diff --git a/lightrag/utils_graph.py b/lightrag/utils_graph.py index c2ccd313..cedad575 100644 --- a/lightrag/utils_graph.py +++ b/lightrag/utils_graph.py @@ -5,26 +5,81 @@ import asyncio from typing import Any, cast from .base import DeletionResult -from .kg.shared_storage import get_graph_db_lock +from .kg.shared_storage import get_storage_keyed_lock from .constants import GRAPH_FIELD_SEP from .utils import compute_mdhash_id, logger from .base import StorageNameSpace +async def _persist_graph_updates( + entities_vdb=None, + relationships_vdb=None, + chunk_entity_relation_graph=None, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> None: + """Unified callback to persist updates after graph operations. + + Ensures all relevant storage instances are properly persisted after + operations like delete, edit, create, or merge. + + Args: + entities_vdb: Entity vector database storage (optional) + relationships_vdb: Relationship vector database storage (optional) + chunk_entity_relation_graph: Graph storage instance (optional) + entity_chunks_storage: Entity-chunk tracking storage (optional) + relation_chunks_storage: Relation-chunk tracking storage (optional) + """ + storages = [] + + # Collect all non-None storage instances + if entities_vdb is not None: + storages.append(entities_vdb) + if relationships_vdb is not None: + storages.append(relationships_vdb) + if chunk_entity_relation_graph is not None: + storages.append(chunk_entity_relation_graph) + if entity_chunks_storage is not None: + storages.append(entity_chunks_storage) + if relation_chunks_storage is not None: + storages.append(relation_chunks_storage) + + # Persist all storage instances in parallel + if storages: + await asyncio.gather( + *[ + cast(StorageNameSpace, storage_inst).index_done_callback() + for storage_inst in storages # type: ignore + ] + ) + + async def adelete_by_entity( - chunk_entity_relation_graph, entities_vdb, relationships_vdb, entity_name: str + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name: str, + entity_chunks_storage=None, + relation_chunks_storage=None, ) -> DeletionResult: """Asynchronously delete an entity and all its relationships. + Also cleans up entity_chunks_storage and relation_chunks_storage to remove chunk tracking. + Args: chunk_entity_relation_graph: Graph storage instance entities_vdb: Vector database storage for entities relationships_vdb: Vector database storage for relationships entity_name: Name of the entity to delete + entity_chunks_storage: Optional KV storage for tracking chunks that reference this entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations """ - graph_db_lock = get_graph_db_lock(enable_logging=False) - # Use graph database lock to ensure atomic graph and vector db operations - async with graph_db_lock: + # Use keyed lock for entity to ensure atomic graph and vector db operations + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): try: # Check if the entity exists if not await chunk_entity_relation_graph.has_node(entity_name): @@ -39,14 +94,45 @@ async def adelete_by_entity( edges = await chunk_entity_relation_graph.get_node_edges(entity_name) related_relations_count = len(edges) if edges else 0 + # Clean up chunk tracking storages before deletion + if entity_chunks_storage is not None: + # Delete entity's entry from entity_chunks_storage + await entity_chunks_storage.delete([entity_name]) + logger.info( + f"Entity Delete: removed chunk tracking for `{entity_name}`" + ) + + if relation_chunks_storage is not None and edges: + # Delete all related relationships from relation_chunks_storage + from .utils import make_relation_chunk_key + + relation_keys_to_delete = [] + for src, tgt in edges: + # Normalize entity order for consistent key generation + normalized_src, normalized_tgt = sorted([src, tgt]) + storage_key = make_relation_chunk_key( + normalized_src, normalized_tgt + ) + relation_keys_to_delete.append(storage_key) + + if relation_keys_to_delete: + await relation_chunks_storage.delete(relation_keys_to_delete) + logger.info( + f"Entity Delete: removed chunk tracking for {len(relation_keys_to_delete)} relations" + ) + await entities_vdb.delete_entity(entity_name) await relationships_vdb.delete_entity_relation(entity_name) await chunk_entity_relation_graph.delete_node(entity_name) - message = f"Entity '{entity_name}' and its {related_relations_count} relationships have been deleted." + message = f"Entity Delete: remove '{entity_name}' and its {related_relations_count} relations" logger.info(message) - await _delete_by_entity_done( - entities_vdb, relationships_vdb, chunk_entity_relation_graph + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, ) return DeletionResult( status="success", @@ -65,40 +151,36 @@ async def adelete_by_entity( ) -async def _delete_by_entity_done( - entities_vdb, relationships_vdb, chunk_entity_relation_graph -) -> None: - """Callback after entity deletion is complete, ensures updates are persisted""" - await asyncio.gather( - *[ - cast(StorageNameSpace, storage_inst).index_done_callback() - for storage_inst in [ # type: ignore - entities_vdb, - relationships_vdb, - chunk_entity_relation_graph, - ] - ] - ) - - async def adelete_by_relation( chunk_entity_relation_graph, relationships_vdb, source_entity: str, target_entity: str, + relation_chunks_storage=None, ) -> DeletionResult: """Asynchronously delete a relation between two entities. + Also cleans up relation_chunks_storage to remove chunk tracking. + Args: chunk_entity_relation_graph: Graph storage instance relationships_vdb: Vector database storage for relationships source_entity: Name of the source entity target_entity: Name of the target entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference this relation """ relation_str = f"{source_entity} -> {target_entity}" - graph_db_lock = get_graph_db_lock(enable_logging=False) - # Use graph database lock to ensure atomic graph and vector db operations - async with graph_db_lock: + # Normalize entity order for undirected graph (ensures consistent key generation) + if source_entity > target_entity: + source_entity, target_entity = target_entity, source_entity + + # Use keyed lock for relation to ensure atomic graph and vector db operations + workspace = relationships_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + sorted_edge_key = sorted([source_entity, target_entity]) + async with get_storage_keyed_lock( + sorted_edge_key, namespace=namespace, enable_logging=False + ): try: # Check if the relation exists edge_exists = await chunk_entity_relation_graph.has_edge( @@ -114,6 +196,19 @@ async def adelete_by_relation( status_code=404, ) + # Clean up chunk tracking storage before deletion + if relation_chunks_storage is not None: + from .utils import make_relation_chunk_key + + # Normalize entity order for consistent key generation + normalized_src, normalized_tgt = sorted([source_entity, target_entity]) + storage_key = make_relation_chunk_key(normalized_src, normalized_tgt) + + await relation_chunks_storage.delete([storage_key]) + logger.info( + f"Relation Delete: removed chunk tracking for `{source_entity}`~`{target_entity}`" + ) + # Delete relation from vector database rel_ids_to_delete = [ compute_mdhash_id(source_entity + target_entity, prefix="rel-"), @@ -127,9 +222,13 @@ async def adelete_by_relation( [(source_entity, target_entity)] ) - message = f"Successfully deleted relation from '{source_entity}' to '{target_entity}'" + message = f"Relation Delete: `{source_entity}`~`{target_entity}` deleted successfully" logger.info(message) - await _delete_relation_done(relationships_vdb, chunk_entity_relation_graph) + await _persist_graph_updates( + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + relation_chunks_storage=relation_chunks_storage, + ) return DeletionResult( status="success", doc_id=relation_str, @@ -147,16 +246,269 @@ async def adelete_by_relation( ) -async def _delete_relation_done(relationships_vdb, chunk_entity_relation_graph) -> None: - """Callback after relation deletion is complete, ensures updates are persisted""" - await asyncio.gather( - *[ - cast(StorageNameSpace, storage_inst).index_done_callback() - for storage_inst in [ # type: ignore - relationships_vdb, - chunk_entity_relation_graph, - ] - ] +async def _edit_entity_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name: str, + updated_data: dict[str, str], + *, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> dict[str, Any]: + """Internal helper that edits an entity without acquiring storage locks. + + This function performs the actual entity edit operations without lock management. + It should only be called by public APIs that have already acquired necessary locks. