Improve parallel handling logic between extraction and merge operation

lightrag/lightrag.py

@@ -46,6 +46,7 @@ from .namespace import NameSpace, make_namespace
 from .operate import (
     chunking_by_token_size,
     extract_entities,
+    merge_nodes_and_edges,
     kg_query,
     mix_kg_vector_query,
     naive_query,
@@ -902,6 +903,7 @@ class LightRAG:
                     semaphore: asyncio.Semaphore,
                 ) -> None:
                     """Process single document"""
+                    file_extraction_stage_ok = False
                     async with semaphore:
                         nonlocal processed_count
                         current_file_number = 0
@@ -919,7 +921,7 @@ class LightRAG:
                                 )
                                 pipeline_status["cur_batch"] = processed_count
 
-                                log_message = f"Processing file ({current_file_number}/{total_files}): {file_path}"
+                                log_message = f"Processing file {current_file_number}/{total_files}: {file_path}"
                                 logger.info(log_message)
                                 pipeline_status["history_messages"].append(log_message)
                                 log_message = f"Processing d-id: {doc_id}"
@@ -986,6 +988,61 @@ class LightRAG:
                                 text_chunks_task,
                             ]
                             await asyncio.gather(*tasks)
+                            file_extraction_stage_ok = True
+
+                        except Exception as e:
+                            # Log error and update pipeline status
+                            error_msg = f"Failed to extrat document {doc_id}: {traceback.format_exc()}"
+                            logger.error(error_msg)
+                            async with pipeline_status_lock:
+                                pipeline_status["latest_message"] = error_msg
+                                pipeline_status["history_messages"].append(error_msg)
+
+                                # Cancel other tasks as they are no longer meaningful
+                                for task in [
+                                    chunks_vdb_task,
+                                    entity_relation_task,
+                                    full_docs_task,
+                                    text_chunks_task,
+                                ]:
+                                    if not task.done():
+                                        task.cancel()
+
+                            # Update document status to failed
+                            await self.doc_status.upsert(
+                                {
+                                    doc_id: {
+                                        "status": DocStatus.FAILED,
+                                        "error": str(e),
+                                        "content": status_doc.content,
+                                        "content_summary": status_doc.content_summary,
+                                        "content_length": status_doc.content_length,
+                                        "created_at": status_doc.created_at,
+                                        "updated_at": datetime.now().isoformat(),
+                                        "file_path": file_path,
+                                    }
+                                }
+                            )
+
+                    # Release semphore before entering to merge stage
+                    if file_extraction_stage_ok:
+                        try:
+                            # Get chunk_results from entity_relation_task
+                            chunk_results = await entity_relation_task                            
+                            await merge_nodes_and_edges(
+                                chunk_results=chunk_results,  # result collected from entity_relation_task
+                                knowledge_graph_inst=self.chunk_entity_relation_graph,
+                                entity_vdb=self.entities_vdb,
+                                relationships_vdb=self.relationships_vdb,
+                                global_config=asdict(self),
+                                pipeline_status=pipeline_status,
+                                pipeline_status_lock=pipeline_status_lock,
+                                llm_response_cache=self.llm_response_cache,
+                                current_file_number=current_file_number,
+                                total_files=total_files,
+                                file_path=file_path,
+                            )
+
                             await self.doc_status.upsert(
                                 {
                                     doc_id: {
@@ -1012,22 +1069,12 @@ class LightRAG:
 
                         except Exception as e:
                             # Log error and update pipeline status
-                            error_msg = f"Failed to process document {doc_id}: {traceback.format_exc()}"
+                            error_msg = f"Merging stage failed in document {doc_id}: {traceback.format_exc()}"
-
                             logger.error(error_msg)
                             async with pipeline_status_lock:
                                 pipeline_status["latest_message"] = error_msg
                                 pipeline_status["history_messages"].append(error_msg)
 
-                                # Cancel other tasks as they are no longer meaningful
-                                for task in [
-                                    chunks_vdb_task,
-                                    entity_relation_task,
-                                    full_docs_task,
-                                    text_chunks_task,
-                                ]:
-                                    if not task.done():
-                                        task.cancel()
                             # Update document status to failed
                             await self.doc_status.upsert(
                                 {
@@ -1101,9 +1148,9 @@ class LightRAG:
 
     async def _process_entity_relation_graph(
         self, chunk: dict[str, Any], pipeline_status=None, pipeline_status_lock=None
-    ) -> None:
+    ) -> list:
         try:
-            await extract_entities(
+            chunk_results = await extract_entities(
                 chunk,
                 knowledge_graph_inst=self.chunk_entity_relation_graph,
                 entity_vdb=self.entities_vdb,
@@ -1113,6 +1160,7 @@ class LightRAG:
                 pipeline_status_lock=pipeline_status_lock,
                 llm_response_cache=self.llm_response_cache,
             )
+            return chunk_results
         except Exception as e:
             error_msg = f"Failed to extract entities and relationships: {str(e)}"
             logger.error(error_msg)

