Add empty checks for NLP graphs

packages/graphrag/graphrag/index/operations/build_noun_graph/build_noun_graph.py

@@ -98,9 +98,11 @@ def _extract_edges(
     Input: nodes_df with schema [id, title, frequency, text_unit_ids]
     Returns: edges_df with schema [source, target, weight, text_unit_ids]
     """
+    if nodes_df.empty:
+        return pd.DataFrame(columns=["source", "target", "weight", "text_unit_ids"])
+
     text_units_df = nodes_df.explode("text_unit_ids")
     text_units_df = text_units_df.rename(columns={"text_unit_ids": "text_unit_id"})
-
     text_units_df = (
         text_units_df.groupby("text_unit_id")
         .agg({"title": lambda x: list(x) if len(x) > 1 else np.nan})

packages/graphrag/graphrag/index/operations/prune_graph.py

@@ -64,6 +64,9 @@ def prune_graph(
         ])
 
     # remove edges by min weight
+    if len(graph.edges) == 0:
+        return graph
+
     if min_edge_weight_pct > 0:
         min_edge_weight = np.percentile(
             [data[schemas.EDGE_WEIGHT] for _, _, data in graph.edges(data=True)],

packages/graphrag/graphrag/index/workflows/extract_graph.py

@@ -121,14 +121,14 @@ async def extract_graph(
         async_type=extraction_async_type,
     )
 
-    if not _validate_data(extracted_entities):
-        error_msg = "Entity Extraction failed. No entities detected during extraction."
+    if len(extracted_entities) == 0:
+        error_msg = "Graph Extraction failed. No entities detected during extraction."
         logger.error(error_msg)
         raise ValueError(error_msg)
 
-    if not _validate_data(extracted_relationships):
+    if len(extracted_relationships) == 0:
         error_msg = (
-            "Entity Extraction failed. No relationships detected during extraction."
+            "Graph Extraction failed. No relationships detected during extraction."
         )
         logger.error(error_msg)
         raise ValueError(error_msg)
@@ -180,8 +180,3 @@ async def get_summarized_entities_relationships(
     extracted_entities.drop(columns=["description"], inplace=True)
     entities = extracted_entities.merge(entity_summaries, on="title", how="left")
     return entities, relationships
-
-
-def _validate_data(df: pd.DataFrame) -> bool:
-    """Validate that the dataframe has data."""
-    return len(df) > 0

packages/graphrag/graphrag/index/workflows/extract_graph_nlp.py

@@ -75,6 +75,19 @@ async def extract_graph_nlp(
         cache=cache,
     )
 
+    if len(extracted_nodes) == 0:
+        error_msg = (
+            "NLP Graph Extraction failed. No entities detected during extraction."
+        )
+        logger.error(error_msg)
+        raise ValueError(error_msg)
+
+    if len(extracted_edges) == 0:
+        error_msg = (
+            "NLP Graph Extraction failed. No relationships detected during extraction."
+        )
+        logger.error(error_msg)
+
     # add in any other columns required by downstream workflows
     extracted_nodes["type"] = "NOUN PHRASE"
     extracted_nodes["description"] = ""

packages/graphrag/graphrag/index/workflows/prune_graph.py

@@ -69,6 +69,16 @@ def prune_graph(
         lcc_only=pruning_config.lcc_only,
     )
 
+    if len(pruned.nodes) == 0:
+        error_msg = "Graph Pruning failed. No entities remain."
+        logger.error(error_msg)
+        raise ValueError(error_msg)
+
+    if len(pruned.edges) == 0:
+        error_msg = "Graph Pruning failed. No relationships remain."
+        logger.error(error_msg)
+        raise ValueError(error_msg)
+
     pruned_nodes, pruned_edges = graph_to_dataframes(
         pruned, node_columns=["title"], edge_columns=["source", "target"]
     )