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + entity_name: Name of the entity to edit + updated_data: Dictionary containing updated attributes (including optional entity_name for renaming) + entity_chunks_storage: Optional KV storage for tracking chunks + relation_chunks_storage: Optional KV storage for tracking relation chunks + + Returns: + Dictionary containing updated entity information + + Note: + Caller must acquire appropriate locks before calling this function. + If renaming (entity_name in updated_data), this function will check if the new name exists. + """ + new_entity_name = updated_data.get("entity_name", entity_name) + is_renaming = new_entity_name != entity_name + + original_entity_name = entity_name + + node_exists = await chunk_entity_relation_graph.has_node(entity_name) + if not node_exists: + raise ValueError(f"Entity '{entity_name}' does not exist") + node_data = await chunk_entity_relation_graph.get_node(entity_name) + + if is_renaming: + existing_node = await chunk_entity_relation_graph.has_node(new_entity_name) + if existing_node: + raise ValueError( + f"Entity name '{new_entity_name}' already exists, cannot rename" + ) + + new_node_data = {**node_data, **updated_data} + new_node_data["entity_id"] = new_entity_name + + if "entity_name" in new_node_data: + del new_node_data[ + "entity_name" + ] # Node data should not contain entity_name field + + if is_renaming: + logger.info(f"Entity Edit: renaming `{entity_name}` to `{new_entity_name}`") + + await chunk_entity_relation_graph.upsert_node(new_entity_name, new_node_data) + + relations_to_update = [] + relations_to_delete = [] + edges = await chunk_entity_relation_graph.get_node_edges(entity_name) + if edges: + for source, target in edges: + edge_data = await chunk_entity_relation_graph.get_edge(source, target) + if edge_data: + relations_to_delete.append( + compute_mdhash_id(source + target, prefix="rel-") + ) + relations_to_delete.append( + compute_mdhash_id(target + source, prefix="rel-") + ) + if source == entity_name: + await chunk_entity_relation_graph.upsert_edge( + new_entity_name, target, edge_data + ) + relations_to_update.append((new_entity_name, target, edge_data)) + else: # target == entity_name + await chunk_entity_relation_graph.upsert_edge( + source, new_entity_name, edge_data + ) + relations_to_update.append((source, new_entity_name, edge_data)) + + await chunk_entity_relation_graph.delete_node(entity_name) + + old_entity_id = compute_mdhash_id(entity_name, prefix="ent-") + await entities_vdb.delete([old_entity_id]) + + await relationships_vdb.delete(relations_to_delete) + + for src, tgt, edge_data in relations_to_update: + normalized_src, normalized_tgt = sorted([src, tgt]) + + description = edge_data.get("description", "") + keywords = edge_data.get("keywords", "") + source_id = edge_data.get("source_id", "") + weight = float(edge_data.get("weight", 1.0)) + + content = f"{normalized_src}\t{normalized_tgt}\n{keywords}\n{description}" + + relation_id = compute_mdhash_id( + normalized_src + normalized_tgt, prefix="rel-" + ) + + relation_data = { + relation_id: { + "content": content, + "src_id": normalized_src, + "tgt_id": normalized_tgt, + "source_id": source_id, + "description": description, + "keywords": keywords, + "weight": weight, + } + } + + await relationships_vdb.upsert(relation_data) + + entity_name = new_entity_name + else: + await chunk_entity_relation_graph.upsert_node(entity_name, new_node_data) + + description = new_node_data.get("description", "") + source_id = new_node_data.get("source_id", "") + entity_type = new_node_data.get("entity_type", "") + content = entity_name + "\n" + description + + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + + entity_data = { + entity_id: { + "content": content, + "entity_name": entity_name, + "source_id": source_id, + "description": description, + "entity_type": entity_type, + } + } + + await entities_vdb.upsert(entity_data) + + if entity_chunks_storage is not None or relation_chunks_storage is not None: + from .utils import make_relation_chunk_key, compute_incremental_chunk_ids + + if entity_chunks_storage is not None: + storage_key = original_entity_name if is_renaming else entity_name + stored_data = await entity_chunks_storage.get_by_id(storage_key) + has_stored_data = ( + stored_data + and isinstance(stored_data, dict) + and stored_data.get("chunk_ids") + ) + + old_source_id = node_data.get("source_id", "") + old_chunk_ids = [cid for cid in old_source_id.split(GRAPH_FIELD_SEP) if cid] + + new_source_id = new_node_data.get("source_id", "") + new_chunk_ids = [cid for cid in new_source_id.split(GRAPH_FIELD_SEP) if cid] + + source_id_changed = set(new_chunk_ids) != set(old_chunk_ids) + + if source_id_changed or not has_stored_data or is_renaming: + existing_full_chunk_ids = [] + if has_stored_data: + existing_full_chunk_ids = [ + cid for cid in stored_data.get("chunk_ids", []) if cid + ] + + if not existing_full_chunk_ids: + existing_full_chunk_ids = old_chunk_ids.copy() + + updated_chunk_ids = compute_incremental_chunk_ids( + existing_full_chunk_ids, old_chunk_ids, new_chunk_ids + ) + + if is_renaming: + await entity_chunks_storage.delete([original_entity_name]) + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": updated_chunk_ids, + "count": len(updated_chunk_ids), + } + } + ) + else: + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": updated_chunk_ids, + "count": len(updated_chunk_ids), + } + } + ) + + logger.info( + f"Entity Edit: find {len(updated_chunk_ids)} chunks related to `{entity_name}`" + ) + + if is_renaming and relation_chunks_storage is not None and relations_to_update: + for src, tgt, edge_data in relations_to_update: + old_src = original_entity_name if src == entity_name else src + old_tgt = original_entity_name if tgt == entity_name else tgt + + old_normalized_src, old_normalized_tgt = sorted([old_src, old_tgt]) + new_normalized_src, new_normalized_tgt = sorted([src, tgt]) + + old_storage_key = make_relation_chunk_key( + old_normalized_src, old_normalized_tgt + ) + new_storage_key = make_relation_chunk_key( + new_normalized_src, new_normalized_tgt + ) + + if old_storage_key != new_storage_key: + old_stored_data = await relation_chunks_storage.get_by_id( + old_storage_key + ) + relation_chunk_ids = [] + + if old_stored_data and isinstance(old_stored_data, dict): + relation_chunk_ids = [ + cid for cid in old_stored_data.get("chunk_ids", []) if cid + ] + else: + relation_source_id = edge_data.get("source_id", "") + relation_chunk_ids = [ + cid + for cid in relation_source_id.split(GRAPH_FIELD_SEP) + if cid + ] + + await relation_chunks_storage.delete([old_storage_key]) + + if relation_chunk_ids: + await relation_chunks_storage.upsert( + { + new_storage_key: { + "chunk_ids": relation_chunk_ids, + "count": len(relation_chunk_ids), + } + } + ) + logger.info( + f"Entity Edit: migrate {len(relations_to_update)} relations after rename" + ) + + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + + logger.info(f"Entity Edit: `{entity_name}` successfully updated") + return await get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + entity_name, + include_vector_data=True, ) @@ -167,10 +519,14 @@ async def aedit_entity( entity_name: str, updated_data: dict[str, str], allow_rename: bool = True, + allow_merge: bool = False, + entity_chunks_storage=None, + relation_chunks_storage=None, ) -> dict[str, Any]: """Asynchronously edit entity information. Updates entity information in the knowledge graph and re-embeds the entity in the vector database. + Also synchronizes entity_chunks_storage and relation_chunks_storage to track chunk references. Args: chunk_entity_relation_graph: Graph storage