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Review by Korbit AI
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| Category | Issue | Status |
|---|---|---|
 |
Over-broad Exception Handling ▹ view |
Files scanned
| File Path | Reviewed |
|---|---|
| src/agentlab/llm/llm_utils.py | ✅ |
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| try: | ||
| return AutoTokenizer.from_pretrained(model_name) | ||
| except OSError: | ||
| logging.info(f"Could not find a tokenizer for model {model_name}. Defaulting to gpt-4.") | ||
| except Exception as e: | ||
| logging.info(f"Could not find a tokenizer for model {model_name}: {e} Defaulting to gpt-4.") |
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Over-broad Exception Handling 
Tell me more
What is the issue?
Using a bare Exception catch is too broad and could mask critical errors that should be handled differently.
Why this matters
This could catch and ignore serious issues like memory errors or import errors that require different handling, potentially making debugging more difficult.
Suggested change ∙ Feature Preview
Catch specific exceptions that are expected in tokenizer loading:
try:
return AutoTokenizer.from_pretrained(model_name)
except (OSError, ValueError) as e:
logging.info(f"Could not find a tokenizer for model {model_name}: {e} Defaulting to gpt-4.")Provide feedback to improve future suggestions
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Changes:
How to reproduce the error
Error that this PR addresses
Description by Korbit AI
What change is being made?
Update the exception handling in
get_tokenizerto catch all exceptions and log the specific error message, then default to "gpt-4".Why are these changes being made?
The change aims to improve the robustness and clarity of the error handling by logging the specific exception encountered when a tokenizer cannot be found, ensuring that unexpected issues are detected and reported more precisely. This approach guarantees fallback to a known default model while maintaining detailed logging information for potential troubleshooting.