliding window.” Instead of just counting requests in fixed blocks of time (like “per minute”), a sliding window counts requests over a rolling period. For example, if the limit is 100 requests per minute, a sliding window tracks requests over the last 60 seconds, no matter when that 60-second window starts.
This method provides smoother and fairer rate limiting compared to fixed windows. If you make 90 requests at the very end of one minute, and 90 requests at the very beginning of the next, a fixed window might let you through. A sliding window would likely rate limit you because you made 180 requests in a very short span. This advanced method helps ensure consistent quota management for AI services. Knowing these concepts helps you understand why your rate limit backoff might still be needed even with careful timing.
5. Cost Optimization Strategies
Good langchain timeout rate limit error handling isn’t just about reliability; it’s also about saving money. Every failed AI call that you retry unnecessarily, or every call that times out after a long wait, can still cost you something. By handling errors smartly, you reduce wasted resources and API calls. This is a crucial part of cost optimization strategies.
If your application blindly retries after hitting a rate limit, it just sends more requests that will probably fail. Each of these might still incur a small cost or count towards your usage, even if it doesn’t get a full response. Proper rate limit backoff prevents these wasteful attempts. Smart error handling directly contributes to your quota management for API usage.
5.1. Smart Retries
The key to cost optimization strategies is not just to retry, but to retry smartly. Don’t retry errors that are unlikely to succeed. For example, if you get an error indicating a “Bad Request” (HTTP 400) because your input was formatted incorrectly, retrying the exact same request will just fail again. This wastes money and API credits.
Your langchain timeout rate limit error handling should distinguish between transient errors (like timeouts or temporary rate limits) and permanent errors (like invalid API keys or malformed prompts). Only transient errors should trigger retries. This approach ensures that recovery after limits is efficient and cost-effective. It stops you from throwing good money after bad.
5.2. Caching Responses
One of the most effective cost optimization strategies for AI applications is caching. If your application asks the same question or similar questions frequently, you don’t need to send a new request to the LLM every single time. Instead, you can store (cache) the response from the first time and return that stored answer for subsequent identical requests.
Caching reduces the number of API calls you make, which directly saves money. It also reduces the likelihood of hitting rate limits because you’re making fewer external requests. LangChain itself offers built-in caching mechanisms that you can enable and configure. This is an excellent way to improve performance and save costs simultaneously.
You can explore LangChain’s caching options in more detail in our blog post [LangChain Caching Strategies Explained](/blog/langchain-caching-strategies-explained). Implementing caching effectively is a powerful tool in your quota management arsenal. It’s like having a local copy of frequently asked questions.
6. Putting It All Together: A Comprehensive Example
Let’s combine several of these best practices into a single, robust example. This code snippet demonstrates langchain timeout rate limit error handling with both a request timeout and a retry mechanism using tenacity for specific transient errors. It shows how you can build a truly resilient AI application.
This example will try to call the LLM, respect a timeout, and if it encounters a simulated openai.APIConnectionError (which could be a timeout) or openai.RateLimitError, it will retry with exponential backoff and jitter. This is a very powerful combination for graceful timeout handling and recovery after limits.
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import time
import random
from tenacity import (
retry,
stop_after_attempt,
wait_exponential,
retry_if_exception_type,
before_sleep_log,
)
import openai
import logging
from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
# Setup basic logging for tenacity output
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
logger = logging.getLogger(__name__)
# --- Simulate different types of OpenAI errors ---
class MockOpenAIRateLimitError(openai.RateLimitError):
def __init__(self, message, *args, **kwargs):
super().__init__(message, *args, **kwargs)
class MockOpenAITimeoutError(openai.APIConnectionError): # Treat connection errors as potential timeouts for retry
def __init__(self, message, *args, **kwargs):
super().__init__(message, request=None, response=None, *args, **kwargs)
class MockOpenAIBadRequestError(openai.BadRequestError): # A permanent error, should NOT retry
def __init__(self, message, *args, **kwargs):
super().__init__(message, response=None, body=None, *args, **kwargs)
# --- The core LLM interaction function with comprehensive error handling ---
call_attempt_count = 0
@retry(
retry=retry_if_exception_type((MockOpenAIRateLimitError, MockOpenAITimeoutError)),
wait=wait_exponential(multiplier=1, min=2, max=20) + random.uniform(0, 1), # Exponential backoff + jitter
stop=stop_after_attempt(7), # Stop after 7 attempts (1 initial + 6 retries)
reraise=True, # Re-raise the last exception if all retries fail
before_sleep=before_sleep_log(logger, logging.INFO, exc_info=True) # Log before sleeping
)
def get_ai_response_robust(llm_instance, prompt_text):
global call_attempt_count
call_attempt_count += 1
logger.info(f"Attempt {call_attempt_count}: Sending request to LLM...")
