mirror of
https://github.com/HKUDS/LightRAG.git
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1117 lines
43 KiB
Python
1117 lines
43 KiB
Python
from __future__ import annotations
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import time
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import asyncio
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from typing import Any, cast
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from .base import DeletionResult
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from .kg.shared_storage import get_graph_db_lock
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from .constants import GRAPH_FIELD_SEP
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from .utils import compute_mdhash_id, logger
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from .base import StorageNameSpace
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async def adelete_by_entity(
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chunk_entity_relation_graph, entities_vdb, relationships_vdb, entity_name: str
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) -> DeletionResult:
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"""Asynchronously delete an entity and all its relationships.
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Args:
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chunk_entity_relation_graph: Graph storage instance
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entities_vdb: Vector database storage for entities
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relationships_vdb: Vector database storage for relationships
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entity_name: Name of the entity to delete
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"""
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graph_db_lock = get_graph_db_lock(enable_logging=False)
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# Use graph database lock to ensure atomic graph and vector db operations
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async with graph_db_lock:
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try:
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# Check if the entity exists
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if not await chunk_entity_relation_graph.has_node(entity_name):
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logger.warning(f"Entity '{entity_name}' not found.")
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return DeletionResult(
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status="not_found",
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doc_id=entity_name,
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message=f"Entity '{entity_name}' not found.",
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status_code=404,
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)
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# Retrieve related relationships before deleting the node
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edges = await chunk_entity_relation_graph.get_node_edges(entity_name)
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related_relations_count = len(edges) if edges else 0
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await entities_vdb.delete_entity(entity_name)
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await relationships_vdb.delete_entity_relation(entity_name)
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await chunk_entity_relation_graph.delete_node(entity_name)
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message = f"Entity '{entity_name}' and its {related_relations_count} relationships have been deleted."
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logger.info(message)
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await _delete_by_entity_done(
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entities_vdb, relationships_vdb, chunk_entity_relation_graph
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)
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return DeletionResult(
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status="success",
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doc_id=entity_name,
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message=message,
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status_code=200,
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)
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except Exception as e:
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error_message = f"Error while deleting entity '{entity_name}': {e}"
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logger.error(error_message)
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return DeletionResult(
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status="fail",
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doc_id=entity_name,
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message=error_message,
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status_code=500,
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)
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async def _delete_by_entity_done(
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entities_vdb, relationships_vdb, chunk_entity_relation_graph
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) -> None:
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"""Callback after entity deletion is complete, ensures updates are persisted"""
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await asyncio.gather(
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*[
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cast(StorageNameSpace, storage_inst).index_done_callback()
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for storage_inst in [ # type: ignore
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entities_vdb,
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relationships_vdb,
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chunk_entity_relation_graph,
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]
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]
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)
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async def adelete_by_relation(
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chunk_entity_relation_graph,
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relationships_vdb,
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source_entity: str,
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target_entity: str,
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) -> DeletionResult:
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"""Asynchronously delete a relation between two entities.
