diff --git a/README-zh.md b/README-zh.md
index 9e892012..685c9468 100644
--- a/README-zh.md
+++ b/README-zh.md
@@ -932,6 +932,94 @@ rag.insert_custom_kg(custom_kg)
+## 删除功能
+
+LightRAG提供了全面的删除功能，允许您删除文档、实体和关系。
+
+
+ 删除实体
+
+您可以通过实体名称删除实体及其所有关联关系：
+
+```python
+# 删除实体及其所有关系（同步版本）
+rag.delete_by_entity("Google")
+
+# 异步版本
+await rag.adelete_by_entity("Google")
+```
+
+删除实体时会：
+- 从知识图谱中移除该实体节点
+- 删除该实体的所有关联关系
+- 从向量数据库中移除相关的嵌入向量
+- 保持知识图谱的完整性
+
+
+
+
+ 删除关系
+
+您可以删除两个特定实体之间的关系：
+
+```python
+# 删除两个实体之间的关系（同步版本）
+rag.delete_by_relation("Google", "Gmail")
+
+# 异步版本
+await rag.adelete_by_relation("Google", "Gmail")
+```
+
+删除关系时会：
+- 移除指定的关系边
+- 从向量数据库中删除关系的嵌入向量
+- 保留两个实体节点及其他关系
+
+
+
+
+ 通过文档ID删除
+
+您可以通过文档ID删除整个文档及其相关的所有知识：
+
+```python
+# 通过文档ID删除（异步版本）
+await rag.adelete_by_doc_id("doc-12345")
+```
+
+通过文档ID删除时的优化处理：
+- **智能清理**：自动识别并删除仅属于该文档的实体和关系
+- **保留共享知识**：如果实体或关系在其他文档中也存在，则会保留并重新构建描述
+- **缓存优化**：清理相关的LLM缓存以减少存储开销
+- **增量重建**：从剩余文档重新构建受影响的实体和关系描述
+
+删除过程包括：
+1. 删除文档相关的所有文本块
+2. 识别仅属于该文档的实体和关系并删除
+3. 重新构建在其他文档中仍存在的实体和关系
+4. 更新所有相关的向量索引
+5. 清理文档状态记录
+
+注意：通过文档ID删除是一个异步操作，因为它涉及复杂的知识图谱重构过程。
+
+
+
+
+ 删除注意事项
+
+**重要提醒：**
+
+1. **不可逆操作**：所有删除操作都是不可逆的，请谨慎使用
+2. **性能考虑**：删除大量数据时可能需要一些时间，特别是通过文档ID删除
+3. **数据一致性**：删除操作会自动维护知识图谱和向量数据库之间的一致性
+4. **备份建议**：在执行重要删除操作前建议备份数据
+
+**批量删除建议：**
+- 对于批量删除操作，建议使用异步方法以获得更好的性能
+- 大规模删除时，考虑分批进行以避免系统负载过高
+
+
+
## 实体合并
diff --git a/README.md b/README.md
index 8fdf4439..31c03bff 100644
--- a/README.md
+++ b/README.md
@@ -988,6 +988,160 @@ These operations maintain data consistency across both the graph database and ve
+## Delete Functions
+
+LightRAG provides comprehensive deletion capabilities, allowing you to delete documents, entities, and relationships.
+
+
+ Delete Entities
+
+You can delete entities by their name along with all associated relationships:
+
+```python
+# Delete entity and all its relationships (synchronous version)
+rag.delete_by_entity("Google")
+
+# Asynchronous version
+await rag.adelete_by_entity("Google")
+```
+
+When deleting an entity:
+- Removes the entity node from the knowledge graph
+- Deletes all associated relationships
+- Removes related embedding vectors from the vector database
+- Maintains knowledge graph integrity
+
+
+
+
+ Delete Relations
+
+You can delete relationships between two specific entities:
+
+```python
+# Delete relationship between two entities (synchronous version)
+rag.delete_by_relation("Google", "Gmail")
+
+# Asynchronous version
+await rag.adelete_by_relation("Google", "Gmail")
+```
+
+When deleting a relationship:
+- Removes the specified relationship edge
+- Deletes the relationship's embedding vector from the vector database
+- Preserves both entity nodes and their other relationships
+
+
+
+
+ Delete by Document ID
+
+You can delete an entire document and all its related knowledge through document ID:
+
+```python
+# Delete by document ID (asynchronous version)
+await rag.adelete_by_doc_id("doc-12345")
+```
+
+Optimized processing when deleting by document ID:
+- **Smart Cleanup**: Automatically identifies and removes entities and relationships that belong only to this document
+- **Preserve Shared Knowledge**: If entities or relationships exist in other documents, they are preserved and their descriptions are rebuilt
+- **Cache Optimization**: Clears related LLM cache to reduce storage overhead
+- **Incremental Rebuilding**: Reconstructs affected entity and relationship descriptions from remaining documents
+
+The deletion process includes:
+1. Delete all text chunks related to the document
+2. Identify and delete entities and relationships that belong only to this document
+3. Rebuild entities and relationships that still exist in other documents
+4. Update all related vector indexes
+5. Clean up document status records
+
+Note: Deletion by document ID is an asynchronous operation as it involves complex knowledge graph reconstruction processes.
