Merge branch 'HKUDS:main' into main

This commit is contained in:
Samuel Chan
2025-01-03 21:18:17 +08:00
committed by GitHub
5 changed files with 412 additions and 22 deletions

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@@ -1,6 +1,6 @@
MIT License MIT License
Copyright (c) 2024 Gustavo Ye Copyright (c) 2025 Gustavo Ye
Permission is hereby granted, free of charge, to any person obtaining a copy Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal of this software and associated documentation files (the "Software"), to deal

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@@ -26,10 +26,11 @@ This repository hosts the code of LightRAG. The structure of this code is based
</div> </div>
## 🎉 News ## 🎉 News
- [x] [2024.12.31]🎯📢LightRAG now supports [deletion by document ID](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
- [x] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise. - [x] [2024.11.25]🎯📢LightRAG now supports seamless integration of [custom knowledge graphs](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg), empowering users to enhance the system with their own domain expertise.
- [x] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author. - [x] [2024.11.19]🎯📢A comprehensive guide to LightRAG is now available on [LearnOpenCV](https://learnopencv.com/lightrag). Many thanks to the blog author.
- [x] [2024.11.12]🎯📢LightRAG now supports [Oracle Database 23ai for all storage types (KV, vector, and graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py). - [x] [2024.11.12]🎯📢LightRAG now supports [Oracle Database 23ai for all storage types (KV, vector, and graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py).
- [x] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete-entity). - [x] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete).
- [x] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge. - [x] [2024.11.09]🎯📢Introducing the [LightRAG Gui](https://lightrag-gui.streamlit.app), which allows you to insert, query, visualize, and download LightRAG knowledge.
- [x] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage). - [x] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
- [x] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`. - [x] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
@@ -412,10 +413,9 @@ custom_kg = {
rag.insert_custom_kg(custom_kg) rag.insert_custom_kg(custom_kg)
``` ```
### Delete Entity ### Delete
```python ```python
# Delete Entity: Deleting entities by their names
rag = LightRAG( rag = LightRAG(
working_dir=WORKING_DIR, working_dir=WORKING_DIR,
llm_model_func=llm_model_func, llm_model_func=llm_model_func,
@@ -426,7 +426,11 @@ rag = LightRAG(
), ),
) )
# Delete Entity: Deleting entities by their names
rag.delete_by_entity("Project Gutenberg") rag.delete_by_entity("Project Gutenberg")
# Delete Document: Deleting entities and relationships associated with the document by doc id
rag.delete_by_doc_id("doc_id")
``` ```
### Multi-file Type Support ### Multi-file Type Support

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@@ -1,5 +1,5 @@
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
__version__ = "1.0.8" __version__ = "1.0.9"
__author__ = "Zirui Guo" __author__ = "Zirui Guo"
__url__ = "https://github.com/HKUDS/LightRAG" __url__ = "https://github.com/HKUDS/LightRAG"

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@@ -43,6 +43,8 @@ from .storage import (
JsonDocStatusStorage, JsonDocStatusStorage,
) )
from .prompt import GRAPH_FIELD_SEP
# future KG integrations # future KG integrations
# from .kg.ArangoDB_impl import ( # from .kg.ArangoDB_impl import (
@@ -680,7 +682,7 @@ class LightRAG:
try: try:
await self.entities_vdb.delete_entity(entity_name) await self.entities_vdb.delete_entity(entity_name)
await self.relationships_vdb.delete_relation(entity_name) await self.relationships_vdb.delete_entity_relation(entity_name)
await self.chunk_entity_relation_graph.delete_node(entity_name) await self.chunk_entity_relation_graph.delete_node(entity_name)
logger.info( logger.info(
@@ -724,3 +726,311 @@ class LightRAG:
Dict with counts for each status Dict with counts for each status
""" """
return await self.doc_status.get_status_counts() return await self.doc_status.get_status_counts()
async def adelete_by_doc_id(self, doc_id: str):
"""Delete a document and all its related data
Args:
doc_id: Document ID to delete
"""
try:
# 1. Get the document status and related data
doc_status = await self.doc_status.get(doc_id)
if not doc_status:
logger.warning(f"Document {doc_id} not found")
return
logger.debug(f"Starting deletion for document {doc_id}")
# 2. Get all related chunks
chunks = await self.text_chunks.filter(
lambda x: x.get("full_doc_id") == doc_id
)
chunk_ids = list(chunks.keys())
logger.debug(f"Found {len(chunk_ids)} chunks to delete")
# 3. Before deleting, check the related entities and relationships for these chunks
for chunk_id in chunk_ids:
# Check entities
entities = [
dp
for dp in self.entities_vdb.client_storage["data"]
if dp.get("source_id") == chunk_id
]
logger.debug(f"Chunk {chunk_id} has {len(entities)} related entities")
# Check relationships
relations = [
dp
for dp in self.relationships_vdb.client_storage["data"]
if dp.get("source_id") == chunk_id
]
logger.debug(f"Chunk {chunk_id} has {len(relations)} related relations")