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Args:
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chunk_entity_relation_graph: Graph storage instance
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relationships_vdb: Vector database storage for relationships
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source_entity: Name of the source entity
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target_entity: Name of the target entity
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"""
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relation_str = f"{source_entity} -> {target_entity}"
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graph_db_lock = get_graph_db_lock(enable_logging=False)
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# Use graph database lock to ensure atomic graph and vector db operations
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async with graph_db_lock:
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try:
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# Check if the relation exists
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edge_exists = await chunk_entity_relation_graph.has_edge(
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source_entity, target_entity
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)
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if not edge_exists:
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message = f"Relation from '{source_entity}' to '{target_entity}' does not exist"
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logger.warning(message)
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return DeletionResult(
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status="not_found",
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doc_id=relation_str,
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message=message,
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status_code=404,
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)
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# Delete relation from vector database
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rel_ids_to_delete = [
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compute_mdhash_id(source_entity + target_entity, prefix="rel-"),
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compute_mdhash_id(target_entity + source_entity, prefix="rel-"),
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]
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await relationships_vdb.delete(rel_ids_to_delete)
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# Delete relation from knowledge graph
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await chunk_entity_relation_graph.remove_edges(
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[(source_entity, target_entity)]
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)
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message = f"Successfully deleted relation from '{source_entity}' to '{target_entity}'"
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logger.info(message)
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await _delete_relation_done(relationships_vdb, chunk_entity_relation_graph)
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return DeletionResult(
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status="success",
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doc_id=relation_str,
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message=message,
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status_code=200,
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)
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except Exception as e:
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error_message = f"Error while deleting relation from '{source_entity}' to '{target_entity}': {e}"
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logger.error(error_message)
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return DeletionResult(
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status="fail",
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doc_id=relation_str,
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message=error_message,
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status_code=500,
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)
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async def _delete_relation_done(relationships_vdb, chunk_entity_relation_graph) -> None:
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"""Callback after relation deletion is complete, ensures updates are persisted"""
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await asyncio.gather(
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*[
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cast(StorageNameSpace, storage_inst).index_done_callback()
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for storage_inst in [ # type: ignore
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relationships_vdb,
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chunk_entity_relation_graph,
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]
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]
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)
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async def aedit_entity(
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chunk_entity_relation_graph,
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entities_vdb,
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relationships_vdb,
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entity_name: str,
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updated_data: dict[str, str],
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allow_rename: bool = True,
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) -> dict[str, Any]:
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"""Asynchronously edit entity information.
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Updates entity information in the knowledge graph and re-embeds the entity in the vector database.
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Args:
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chunk_entity_relation_graph: Graph storage instance
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entities_vdb: Vector database storage for entities
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relationships_vdb: Vector database storage for relationships
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entity_name: Name of the entity to edit
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updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "entity_type": "new type"}
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allow_rename: Whether to allow entity renaming, defaults to True
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Returns:
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Dictionary containing updated entity information
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"""
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graph_db_lock = get_graph_db_lock(enable_logging=False)
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# Use graph database lock to ensure atomic graph and vector db operations
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async with graph_db_lock:
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try:
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# 1. Get current entity information
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node_exists = await chunk_entity_relation_graph.has_node(entity_name)
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if not node_exists:
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raise ValueError(f"Entity '{entity_name}' does not exist")
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node_data = await chunk_entity_relation_graph.get_node(entity_name)
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# Check if entity is being renamed
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new_entity_name = updated_data.get("entity_name", entity_name)
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is_renaming = new_entity_name != entity_name
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# If renaming, check if new name already exists
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if is_renaming:
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if not allow_rename:
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raise ValueError(
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"Entity renaming is not allowed. Set allow_rename=True to enable this feature"
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)
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existing_node = await chunk_entity_relation_graph.has_node(
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new_entity_name
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)
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if existing_node:
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raise ValueError(
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f"Entity name '{new_entity_name}' already exists, cannot rename"
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)
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# 2. Update entity information in the graph
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new_node_data = {**node_data, **updated_data}
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new_node_data["entity_id"] = new_entity_name
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if "entity_name" in new_node_data:
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del new_node_data[
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"entity_name"
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] # Node data should not contain entity_name field
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# If renaming entity
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if is_renaming:
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logger.info(f"Renaming entity '{entity_name}' to '{new_entity_name}'")
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# Create new entity
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await chunk_entity_relation_graph.upsert_node(
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new_entity_name, new_node_data
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)
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# Store relationships that need to be updated
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relations_to_update = []
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relations_to_delete = []
