Bump ty and ruff pins

.git-blame-ignore-revs

@@ -0,0 +1,2 @@
+# ruff format, line length 100
+65ca0f267e1804971ddb71ea2f85a02cad9f8294

.github/workflows/ci.yml

@@ -6,10 +6,14 @@ on:
 jobs:
   label-gate:
     uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/label-gate.yml@main
+  ruff:
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/ruff.yml@ruff-ty-workflows
+  ty:
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/ty.yml@ruff-ty-workflows
   build-test-sqlite:
-    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test.yml@main
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test-sqlite.yml@ruff-ty-workflows
   build-test-postgres:
-    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test-postgres-v2.yml@main
+    uses: AustralianCancerDataNetwork/cava-devops/.github/workflows/build-test-postgres-v3.yml@ruff-ty-workflows
     with:
       postgres-db: omop_graph_test
       setup-commands: |

README.md

@@ -80,8 +80,8 @@ This classification drives traversal filtering and scoring.
 from omop_graph.graph.paths import find_shortest_paths
 from omop_graph.extensions.omop_alchemy import PredicateKind
 
-ingredient = kg.concept_id_by_code("RxNorm", "6809")    # Metformin
-drug = kg.concept_id_by_code("RxNorm", "860975")         # Metformin 500 MG Oral Tablet
+ingredient = kg.concept_id_by_code("RxNorm", "6809")  # Metformin
+drug = kg.concept_id_by_code("RxNorm", "860975")  # Metformin 500 MG Oral Tablet
 
 paths, trace = find_shortest_paths(
     kg,

docs/graph/kg.md

@@ -66,8 +66,10 @@ from omop_graph.graph.kg import KnowledgeGraph, KnowledgeGraphEmbeddingConfigura
 from omop_emb.config import MetricType
 
 resolver = Resolver.from_active_config()
-resolved_vector_store = resolver.resolve_vector_store("vector_store")   # a [vector_stores.*] entry name
-resolved_model = resolver.resolve_model("embedding-model")              # a [models.*] entry name
+resolved_vector_store = resolver.resolve_vector_store(
+    "vector_store"
+)  # a [vector_stores.*] entry name
+resolved_model = resolver.resolve_model("embedding-model")  # a [models.*] entry name
 backend = open_vector_store_writer(resolved_vector_store)
 
 emb_config = KnowledgeGraphEmbeddingConfiguration(

docs/mkdocs_macros.py

@@ -1,28 +1,27 @@
-import pandas as pd
 import pathlib as pl
-from typing import Optional, List, Mapping
+from collections.abc import Mapping
+
+import pandas as pd
+
 
 def define_env(env):
     @env.macro
     def to_grouped_table(
-        file_path: str, 
-        group_cols: List[int] = [0], 
-        displayed_cols: Optional[List[int]] = None, 
-        sort_cols: Optional[List[int]] = None, 
-        col_mappings: Optional[Mapping[str, str]] = None
+        file_path: str,
+        group_cols: list[int] | None = None,
+        displayed_cols: list[int] | None = None,
+        sort_cols: list[int] | None = None,
+        col_mappings: Mapping[str, str] | None = None,
     ):
         if group_cols is None:
             group_cols = [0]
 
         absolute_path = (pl.Path(env.project_dir) / file_path).resolve()
-        
+
         if not absolute_path.exists():
             return f"**Error:** Could not find file at `{absolute_path}`"
-
-        suffix_loader_map = {
-            ".csv": pd.read_csv,
-            ".xlsx": pd.read_excel
-        }
+
+        suffix_loader_map = {".csv": pd.read_csv, ".xlsx": pd.read_excel}
         loader_fn = suffix_loader_map[absolute_path.suffix]
 
         df = loader_fn(absolute_path, usecols=displayed_cols)
@@ -35,36 +34,41 @@ def to_grouped_table(
             df = df.rename(columns=col_mappings)
 