instance @@ -179,199 +535,182 @@ async def aedit_entity( entity_name: Name of the entity to edit updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "entity_type": "new type"} allow_rename: Whether to allow entity renaming, defaults to True + allow_merge: Whether to merge into an existing entity when renaming to an existing name, defaults to False + entity_chunks_storage: Optional KV storage for tracking chunks that reference this entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations Returns: - Dictionary containing updated entity information + Dictionary containing updated entity information and operation summary with the following structure: + { + "entity_name": str, # Name of the entity + "description": str, # Entity description + "entity_type": str, # Entity type + "source_id": str, # Source chunk IDs + ... # Other entity properties + "operation_summary": { + "merged": bool, # Whether entity was merged + "merge_status": str, # "success" | "failed" | "not_attempted" + "merge_error": str | None, # Error message if merge failed + "operation_status": str, # "success" | "partial_success" | "failure" + "target_entity": str | None, # Target entity name if renaming/merging + "final_entity": str, # Final entity name after operation + "renamed": bool # Whether entity was renamed + } + } + + operation_status values: + - "success": Operation completed successfully (update/rename/merge all succeeded) + - "partial_success": Non-name updates succeeded but merge failed + - "failure": Operation failed completely + + merge_status values: + - "success": Entity successfully merged into target + - "failed": Merge operation failed + - "not_attempted": No merge was attempted (normal update/rename) """ - graph_db_lock = get_graph_db_lock(enable_logging=False) - # Use graph database lock to ensure atomic graph and vector db operations - async with graph_db_lock: + new_entity_name = updated_data.get("entity_name", entity_name) + is_renaming = new_entity_name != entity_name + + lock_keys = sorted({entity_name, new_entity_name}) if is_renaming else [entity_name] + + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + + operation_summary: dict[str, Any] = { + "merged": False, + "merge_status": "not_attempted", + "merge_error": None, + "operation_status": "success", + "target_entity": None, + "final_entity": new_entity_name if is_renaming else entity_name, + "renamed": is_renaming, + } + async with get_storage_keyed_lock( + lock_keys, namespace=namespace, enable_logging=False + ): try: - # 1. Get current entity information - node_exists = await chunk_entity_relation_graph.has_node(entity_name) - if not node_exists: - raise ValueError(f"Entity '{entity_name}' does not exist") - node_data = await chunk_entity_relation_graph.get_node(entity_name) + if is_renaming and not allow_rename: + raise ValueError( + "Entity renaming is not allowed. Set allow_rename=True to enable this feature" + ) - # Check if entity is being renamed - new_entity_name = updated_data.get("entity_name", entity_name) - is_renaming = new_entity_name != entity_name - - # If renaming, check if new name already exists if is_renaming: - if not allow_rename: - raise ValueError( - "Entity renaming is not allowed. Set allow_rename=True to enable this feature" - ) - - existing_node = await chunk_entity_relation_graph.has_node( + target_exists = await chunk_entity_relation_graph.has_node( new_entity_name ) - if existing_node: - raise ValueError( - f"Entity name '{new_entity_name}' already exists, cannot rename" + if target_exists: + if not allow_merge: + raise ValueError( + f"Entity name '{new_entity_name}' already exists, cannot rename" + ) + + logger.info( + f"Entity Edit: `{entity_name}` will be merged into `{new_entity_name}`" ) - # 2. Update entity information in the graph - new_node_data = {**node_data, **updated_data} - new_node_data["entity_id"] = new_entity_name - - if "entity_name" in new_node_data: - del new_node_data[ - "entity_name" - ] # Node data should not contain entity_name field - - # If renaming entity - if is_renaming: - logger.info(f"Renaming entity '{entity_name}' to '{new_entity_name}'") - - # Create new entity - await chunk_entity_relation_graph.upsert_node( - new_entity_name, new_node_data - ) - - # Store relationships that need to be updated - relations_to_update = [] - relations_to_delete = [] - # Get all edges related to the original entity - edges = await chunk_entity_relation_graph.get_node_edges(entity_name) - if edges: - # Recreate edges for the new entity - for source, target in edges: - edge_data = await chunk_entity_relation_graph.get_edge( - source, target - ) - if edge_data: - relations_to_delete.append( - compute_mdhash_id(source + target, prefix="rel-") - ) - relations_to_delete.append( - compute_mdhash_id(target + source, prefix="rel-") - ) - if source == entity_name: - await chunk_entity_relation_graph.upsert_edge( - new_entity_name, target, edge_data - ) - relations_to_update.append( - (new_entity_name, target, edge_data) - ) - else: # target == entity_name - await chunk_entity_relation_graph.upsert_edge( - source, new_entity_name, edge_data - ) - relations_to_update.append( - (source, new_entity_name, edge_data) - ) - - # Delete old entity - await chunk_entity_relation_graph.delete_node(entity_name) - - # Delete old entity record from vector database - old_entity_id = compute_mdhash_id(entity_name, prefix="ent-") - await entities_vdb.delete([old_entity_id]) - logger.info( - f"Deleted old entity '{entity_name}' and its vector embedding from database" - ) - - # Delete old relation records from vector database - await relationships_vdb.delete(relations_to_delete) - logger.info( - f"Deleted {len(relations_to_delete)} relation records for entity '{entity_name}' from vector database" - ) - - # Update relationship vector representations - for src, tgt, edge_data in relations_to_update: - description = edge_data.get("description", "") - keywords = edge_data.get("keywords", "") - source_id = edge_data.get("source_id", "") - weight = float(edge_data.get("weight", 1.0)) - - # Create new content for embedding - content = f"{src}\t{tgt}\n{keywords}\n{description}" - - # Calculate relationship ID - relation_id = compute_mdhash_id(src + tgt, prefix="rel-") - - # Prepare data for vector database update - relation_data = { - relation_id: { - "content": content, - "src_id": src, - "tgt_id": tgt, - "source_id": source_id, - "description": description, - "keywords": keywords, - "weight": weight, - } + # Track whether non-name updates were applied + non_name_updates_applied = False + non_name_updates = { + key: value + for key, value in updated_data.items() + if key != "entity_name" } - # Update vector database - await relationships_vdb.upsert(relation_data) + # Apply non-name updates first + if non_name_updates: + try: + logger.info( + "Entity Edit: applying non-name updates before merge" + ) + await _edit_entity_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + entity_name, + non_name_updates, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + non_name_updates_applied = True + except Exception as update_error: + # If update fails, re-raise immediately + logger.error( + f"Entity Edit: non-name updates failed: {update_error}" + ) + raise - # Update working entity name to new name - entity_name = new_entity_name - else: - # If not renaming, directly update node data - await chunk_entity_relation_graph.upsert_node( - entity_name, new_node_data - ) + # Attempt to merge entities + try: + merge_result = await _merge_entities_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + [entity_name], + new_entity_name, + merge_strategy=None, + target_entity_data=None, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) - # 3. Recalculate entity's vector representation and update vector database - description = new_node_data.get("description", "") - source_id = new_node_data.get("source_id", "") - entity_type = new_node_data.get("entity_type", "") - content = entity_name + "\n" + description + # Merge succeeded + operation_summary.update( + { + "merged": True, + "merge_status": "success", + "merge_error": None, + "operation_status": "success", + "target_entity": new_entity_name, + "final_entity": new_entity_name, + } + ) + return {**merge_result, "operation_summary": operation_summary} - # Calculate entity ID - entity_id = compute_mdhash_id(entity_name, prefix="ent-") + except Exception as merge_error: + # Merge failed, but update may have succeeded + logger.error(f"Entity Edit: merge failed: {merge_error}") - # Prepare data for vector database update - entity_data = { - entity_id: { - "content": content, - "entity_name": entity_name, - "source_id": source_id, - "description": description, - "entity_type": entity_type, - } - } + # Return partial success status (update succeeded but merge failed) + operation_summary.update( + { + "merged": False, + "merge_status": "failed", + "merge_error": str(merge_error), + "operation_status": "partial_success" + if non_name_updates_applied + else "failure", + "target_entity": new_entity_name, + "final_entity": entity_name, # Keep source entity name + } + ) - # Update vector database - await entities_vdb.upsert(entity_data) + # Get current entity info (with applied updates if any) + entity_info = await get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + entity_name, + include_vector_data=True, + ) + return {**entity_info, "operation_summary": operation_summary} - # 4. Save changes - await _edit_entity_done( - entities_vdb, relationships_vdb, chunk_entity_relation_graph - ) - - logger.info(f"Entity '{entity_name}' successfully updated") - return await get_entity_info( + # Normal edit flow (no merge involved) + edit_result = await _edit_entity_impl( chunk_entity_relation_graph, entities_vdb, + relationships_vdb, entity_name, - include_vector_data=True, + updated_data, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, ) + operation_summary["operation_status"] = "success" + return {**edit_result, "operation_summary": operation_summary} + except Exception as e: logger.error(f"Error while editing entity '{entity_name}': {e}") raise -async def _edit_entity_done( - entities_vdb, relationships_vdb, chunk_entity_relation_graph -) -> None: - """Callback after entity editing is complete, ensures updates are persisted""" - await asyncio.gather( - *[ - cast(StorageNameSpace, storage_inst).index_done_callback() - for storage_inst in [ # type: ignore - entities_vdb, - relationships_vdb, - chunk_entity_relation_graph, - ] - ] - ) - - async def aedit_relation( chunk_entity_relation_graph, entities_vdb, @@ -379,10 +718,12 @@ async def aedit_relation( source_entity: str, target_entity: str, updated_data: dict[str, Any], + relation_chunks_storage=None, ) -> dict[str, Any]: """Asynchronously edit relation information. Updates relation (edge) information in the knowledge graph and re-embeds the relation in the vector database. + Also synchronizes the relation_chunks_storage to track which chunks reference this relation. Args: chunk_entity_relation_graph: Graph storage instance @@ -391,13 +732,22 @@ async def aedit_relation( source_entity: Name of the source entity target_entity: Name of the target entity updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "keywords": "new keywords"} + relation_chunks_storage: Optional KV storage for tracking chunks that reference this relation Returns: Dictionary containing updated relation information """ - graph_db_lock = get_graph_db_lock(enable_logging=False) - # Use graph database lock to ensure atomic graph and vector db operations - async with graph_db_lock: + # Normalize entity order for undirected graph (ensures consistent key generation) + if source_entity > target_entity: + source_entity, target_entity = target_entity, source_entity + + # Use keyed lock for relation to ensure atomic graph and vector db operations + workspace = relationships_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + sorted_edge_key = sorted([source_entity, target_entity]) + async with get_storage_keyed_lock( + sorted_edge_key, namespace=namespace, enable_logging=False + ): try: # 1. Get current relation information edge_exists = await chunk_entity_relation_graph.has_edge( @@ -411,12 +761,14 @@ async def aedit_relation( source_entity, target_entity ) # Important: First delete the old relation record from the vector database - old_relation_id = compute_mdhash_id( - source_entity + target_entity, prefix="rel-" - ) - await relationships_vdb.delete([old_relation_id]) - logger.info( - f"Deleted old relation record from vector database for relation {source_entity} -> {target_entity}" + # Delete both permutations to handle relationships created before normalization + rel_ids_to_delete = [ + compute_mdhash_id(source_entity + target_entity, prefix="rel-"), + compute_mdhash_id(target_entity + source_entity, prefix="rel-"), + ] + await relationships_vdb.delete(rel_ids_to_delete) + logger.debug( + f"Relation Delete: delete vdb for `{source_entity}`~`{target_entity}`" ) # 2. Update relation information in the graph @@ -455,11 +807,79 @@ async def aedit_relation( # Update vector database await relationships_vdb.upsert(relation_data) - # 4. Save changes - await _edit_relation_done(relationships_vdb, chunk_entity_relation_graph) + # 4. Update relation_chunks_storage in two scenarios: + # - source_id has changed (edit scenario) + # - relation_chunks_storage has no existing data (migration/initialization scenario) + if relation_chunks_storage is not None: + from .utils import ( + make_relation_chunk_key, + compute_incremental_chunk_ids, + ) + + storage_key = make_relation_chunk_key(source_entity, target_entity) + + # Check if storage has existing data + stored_data = await relation_chunks_storage.get_by_id(storage_key) + has_stored_data = ( + stored_data + and isinstance(stored_data, dict) + and stored_data.get("chunk_ids") + ) + + # Get old and new source_id + old_source_id = edge_data.get("source_id", "") + old_chunk_ids = [ + cid for cid in old_source_id.split(GRAPH_FIELD_SEP) if cid + ] + + new_source_id = new_edge_data.get("source_id", "") + new_chunk_ids = [ + cid for cid in new_source_id.split(GRAPH_FIELD_SEP) if cid + ] + + source_id_changed = set(new_chunk_ids) != set(old_chunk_ids) + + # Update if: source_id changed OR storage has no data + if source_id_changed or not has_stored_data: + # Get existing full chunk_ids from storage + existing_full_chunk_ids = [] + if has_stored_data: + existing_full_chunk_ids = [ + cid for cid in stored_data.get("chunk_ids", []) if cid + ] + + # If no stored data exists, use old source_id as baseline + if not existing_full_chunk_ids: + existing_full_chunk_ids = old_chunk_ids.copy() + + # Use utility function to compute incremental updates + updated_chunk_ids = compute_incremental_chunk_ids( + existing_full_chunk_ids, old_chunk_ids, new_chunk_ids + ) + + # Update storage (Update even if updated_chunk_ids is empty) + await relation_chunks_storage.upsert( + { + storage_key: { + "chunk_ids": updated_chunk_ids, + "count": len(updated_chunk_ids), + } + } + ) + + logger.info( + f"Relation Delete: update chunk tracking for `{source_entity}`~`{target_entity}`" + ) + + # 5. Save changes + await _persist_graph_updates( + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + relation_chunks_storage=relation_chunks_storage, + ) logger.info( - f"Relation from '{source_entity}' to '{target_entity}' successfully updated" + f"Relation Delete: `{source_entity}`~`{target_entity}`' successfully updated" ) return await get_relation_info( chunk_entity_relation_graph, @@ -475,29 +895,19 @@ async def aedit_relation( raise -async def _edit_relation_done(relationships_vdb, chunk_entity_relation_graph) -> None: - """Callback after relation editing is complete, ensures updates are persisted""" - await asyncio.gather( - *[ - cast(StorageNameSpace, storage_inst).index_done_callback() - for storage_inst in [ # type: ignore - relationships_vdb, - chunk_entity_relation_graph, - ] - ] - ) - - async def acreate_entity( chunk_entity_relation_graph, entities_vdb, relationships_vdb, entity_name: str, entity_data: dict[str, Any], + entity_chunks_storage=None, + relation_chunks_storage=None, ) -> dict[str, Any]: """Asynchronously create a new entity. Creates a new entity in the knowledge graph and adds it to the vector database. + Also synchronizes entity_chunks_storage to track chunk