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# Get all edges related to the original entity
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edges = await chunk_entity_relation_graph.get_node_edges(entity_name)
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if edges:
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# Recreate edges for the new entity
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for source, target in edges:
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edge_data = await chunk_entity_relation_graph.get_edge(
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source, target
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)
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if edge_data:
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relations_to_delete.append(
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compute_mdhash_id(source + target, prefix="rel-")
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)
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relations_to_delete.append(
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compute_mdhash_id(target + source, prefix="rel-")
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)
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if source == entity_name:
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await chunk_entity_relation_graph.upsert_edge(
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new_entity_name, target, edge_data
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)
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relations_to_update.append(
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(new_entity_name, target, edge_data)
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)
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else: # target == entity_name
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await chunk_entity_relation_graph.upsert_edge(
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source, new_entity_name, edge_data
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)
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relations_to_update.append(
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(source, new_entity_name, edge_data)
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)
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# Delete old entity
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await chunk_entity_relation_graph.delete_node(entity_name)
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# Delete old entity record from vector database
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old_entity_id = compute_mdhash_id(entity_name, prefix="ent-")
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await entities_vdb.delete([old_entity_id])
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logger.info(
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f"Deleted old entity '{entity_name}' and its vector embedding from database"
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)
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# Delete old relation records from vector database
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await relationships_vdb.delete(relations_to_delete)
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logger.info(
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f"Deleted {len(relations_to_delete)} relation records for entity '{entity_name}' from vector database"
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)
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# Update relationship vector representations
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for src, tgt, edge_data in relations_to_update:
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description = edge_data.get("description", "")
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keywords = edge_data.get("keywords", "")
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source_id = edge_data.get("source_id", "")
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weight = float(edge_data.get("weight", 1.0))
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# Create new content for embedding
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content = f"{src}\t{tgt}\n{keywords}\n{description}"
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# Calculate relationship ID
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relation_id = compute_mdhash_id(src + tgt, prefix="rel-")
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# Prepare data for vector database update
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relation_data = {
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relation_id: {
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"content": content,
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"src_id": src,
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"tgt_id": tgt,
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"source_id": source_id,
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"description": description,
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"keywords": keywords,
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"weight": weight,
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}
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}
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# Update vector database
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await relationships_vdb.upsert(relation_data)
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# Update working entity name to new name
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entity_name = new_entity_name
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else:
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# If not renaming, directly update node data
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await chunk_entity_relation_graph.upsert_node(
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entity_name, new_node_data
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)
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# 3. Recalculate entity's vector representation and update vector database
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description = new_node_data.get("description", "")
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source_id = new_node_data.get("source_id", "")
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entity_type = new_node_data.get("entity_type", "")
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content = entity_name + "\n" + description
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# Calculate entity ID
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entity_id = compute_mdhash_id(entity_name, prefix="ent-")
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# Prepare data for vector database update
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entity_data = {
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entity_id: {
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"content": content,
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"entity_name": entity_name,
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"source_id": source_id,
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"description": description,
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"entity_type": entity_type,
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}
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}
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# Update vector database
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await entities_vdb.upsert(entity_data)
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# 4. Save changes
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await _edit_entity_done(
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entities_vdb, relationships_vdb, chunk_entity_relation_graph
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)
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logger.info(f"Entity '{entity_name}' successfully updated")
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return await get_entity_info(
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chunk_entity_relation_graph,
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entities_vdb,
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entity_name,
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include_vector_data=True,
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)
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except Exception as e:
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logger.error(f"Error while editing entity '{entity_name}': {e}")
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raise
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async def _edit_entity_done(
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entities_vdb, relationships_vdb, chunk_entity_relation_graph
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) -> None:
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"""Callback after entity editing is complete, ensures updates are persisted"""
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await asyncio.gather(
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*[
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cast(StorageNameSpace, storage_inst).index_done_callback()
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for storage_inst in [ # type: ignore
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entities_vdb,
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relationships_vdb,
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chunk_entity_relation_graph,
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]
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]
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)
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async def aedit_relation(
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chunk_entity_relation_graph,
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entities_vdb,
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relationships_vdb,
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source_entity: str,
|
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target_entity: str,
|
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updated_data: dict[str, Any],
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) -> dict[str, Any]:
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"""Asynchronously edit relation information.
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Updates relation (edge) information in the knowledge graph and re-embeds the relation in the vector database.