         df = df.dropna(subset=df.columns[group_cols])
-        
+
         span_counts_map = {}
         for i, col_idx in enumerate(group_cols):
-            relevant_cols = group_cols[:i+1]
-            group_changes = (df.iloc[:, relevant_cols] != df.iloc[:, relevant_cols].shift()).any(axis=1)
+            relevant_cols = group_cols[: i + 1]
+            group_changes = (df.iloc[:, relevant_cols] != df.iloc[:, relevant_cols].shift()).any(
+                axis=1
+            )
             span_counts_map[col_idx] = group_changes.cumsum().value_counts(sort=False).tolist()
 
         table_style = "width: 100%; border-collapse: collapse; margin-bottom: 1rem;"
         cell_style = "border: 1px solid var(--md-typeset-table-color, #e0e0e0); padding: 12px; text-align: left;"
-        header_style = cell_style + " background-color: var(--md-default-bg-color, #f5f5f5); font-weight: bold;"
-
+        header_style = (
+            cell_style
+            + " background-color: var(--md-default-bg-color, #f5f5f5); font-weight: bold;"
+        )
+
         html = f'<table style="{table_style}">'
         html += "<thead><tr>"
-        
-        for col in df.columns: 
+
+        for col in df.columns:
             html += f'<th style="{header_style}">{col}</th>'
         html += "</tr></thead>"
-        
+
         html += "<tbody>"
-        
+
         span_idx_map = {c: 0 for c in group_cols}
         rows_to_skip_map = {c: 0 for c in group_cols}
-        
+
         for i, row in df.iterrows():
             highest_change_level = -1
             for level, col_idx in enumerate(group_cols):
                 if rows_to_skip_map[col_idx] == 0:
                     highest_change_level = level
                     break
-            
+
             if highest_change_level == 0 and i > 0:
                 html += '<tr style="border-top: 2px solid var(--md-default-fg-color, #666);">'
             elif highest_change_level > 0 and i > 0:
@@ -77,16 +81,19 @@ def to_grouped_table(
                 if j in group_cols:
                     if rows_to_skip_map[j] == 0:
                         span = span_counts_map[j][span_idx_map[j]]
-                        group_style = cell_style + " vertical-align: middle; font-weight: bold; background-color: var(--md-code-bg-color, #fafafa);"
+                        group_style = (
+                            cell_style
+                            + " vertical-align: middle; font-weight: bold; background-color: var(--md-code-bg-color, #fafafa);"
+                        )
                         html += f'<td rowspan="{span}" style="{group_style}">{display_val}</td>'
                         rows_to_skip_map[j] = span - 1
                         span_idx_map[j] += 1
                     else:
                         rows_to_skip_map[j] -= 1
-                        continue 
+                        continue
                 else:
                     html += f'<td style="{cell_style}">{display_val}</td>'
             html += "</tr>"
-            
+
         html += "</tbody></table>"
-        return html
+        return html

docs/reasoning/grounding.md

@@ -66,7 +66,7 @@ from omop_alchemy.cdm.query import ConceptFilter
 from omop_graph.extensions.omop_alchemy import PredicateKind
 
 constraints = GroundingConstraints(
-    parent_ids=(441484,),   # 'Clinical Finding' — only accept descendants of this ancestor
+    parent_ids=(441484,),  # 'Clinical Finding' — only accept descendants of this ancestor
     search_constraint=ConceptFilter(
         domains=("Condition",),
         vocabularies=("SNOMED",),
@@ -80,7 +80,7 @@ results = ground_term(
     resolver_pipeline=pipeline,
     kg=kg,
     query="chest pain",
-    query_embedding=None,   # pass a precomputed vector, or None to skip embedding-based scoring
+    query_embedding=None,  # pass a precomputed vector, or None to skip embedding-based scoring
     constraints=constraints,
 )
 ```

docs/reasoning/resolvers.md

@@ -28,9 +28,9 @@ constraints = GroundingConstraints(
     search_constraint=ConceptFilter(
         domains=parsed_domains,
         vocabularies=parsed_vocabs,
-        require_standard=True  # We prioritize Standard Concepts
+        require_standard=True,  # We prioritize Standard Concepts
     ),
-    max_depth=6  # How deep to traverse the hierarchy
+    max_depth=6,  # How deep to traverse the hierarchy
 )
 
 # The resolver pipeline stops once it finds a high-confidence match