marcopan
292048f777
Due cambiamenti interconnessi da user review:
1. language come parametro workspace (spec decisione 9):
- Config.language (default 'en') + workspaces PSD con 'language: it'
- Generalizza Thoth oltre l'italiano: descrizioni tabelle/colonne ed evidence
sono nel workspace language; le istruzioni della skill restano in inglese
(piu' affidabili per modelli piccoli, meno ambigue)
2. Skill riscritta in INGLESE preservando la semantica COMPLETA dell'originale
(autocritica: la mia riscrittura precedente aveva perso ~10 vincoli precisi):
- 'promuovere' ambiguo (3 accezioni: phase advance / recommend / memory promote)
-> 'never advance a phase or record a decision without confirmation'
- recuperati vincoli persi: choice-is-confirmation (no reviewer_confirm dopo
reviewer_decide), reviewer_select SOLO per iterazione no-decision, messaggi
auto-contenuti obbligatori, artefatto = superficie di decisione (gate rilegge
da disco per CTE/SQL), candidati con provenienza+score non verita', opzione
'leave ambiguity open', F1 passa lista completa non solo ultima
- language contract esplicito (istruzioni EN, output nel workspace language)
Sottomoduli cte/memoria/rewriting/sql-generation in inglese, semantica tecnica
intatta (regole AV-SQL, dim_time trick, max 5 memorie solo 3 tipi riusabili).
Verifica: 0 residui nsp/chirone, tutti i tht <cmd> citati registrati, 165 passed.
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2.7 KiB
Final SQL generation technique
Adapted from the "query generation" step of AV-SQL (recursive divide-and-conquer) and its review checklist.
Generation (divide-and-conquer)
- Divide: decompose the rewritten question into sub-questions, each aimed at a piece of information or logic (a population, a filter, an aggregate).
- Conquer: for each sub-question formulate a pseudo-SQL, with placeholders for sub-questions not yet resolved. The CTEs already tested in Phase 6 are the preferred building blocks: reuse them by name, with their known outcome.
- Recombine: replace placeholders bottom-up until the full SQL. The final SQL may include the CTEs in its own WITH.
- Dialect: PostgreSQL. Copy table and column names EXACTLY from the schema context; never invent objects.
The file sessions/<id>/sql_final.sql must contain ONLY the SQL, clean and
copy-pasteable: no rationale comments (that lives in the audit artifacts).
Time dimension (analysis by year/month/quarter)
Fact tables have data_time_key (integer, format YYYYMMDD): it is the FK to
dim_time.day_key. This FK is NOT declared in the DWH (facts have
foreign_keys: []), so it will NOT appear in schema_linking.json: you must add it
by hand to the join.
- To extract year, month, quarter, semester etc. do
JOIN dim_time dt ON dt.day_key = <fact>.data_time_keyand use the dimension's columns:dt.year,dt.month,dt.quarter,dt.semester,dt.full_date,dt.month_name_it,dt.year_month. - Do NOT do arithmetic on the key (e.g.
data_time_key / 10000for the year): it works by accident but is fragile and breaks as soon as you need to format a date or do a cast. Always usedim_time. - "Last N years from the most recent year":
dt.year >= (SELECT MAX(year) FROM dim_time WHERE day_key IN (SELECT data_time_key FROM <fact>)) - (N-1), or compute the max year on the rows actually present in the fact.
Review checklist (on errors or suspicious results)
- Do the returned columns answer the question exactly?
- Do the filters (WHERE/HAVING) reflect ALL the conditions of the rewritten question?
- Are aggregations, groupings and orderings the required ones?
- Empty or zero result: almost always indicates a problem in conditions or joins.
Verify the filter values with
tht search "<value>"(match on real values). - Do the joins follow those promoted in
schema_linking.json? (exception: the FKdata_time_key → dim_time.day_keyis not declared, see the time section.) - Time analyses: are you using
JOIN dim_timeand not key arithmetic?
Every substantive revision is recorded with:
reviewer_decide(options:[{label:"Register revision", type:"sql_revised", subject:"sql_final", detail:"<what changed>", rationale:"<why>"}], allow_other:false).