[SC 10121] Add demo notebook for the code explainer

[AnilSorathiya]

Pull Request Description

The Code explainer notebook has been added in this change.

What

The notebook will use for demonstrating the new llm feature called code explainer.

Why

It is one of customer requirements.

How to Test

1. Register model using model source code template in UI
2. Run this notebook with the credentials.
3. The document contents will be automatically generated using this notebook.

Pull Request Dependencies

https://github.com/validmind/backend/pull/1500

External Release Notes

Deployment Notes

Breaking Changes

no breaking changes

Screenshots/Videos (Frontend Only)

Checklist

• PR body describes what, why, and how to test
• Release notes written
• Deployment notes written
• Breaking changes identified
• Labels applied
• PR linked to Shortcut
• Screenshots/videos added (Frontend)
• Unit tests added (Backend)
• Tested locally
• Documentation updated (if required)

Areas Needing Special Review

Additional Notes

[github-actions]

Pull requests must include a description in the release notes section.

[github-actions]

Pull requests must include a description in the release notes section.

[cachafla]

Looks good! I'd just add the following to finalize the notebook:

1. The typical intro/outro template content that we have in the other notebooks
2. Very short paragraph at the beginning of each section i.e. Codebase Overview, Environment and Dependencies, etc.

[AnilSorathiya]

Looks good! I'd just add the following to finalize the notebook:

1. The typical intro/outro template content that we have in the other notebooks
2. Very short paragraph at the beginning of each section i.e. Codebase Overview, Environment and Dependencies, etc.

yes. done thanks

[juanmleng]

I noticed that some of the sections in the notebook (e.g., Evaluation and Validation, Logging and Monitoring, Inference and Scoring, Testing Strategy, Versioning, and Security) assume details that are not included in the customer_churn_full_suite.py code. In those cases, this may cause the code explainer to hallucinate? If that is the case, I would suggest aligning the examples in the notebook with what is implemented in the script.

[AnilSorathiya]

I noticed that some of the sections in the notebook (e.g., Evaluation and Validation, Logging and Monitoring, Inference and Scoring, Testing Strategy, Versioning, and Security) assume details that are not included in the customer_churn_full_suite.py code. In those cases, this may cause the code explainer to hallucinate? If that is the case, I would suggest aligning the examples in the notebook with what is implemented in the script.

Thanks. Updated the customer_churn_full_suite.py to reduce hallucination.

[juanmleng]

Great to have a brand new LLM feature! Well done. I just left a couple of minor comments.

[github-actions]

PR Summary

This pull request introduces a comprehensive script and Jupyter notebook for documenting and explaining a customer churn prediction model using the ValidMind library. The key additions include:

1. Customer Churn Model Script: A Python script (customer_churn_full_suite.py) that demonstrates the process of documenting a machine learning model using the ValidMind library. It includes:

   • Initialization of the ValidMind library and configuration management.
   • Loading and preprocessing of a sample dataset for customer churn prediction.
   • Training of an XGBoost classification model.
   • Model versioning and artifact management.
   • Running a full suite of documentation tests using ValidMind.
   • Model inference and scoring.

2. Code Explainer Notebook: A Jupyter notebook (quickstart_code_explainer_demo.ipynb) that provides a step-by-step guide on using the ValidMind code explainer to generate comprehensive documentation for a codebase. It covers:

   • Overview of the ValidMind code explainer and its benefits.
   • Instructions for installing and initializing the ValidMind library.
   • Detailed sections for code analysis, including environment setup, data handling, model implementation, training pipeline, evaluation, inference, configuration management, testing, logging, versioning, security, and example runs.

These additions aim to enhance the documentation and understanding of the model and codebase, facilitating better collaboration and compliance with best practices.

Test Suggestions

• Test the loading and preprocessing of the customer churn dataset to ensure data integrity.
• Verify the training process of the XGBoost model, including hyperparameter settings and early stopping.
• Check the model versioning and artifact saving functionality for correct file creation and retrieval.
• Run the full suite of ValidMind documentation tests and verify the generated documentation.
• Test the inference and scoring methods for accuracy and performance on new data.
• Validate the code explainer notebook by running each cell and ensuring expected outputs are generated.
• Ensure that the logging configuration captures all necessary information during script execution.