lightrag/operate.py

@@ -476,6 +476,139 @@ async def _merge_edges_then_upsert(
     return edge_data
 
 
+async def merge_nodes_and_edges(
+    chunk_results: list,
+    knowledge_graph_inst: BaseGraphStorage,
+    entity_vdb: BaseVectorStorage,
+    relationships_vdb: BaseVectorStorage,
+    global_config: dict[str, str],
+    pipeline_status: dict = None,
+    pipeline_status_lock=None,
+    llm_response_cache: BaseKVStorage | None = None,
+    current_file_number: int = 0,
+    total_files: int = 0,
+    file_path: str = "unknown_source",
+) -> None:
+    """Merge nodes and edges from extraction results
+    
+    Args:
+        chunk_results: List of tuples (maybe_nodes, maybe_edges) containing extracted entities and relationships
+        knowledge_graph_inst: Knowledge graph storage
+        entity_vdb: Entity vector database
+        relationships_vdb: Relationship vector database
+        global_config: Global configuration
+        pipeline_status: Pipeline status dictionary
+        pipeline_status_lock: Lock for pipeline status
+        llm_response_cache: LLM response cache
+    """
+    # Get lock manager from shared storage
+    from .kg.shared_storage import get_graph_db_lock
+    graph_db_lock = get_graph_db_lock(enable_logging=False)
+    
+    # Collect all nodes and edges from all chunks
+    all_nodes = defaultdict(list)
+    all_edges = defaultdict(list)
+
+    for maybe_nodes, maybe_edges in chunk_results:
+        # Collect nodes
+        for entity_name, entities in maybe_nodes.items():
+            all_nodes[entity_name].extend(entities)
+
+        # Collect edges with sorted keys for undirected graph
+        for edge_key, edges in maybe_edges.items():
+            sorted_edge_key = tuple(sorted(edge_key))
+            all_edges[sorted_edge_key].extend(edges)
+
+    # Centralized processing of all nodes and edges
+    entities_data = []
+    relationships_data = []
+
+    # Merge nodes and edges
+    # Use graph database lock to ensure atomic merges and updates
+    async with graph_db_lock:
+        async with pipeline_status_lock:
+            log_message = f"Merging nodes/edges {current_file_number}/{total_files}: {file_path}"
+            logger.info(log_message)
+            pipeline_status["latest_message"] = log_message
+            pipeline_status["history_messages"].append(log_message)
+
+        # Process and update all entities at once
+        for entity_name, entities in all_nodes.items():
+            entity_data = await _merge_nodes_then_upsert(
+                entity_name,
+                entities,
+                knowledge_graph_inst,
+                global_config,
+                pipeline_status,
+                pipeline_status_lock,
+                llm_response_cache,
+            )
+            entities_data.append(entity_data)
+
+        # Process and update all relationships at once
+        for edge_key, edges in all_edges.items():
+            edge_data = await _merge_edges_then_upsert(
+                edge_key[0],
+                edge_key[1],
+                edges,
+                knowledge_graph_inst,
+                global_config,
+                pipeline_status,
+                pipeline_status_lock,
+                llm_response_cache,
+            )
+            if edge_data is not None:
+                relationships_data.append(edge_data)
+
+        # Update total counts
+        total_entities_count = len(entities_data)
+        total_relations_count = len(relationships_data)
+
+        log_message = f"Updating {total_entities_count} entities  {current_file_number}/{total_files}: {file_path}"
+        logger.info(log_message)
+        if pipeline_status is not None:
+            async with pipeline_status_lock:
+                pipeline_status["latest_message"] = log_message
+                pipeline_status["history_messages"].append(log_message)
+
+        # Update vector databases with all collected data
+        if entity_vdb is not None and entities_data:
+            data_for_vdb = {
+                compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
+                    "entity_name": dp["entity_name"],
+                    "entity_type": dp["entity_type"],
+                    "content": f"{dp['entity_name']}\n{dp['description']}",
+                    "source_id": dp["source_id"],
+                    "file_path": dp.get("file_path", "unknown_source"),
+                }
+                for dp in entities_data
+            }
+            await entity_vdb.upsert(data_for_vdb)
+
+        log_message = (
+            f"Updating {total_relations_count} relations {current_file_number}/{total_files}: {file_path}"
+        )
+        logger.info(log_message)
+        if pipeline_status is not None:
+            async with pipeline_status_lock:
+                pipeline_status["latest_message"] = log_message
+                pipeline_status["history_messages"].append(log_message)
+
+        if relationships_vdb is not None and relationships_data:
+            data_for_vdb = {
+                compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
+                    "src_id": dp["src_id"],
+                    "tgt_id": dp["tgt_id"],
+                    "keywords": dp["keywords"],
+                    "content": f"{dp['src_id']}\t{dp['tgt_id']}\n{dp['keywords']}\n{dp['description']}",
+                    "source_id": dp["source_id"],
+                    "file_path": dp.get("file_path", "unknown_source"),
+                }
+                for dp in relationships_data
+            }
+            await relationships_vdb.upsert(data_for_vdb)
+
+
 async def extract_entities(
     chunks: dict[str, TextChunkSchema],
     knowledge_graph_inst: BaseGraphStorage,
@@ -485,7 +618,7 @@ async def extract_entities(
     pipeline_status: dict = None,
     pipeline_status_lock=None,
     llm_response_cache: BaseKVStorage | None = None,
-) -> None:
+) -> list:
     use_llm_func: callable = global_config["llm_model_func"]
     entity_extract_max_gleaning = global_config["entity_extract_max_gleaning"]
 