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Args:
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chunk_entity_relation_graph: Graph storage instance
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entities_vdb: Vector database storage for entities
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relationships_vdb: Vector database storage for relationships
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source_entity: Name of the source entity
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target_entity: Name of the target entity
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updated_data: Dictionary containing updated attributes, e.g. {"description": "new description", "keywords": "new keywords"}
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Returns:
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Dictionary containing updated relation information
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"""
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graph_db_lock = get_graph_db_lock(enable_logging=False)
|
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# Use graph database lock to ensure atomic graph and vector db operations
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async with graph_db_lock:
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try:
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# 1. Get current relation information
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edge_exists = await chunk_entity_relation_graph.has_edge(
|
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source_entity, target_entity
|
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)
|
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if not edge_exists:
|
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raise ValueError(
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f"Relation from '{source_entity}' to '{target_entity}' does not exist"
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)
|
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edge_data = await chunk_entity_relation_graph.get_edge(
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source_entity, target_entity
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)
|
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# Important: First delete the old relation record from the vector database
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old_relation_id = compute_mdhash_id(
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source_entity + target_entity, prefix="rel-"
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)
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await relationships_vdb.delete([old_relation_id])
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logger.info(
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f"Deleted old relation record from vector database for relation {source_entity} -> {target_entity}"
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)
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# 2. Update relation information in the graph
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new_edge_data = {**edge_data, **updated_data}
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await chunk_entity_relation_graph.upsert_edge(
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source_entity, target_entity, new_edge_data
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)
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# 3. Recalculate relation's vector representation and update vector database
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description = new_edge_data.get("description", "")
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keywords = new_edge_data.get("keywords", "")
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source_id = new_edge_data.get("source_id", "")
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weight = float(new_edge_data.get("weight", 1.0))
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# Create content for embedding
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content = f"{source_entity}\t{target_entity}\n{keywords}\n{description}"
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|
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# Calculate relation ID
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relation_id = compute_mdhash_id(
|
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source_entity + target_entity, prefix="rel-"
|
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)
|
|
|
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# Prepare data for vector database update
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relation_data = {
|
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relation_id: {
|
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"content": content,
|
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"src_id": source_entity,
|
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"tgt_id": target_entity,
|
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"source_id": source_id,
|
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"description": description,
|
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"keywords": keywords,
|
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"weight": weight,
|
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}
|
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}
|
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|
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# Update vector database
|
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await relationships_vdb.upsert(relation_data)
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|
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# 4. Save changes
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await _edit_relation_done(relationships_vdb, chunk_entity_relation_graph)
|
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|
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logger.info(
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f"Relation from '{source_entity}' to '{target_entity}' successfully updated"
|
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)
|
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return await get_relation_info(
|
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chunk_entity_relation_graph,
|
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relationships_vdb,
|
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source_entity,
|
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target_entity,
|
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include_vector_data=True,
|
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)
