diff --git a/.gitignore b/.gitignore
index 65aaaa02..942c2c25 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,3 +11,4 @@ neo4jWorkDir/
ignore_this.txt
.venv/
*.ignore.*
+.ruff_cache/
diff --git a/README.md b/README.md
index f0276bcd..cb4e2b02 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,7 @@
- 
+
@@ -22,7 +22,8 @@ This repository hosts the code of LightRAG. The structure of this code is based
## 🎉 News
-- [x] [2024.11.11]🎯📢You can [use Oracle Database 23ai for all storage types (kv/vector/graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py) now.
+- [x] [2024.11.12]🎯📢You can [use Oracle Database 23ai for all storage types (kv/vector/graph)](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py) now.
+- [x] [2024.11.11]🎯📢LightRAG now supports [deleting entities by their names](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete-entity).
- [x] [2024.11.09]🎯📢Now comes [LightRAG Gui](https://lightrag-gui.streamlit.app) that lets you insert, query, visualize, and download LightRAG knowledge.
- [x] [2024.11.04]🎯📢You can [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage) now.
- [x] [2024.10.29]🎯📢LightRAG now supports multiple file types, including PDF, DOC, PPT, and CSV via `textract`.
@@ -319,6 +320,23 @@ with open("./newText.txt") as f:
rag.insert(f.read())
```
+### Delete Entity
+
+```python
+# Delete Entity: Deleting entities by their names
+rag = LightRAG(
+ working_dir=WORKING_DIR,
+ llm_model_func=llm_model_func,
+ embedding_func=EmbeddingFunc(
+ embedding_dim=embedding_dimension,
+ max_token_size=8192,
+ func=embedding_func,
+ ),
+)
+
+rag.delete_by_entity("Project Gutenberg")
+```
+
### Multi-file Type Support
The `textract` supports reading file types such as TXT, DOCX, PPTX, CSV, and PDF.
diff --git a/lightrag/__init__.py b/lightrag/__init__.py
index b73db1b9..6d9003ff 100644
--- a/lightrag/__init__.py
+++ b/lightrag/__init__.py
@@ -1,5 +1,5 @@
from .lightrag import LightRAG as LightRAG, QueryParam as QueryParam
-__version__ = "0.0.9"
+__version__ = "1.0.0"
__author__ = "Zirui Guo"
__url__ = "https://github.com/HKUDS/LightRAG"
diff --git a/lightrag/base.py b/lightrag/base.py
index b88acae2..379efeb3 100644
--- a/lightrag/base.py
+++ b/lightrag/base.py
@@ -118,7 +118,7 @@ class BaseGraphStorage(StorageNameSpace):
):
raise NotImplementedError
- async def clustering(self, algorithm: str):
+ async def delete_node(self, node_id: str):
raise NotImplementedError
async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
diff --git a/lightrag/kg/oracle_impl.py b/lightrag/kg/oracle_impl.py
index 1d8b5002..7340ad7d 100644
--- a/lightrag/kg/oracle_impl.py
+++ b/lightrag/kg/oracle_impl.py
@@ -592,7 +592,9 @@ TABLES = {
workspace varchar(1024),
doc_name varchar(1024),
content CLOB,
- meta JSON
+ meta JSON,
+ createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+ updatetime TIMESTAMP DEFAULT NULL
)"""},
"LIGHTRAG_DOC_CHUNKS":
@@ -603,7 +605,9 @@ TABLES = {
chunk_order_index NUMBER,
tokens NUMBER,
content CLOB,
- content_vector VECTOR
+ content_vector VECTOR,
+ createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+ updatetime TIMESTAMP DEFAULT NULL
)"""},
"LIGHTRAG_GRAPH_NODES":
@@ -615,7 +619,9 @@ TABLES = {
description CLOB,
source_chunk_id varchar(256),
content CLOB,
- content_vector VECTOR
+ content_vector VECTOR,
+ createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+ updatetime TIMESTAMP DEFAULT NULL
)"""},
"LIGHTRAG_GRAPH_EDGES":
{"ddl":"""CREATE TABLE LIGHTRAG_GRAPH_EDGES (
@@ -628,13 +634,18 @@ TABLES = {
description CLOB,
source_chunk_id varchar(256),
content CLOB,
- content_vector VECTOR
+ content_vector VECTOR,
+ createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+ updatetime TIMESTAMP DEFAULT NULL
)"""},
"LIGHTRAG_LLM_CACHE":
{"ddl":"""CREATE TABLE LIGHTRAG_LLM_CACHE (
id varchar(256) PRIMARY KEY,
+ send clob,
return clob,
- model varchar(1024)
+ model varchar(1024),
+ createtime TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
+ updatetime TIMESTAMP DEFAULT NULL
)"""},
"LIGHTRAG_GRAPH":
diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index 40f4eca3..52786970 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -351,3 +351,34 @@ class LightRAG:
continue
tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
await asyncio.gather(*tasks)
+
+ def delete_by_entity(self, entity_name: str):
+ loop = always_get_an_event_loop()
+ return loop.run_until_complete(self.adelete_by_entity(entity_name))
+
+ async def adelete_by_entity(self, entity_name: str):
+ entity_name = f'"{entity_name.upper()}"'
+
+ try:
+ await self.entities_vdb.delete_entity(entity_name)
+ await self.relationships_vdb.delete_relation(entity_name)
+ await self.chunk_entity_relation_graph.delete_node(entity_name)
+
+ logger.info(
+ f"Entity '{entity_name}' and its relationships have been deleted."
