SDMX metadata enrichment tools for schema mapping

[rohitkumarbhagat]

Add SDMX metadata enrichment tooling to find items, fetch enriched descriptions, and merge results for improved schema mapping.

[gemini-code-assist]

Summary of Changes

Hello @rohitkumarbhagat, 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 robust set of agentic tools designed to streamline and enhance SDMX metadata enrichment. The workflow involves identifying specific metadata elements that lack sufficient detail, using AI-powered web searches to gather more comprehensive descriptions, and then merging these enriched descriptions back into the primary metadata. This automation significantly improves the quality and completeness of SDMX metadata, which is crucial for accurate schema mapping and data interpretation.

Highlights

• Metadata Selection Tool: A new Python script (metadata_enricher_find.py) is introduced to intelligently select SDMX codes and concepts requiring enrichment and generate targeted web search queries.
• Description Fetching Tool: Another new Python script (metadata_enricher_fetch.py) leverages the Gemini CLI to execute the generated queries, fetching and populating enriched_description fields for the selected metadata items.
• Metadata Merging Tool: A third Python script (metadata_enricher_merge.py) is added to seamlessly integrate the newly fetched enriched_description values back into the original SDMX metadata JSON structure.
• Comprehensive Documentation and Testing: Each new tool comes with dedicated unit tests and a clear README file outlining its purpose, CLI usage, and expected outputs. Jinja2 templates are also added for prompt generation.

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[gemini-code-assist]

Code Review

This pull request introduces a set of tools for SDMX metadata enrichment, including finding items to enrich, fetching data with Gemini, and merging the results. The implementation is well-structured with separate scripts for each step, corresponding tests, and documentation.

My review focuses on two main points:

1. A critical security vulnerability in metadata_enricher_fetch.py and metadata_enricher_find.py due to the use of shell=True with user-provided input, which could lead to command injection. I've provided detailed comments on how to refactor this to improve security.
2. A significant amount of code duplication between metadata_enricher_fetch.py and metadata_enricher_find.py. I've suggested refactoring this into a base class to improve maintainability.

Apart from these points, the code is clear and the new functionality is well-tested. The README provides good guidance on how to use the new tools.