|
|
except Exception as e:
|
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logger.error(
|
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f"Error while editing relation from '{source_entity}' to '{target_entity}': {e}"
|
|
)
|
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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,
|
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]
|
|
]
|
|
)
|
|
|
|
|
|
async def acreate_entity(
|
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chunk_entity_relation_graph,
|
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entities_vdb,
|
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relationships_vdb,
|
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entity_name: str,
|
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entity_data: dict[str, Any],
|
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) -> dict[str, Any]:
|
|
"""Asynchronously create a new entity.
|
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|
|
Creates a new entity in the knowledge graph and adds it to the vector database.
|
|
|
|
Args:
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chunk_entity_relation_graph: Graph storage instance
|
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entities_vdb: Vector database storage for entities
|
|
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"}
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|
|
|
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:
|
|
try:
|
|
# Check if entity already exists
|
|
existing_node = await chunk_entity_relation_graph.has_node(entity_name)
|
|
if existing_node:
|
|
raise ValueError(f"Entity '{entity_name}' already exists")
|
|
|
|
# Prepare node data with defaults if missing
|
|
node_data = {
|
|
"entity_id": entity_name,
|
|
"entity_type": entity_data.get("entity_type", "UNKNOWN"),
|
|
"description": entity_data.get("description", ""),
|
|
"source_id": entity_data.get("source_id", "manual_creation"),
|
|
"file_path": entity_data.get("file_path", "manual_creation"),
|
|
"created_at": int(time.time()),
|
|
}
|
|
|
|
# Add entity to knowledge graph
|
|
await chunk_entity_relation_graph.upsert_node(entity_name, node_data)
|
|
|
|
# Prepare content for entity
|
|
description = node_data.get("description", "")
|
|
source_id = node_data.get("source_id", "")
|
|
entity_type = node_data.get("entity_type", "")
|
|
content = entity_name + "\n" + description
|
|
|
|
# Calculate entity ID
|
|
entity_id = compute_mdhash_id(entity_name, prefix="ent-")
|
|
|
|
# Prepare data for vector database update
|
|
entity_data_for_vdb = {
|
|
entity_id: {
|
|
"content": content,
|
|
"entity_name": entity_name,
|
|
"source_id": source_id,
|
|
"description": description,
|
|
"entity_type": entity_type,
|
|
"file_path": entity_data.get("file_path", "manual_creation"),
|
|
}
|
|
}
|
|
|
|
# Update vector database
|
|
await entities_vdb.upsert(entity_data_for_vdb)
|
|
|
|
# Save changes
|
|
await _edit_entity_done(
|
|
entities_vdb, relationships_vdb, chunk_entity_relation_graph
|
|
)
|
|
|
|
logger.info(f"Entity '{entity_name}' successfully created")
|
|
return await get_entity_info(
|
|
chunk_entity_relation_graph,
|
|
entities_vdb,
|
|
entity_name,
|
|
include_vector_data=True,
|
|
)
|
|
except Exception as e:
|
|
logger.error(f"Error while creating entity '{entity_name}': {e}")
|
|
raise
|
|
|
|
|
|
async def acreate_relation(
|
|
chunk_entity_relation_graph,
|
|
entities_vdb,
|
|
relationships_vdb,
|
|
source_entity: str,
|
|
target_entity: str,
|
|
relation_data: dict[str, Any],
|
|
) -> 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.
|
|
|
|
Args:
|
|
chunk_entity_relation_graph: Graph storage instance
|
|
entities_vdb: Vector database storage for entities
|
|
relationships_vdb: Vector database storage for relationships
|
|
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"}
|
|
|
|
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:
|
|
try:
|
|
# Check if both entities exist
|
|
source_exists = await chunk_entity_relation_graph.has_node(source_entity)
|
|
target_exists = await chunk_entity_relation_graph.has_node(target_entity)
|
|
|
|
if not source_exists:
|
|
raise ValueError(f"Source entity '{source_entity}' does not exist")
|
|
if not target_exists:
|
|
raise ValueError(f"Target entity '{target_entity}' does not exist")
|
|
|
|
# Check if relation already exists
|
|
existing_edge = await chunk_entity_relation_graph.has_edge(
|
|
source_entity, target_entity
|
|
)
|
|
if existing_edge:
|
|
raise ValueError(
|
|
f"Relation from '{source_entity}' to '{target_entity}' already exists"
|
|
)
|
|
|
|
# Prepare edge data with defaults if missing
|
|
edge_data = {
|
|
"description": relation_data.get("description", ""),
|
|
"keywords": relation_data.get("keywords", ""),
|
|
"source_id": relation_data.get("source_id", "manual_creation"),
|
|
"weight": float(relation_data.get("weight", 1.0)),
|
|
"file_path": relation_data.get("file_path", "manual_creation"),
|
|
"created_at": int(time.time()),
|
|
}
|
|
|
|
# Add relation to knowledge graph
|
|
await chunk_entity_relation_graph.upsert_edge(
|
|
source_entity, target_entity, edge_data
|
|
)
|
|
|
|
# Prepare content for embedding
|
|
description = edge_data.get("description", "")
|
|
keywords = edge_data.get("keywords", "")
|
|
source_id = edge_data.get("source_id", "")
|
|
weight = edge_data.get("weight", 1.0)
|
|
|
|
# Create content for embedding
|
|
content = f"{keywords}\t{source_entity}\n{target_entity}\n{description}"
|
|
|
|
# Calculate relation ID
|
|
relation_id = compute_mdhash_id(
|
|
source_entity + target_entity, prefix="rel-"
|
|
)
|
|
|
|
# Prepare data for vector database update
|
|
relation_data_for_vdb = {
|
|
relation_id: {
|
|
"content": content,
|
|
"src_id": source_entity,
|
|
"tgt_id": target_entity,
|
|
"source_id": source_id,
|
|
"description": description,
|
|
"keywords": keywords,
|
|
"weight": weight,
|
|
"file_path": relation_data.get("file_path", "manual_creation"),
|
|
}
|
|
}
|
|
|
|
# Update vector database
|
|
await relationships_vdb.upsert(relation_data_for_vdb)
|
|
|
|
# Save changes
|
|
await _edit_relation_done(relationships_vdb, chunk_entity_relation_graph)
|
|
|
|
logger.info(
|
|
f"Relation from '{source_entity}' to '{target_entity}' successfully created"
|
|
)
|
|
return await get_relation_info(
|
|
chunk_entity_relation_graph,
|
|
relationships_vdb,
|
|
source_entity,
|
|
target_entity,
|
|
include_vector_data=True,
|
|
)
|
|
except Exception as e:
|
|
logger.error(
|
|
f"Error while creating relation from '{source_entity}' to '{target_entity}': {e}"
|
|
)
|
|
raise
|
|
|
|
|
|
async def amerge_entities(
|
|
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,
|
|
) -> 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.