# Simulate different error conditions based on attempt number
if call_attempt_count == 1:
logger.warning(" Simulating RateLimitError for demonstration.")
raise MockOpenAIRateLimitError("Too many requests!", json_body={"error": {"code": "rate_limit_exceeded"}})
elif call_attempt_count == 2:
logger.warning(" Simulating TimeoutError for demonstration.")
raise MockOpenAITimeoutError("The connection timed out after 15 seconds.", request=None)
elif call_attempt_count == 3:
logger.error(" Simulating a permanent BadRequestError. This should NOT be retried normally.")
# If this error were configured to be retried, it would loop forever
raise MockOpenAIBadRequestError("Invalid request format.", response=None, body={"message": "Invalid prompt"})
else:
# On subsequent attempts, succeed
logger.info(f" Attempt {call_attempt_count}: LLM call succeeded after retries!")
return llm_instance.invoke([HumanMessage(content=prompt_text)])
# --- Main script execution ---
if __name__ == "__main__":
print("\n--- Comprehensive LangChain Error Handling Example ---")
# Initialize LLM with a specific request timeout
robust_llm = ChatOpenAI(
model="gpt-3.5-turbo",
temperature=0.7,
request_timeout=15, # Individual LLM call timeout
api_key="YOUR_OPENAI_API_KEY" # Replace with your actual key
)
prompt_template = ChatPromptTemplate.from_template("Explain {topic} in simple terms.")
output_parser = StrOutputParser()
# Create a simple chain
robust_chain = prompt_template | robust_llm | output_parser
# --- Test Case 1: Expected to succeed after retries ---
print("\n--- Test Case 1: Transient Errors (Rate Limit, Timeout) ---")
call_attempt_count = 0 # Reset counter for new test case
try:
response = get_ai_response_robust(robust_llm, "quantum computing")
print("\nSUCCESS: Final response for 'quantum computing':", response.content[:100], "...")
except (MockOpenAIRateLimitError, MockOpenAITimeoutError) as e:
print(f"\nFAILURE: Operation failed after all retries due to {type(e).__name__}: {e}")
except MockOpenAIBadRequestError as e:
print(f"\nFAILURE: Operation failed due to {type(e).__name__} (should not have retried this if configured correctly): {e}")
except Exception as e:
print(f"\nFAILURE: An unexpected error occurred: {e}")
# --- Test Case 2: Permanent Error (Bad Request) - Demonstrates smart retries ---
# Here, we show that if the error is PERMANENT, blindly retrying is bad.
# The `retry_if_exception_type` in tenacity is key here.
# For a real scenario, you'd configure `get_ai_response_robust` to NOT retry MockOpenAIBadRequestError
# But for this demo, we'll let it try and fail early.
print("\n--- Test Case 2: Permanent Error (Bad Request) ---")
call_attempt_count = 0 # Reset counter
# Temporarily modify the retry logic for demonstration
# In a real app, you'd have a separate function or more nuanced retry_if_exception_type
@retry(
retry=retry_if_exception_type(MockOpenAIBadRequestError), # THIS IS BAD, FOR DEMO ONLY
stop=stop_after_attempt(2), # Fail fast
reraise=True,
before_sleep=before_sleep_log(logger, logging.INFO, exc_info=True)
)
def get_ai_response_bad_request_test(llm_instance, prompt_text):
global call_attempt_count
call_attempt_count += 1
logger.info(f"Attempt {call_attempt_count}: Sending request for bad request test...")
raise MockOpenAIBadRequestError("Invalid request format for 'nonsense'.", response=None, body={"message": "Invalid prompt"})
try:
# This call will always raise MockOpenAIBadRequestError and stop quickly
get_ai_response_bad_request_test(robust_llm, "nonsense prompt that causes bad request")
except MockOpenAIBadRequestError as e:
print(f"\nFAILURE: Correctly stopped retrying for permanent error {type(e).__name__}: {e}")
except Exception as e:
print(f"\nFAILURE: An unexpected error occurred in bad request test: {e}")
print("\n--- End of Comprehensive Example ---")
This comprehensive example showcases multiple aspects of langchain timeout rate limit error handling. You can see how an individual LLM has a timeout, how tenacity handles retries for transient errors like rate limits and connection issues, and how it avoids retrying permanent errors. The logging provides clear visibility into the retry process. This approach builds incredibly resilient AI applications.
Conclusion
Building AI applications with LangChain is exciting, but real-world conditions mean you’ll encounter timeouts and rate limits. Mastering langchain timeout rate limit error handling is not just a good idea; it’s essential for creating reliable, efficient, and cost-effective systems. By understanding and implementing strategies for timeout configuration, request timeout handling, rate limit detection, and rate limit backoff, you empower your applications to recover gracefully from challenges.
Remember to leverage tools like tenacity for smart retries with graceful timeout handling and jitter. Always consider recovery after limits by planning for fallbacks and using circuit breakers for persistent issues. Finally, don’t forget that effective cost optimization strategies are intertwined with smart error handling and quota management. Implement these best practices, and you’ll build robust AI solutions that truly stand the test of time and traffic. Your AI helper will thank you for making it so resilient!
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