# Continue with the original deletion process...
# 4. Delete chunks from vector database
if chunk_ids:
await self.chunks_vdb.delete(chunk_ids)
await self.text_chunks.delete(chunk_ids)
# 5. Find and process entities and relationships that have these chunks as source
# Get all nodes in the graph
nodes = self.chunk_entity_relation_graph._graph.nodes(data=True)
edges = self.chunk_entity_relation_graph._graph.edges(data=True)
# Track which entities and relationships need to be deleted or updated
entities_to_delete = set()
entities_to_update = {} # entity_name -> new_source_id
relationships_to_delete = set()
relationships_to_update = {} # (src, tgt) -> new_source_id
# Process entities
for node, data in nodes:
if "source_id" in data:
# Split source_id using GRAPH_FIELD_SEP
sources = set(data["source_id"].split(GRAPH_FIELD_SEP))
sources.difference_update(chunk_ids)
if not sources:
entities_to_delete.add(node)
logger.debug(
f"Entity {node} marked for deletion - no remaining sources"
)
else:
new_source_id = GRAPH_FIELD_SEP.join(sources)
entities_to_update[node] = new_source_id
logger.debug(
f"Entity {node} will be updated with new source_id: {new_source_id}"
)
# Process relationships
for src, tgt, data in edges:
if "source_id" in data:
# Split source_id using GRAPH_FIELD_SEP
sources = set(data["source_id"].split(GRAPH_FIELD_SEP))
sources.difference_update(chunk_ids)
if not sources:
relationships_to_delete.add((src, tgt))
logger.debug(
f"Relationship {src}-{tgt} marked for deletion - no remaining sources"
)
else:
new_source_id = GRAPH_FIELD_SEP.join(sources)
relationships_to_update[(src, tgt)] = new_source_id
logger.debug(
f"Relationship {src}-{tgt} will be updated with new source_id: {new_source_id}"
)
# Delete entities
if entities_to_delete:
for entity in entities_to_delete:
await self.entities_vdb.delete_entity(entity)
logger.debug(f"Deleted entity {entity} from vector DB")
self.chunk_entity_relation_graph.remove_nodes(list(entities_to_delete))
logger.debug(f"Deleted {len(entities_to_delete)} entities from graph")
# Update entities
for entity, new_source_id in entities_to_update.items():
node_data = self.chunk_entity_relation_graph._graph.nodes[entity]
node_data["source_id"] = new_source_id
await self.chunk_entity_relation_graph.upsert_node(entity, node_data)
logger.debug(
f"Updated entity {entity} with new source_id: {new_source_id}"
)
# Delete relationships
if relationships_to_delete:
for src, tgt in relationships_to_delete:
rel_id_0 = compute_mdhash_id(src + tgt, prefix="rel-")
rel_id_1 = compute_mdhash_id(tgt + src, prefix="rel-")
await self.relationships_vdb.delete([rel_id_0, rel_id_1])
logger.debug(f"Deleted relationship {src}-{tgt} from vector DB")
self.chunk_entity_relation_graph.remove_edges(
list(relationships_to_delete)
)
logger.debug(
f"Deleted {len(relationships_to_delete)} relationships from graph"
)
# Update relationships
for (src, tgt), new_source_id in relationships_to_update.items():
edge_data = self.chunk_entity_relation_graph._graph.edges[src, tgt]
edge_data["source_id"] = new_source_id
await self.chunk_entity_relation_graph.upsert_edge(src, tgt, edge_data)
logger.debug(
f"Updated relationship {src}-{tgt} with new source_id: {new_source_id}"
)
# 6. Delete original document and status
await self.full_docs.delete([doc_id])
await self.doc_status.delete([doc_id])
# 7. Ensure all indexes are updated
await self._insert_done()
logger.info(
f"Successfully deleted document {doc_id} and related data. "
f"Deleted {len(entities_to_delete)} entities and {len(relationships_to_delete)} relationships. "
f"Updated {len(entities_to_update)} entities and {len(relationships_to_update)} relationships."