+ )
+ await self._delete_by_entity_done()
+ except Exception as e:
+ logger.error(f"Error while deleting entity '{entity_name}': {e}")
+
+ async def _delete_by_entity_done(self):
+ tasks = []
+ for storage_inst in [
+ self.entities_vdb,
+ self.relationships_vdb,
+ self.chunk_entity_relation_graph,
+ ]:
+ if storage_inst is None:
+ continue
+ tasks.append(cast(StorageNameSpace, storage_inst).index_done_callback())
+ await asyncio.gather(*tasks)
diff --git a/lightrag/storage.py b/lightrag/storage.py
index 61bebf2d..9a4c3d4c 100644
--- a/lightrag/storage.py
+++ b/lightrag/storage.py
@@ -7,7 +7,13 @@ import networkx as nx
import numpy as np
from nano_vectordb import NanoVectorDB
-from .utils import load_json, logger, write_json
+from .utils import (
+ logger,
+ load_json,
+ write_json,
+ compute_mdhash_id,
+)
+
from .base import (
BaseGraphStorage,
BaseKVStorage,
@@ -111,6 +117,43 @@ class NanoVectorDBStorage(BaseVectorStorage):
]
return results
+ @property
+ def client_storage(self):
+ return getattr(self._client, "_NanoVectorDB__storage")
+
+ async def delete_entity(self, entity_name: str):
+ try:
+ entity_id = [compute_mdhash_id(entity_name, prefix="ent-")]
+
+ if self._client.get(entity_id):
+ self._client.delete(entity_id)
+ logger.info(f"Entity {entity_name} have been deleted.")
+ else:
+ logger.info(f"No entity found with name {entity_name}.")
+ except Exception as e:
+ logger.error(f"Error while deleting entity {entity_name}: {e}")
+
+ async def delete_relation(self, entity_name: str):
+ try:
+ relations = [
+ dp
+ for dp in self.client_storage["data"]
+ if dp["src_id"] == entity_name or dp["tgt_id"] == entity_name
+ ]
+ ids_to_delete = [relation["__id__"] for relation in relations]
+
+ if ids_to_delete:
+ self._client.delete(ids_to_delete)
+ logger.info(
+ f"All relations related to entity {entity_name} have been deleted."
+ )
+ else:
+ logger.info(f"No relations found for entity {entity_name}.")
+ except Exception as e:
+ logger.error(
+ f"Error while deleting relations for entity {entity_name}: {e}"
+ )
+
async def index_done_callback(self):
self._client.save()
@@ -228,6 +271,18 @@ class NetworkXStorage(BaseGraphStorage):
):
self._graph.add_edge(source_node_id, target_node_id, **edge_data)
+ async def delete_node(self, node_id: str):
+ """
+ Delete a node from the graph based on the specified node_id.
+
+ :param node_id: The node_id to delete
+ """
+ if self._graph.has_node(node_id):
+ self._graph.remove_node(node_id)
+ logger.info(f"Node {node_id} deleted from the graph.")
+ else:
+ logger.warning(f"Node {node_id} not found in the graph for deletion.")
+
async def embed_nodes(self, algorithm: str) -> tuple[np.ndarray, list[str]]:
if algorithm not in self._node_embed_algorithms:
raise ValueError(f"Node embedding algorithm {algorithm} not supported")