references. Args: chunk_entity_relation_graph: Graph storage instance @@ -505,13 +915,18 @@ async def acreate_entity( relationships_vdb: Vector database storage for relationships entity_name: Name of the new entity entity_data: Dictionary containing entity attributes, e.g. {"description": "description", "entity_type": "type"} + entity_chunks_storage: Optional KV storage for tracking chunks that reference this entity + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations Returns: Dictionary containing created entity information """ - graph_db_lock = get_graph_db_lock(enable_logging=False) - # Use graph database lock to ensure atomic graph and vector db operations - async with graph_db_lock: + # Use keyed lock for entity to ensure atomic graph and vector db operations + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + [entity_name], namespace=namespace, enable_logging=False + ): try: # Check if entity already exists existing_node = await chunk_entity_relation_graph.has_node(entity_name) @@ -555,12 +970,34 @@ async def acreate_entity( # Update vector database await entities_vdb.upsert(entity_data_for_vdb) + # Update entity_chunks_storage to track chunk references + if entity_chunks_storage is not None: + source_id = node_data.get("source_id", "") + chunk_ids = [cid for cid in source_id.split(GRAPH_FIELD_SEP) if cid] + + if chunk_ids: + await entity_chunks_storage.upsert( + { + entity_name: { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + } + ) + logger.info( + f"Entity Create: tracked {len(chunk_ids)} chunks for `{entity_name}`" + ) + # Save changes - await _edit_entity_done( - entities_vdb, relationships_vdb, chunk_entity_relation_graph + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, ) - logger.info(f"Entity '{entity_name}' successfully created") + logger.info(f"Entity Create: '{entity_name}' successfully created") return await get_entity_info( chunk_entity_relation_graph, entities_vdb, @@ -579,10 +1016,12 @@ async def acreate_relation( source_entity: str, target_entity: str, relation_data: dict[str, Any], + relation_chunks_storage=None, ) -> dict[str, Any]: """Asynchronously create a new relation between entities. Creates a new relation (edge) in the knowledge graph and adds it to the vector database. + Also synchronizes relation_chunks_storage to track chunk references. Args: chunk_entity_relation_graph: Graph storage instance @@ -591,13 +1030,18 @@ async def acreate_relation( source_entity: Name of the source entity target_entity: Name of the target entity relation_data: Dictionary containing relation attributes, e.g. {"description": "description", "keywords": "keywords"} + relation_chunks_storage: Optional KV storage for tracking chunks that reference this relation Returns: Dictionary containing created relation information """ - graph_db_lock = get_graph_db_lock(enable_logging=False) - # Use graph database lock to ensure atomic graph and vector db operations - async with graph_db_lock: + # Use keyed lock for relation to ensure atomic graph and vector db operations + workspace = relationships_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + sorted_edge_key = sorted([source_entity, target_entity]) + async with get_storage_keyed_lock( + sorted_edge_key, namespace=namespace, enable_logging=False + ): try: # Check if both entities exist source_exists = await chunk_entity_relation_graph.has_node(source_entity) @@ -632,6 +1076,10 @@ async def acreate_relation( source_entity, target_entity, edge_data ) + # Normalize entity order for undirected relation vector (ensures consistent key generation) + if source_entity > target_entity: + source_entity, target_entity = target_entity, source_entity + # Prepare content for embedding description = edge_data.get("description", "") keywords = edge_data.get("keywords", "") @@ -663,11 +1111,39 @@ async def acreate_relation( # Update vector database await relationships_vdb.upsert(relation_data_for_vdb) + # Update relation_chunks_storage to track chunk references + if relation_chunks_storage is not None: + from .utils import make_relation_chunk_key + + # Normalize entity order for consistent key generation + normalized_src, normalized_tgt = sorted([source_entity, target_entity]) + storage_key = make_relation_chunk_key(normalized_src, normalized_tgt) + + source_id = edge_data.get("source_id", "") + chunk_ids = [cid for cid in source_id.split(GRAPH_FIELD_SEP) if cid] + + if chunk_ids: + await relation_chunks_storage.upsert( + { + storage_key: { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + } + ) + logger.info( + f"Relation Create: tracked {len(chunk_ids)} chunks for `{source_entity}`~`{target_entity}`" + ) + # Save changes - await _edit_relation_done(relationships_vdb, chunk_entity_relation_graph) + await _persist_graph_updates( + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + relation_chunks_storage=relation_chunks_storage, + ) logger.info( - f"Relation from '{source_entity}' to '{target_entity}' successfully created" + f"Relation Create: `{source_entity}`~`{target_entity}` successfully created" ) return await get_relation_info( chunk_entity_relation_graph, @@ -683,6 +1159,372 @@ async def acreate_relation( raise +async def _merge_entities_impl( + chunk_entity_relation_graph, + entities_vdb, + relationships_vdb, + source_entities: list[str], + target_entity: str, + *, + merge_strategy: dict[str, str] = None, + target_entity_data: dict[str, Any] = None, + entity_chunks_storage=None, + relation_chunks_storage=None, +) -> dict[str, Any]: + """Internal helper that merges entities without acquiring storage locks. + + This function performs the actual entity merge operations without lock management. + It should only be called by public APIs that have already acquired necessary locks. + + Args: + chunk_entity_relation_graph: Graph storage instance + entities_vdb: Vector database storage for entities + relationships_vdb: Vector database storage for relationships + source_entities: List of source entity names to merge + target_entity: Name of the target entity after merging + merge_strategy: Deprecated. Merge strategy for each field (optional) + target_entity_data: Dictionary of specific values to set for target entity (optional) + entity_chunks_storage: Optional KV storage for tracking chunks + relation_chunks_storage: Optional KV storage for tracking relation chunks + + Returns: + Dictionary containing the merged entity information + + Note: + Caller must acquire appropriate locks before calling this function. + All source entities and the target entity should be locked together. + """ + # Default merge strategy for entities + default_entity_merge_strategy = { + "description": "concatenate", + "entity_type": "keep_first", + "source_id": "join_unique", + "file_path": "join_unique", + } + effective_entity_merge_strategy = default_entity_merge_strategy + if merge_strategy: + logger.warning( + "Entity Merge: merge_strategy parameter is deprecated and will be ignored in a future release." + ) + effective_entity_merge_strategy = { + **default_entity_merge_strategy, + **merge_strategy, + } + target_entity_data = {} if target_entity_data is None else target_entity_data + + # 1. Check if all source entities exist + source_entities_data = {} + for entity_name in source_entities: + node_exists = await chunk_entity_relation_graph.has_node(entity_name) + if not node_exists: + raise ValueError(f"Source entity '{entity_name}' does not exist") + node_data = await chunk_entity_relation_graph.get_node(entity_name) + source_entities_data[entity_name] = node_data + + # 2. Check if target entity exists and get its data if it does + target_exists = await chunk_entity_relation_graph.has_node(target_entity) + existing_target_entity_data = {} + if target_exists: + existing_target_entity_data = await chunk_entity_relation_graph.get_node( + target_entity + ) + + # 3. Merge entity data + merged_entity_data = _merge_attributes( + list(source_entities_data.values()) + + ([existing_target_entity_data] if target_exists else []), + effective_entity_merge_strategy, + filter_none_only=False, # Use entity behavior: filter falsy values + ) + + # Apply any explicitly provided target entity data (overrides merged data) + for