)
# Add verification step
async def verify_deletion():
# Verify if the document has been deleted
if await self.full_docs.get_by_id(doc_id):
logger.error(f"Document {doc_id} still exists in full_docs")
# Verify if chunks have been deleted
remaining_chunks = await self.text_chunks.filter(
lambda x: x.get("full_doc_id") == doc_id
)
if remaining_chunks:
logger.error(f"Found {len(remaining_chunks)} remaining chunks")
# Verify entities and relationships
for chunk_id in chunk_ids:
# Check entities
entities_with_chunk = [
dp
for dp in self.entities_vdb.client_storage["data"]
if chunk_id
in (dp.get("source_id") or "").split(GRAPH_FIELD_SEP)
]
if entities_with_chunk:
logger.error(
f"Found {len(entities_with_chunk)} entities still referencing chunk {chunk_id}"
)
# Check relationships
relations_with_chunk = [
dp
for dp in self.relationships_vdb.client_storage["data"]
if chunk_id
in (dp.get("source_id") or "").split(GRAPH_FIELD_SEP)
]
if relations_with_chunk:
logger.error(
f"Found {len(relations_with_chunk)} relations still referencing chunk {chunk_id}"
)
await verify_deletion()
except Exception as e:
logger.error(f"Error while deleting document {doc_id}: {e}")
def delete_by_doc_id(self, doc_id: str):
"""Synchronous version of adelete"""
return asyncio.run(self.adelete_by_doc_id(doc_id))
async def get_entity_info(
self, entity_name: str, include_vector_data: bool = False
):
"""Get detailed information of an entity
Args:
entity_name: Entity name (no need for quotes)
include_vector_data: Whether to include data from the vector database
Returns:
dict: A dictionary containing entity information, including:
- entity_name: Entity name
- source_id: Source document ID
- graph_data: Complete node data from the graph database
- vector_data: (optional) Data from the vector database
"""
entity_name = f'"{entity_name.upper()}"'
# Get information from the graph
node_data = await self.chunk_entity_relation_graph.get_node(entity_name)
source_id = node_data.get("source_id") if node_data else None
result = {
"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 = self.entities_vdb._client.get([entity_id])
result["vector_data"] = vector_data[0] if vector_data else None
return result
def get_entity_info_sync(self, entity_name: str, include_vector_data: bool = False):
"""Synchronous version of getting entity information
Args:
entity_name: Entity name (no need for quotes)
include_vector_data: Whether to include data from the vector database
"""
try:
import tracemalloc
tracemalloc.start()
return asyncio.run(self.get_entity_info(entity_name, include_vector_data))
finally:
tracemalloc.stop()
async def get_relation_info(
self, src_entity: str, tgt_entity: str, include_vector_data: bool = False
):
"""Get detailed information of a relationship
Args:
src_entity: Source entity name (no need for quotes)
tgt_entity: Target entity name (no need for quotes)
include_vector_data: Whether to include data from the vector database
Returns:
dict: A dictionary containing relationship information, including:
- src_entity: Source entity name
- tgt_entity: Target entity name
- source_id: Source document ID
- graph_data: Complete edge data from the graph database
- vector_data: (optional) Data from the vector database
"""
src_entity = f'"{src_entity.upper()}"'
tgt_entity = f'"{tgt_entity.upper()}"'
# Get information from the graph
edge_data = await self.chunk_entity_relation_graph.get_edge(
src_entity, tgt_entity
)
source_id = edge_data.get("source_id") if edge_data else None
result = {
"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 = self.relationships_vdb._client.get([rel_id])
result["vector_data"] = vector_data[0] if vector_data else None
return result
def get_relation_info_sync(
self, src_entity: str, tgt_entity: str, include_vector_data: bool = False
):
"""Synchronous version of getting relationship information
Args:
src_entity: Source entity name (no need for quotes)
tgt_entity: Target entity name (no need for quotes)
include_vector_data: Whether to include data from the vector database
"""
try:
import tracemalloc
tracemalloc.start()
return asyncio.run(
self.get_relation_info(src_entity, tgt_entity, include_vector_data)
)
finally:
tracemalloc.stop()

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@@ -32,6 +32,7 @@ class JsonKVStorage(BaseKVStorage):
working_dir = self.global_config["working_dir"] working_dir = self.global_config["working_dir"]
self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json") self._file_name = os.path.join(working_dir, f"kv_store_{self.namespace}.json")
self._data = load_json(self._file_name) or {} self._data = load_json(self._file_name) or {}
self._lock = asyncio.Lock()
logger.info(f"Load KV {self.namespace} with {len(self._data)} data") logger.info(f"Load KV {self.namespace} with {len(self._data)} data")
async def all_keys(self) -> list[str]: async def all_keys(self) -> list[str]:
@@ -66,6 +67,35 @@ class JsonKVStorage(BaseKVStorage):
async def drop(self): async def drop(self):
self._data = {} self._data = {}
async def filter(self, filter_func):
"""Filter key-value pairs based on a filter function
Args:
filter_func: The filter function, which takes a value as an argument and returns a boolean value
Returns:
Dict: Key-value pairs that meet the condition
"""
result = {}
async with self._lock:
for key, value in self._data.items():
if filter_func(value):
result[key] = value
return result
async def delete(self, ids: list[str]):
"""Delete data with specified IDs
Args:
ids: List of IDs to delete
"""
async with self._lock:
for id in ids:
if id in self._data:
del self._data[id]
await self.index_done_callback()
logger.info(f"Successfully deleted {len(ids)} items from {self.namespace}")
@dataclass @dataclass
class NanoVectorDBStorage(BaseVectorStorage): class NanoVectorDBStorage(BaseVectorStorage):
@@ -150,38 +180,54 @@ class NanoVectorDBStorage(BaseVectorStorage):
def client_storage(self): def client_storage(self):
return getattr(self._client, "_NanoVectorDB__storage") return getattr(self._client, "_NanoVectorDB__storage")
async def delete(self, ids: list[str]):
"""Delete vectors with specified IDs
Args:
ids: List of vector IDs to be deleted
"""
try:
self._client.delete(ids)
logger.info(
f"Successfully deleted {len(ids)} vectors from {self.namespace}"
)
except Exception as e:
logger.error(f"Error while deleting vectors from {self.namespace}: {e}")
async def delete_entity(self, entity_name: str): async def delete_entity(self, entity_name: str):
try: try:
entity_id = [compute_mdhash_id(entity_name, prefix="ent-")] entity_id = compute_mdhash_id(entity_name, prefix="ent-")
logger.debug(
if self._client.get(entity_id): f"Attempting to delete entity {entity_name} with ID {entity_id}"
self._client.delete(entity_id) )
logger.info(f"Entity {entity_name} have been deleted.") # Check if the entity exists
if self._client.get([entity_id]):
await self.delete([entity_id])
logger.debug(f"Successfully deleted entity {entity_name}")
else: else:
logger.info(f"No entity found with name {entity_name}.") logger.debug(f"Entity {entity_name} not found in storage")
except Exception as e: except Exception as e:
logger.error(f"Error while deleting entity {entity_name}: {e}") logger.error(f"Error deleting entity {entity_name}: {e}")
async def delete_relation(self, entity_name: str): async def delete_entity_relation(self, entity_name: str):
try: try:
relations = [ relations = [
dp dp
for dp in self.client_storage["data"] for dp in self.client_storage["data"]
if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name
] ]
logger.debug(f"Found {len(relations)} relations for entity {entity_name}")
ids_to_delete = [relation["__id__"] for relation in relations] ids_to_delete = [relation["__id__"] for relation in relations]
if ids_to_delete: if ids_to_delete:
self._client.delete(ids_to_delete) await self.delete(ids_to_delete)
logger.info( logger.debug(
f"All relations related to entity {entity_name} have been deleted." f"Deleted {len(ids_to_delete)} relations for {entity_name}"
) )
else: else:
logger.info(f"No relations found for entity {entity_name}.") logger.debug(f"No relations found for entity {entity_name}")
except Exception as e: except Exception as e:
logger.error( logger.error(f"Error deleting relations for {entity_name}: {e}")
f"Error while deleting relations for entity {entity_name}: {e}"
)
async def index_done_callback(self): async def index_done_callback(self):
self._client.save() self._client.save()
@@ -329,6 +375,26 @@ class NetworkXStorage(BaseGraphStorage):
nodes_ids = [self._graph.nodes[node_id]["id"] for node_id in nodes] nodes_ids = [self._graph.nodes[node_id]["id"] for node_id in nodes]
return embeddings, nodes_ids return embeddings, nodes_ids
def remove_nodes(self, nodes: list[str]):
"""Delete multiple nodes
Args:
nodes: List of node IDs to be deleted
"""
for node in nodes:
if self._graph.has_node(node):
self._graph.remove_node(node)
def remove_edges(self, edges: list[tuple[str, str]]):
"""Delete multiple edges
Args:
edges: List of edges to be deleted, each edge is a (source, target) tuple
"""
for source, target in edges:
if self._graph.has_edge(source, target):
self._graph.remove_edge(source, target)
@dataclass @dataclass
class JsonDocStatusStorage(DocStatusStorage): class JsonDocStatusStorage(DocStatusStorage):
@@ -378,3 +444,13 @@ class JsonDocStatusStorage(DocStatusStorage):
self._data.update(data) self._data.update(data)
await self.index_done_callback() await self.index_done_callback()
return data return data
async def get(self, doc_id: str) -> Union[DocProcessingStatus, None]:
"""Get document status by ID"""
return self._data.get(doc_id)
async def delete(self, doc_ids: list[str]):
"""Delete document status by IDs"""
for doc_id in doc_ids:
self._data.pop(doc_id, None)
await self.index_done_callback()