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This pull request updates the golden files automatically via Cloud Build. Please review the changes carefully. Cloud Build Log

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Summary of Changes

Hello @datacommons-robot-author, 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 automatically updates integration test golden files, reflecting recent changes in the backend's query processing and Statistical Variable (SV) matching algorithms. The updates demonstrate refined query interpretation, including the integration of Large Language Models for certain query types, and adjustments to how SVs are identified and scored. These changes ensure that the integration tests accurately reflect the current system behavior.

Highlights

  • Automated Golden File Updates: This pull request automatically updates integration test golden files, reflecting recent changes in the system's query processing and Statistical Variable (SV) matching logic.
  • Query Processing Enhancements: The debug_info.json files show updates to how queries are processed, including changes in query_with_places_removed and query_transformations fields, indicating refined parsing and simplification.
  • Large Language Model (LLM) Integration: For certain queries, an llm_response object has been introduced in the debug logs, demonstrating the system's use of LLMs to interpret query intent and identify relevant metrics.
  • Divergent Query Handling: Different queries now exhibit distinct behaviors in SV matching and LLM logging, suggesting specialized processing paths based on the query's structure or content.
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Code Review

This pull request updates golden files for integration tests.

The changes for the compareobesityvs.poverty test case appear to be an improvement, with better metric detection from the new LLM-based flow. However, there's a potentially problematic value in the debug logs for sv_detection_query_index_types.

More critically, the changes for the numberofpoorhispanicwomenwithphd test case show a significant regression. The new version fails to detect any statistical variables, whereas the previous version was successful. This seems to be caused by an issue in the query processing logic. This regression should be addressed.

