Fix chunk size too large error
chunk size too large error occurs when the input text chunk exceeds the model's token limit or API constraints. To fix it, reduce the chunk size by splitting your input into smaller segments before sending to the model, ensuring each chunk fits within the allowed token limit.config_error chunk size too large error.Why this happens
The chunk size too large error arises when you send input chunks that exceed the maximum token limit supported by the AI model or API. For example, if you split a large document into chunks of 5000 tokens but the model only supports 4096 tokens per request, the API will reject the request with this error.
Typical triggering code looks like this:
chunks = text_splitter.split_text(document, chunk_size=5000)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": chunk} for chunk in chunks]
)This causes the error because chunk_size=5000 exceeds the model's token limit.
chunks = text_splitter.split_text(document, chunk_size=5000)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": chunk} for chunk in chunks]
)
# Error: chunk size too largeopenai.error.InvalidRequestError: chunk size too large
The fix
Reduce the chunk_size to a value within the model's token limit, typically 2048 or 4096 tokens depending on the model. This ensures each chunk fits in a single API request.
Example corrected code:
from openai import OpenAI
import os
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# Use a smaller chunk size, e.g., 1000 tokens
chunks = text_splitter.split_text(document, chunk_size=1000)
for chunk in chunks:
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": chunk}]
)
print(response.choices[0].message.content)
# This works because chunk_size=1000 fits within token limitsResponse text from model for each chunk printed without error
Preventing it in production
Implement validation to check chunk sizes before sending requests. Use token counting libraries (like tiktoken for OpenAI models) to ensure chunks do not exceed limits.
Incorporate retry logic with exponential backoff for transient errors. Also, consider dynamically adjusting chunk sizes based on model limits or API error feedback.
Example best practices:
- Count tokens per chunk before API call.
- Set chunk size conservatively below model max tokens.
- Use retries with backoff on errors.
- Log and monitor chunk sizes and errors.
Key Takeaways
- Always ensure chunk sizes fit within the model's token limits to avoid errors.
- Use token counting tools to validate chunk sizes before API calls.
- Implement retries with exponential backoff to handle transient API errors.