Neurobyte

v0.2.3

NOTEBOOK_CONTEXT_ENGINE

The standard for preparing Jupyter Notebooks for LLM interactions. Neurobyte v0.2.3 introduces a strictly typed Audit → Format → Export pipeline that prevents hallucinations, sanitizes secrets, and optimizes context windows. Built for high-stakes production environments.

GitHub PyPI
neurobyte-cli
❯ neurobyte export analysis.ipynb --json
... reading notebook ...
[EXTRACTED] 14 code cells found.
[REDACTED] API_KEY pattern detected in Cell 3.
[SUMMARY] "Data ingestion and cleaning logic."
Context exported to ./context/analysis.json

The v0.2.3 Workflow

  • 1. Audit: Pre-flight checks for your notebook. Detects leaked secrets (API keys), cyclomatic complexity spikes, and non-reproducible paths before they enter your pipeline.
  • 2. Format: Token optimization engine. Hoists scattered imports to the top and applies ruff formatting to ensure code is canonical and easy for LLMs to parse.
  • 3. Export: High-fidelity context generation. Outputs to XML, JSON, or text with smart redaction and optional markdown narratives.

Modern CLI (v2)

  • › Enhanced Help: Clearer output with examples.
  • › Type Safety: Improved validation ensures fewer runtime errors.

Production-Grade

  • › Verified Redaction: Sensitive data stripping backed by security tests.
  • › Confirmed Robustness: >70% test coverage & mypy strict compliance.

Developer Experience

  • › Deep Introspection: New --verbose flag for granular logs.
  • › Standard Tooling: Modern packaging & Makefile workflows.
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