key, value in target_entity_data.items(): + merged_entity_data[key] = value + + # 4. Get all relationships of the source entities and target entity (if exists) + all_relations = [] + entities_to_collect = source_entities.copy() + + # If target entity exists and not already in source_entities, add it + if target_exists and target_entity not in source_entities: + entities_to_collect.append(target_entity) + + for entity_name in entities_to_collect: + # Get all relationships of the entities + edges = await chunk_entity_relation_graph.get_node_edges(entity_name) + if edges: + for src, tgt in edges: + # Ensure src is the current entity + if src == entity_name: + edge_data = await chunk_entity_relation_graph.get_edge(src, tgt) + all_relations.append((src, tgt, edge_data)) + + # 5. Create or update the target entity + merged_entity_data["entity_id"] = target_entity + if not target_exists: + await chunk_entity_relation_graph.upsert_node(target_entity, merged_entity_data) + logger.info(f"Entity Merge: created target '{target_entity}'") + else: + await chunk_entity_relation_graph.upsert_node(target_entity, merged_entity_data) + logger.info(f"Entity Merge: Updated target '{target_entity}'") + + # 6. Recreate all relations pointing to the target entity in KG + # Also collect chunk tracking information in the same loop + relation_updates = {} # Track relationships that need to be merged + relations_to_delete = [] + + # Initialize chunk tracking variables + relation_chunk_tracking = {} # key: storage_key, value: list of chunk_ids + old_relation_keys_to_delete = [] + + for src, tgt, edge_data in all_relations: + relations_to_delete.append(compute_mdhash_id(src + tgt, prefix="rel-")) + relations_to_delete.append(compute_mdhash_id(tgt + src, prefix="rel-")) + + # Collect old chunk tracking key for deletion + if relation_chunks_storage is not None: + from .utils import make_relation_chunk_key + + old_storage_key = make_relation_chunk_key(src, tgt) + old_relation_keys_to_delete.append(old_storage_key) + + new_src = target_entity if src in source_entities else src + new_tgt = target_entity if tgt in source_entities else tgt + + # Skip relationships between source entities to avoid self-loops + if new_src == new_tgt: + logger.info(f"Entity Merge: skipping `{src}`~`{tgt}` to avoid self-loop") + continue + + # Normalize entity order for consistent duplicate detection (undirected relationships) + normalized_src, normalized_tgt = sorted([new_src, new_tgt]) + relation_key = f"{normalized_src}|{normalized_tgt}" + + # Process chunk tracking for this relation + if relation_chunks_storage is not None: + storage_key = make_relation_chunk_key(normalized_src, normalized_tgt) + + # Get chunk_ids from storage for this original relation + stored = await relation_chunks_storage.get_by_id(old_storage_key) + + if stored is not None and isinstance(stored, dict): + chunk_ids = [cid for cid in stored.get("chunk_ids", []) if cid] + else: + # Fallback to source_id from graph + source_id = edge_data.get("source_id", "") + chunk_ids = [cid for cid in source_id.split(GRAPH_FIELD_SEP) if cid] + + # Accumulate chunk_ids with ordered deduplication + if storage_key not in relation_chunk_tracking: + relation_chunk_tracking[storage_key] = [] + + existing_chunks = set(relation_chunk_tracking[storage_key]) + for chunk_id in chunk_ids: + if chunk_id not in existing_chunks: + existing_chunks.add(chunk_id) + relation_chunk_tracking[storage_key].append(chunk_id) + + if relation_key in relation_updates: + # Merge relationship data + existing_data = relation_updates[relation_key]["data"] + merged_relation = _merge_attributes( + [existing_data, edge_data], + { + "description": "concatenate", + "keywords": "join_unique_comma", + "source_id": "join_unique", + "file_path": "join_unique", + "weight": "max", + }, + filter_none_only=True, # Use relation behavior: only filter None + ) + relation_updates[relation_key]["data"] = merged_relation + logger.debug( + f"Entity Merge: deduplicating relation `{normalized_src}`~`{normalized_tgt}`" + ) + else: + relation_updates[relation_key] = { + "graph_src": new_src, + "graph_tgt": new_tgt, + "norm_src": normalized_src, + "norm_tgt": normalized_tgt, + "data": edge_data.copy(), + } + + # Apply relationship updates + logger.info(f"Entity Merge: updatign {len(relation_updates)} relations") + for rel_data in relation_updates.values(): + await chunk_entity_relation_graph.upsert_edge( + rel_data["graph_src"], rel_data["graph_tgt"], rel_data["data"] + ) + logger.info( + f"Entity Merge: updating relation `{rel_data['graph_src']}`~`{rel_data['graph_tgt']}`" + ) + + # Update relation chunk tracking storage + if relation_chunks_storage is not None and all_relations: + if old_relation_keys_to_delete: + await relation_chunks_storage.delete(old_relation_keys_to_delete) + + if relation_chunk_tracking: + updates = {} + for storage_key, chunk_ids in relation_chunk_tracking.items(): + updates[storage_key] = { + "chunk_ids": chunk_ids, + "count": len(chunk_ids), + } + + await relation_chunks_storage.upsert(updates) + logger.info( + f"Entity Merge: {len(updates)} relation chunk tracking records updated" + ) + + # 7. Update relationship vector representations + logger.debug( + f"Entity Merge: deleting {len(relations_to_delete)} relations from vdb" + ) + await relationships_vdb.delete(relations_to_delete) + + for rel_data in relation_updates.values(): + edge_data = rel_data["data"] + normalized_src = rel_data["norm_src"] + normalized_tgt = rel_data["norm_tgt"] + + description = edge_data.get("description", "") + keywords = edge_data.get("keywords", "") + source_id = edge_data.get("source_id", "") + weight = float(edge_data.get("weight", 1.0)) + + # Use normalized order for content and relation ID + content = f"{keywords}\t{normalized_src}\n{normalized_tgt}\n{description}" + relation_id = compute_mdhash_id(normalized_src + normalized_tgt, prefix="rel-") + + relation_data_for_vdb = { + relation_id: { + "content": content, + "src_id": normalized_src, + "tgt_id": normalized_tgt, + "source_id": source_id, + "description": description, + "keywords": keywords, + "weight": weight, + } + } + await relationships_vdb.upsert(relation_data_for_vdb) + logger.debug( + f"Entity Merge: updating vdb `{normalized_src}`~`{normalized_tgt}`" + ) + + logger.info(f"Entity Merge: {len(relation_updates)} relations in vdb updated") + + # 8. Update entity vector representation + description = merged_entity_data.get("description", "") + source_id = merged_entity_data.get("source_id", "") + entity_type = merged_entity_data.get("entity_type", "") + content = target_entity + "\n" + description + + entity_id = compute_mdhash_id(target_entity, prefix="ent-") + entity_data_for_vdb = { + entity_id: { + "content": content, + "entity_name": target_entity, + "source_id": source_id, + "description": description, + "entity_type": entity_type, + } + } + await entities_vdb.upsert(entity_data_for_vdb) + logger.info(f"Entity Merge: updating vdb `{target_entity}`") + + # 9. Merge entity chunk tracking (source entities first, then target entity) + if entity_chunks_storage is not None: + all_chunk_id_lists = [] + + # Build list of entities to process (source entities first, then target entity) + entities_to_process = [] + + # Add source entities first (excluding target if it's already in source list) + for entity_name in source_entities: + if entity_name != target_entity: + entities_to_process.append(entity_name) + + # Add target entity last (if it exists) + if target_exists: + entities_to_process.append(target_entity) + + # Process all entities in order with unified logic + for entity_name in entities_to_process: + stored = await entity_chunks_storage.get_by_id(entity_name) + if stored and isinstance(stored, dict): + chunk_ids = [cid for cid in stored.get("chunk_ids", []) if cid] + if chunk_ids: + all_chunk_id_lists.append(chunk_ids) + + # Merge chunk_ids with ordered deduplication (preserves order, source entities first) + merged_chunk_ids = [] + seen = set() + for chunk_id_list in all_chunk_id_lists: + for chunk_id in chunk_id_list: + if chunk_id not in seen: + seen.add(chunk_id) + merged_chunk_ids.append(chunk_id) + + # Delete source