Handle token limit error gracefully
Quick answer
A
token limit error occurs when your input plus expected output exceeds the model's maximum context window. Handle it gracefully by detecting the error, truncating or summarizing input to fit within the limit, and optionally retrying the request with adjusted input. ERROR TYPE
api_error ⚡ QUICK FIX
Catch the token limit error and truncate or summarize your input to fit within the model's context window before retrying the API call.
Why this happens
Large language models have a fixed context window size, which limits the total number of tokens (input + output) they can process in a single request. If your prompt plus the expected completion tokens exceed this limit, the API returns a token limit error. For example, sending a very long conversation history or document without trimming can trigger this error.
Typical error output looks like:
{"error": {"message": "This model's maximum context length is 8192 tokens, but you requested 9000 tokens.", "type": "invalid_request_error"}}Example of problematic code:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
messages = [{"role": "user", "content": "Very long text exceeding token limit..."}]
response = client.chat.completions.create(
model="gpt-4o",
messages=messages,
max_tokens=1024
)
print(response.choices[0].message.content) output
{"error": {"message": "This model's maximum context length is 8192 tokens, but you requested 9000 tokens.", "type": "invalid_request_error"}} The fix
To fix this, catch the token limit error and reduce the input size by truncating or summarizing the prompt. Then retry the API call with the adjusted input. This ensures the total tokens fit within the model's context window.
Example fixed code with truncation:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# Function to truncate text to approximate token limit
# (Use a tokenizer library for precise token counting in production)
def truncate_text(text, max_tokens=7000):
# Simple heuristic: assume 4 chars per token
max_chars = max_tokens * 4
return text[:max_chars]
try:
long_text = "Very long text exceeding token limit..." * 1000
truncated_text = truncate_text(long_text)
messages = [{"role": "user", "content": truncated_text}]
response = client.chat.completions.create(
model="gpt-4o",
messages=messages,
max_tokens=1024
)
print(response.choices[0].message.content)
except Exception as e:
print(f"Error: {e}") output
Here is the response based on the truncated input...
Preventing it in production
- Implement input validation to estimate token count before sending requests, using tokenizer libraries like
tiktoken. - Automatically truncate or summarize long inputs to fit within the model's context window minus expected output tokens.
- Use exponential backoff and retry logic to handle transient errors gracefully.
- Consider chunking large documents and processing them sequentially or with retrieval-augmented generation (RAG) to stay within limits.
Key Takeaways
- Always check and respect the model's maximum context window to avoid token limit errors.
- Use token counting libraries to pre-validate input length before API calls.
- Implement truncation or summarization to fit inputs within token limits.
- Add retry logic with backoff to handle transient API errors gracefully.