modify smoke tests to include csv table provider

.semversioner/next-release/patch-20260213160631396575.json

@@ -0,0 +1,4 @@
+{
+  "type": "patch",
+  "description": "add csv table smoke tests"
+}

packages/graphrag-storage/graphrag_storage/tables/csv_table_provider.py

@@ -70,7 +70,7 @@ async def read_dataframe(self, table_name: str) -> pd.DataFrame:
             # Handle empty CSV (pandas can't parse files with no columns)
             if not csv_data or csv_data.strip() == "":
                 return pd.DataFrame()
-            return pd.read_csv(StringIO(csv_data))
+            return pd.read_csv(StringIO(csv_data), keep_default_na=False)
         except Exception:
             logger.exception("error loading table from storage: %s", filename)
             raise

tests/fixtures/text/config.json

@@ -21,9 +21,12 @@
                 300
             ],
             "max_runtime": 30,
+            "nan_allowed_columns": [
+                "description"
+            ],
             "expected_artifacts": [
-                "entities.parquet",
-                "relationships.parquet"
+                "entities.csv",
+                "relationships.csv"
             ]
         },
         "create_communities": {
@@ -32,7 +35,7 @@
                 30
             ],
             "max_runtime": 30,
-            "expected_artifacts": ["communities.parquet"]
+            "expected_artifacts": ["communities.csv"]
         },
         "create_community_reports_text": {
             "row_range": [
@@ -51,7 +54,7 @@
                 "size"
             ],
             "max_runtime": 2000,
-            "expected_artifacts": ["community_reports.parquet"]
+            "expected_artifacts": ["community_reports.csv"]
         },
         "create_final_text_units": {
             "row_range": [
@@ -64,7 +67,7 @@
                 "covariate_ids"
             ],
             "max_runtime": 30,
-            "expected_artifacts": ["text_units.parquet"]
+            "expected_artifacts": ["text_units.csv"]
         },
         "create_final_documents": {
             "row_range": [
@@ -75,7 +78,7 @@
                 "raw_data"
             ],
             "max_runtime": 30,
-            "expected_artifacts": ["documents.parquet"]
+            "expected_artifacts": ["documents.csv"]
         },
         "generate_text_embeddings": {
             "row_range": [
@@ -84,9 +87,9 @@
             ],
             "max_runtime": 150,
             "expected_artifacts": [
-                "embeddings.text_unit_text.parquet",
-                "embeddings.entity_description.parquet",
-                "embeddings.community_full_content.parquet"
+                "embeddings.text_unit_text.csv",
+                "embeddings.entity_description.csv",
+                "embeddings.community_full_content.csv"
             ]
         }
     },

tests/fixtures/text/settings.yml

@@ -29,6 +29,9 @@ vector_store:
   api_key: ${AZURE_AI_SEARCH_API_KEY}
   container_name: "simple_text_ci"
 
+table_provider:
+  type: csv
+
 community_reports:
   prompt: "prompts/community_report.txt"
   max_length: 2000

tests/smoke/test_fixtures.py

@@ -178,25 +178,29 @@ def __assert_indexer_outputs(
             for artifact in workflow_artifacts:
                 if artifact.endswith(".parquet"):
                     output_df = pd.read_parquet(output_path / artifact)
-
-                    # Check number of rows between range
-                    assert (
-                        config["row_range"][0]
-                        <= len(output_df)
-                        <= config["row_range"][1]
-                    ), (
-                        f"Expected between {config['row_range'][0]} and {config['row_range'][1]}, found: {len(output_df)} for file: {artifact}"
+                elif artifact.endswith(".csv"):
+                    output_df = pd.read_csv(
+                        output_path / artifact, keep_default_na=False
                     )
+                else:
+                    continue
+
+                # Check number of rows between range
+                assert (
+                    config["row_range"][0] <= len(output_df) <= config["row_range"][1]
+                ), (
+                    f"Expected between {config['row_range'][0]} and {config['row_range'][1]}, found: {len(output_df)} for file: {artifact}"
+                )
 
-                    # Get non-nan rows
-                    nan_df = output_df.loc[
-                        :,
-                        ~output_df.columns.isin(config.get("nan_allowed_columns", [])),
-                    ]
-                    nan_df = nan_df[nan_df.isna().any(axis=1)]
-                    assert len(nan_df) == 0, (
-                        f"Found {len(nan_df)} rows with NaN values for file: {artifact} on columns: {nan_df.columns[nan_df.isna().any()].tolist()}"
-                    )
+                # Get non-nan rows
+                nan_df = output_df.loc[
+                    :,
+                    ~output_df.columns.isin(config.get("nan_allowed_columns", [])),
+                ]
+                nan_df = nan_df[nan_df.isna().any(axis=1)]
+                assert len(nan_df) == 0, (
+                    f"Found {len(nan_df)} rows with NaN values for file: {artifact} on columns: {nan_df.columns[nan_df.isna().any()].tolist()}"
+                )
 
     def __run_query(self, root: Path, query_config: dict[str, str]):
         command = [