entities' chunk tracking records + entity_keys_to_delete = [e for e in source_entities if e != target_entity] + if entity_keys_to_delete: + await entity_chunks_storage.delete(entity_keys_to_delete) + + # Update target entity's chunk tracking + if merged_chunk_ids: + await entity_chunks_storage.upsert( + { + target_entity: { + "chunk_ids": merged_chunk_ids, + "count": len(merged_chunk_ids), + } + } + ) + logger.info( + f"Entity Merge: find {len(merged_chunk_ids)} chunks related to '{target_entity}'" + ) + + # 10. Delete source entities + for entity_name in source_entities: + if entity_name == target_entity: + logger.warning( + f"Entity Merge: source entity'{entity_name}' is same as target entity" + ) + continue + + logger.info(f"Entity Merge: deleting '{entity_name}' from KG and vdb") + + # Delete entity node and related edges from knowledge graph + await chunk_entity_relation_graph.delete_node(entity_name) + + # Delete entity record from vector database + entity_id = compute_mdhash_id(entity_name, prefix="ent-") + await entities_vdb.delete([entity_id]) + + # 11. Save changes + await _persist_graph_updates( + entities_vdb=entities_vdb, + relationships_vdb=relationships_vdb, + chunk_entity_relation_graph=chunk_entity_relation_graph, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, + ) + + logger.info( + f"Entity Merge: successfully merged {len(source_entities)} entities into '{target_entity}'" + ) + return await get_entity_info( + chunk_entity_relation_graph, + entities_vdb, + target_entity, + include_vector_data=True, + ) + + async def amerge_entities( chunk_entity_relation_graph, entities_vdb, @@ -691,11 +1533,14 @@ async def amerge_entities( target_entity: str, merge_strategy: dict[str, str] = None, target_entity_data: dict[str, Any] = None, + entity_chunks_storage=None, + relation_chunks_storage=None, ) -> dict[str, Any]: """Asynchronously merge multiple entities into one entity. Merges multiple source entities into a target entity, handling all relationships, and updating both the knowledge graph and vector database. + Also merges chunk tracking information from entity_chunks_storage and relation_chunks_storage. Args: chunk_entity_relation_graph: Graph storage instance @@ -703,314 +1548,82 @@ async def amerge_entities( relationships_vdb: Vector database storage for relationships source_entities: List of source entity names to merge target_entity: Name of the target entity after merging - merge_strategy: Merge strategy configuration, e.g. {"description": "concatenate", "entity_type": "keep_first"} - Supported strategies: - - "concatenate": Concatenate all values (for text fields) - - "keep_first": Keep the first non-empty value - - "keep_last": Keep the last non-empty value - - "join_unique": Join all unique values (for fields separated by delimiter) + merge_strategy: Deprecated (Each field uses its own default strategy). If provided, + customizations are applied but a warning is logged. target_entity_data: Dictionary of specific values to set for the target entity, overriding any merged values, e.g. {"description": "custom description", "entity_type": "PERSON"} + entity_chunks_storage: Optional KV storage for tracking chunks that reference entities + relation_chunks_storage: Optional KV storage for tracking chunks that reference relations Returns: Dictionary containing the merged entity information """ - graph_db_lock = get_graph_db_lock(enable_logging=False) - # Use graph database lock to ensure atomic graph and vector db operations - async with graph_db_lock: + # Collect all entities involved (source + target) and lock them all in sorted order + all_entities = set(source_entities) + all_entities.add(target_entity) + lock_keys = sorted(all_entities) + + workspace = entities_vdb.global_config.get("workspace", "") + namespace = f"{workspace}:GraphDB" if workspace else "GraphDB" + async with get_storage_keyed_lock( + lock_keys, namespace=namespace, enable_logging=False + ): try: - # Default merge strategy - default_strategy = { - "description": "concatenate", - "entity_type": "keep_first", - "source_id": "join_unique", - } - - merge_strategy = ( - default_strategy - if merge_strategy is None - else {**default_strategy, **merge_strategy} - ) - target_entity_data = ( - {} if target_entity_data is None else target_entity_data - ) - - # 1. Check if all source entities exist - source_entities_data = {} - for entity_name in source_entities: - node_exists = await chunk_entity_relation_graph.has_node(entity_name) - if not node_exists: - raise ValueError(f"Source entity '{entity_name}' does not exist") - node_data = await chunk_entity_relation_graph.get_node(entity_name) - source_entities_data[entity_name] = node_data - - # 2. Check if target entity exists and get its data if it does - target_exists = await chunk_entity_relation_graph.has_node(target_entity) - existing_target_entity_data = {} - if target_exists: - existing_target_entity_data = ( - await chunk_entity_relation_graph.get_node(target_entity) - ) - logger.info( - f"Target entity '{target_entity}' already exists, will merge data" - ) - - # 3. Merge entity data - merged_entity_data = _merge_entity_attributes( - list(source_entities_data.values()) - + ([existing_target_entity_data] if target_exists else []), - merge_strategy, - ) - - # Apply any explicitly provided target entity data (overrides merged data) - for key, value in target_entity_data.items(): - merged_entity_data[key] = value - - # 4. Get all relationships of the source entities - all_relations = [] - for entity_name in source_entities: - # Get all relationships of the source entities - edges = await chunk_entity_relation_graph.get_node_edges(entity_name) - if edges: - for src, tgt in edges: - # Ensure src is the current entity - if src == entity_name: - edge_data = await chunk_entity_relation_graph.get_edge( - src, tgt - ) - all_relations.append((src, tgt, edge_data)) - - # 5. Create or update the target entity - merged_entity_data["entity_id"] = target_entity - if not target_exists: - await chunk_entity_relation_graph.upsert_node( - target_entity, merged_entity_data - ) - logger.info(f"Created new target entity '{target_entity}'") - else: - await chunk_entity_relation_graph.upsert_node( - target_entity, merged_entity_data - ) - logger.info(f"Updated existing target entity '{target_entity}'") - - # 6. Recreate all relationships, pointing to the target entity - relation_updates = {} # Track relationships that need to be merged - relations_to_delete = [] - - for src, tgt, edge_data in all_relations: - relations_to_delete.append(compute_mdhash_id(src + tgt, prefix="rel-")) - relations_to_delete.append(compute_mdhash_id(tgt + src, prefix="rel-")) - new_src = target_entity if src in source_entities else src - new_tgt = target_entity if tgt in source_entities else tgt - - # Skip relationships between source entities to avoid self-loops - if new_src == new_tgt: - logger.info( - f"Skipping relationship between source entities: {src} -> {tgt} to avoid self-loop" - ) - continue - - # Check if the same relationship already exists - relation_key = f"{new_src}|{new_tgt}" - if relation_key in relation_updates: - # Merge relationship data - existing_data = relation_updates[relation_key]["data"] - merged_relation = _merge_relation_attributes( - [existing_data, edge_data], - { - "description": "concatenate", - "keywords": "join_unique", - "source_id": "join_unique", - "weight": "max", - }, - ) - relation_updates[relation_key]["data"] = merged_relation - logger.info( - f"Merged duplicate relationship: {new_src} -> {new_tgt}" - ) - else: - relation_updates[relation_key] = { - "src": new_src, - "tgt": new_tgt, - "data": edge_data.copy(), - } - - # Apply relationship updates - for rel_data in relation_updates.values(): - await chunk_entity_relation_graph.upsert_edge( - rel_data["src"], rel_data["tgt"], rel_data["data"] - ) - logger.info( - f"Created or updated relationship: {rel_data['src']} -> {rel_data['tgt']}" - ) - - # Delete relationships records from vector database - await relationships_vdb.delete(relations_to_delete) - logger.info( - f"Deleted {len(relations_to_delete)} relation records for entity from vector database" - ) - - # 7. Update