chore: auto-commit (26 archivos)
- python/functions/bigquery/bq_auth.md - python/functions/bigquery/bq_load_from_file.md - python/functions/bigquery/bq_load_from_gcs.md - python/functions/bigquery/client.py - python/functions/bigquery/queries.py - python/functions/datascience/__init__.py - python/functions/datascience/decode_qr_image.py - python/functions/datascience/load_bq_table_to_duckdb.md - python/functions/datascience/load_bq_table_to_duckdb.py - python/functions/pipelines/profile_bq_table.md - ... Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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@@ -121,6 +121,7 @@ def profile_database(
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write_report: bool = True,
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min_inclusion: float = 0.9,
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emit_pdf: bool = False,
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run_llm: bool = False,
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) -> dict:
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"""Perfila una base DuckDB entera + sus relaciones inter-tabla.
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@@ -141,6 +142,9 @@ def profile_database(
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render_eda_pdf_relational (resumen de tablas + relaciones FK + join
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graph) junto a los reports y devuelve su ruta en report_pdf_path. Con
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False no se toca el PDF (retrocompatible) y report_pdf_path es None.
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run_llm: si True (default False) activa la capa LLM interpretativa de
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profile_table para CADA tabla (una llamada LLM por tabla sobre el
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perfil agregado, nunca filas crudas).
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Returns:
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dict dict-no-throw. En exito:
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@@ -177,7 +181,9 @@ def profile_database(
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# 2) Perfilar cada tabla (tolerando fallos individuales).
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for table in tables:
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r = profile_table(db_path, table, sample=sample, write_report=False)
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r = profile_table(
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db_path, table, sample=sample, write_report=False, run_llm=run_llm
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)
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if r.get("status") == "ok":
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prof = r["profile"]
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table_profiles.append(prof)
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