refactor composer to use sqlalchemy, avoid string concatenations and follow conventions

api/recon/build.py

@@ -137,6 +137,11 @@ def merge_into_recon(dataset: str, extracted: dict[str, Any]) -> Recon:
 
     relationships = [Relationship.parse(r) for r in (aug.get("relationships", []) or [])]
 
+    # Storage-encoding overrides (optional). Keyed by Column.semantic_type; each
+    # value maps a role (e.g. "as_date") to a SQL template with a `{col}`
+    # placeholder. The composer merges these over its built-in defaults.
+    encodings: dict[str, dict[str, str]] = aug.get("encodings", {}) or {}
+
     column_to_tables: dict[str, list[str]] = {}
     for t in tables.values():
         for c in t.columns:
@@ -150,6 +155,7 @@ def merge_into_recon(dataset: str, extracted: dict[str, Any]) -> Recon:
         metrics=metrics,
         column_to_tables=column_to_tables,
         relationships=relationships,
+        encodings=encodings,
     )
 
 

api/recon/types.py

@@ -151,6 +151,12 @@ class Recon:
     metrics: dict[str, Metric]
     column_to_tables: dict[str, list[str]] = field(default_factory=dict)
     relationships: list[Relationship] = field(default_factory=list)
+    # Storage-encoding overrides keyed by Column.semantic_type. Each value is a
+    # map of role → SQL template with a `{col}` placeholder (e.g.
+    # {"date_yymmdd": {"as_date": "TO_DATE(LPAD({col}::text, 6, '0'), 'YYMMDD')"}}).
+    # The composer merges these over its DEFAULT_ENCODINGS — the dataset wins.
+    # Empty for datasets whose columns are stored in native types.
+    encodings: dict[str, dict[str, str]] = field(default_factory=dict)
 
     # ── Read-side helpers used by Analyses + the composer ──
 
@@ -299,6 +305,7 @@ class Recon:
             "metrics": {n: m.to_dict() for n, m in self.metrics.items()},
             "column_to_tables": self.column_to_tables,
             "relationships": [r.to_dict() for r in self.relationships],
+            "encodings": self.encodings,
         }
 
     @classmethod
@@ -309,4 +316,5 @@ class Recon:
             metrics={n: Metric.from_dict(m) for n, m in d.get("metrics", {}).items()},
             column_to_tables=d.get("column_to_tables", {}),
             relationships=[Relationship(**r) for r in d.get("relationships", [])],
+            encodings=d.get("encodings", {}) or {},
         )