Add script to generate provisional nodes

[vish-cs]

No description provided.

[gemini-code-assist]

Summary of Changes

Hello, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request introduces a new Python utility script designed to automate the identification and generation of 'provisional nodes' within a knowledge graph system. By scanning existing data definition files (.mcf), the script helps maintain data consistency by flagging and creating placeholder definitions for entities that are referenced but not formally defined, with an optional check against a Spanner database to prevent redundant provisional node creation.

Highlights

• New Script for Provisional Node Generation: A new Python script, generate_provisional_nodes.py, has been added to automate the identification and creation of provisional nodes.
• MCF File Scanning: The script scans a specified directory for .mcf files to identify nodes that are referenced in properties or values but are not explicitly defined within the local files.
• Spanner Integration: It includes an optional feature to check Google Cloud Spanner to determine if these 'missing' nodes already exist in the database, preventing redundant provisional node creation.
• Provisional Node Output: The script generates a provisional_nodes.mcf file containing definitions for truly missing nodes, classifying them as either dcs:Property or dcs:ProvisionalNode based on their usage.

Changelog

• import-automation/executor/generate_provisional_nodes.py
  • Added a new script to generate provisional nodes.
  • Implemented logic to scan MCF files for defined and referenced nodes.
  • Included functionality to query Google Cloud Spanner for existing nodes.
  • Provided options to skip Spanner checks and specify a scan directory.

Activity

• No specific activity (comments, reviews, progress updates) has been recorded for this pull request yet.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for GitHub and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request introduces a new script to generate provisional nodes from MCF files. The script scans a directory, identifies undefined node references, checks their existence in Spanner, and generates an MCF file for the missing nodes. My review focuses on improving the script's robustness, efficiency, and maintainability. I've identified a potential bug in parsing MCF files due to a restrictive regex, suggested improvements for memory efficiency when reading files, and recommended making configurations more flexible and centralizing constants.