@@ -530,15 +663,6 @@ async def extract_entities(
 
     processed_chunks = 0
     total_chunks = len(ordered_chunks)
-    total_entities_count = 0
-    total_relations_count = 0
-
-    # Get lock manager from shared storage
-    from .kg.shared_storage import get_graph_db_lock
-
-    graph_db_lock = get_graph_db_lock(enable_logging=False)
-
-    # Use the global use_llm_func_with_cache function from utils.py
 
     async def _process_extraction_result(
         result: str, chunk_key: str, file_path: str = "unknown_source"
@@ -708,102 +832,9 @@ async def extract_entities(
 
     # If all tasks completed successfully, collect results
     chunk_results = [task.result() for task in tasks]
-
+    
-    # Collect all nodes and edges from all chunks
+    # Return the chunk_results for later processing in merge_nodes_and_edges
-    all_nodes = defaultdict(list)
+    return chunk_results
-    all_edges = defaultdict(list)
-
-    for maybe_nodes, maybe_edges in chunk_results:
-        # Collect nodes
-        for entity_name, entities in maybe_nodes.items():
-            all_nodes[entity_name].extend(entities)
-
-        # Collect edges with sorted keys for undirected graph
-        for edge_key, edges in maybe_edges.items():
-            sorted_edge_key = tuple(sorted(edge_key))
-            all_edges[sorted_edge_key].extend(edges)
-
-    # Centralized processing of all nodes and edges
-    entities_data = []
-    relationships_data = []
-
-    # Use graph database lock to ensure atomic merges and updates
-    async with graph_db_lock:
-        # Process and update all entities at once
-        for entity_name, entities in all_nodes.items():
-            entity_data = await _merge_nodes_then_upsert(
-                entity_name,
-                entities,
-                knowledge_graph_inst,
-                global_config,
-                pipeline_status,
-                pipeline_status_lock,
-                llm_response_cache,
-            )
-            entities_data.append(entity_data)
-
-        # Process and update all relationships at once
-        for edge_key, edges in all_edges.items():
-            edge_data = await _merge_edges_then_upsert(
-                edge_key[0],
-                edge_key[1],
-                edges,
-                knowledge_graph_inst,
-                global_config,
-                pipeline_status,
-                pipeline_status_lock,
-                llm_response_cache,
-            )
-            if edge_data is not None:
-                relationships_data.append(edge_data)
-
-        # Update total counts
-        total_entities_count = len(entities_data)
-        total_relations_count = len(relationships_data)
-
-        log_message = f"Updating vector storage: {total_entities_count} entities..."
-        logger.info(log_message)
-        if pipeline_status is not None:
-            async with pipeline_status_lock:
-                pipeline_status["latest_message"] = log_message
-                pipeline_status["history_messages"].append(log_message)
-
-        # Update vector databases with all collected data
-        if entity_vdb is not None and entities_data:
-            data_for_vdb = {
-                compute_mdhash_id(dp["entity_name"], prefix="ent-"): {
-                    "entity_name": dp["entity_name"],
-                    "entity_type": dp["entity_type"],
-                    "content": f"{dp['entity_name']}\n{dp['description']}",
-                    "source_id": dp["source_id"],
-                    "file_path": dp.get("file_path", "unknown_source"),
-                }
-                for dp in entities_data
-            }
-            await entity_vdb.upsert(data_for_vdb)
-
-        log_message = (
-            f"Updating vector storage: {total_relations_count} relationships..."
-        )
-        logger.info(log_message)
-        if pipeline_status is not None:
-            async with pipeline_status_lock:
-                pipeline_status["latest_message"] = log_message
-                pipeline_status["history_messages"].append(log_message)
-
-        if relationships_vdb is not None and relationships_data:
-            data_for_vdb = {
-                compute_mdhash_id(dp["src_id"] + dp["tgt_id"], prefix="rel-"): {
-                    "src_id": dp["src_id"],
-                    "tgt_id": dp["tgt_id"],
-                    "keywords": dp["keywords"],
-                    "content": f"{dp['src_id']}\t{dp['tgt_id']}\n{dp['keywords']}\n{dp['description']}",
-                    "source_id": dp["source_id"],
-                    "file_path": dp.get("file_path", "unknown_source"),
-                }
-                for dp in relationships_data
-            }
-            await relationships_vdb.upsert(data_for_vdb)
 
 
 async def kg_query(