entity vector representation - description = merged_entity_data.get("description", "") - source_id = merged_entity_data.get("source_id", "") - entity_type = merged_entity_data.get("entity_type", "") - content = target_entity + "\n" + description - - entity_id = compute_mdhash_id(target_entity, prefix="ent-") - entity_data_for_vdb = { - entity_id: { - "content": content, - "entity_name": target_entity, - "source_id": source_id, - "description": description, - "entity_type": entity_type, - } - } - - await entities_vdb.upsert(entity_data_for_vdb) - - # 8. Update relationship vector representations - for rel_data in relation_updates.values(): - src = rel_data["src"] - tgt = rel_data["tgt"] - edge_data = rel_data["data"] - - description = edge_data.get("description", "") - keywords = edge_data.get("keywords", "") - source_id = edge_data.get("source_id", "") - weight = float(edge_data.get("weight", 1.0)) - - content = f"{keywords}\t{src}\n{tgt}\n{description}" - relation_id = compute_mdhash_id(src + tgt, prefix="rel-") - - relation_data_for_vdb = { - relation_id: { - "content": content, - "src_id": src, - "tgt_id": tgt, - "source_id": source_id, - "description": description, - "keywords": keywords, - "weight": weight, - } - } - - await relationships_vdb.upsert(relation_data_for_vdb) - - # 9. Delete source entities - for entity_name in source_entities: - if entity_name == target_entity: - logger.info( - f"Skipping deletion of '{entity_name}' as it's also the target entity" - ) - continue - - # Delete entity node from knowledge graph - await chunk_entity_relation_graph.delete_node(entity_name) - - # Delete entity record from vector database - entity_id = compute_mdhash_id(entity_name, prefix="ent-") - await entities_vdb.delete([entity_id]) - - logger.info( - f"Deleted source entity '{entity_name}' and its vector embedding from database" - ) - - # 10. Save changes - await _merge_entities_done( - entities_vdb, relationships_vdb, chunk_entity_relation_graph - ) - - logger.info( - f"Successfully merged {len(source_entities)} entities into '{target_entity}'" - ) - return await get_entity_info( + return await _merge_entities_impl( chunk_entity_relation_graph, entities_vdb, + relationships_vdb, + source_entities, target_entity, - include_vector_data=True, + merge_strategy=merge_strategy, + target_entity_data=target_entity_data, + entity_chunks_storage=entity_chunks_storage, + relation_chunks_storage=relation_chunks_storage, ) - except Exception as e: logger.error(f"Error merging entities: {e}") raise -def _merge_entity_attributes( - entity_data_list: list[dict[str, Any]], merge_strategy: dict[str, str] +def _merge_attributes( + data_list: list[dict[str, Any]], + merge_strategy: dict[str, str], + filter_none_only: bool = False, ) -> dict[str, Any]: - """Merge attributes from multiple entities. + """Merge attributes from multiple entities or relationships. + + This unified function handles merging of both entity and relationship attributes, + applying different merge strategies per field. Args: - entity_data_list: List of dictionaries containing entity data - merge_strategy: Merge strategy for each field + data_list: List of dictionaries containing entity or relationship data + merge_strategy: Merge strategy for each field. Supported strategies: + - "concatenate": Join all values with GRAPH_FIELD_SEP + - "keep_first": Keep the first non-empty value + - "keep_last": Keep the last non-empty value + - "join_unique": Join unique items separated by GRAPH_FIELD_SEP + - "join_unique_comma": Join unique items separated by comma and space + - "max": Keep the maximum numeric value (for numeric fields) + filter_none_only: If True, only filter None values (keep empty strings, 0, etc.). + If False, filter all falsy values. Default is False for backward compatibility. Returns: - Dictionary containing merged entity data + Dictionary containing merged data """ merged_data = {} # Collect all possible keys all_keys = set() - for data in entity_data_list: + for data in data_list: all_keys.update(data.keys()) # Merge values for each key for key in all_keys: - # Get all values for this key - values = [data.get(key) for data in entity_data_list if data.get(key)] - - if not values: - continue - - # Merge values according to strategy - strategy = merge_strategy.get(key, "keep_first") - - if strategy == "concatenate": - merged_data[key] = "\n\n".join(values) - elif strategy == "keep_first": - merged_data[key] = values[0] - elif strategy == "keep_last": - merged_data[key] = values[-1] - elif strategy == "join_unique": - # Handle fields separated by GRAPH_FIELD_SEP - unique_items = set() - for value in values: - items = value.split(GRAPH_FIELD_SEP) - unique_items.update(items) - merged_data[key] = GRAPH_FIELD_SEP.join(unique_items) + # Get all values for this key based on filtering mode + if filter_none_only: + values = [data.get(key) for data in data_list if data.get(key) is not None] else: - # Default strategy - merged_data[key] = values[0] - - return merged_data - - -def _merge_relation_attributes( - relation_data_list: list[dict[str, Any]], merge_strategy: dict[str, str] -) -> dict[str, Any]: - """Merge attributes from multiple relationships. - - Args: - relation_data_list: List of dictionaries containing relationship data - merge_strategy: Merge strategy for each field - - Returns: - Dictionary containing merged relationship data - """ - merged_data = {} - - # Collect all possible keys - all_keys = set() - for data in relation_data_list: - all_keys.update(data.keys()) - - # Merge values for each key - for key in all_keys: - # Get all values for this key - values = [ - data.get(key) for data in relation_data_list if data.get(key) is not None - ] + values = [data.get(key) for data in data_list if data.get(key)] if not values: continue @@ -1019,7 +1632,8 @@ def _merge_relation_attributes( strategy = merge_strategy.get(key, "keep_first") if strategy == "concatenate": - merged_data[key] = "\n\n".join(str(v) for v in values) + # Convert all values to strings and join with GRAPH_FIELD_SEP + merged_data[key] = GRAPH_FIELD_SEP.join(str(v) for v in values) elif strategy == "keep_first": merged_data[key] = values[0] elif strategy == "keep_last": @@ -1031,35 +1645,27 @@ def _merge_relation_attributes( items = str(value).split(GRAPH_FIELD_SEP) unique_items.update(items) merged_data[key] = GRAPH_FIELD_SEP.join(unique_items) + elif strategy == "join_unique_comma": + # Handle fields separated by comma, join unique items with comma + unique_items = set() + for value in values: + items = str(value).split(",") + unique_items.update(item.strip() for item in items if item.strip()) + merged_data[key] = ",".join(sorted(unique_items)) elif strategy == "max": # For numeric fields like weight try: merged_data[key] = max(float(v) for v in values) except (ValueError, TypeError): + # Fallback to first value if conversion fails merged_data[key] = values[0] else: - # Default strategy + # Default strategy: keep first value merged_data[key] = values[0] return merged_data -async def _merge_entities_done( - entities_vdb, relationships_vdb, chunk_entity_relation_graph -) -> None: - """Callback after entity merging is complete, ensures updates are persisted""" - await asyncio.gather( - *[ - cast(StorageNameSpace, storage_inst).index_done_callback() - for storage_inst in [ # type: ignore - entities_vdb, - relationships_vdb, - chunk_entity_relation_graph, - ] - ] - ) - - async def get_entity_info( chunk_entity_relation_graph, entities_vdb, @@ -1094,7 +1700,18 @@ async def get_relation_info( tgt_entity: str, include_vector_data: bool = False, ) -> dict[str, str | None | dict[str, str]]: - """Get detailed information of a relationship""" + """ + Get detailed information of a relationship between two entities. + Relationship is unidirectional, swap src_entity and tgt_entity does not change the relationship. + + Args: + src_entity: Source entity name + tgt_entity: Target entity name + include_vector_data: Whether to include vector database information + + Returns: + Dictionary containing relationship information + """ # Get information from the graph edge_data = await chunk_entity_relation_graph.get_edge(src_entity, tgt_entity)