|
|
|
|
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: 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)
|
|
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"}
|
|
|
|
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:
|
|
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(
|
|
chunk_entity_relation_graph,
|
|
entities_vdb,
|
|
target_entity,
|
|
include_vector_data=True,
|
|
)
|
|
|
|
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]
|
|
) -> dict[str, Any]:
|
|
"""Merge attributes from multiple entities.
|
|
|
|
Args:
|
|
entity_data_list: List of dictionaries containing entity data
|
|
merge_strategy: Merge strategy for each field
|
|
|
|
Returns:
|
|
Dictionary containing merged entity data
|
|
"""
|
|
merged_data = {}
|
|
|
|
# Collect all possible keys
|
|
all_keys = set()
|
|
for data in entity_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)
|
|
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
|
|
]
|
|
|
|
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(str(v) for v in 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 = str(value).split(GRAPH_FIELD_SEP)
|
|
unique_items.update(items)
|
|
merged_data[key] = GRAPH_FIELD_SEP.join(unique_items)
|
|
elif strategy == "max":
|
|
# For numeric fields like weight
|
|
try:
|
|
merged_data[key] = max(float(v) for v in values)
|
|
except (ValueError, TypeError):
|
|
merged_data[key] = values[0]
|
|
else:
|
|
# Default strategy
|
|
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,
|
|
entity_name: str,
|
|
include_vector_data: bool = False,
|
|
) -> dict[str, str | None | dict[str, str]]:
|
|
"""Get detailed information of an entity"""
|
|
|
|
# Get information from the graph
|
|
node_data = await chunk_entity_relation_graph.get_node(entity_name)
|
|
source_id = node_data.get("source_id") if node_data else None
|
|
|
|
result: dict[str, str | None | dict[str, str]] = {
|
|
"entity_name": entity_name,
|
|
"source_id": source_id,
|
|
"graph_data": node_data,
|
|
}
|
|
|
|
# Optional: Get vector database information
|
|
if include_vector_data:
|
|
entity_id = compute_mdhash_id(entity_name, prefix="ent-")
|
|
vector_data = await entities_vdb.get_by_id(entity_id)
|
|
result["vector_data"] = vector_data
|
|
|
|
return result
|
|
|
|
|
|
async def get_relation_info(
|
|
chunk_entity_relation_graph,
|
|
relationships_vdb,
|
|
src_entity: str,
|
|
tgt_entity: str,
|
|
include_vector_data: bool = False,
|
|
) -> dict[str, str | None | dict[str, str]]:
|
|
"""Get detailed information of a relationship"""
|
|
|
|
# Get information from the graph
|
|
edge_data = await chunk_entity_relation_graph.get_edge(src_entity, tgt_entity)
|
|
source_id = edge_data.get("source_id") if edge_data else None
|
|
|
|
result: dict[str, str | None | dict[str, str]] = {
|
|
"src_entity": src_entity,
|
|
"tgt_entity": tgt_entity,
|
|
"source_id": source_id,
|
|
"graph_data": edge_data,
|
|
}
|
|
|
|
# Optional: Get vector database information
|
|
if include_vector_data:
|
|
rel_id = compute_mdhash_id(src_entity + tgt_entity, prefix="rel-")
|
|
vector_data = await relationships_vdb.get_by_id(rel_id)
|
|
result["vector_data"] = vector_data
|
|
|
|
return result
|