In modern data science organizations, Jupyter Notebooks ( .ipynb ) serve as the primary medium for exploratory data analysis (EDA), model training, hypothesis validation, and architectural experiments. However, standard enterprise knowledge graphs and LLM retrieval pipelines have historically struggled to ingest notebooks safely and meaningfully:

Security Vulnerabilities (OWASP LLM02): Ingesting external notebooks that contain untrusted Python or shell magics ( !curl , os.system ) risks accidental code execution if runtime environments ( ipykernel , exec() , eval() ) are triggered during parsing.

Noise & Bloat: A single uncleaned notebook can contain tens of megabytes of raw Base64 PNG plots, ANSI color codes from failed stack traces, and verbose progress bar outputs.

Stale Knowledge & Orphaned Entities: When a data scientist deletes or refactors a notebook, stale entity nodes and outdated embeddings often remain indefinitely in vector and graph storage.

As part of the WeMakeDevs × Cognee Mergetober…