Building a Self-Healing Data Pipeline That Fixes Its Own Python Errors

A self-healing data pipeline can be built to automatically fix common Python errors that occur during data ingestion. The system employs a "Try-Heal-Retry" architecture where a failure triggers an exception handler that captures the error message and a snippet of the source file. This context is then sent to a Large Language Model (LLM), which diagnoses the problem and proposes corrected parameters for the data loading function. The script uses Pydantic to ensure the LLM returns a structured JSON response and the tenacity library to cleanly manage the retry logic with the new parameters. While effective, the author also discusses important considerations like the potential cost of API calls and data privacy risks when sending file contents to an external service.