Fix: Correct location extraction logic and RAG other mode initialization

[ARYANPATEL-BIT]

Description

This PR fixes critical data parsing and logic errors in the Government RAG benchmarking example that previously broke location-based filtering and caused the [other] mode to crash.

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Changes

1. Location Extraction Overhaul (basemodel.py)

Issue

• Used os.path.basename(os.getcwd()) to determine province
• Always returned project root (e.g., "ianvs")
• Broke RAG filtering logic completely

Fix

• Added _load_locations_from_dataset(data) helper method

Strategy

• Primary: Reads .jsonl dataset and extracts province from level_4_dim
• Fallback: Uses NLP-based text matching + internal dictionary to detect and translate Chinese province names

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2. Fixed Missing all_locations Attribute

Issue

• [other] mode used self.all_locations without initialization
• Caused AttributeError

Fix

• Properly initializes self.all_locations during location extraction
• Stores all valid provinces dynamically
• Enables correct inverse filtering logic

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Testing Performed

• ✅ Verified correct province mapping per query (e.g., "Shanghai")
• ✅ Confirmed RAG filtering works as expected
• ✅ Tested [other] mode → no crashes, correct inverted dataset behavior

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Type of Change

• Bug fix (non-breaking change)
• Performance improvement

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Impact

• Restores correct RAG filtering behavior
• Fixes [other] mode crash
• Improves robustness of location detection across environments

Fixes : #379

[kubeedge-bot]

Welcome @ARYANPATEL-BIT! It looks like this is your first PR to kubeedge/ianvs 🎉

[kubeedge-bot]

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[gemini-code-assist]

Code Review

This pull request improves location-based RAG filtering by extracting province information directly from the dataset or query text, replacing the previous unreliable directory-based detection. Review feedback suggests optimizing the location extraction logic by ensuring all_locations only contains detected provinces to avoid inefficient searches and invalid path lookups. Additionally, it is recommended to cache the dataset parsing results to prevent performance degradation during the prediction process.

[ARYANPATEL-BIT]

/assign @MooreZheng