Comment on lines +8 to +13
"query_with_places_removed": "",
"sv_matching": {
"CosineScore": [
0.79858,
0.78833,
0.77635,
0.77406,
0.74103,
0.73347,
0.72283,
0.72257,
0.69955,
0.6995,
0.69762,
0.69743,
0.696,
0.69534,
0.69452,
0.69396,
0.69354,
0.69084,
0.6907,
0.68967,
0.68895,
0.68862,
0.68633,
0.68592,
0.68275,
0.68221,
0.68156,
0.67987,
0.67686,
0.67631,
0.67515,
0.6745,
0.67257,
0.6718,
0.66917,
0.669,
0.66822,
0.66781,
0.66507,
0.66487
],
"MultiSV": {
"Candidates": [
{
"AggCosineScore": 0.8315,
"DelimBased": false,
"Parts": [
{
"CosineScore": [
0.83118,
0.831,
0.82552,
0.81955,
0.81456,
0.80907,
0.80864,
0.80744,
0.8003,
0.79858,
0.78782
],
"QueryPart": "number of poor hispanic",
"SV": [
"Count_Person_BelowPovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_HispanicOrLatino",
"Count_Person_Female_AbovePovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_AbovePovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Household_WithoutFoodStampsInThePast12Months_HispanicOrLatino",
"Count_Person_Male_AbovePovertyLevelInThePast12Months_BlackOrAfricanAmericanAlone",
"Count_Person_Male_BelowPovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_Female_BelowPovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_NoHealthInsurance_HispanicOrLatino",
"Count_Person_WithDisability_HispanicOrLatino",
"Count_Person_15OrMoreYears_Separated_HispanicOrLatino"
]
},
{
"CosineScore": [
0.83183,
0.80299
],
"QueryPart": "women phd",
"SV": [
"Count_Person_25OrMoreYears_EducationalAttainmentDoctorateDegree_Female",
"Count_Person_25OrMoreYears_Female_DoctorateDegree_AsFractionOf_Count_Person_25OrMoreYears_Female"
]
}
]
},
{
"AggCosineScore": 0.8259,
"DelimBased": false,
"Parts": [
{
"CosineScore": [
0.83667,
0.79727
],
"QueryPart": "number of poor",
"SV": [
"dc/topic/Poverty",
"Count_Person_Rural_BelowPovertyLevelInThePast12Months"
]
},
{
"CosineScore": [
0.81515,
0.7886,
0.77756,
0.77521
],
"QueryPart": "hispanic women phd",
"SV": [
"dc/06f0jf8xvzw4f",
"Count_Person_Female_HispanicOrLatino",
"dc/3w039ndqy7qv1",
"dc/topic/HispanicOrLatinoFemalePopulationByAge"
]
}
]
},
{
"AggCosineScore": 0.8071,
"DelimBased": false,
"Parts": [
{
"CosineScore": [
0.84959,
0.8335
],
"QueryPart": "number of poor hispanic women",
"SV": [
"Count_Person_Female_BelowPovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_Female_HispanicOrLatino"
]
},
{
"CosineScore": [
0.7647,
0.74767,
0.73771,
0.73628,
0.73059
],
"QueryPart": "phd",
"SV": [
"Count_Person_EducationalAttainmentDoctorateDegree",
"Count_Person_25OrMoreYears_EducationalAttainmentDoctorateDegree_Female",
"Count_Person_25OrMoreYears_DoctorateDegree_AsFractionOf_Count_Person_25OrMoreYears",
"Count_Person_25OrMoreYears_EducationalAttainmentDoctorateDegree_Male",
"Count_Person_25OrMoreYears_Female_DoctorateDegree_AsFractionOf_Count_Person_25OrMoreYears_Female"
]
}
]
}
]
},
"CosineScore": [],
"MultiSV": {},
"Query": "number of poor hispanic women with phd",
"SV": [
"dc/06f0jf8xvzw4f",
"Count_Person_Female_BelowPovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_Female_HispanicOrLatino",
"dc/3w039ndqy7qv1",
"dc/topic/HispanicOrLatinoFemalePopulationByAge",
"dc/9cqv67nn7pn1b",
"Median_Age_Person_Female_HispanicOrLatino",
"Count_Person_25OrMoreYears_EducationalAttainmentDoctorateDegree_Female",
"Count_Person_Female_AbovePovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_BelowPovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_15OrMoreYears_Widowed_HispanicOrLatino",
"Count_Person_15OrMoreYears_Divorced_HispanicOrLatino",
"Count_Household_HouseholderRaceHispanicOrLatino_SingleMotherFamilyHousehold",
"Count_Person_Female_NotHispanicOrLatino",
"Count_Student_HispanicOrLatino",
"Count_Person_25OrMoreYears_Female_DoctorateDegree_AsFractionOf_Count_Person_25OrMoreYears_Female",
"dc/0jtctjm33mgh1",
"Count_Person_WithDisability_HispanicOrLatino",
"Count_Person_15OrMoreYears_MarriedAndNotSeparated_HispanicOrLatino",
"Count_Person_HispanicOrLatino_ResidesInCollegeOrUniversityStudentHousing",
"Count_Household_WithoutFoodStampsInThePast12Months_HispanicOrLatino",
"Count_Person_HispanicOrLatino",
"Count_Person_AbovePovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_Female_BelowPovertyLevelInThePast12Months_TwoOrMoreRaces",
"Count_Person_Male_AbovePovertyLevelInThePast12Months_BlackOrAfricanAmericanAlone",
"Count_Person_15OrMoreYears_NeverMarried_HispanicOrLatino",
"Count_Person_Male_BelowPovertyLevelInThePast12Months_HispanicOrLatino",
"Count_Person_Female_BelowPovertyLevelInThePast12Months",
"dc/topic/PovertyByGender",
"Count_Person_Female_AbovePovertyLevelInThePast12Months_TwoOrMoreRaces",
"Count_Person_HispanicOrLatino_ResidesInNursingFacilities",
"Count_Person_NoHealthInsurance_HispanicOrLatino",
"dc/5hc4etrfyj9qg",
"Count_Person_Male_HispanicOrLatino",
"Count_Person_Female_BelowPovertyLevelInThePast12Months_WhiteAlone",
"dc/hyfn2tlyz48lb",
"Count_Person_25OrMoreYears_EducationalAttainmentSomeCollegeLessThan1Year_Female",
"Count_Person_25OrMoreYears_EducationalAttainmentBachelorsDegreeOrHigher_Female",
"Count_Person_Female_AbovePovertyLevelInThePast12Months_WhiteAlone",
"dc/epw58ne8mytn5"
]
"SV": []
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critical

This change indicates a significant regression for this test case. The previous version correctly identified numerous statistical variables (SVs), but the new version finds none (SV is empty).

The root cause appears to be that query_with_places_removed is now an empty string. With an empty query, it's expected that no SVs would be matched. This suggests a bug in the upstream query processing logic that strips the entire query.

Comment on lines 231 to +233
"sv_detection_query_index_types": [
"base_uae_mem"
],
"sv_detection_query_input": "compare obesity vs poverty",
"sv_detection_query_stop_words_removal": "obesity poverty"
""
]
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medium

The sv_detection_query_index_types is now [""]. Previously, it was ["base_uae_mem"]. Using an array with an empty string as an index type is ambiguous and could be a bug. If the intention is to not use any embeddings index, it would be clearer to use an empty array []. This might indicate an issue where the embeddings index is not being correctly passed or used in the new LLM-based detection flow.

@rohitkumarbhagat
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rohitkumarbhagat commented Jan 23, 2026

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