+
+
+
+**Important Reminders:**
+
+1. **Irreversible Operations**: All deletion operations are irreversible, please use with caution
+2. **Performance Considerations**: Deleting large amounts of data may take some time, especially deletion by document ID
+3. **Data Consistency**: Deletion operations automatically maintain consistency between the knowledge graph and vector database
+4. **Backup Recommendations**: Consider backing up data before performing important deletion operations
+
+**Batch Deletion Recommendations:**
+- For batch deletion operations, consider using asynchronous methods for better performance
+- For large-scale deletions, consider processing in batches to avoid excessive system load
+
+## Entity Merging
+
+
+ Merge Entities and Their Relationships
+
+LightRAG now supports merging multiple entities into a single entity, automatically handling all relationships:
+
+```python
+# Basic entity merging
+rag.merge_entities(
+ source_entities=["Artificial Intelligence", "AI", "Machine Intelligence"],
+ target_entity="AI Technology"
+)
+```
+
+With custom merge strategy:
+
+```python
+# Define custom merge strategy for different fields
+rag.merge_entities(
+ source_entities=["John Smith", "Dr. Smith", "J. Smith"],
+ target_entity="John Smith",
+ merge_strategy={
+ "description": "concatenate", # Combine all descriptions
+ "entity_type": "keep_first", # Keep the entity type from the first entity
+ "source_id": "join_unique" # Combine all unique source IDs
+ }
+)
+```
+
+With custom target entity data:
+
+```python
+# Specify exact values for the merged entity
+rag.merge_entities(
+ source_entities=["New York", "NYC", "Big Apple"],
+ target_entity="New York City",
+ target_entity_data={
+ "entity_type": "LOCATION",
+ "description": "New York City is the most populous city in the United States.",
+ }
+)
+```
+
+Advanced usage combining both approaches:
+
+```python
+# Merge company entities with both strategy and custom data
+rag.merge_entities(
+ source_entities=["Microsoft Corp", "Microsoft Corporation", "MSFT"],
+ target_entity="Microsoft",
+ merge_strategy={
+ "description": "concatenate", # Combine all descriptions
+ "source_id": "join_unique" # Combine source IDs
+ },
+ target_entity_data={
+ "entity_type": "ORGANIZATION",
+ }
+)
+```
+
+When merging entities:
+
+* All relationships from source entities are redirected to the target entity
+* Duplicate relationships are intelligently merged
+* Self-relationships (loops) are prevented
+* Source entities are removed after merging
+* Relationship weights and attributes are preserved
+
+
+
## Entity Merging
diff --git a/lightrag/base.py b/lightrag/base.py
index e66a67db..b8e4d642 100644
--- a/lightrag/base.py
+++ b/lightrag/base.py
@@ -278,20 +278,20 @@ class BaseKVStorage(StorageNameSpace, ABC):
False: if the cache drop failed, or the cache mode is not supported
"""
- async def drop_cache_by_chunk_ids(self, chunk_ids: list[str] | None = None) -> bool:
- """Delete specific cache records from storage by chunk IDs
+ # async def drop_cache_by_chunk_ids(self, chunk_ids: list[str] | None = None) -> bool:
+ # """Delete specific cache records from storage by chunk IDs
- Importance notes for in-memory storage:
- 1. Changes will be persisted to disk during the next index_done_callback
- 2. update flags to notify other processes that data persistence is needed
+ # Importance notes for in-memory storage:
+ # 1. Changes will be persisted to disk during the next index_done_callback
+ # 2. update flags to notify other processes that data persistence is needed
- Args:
- chunk_ids (list[str]): List of chunk IDs to be dropped from storage
+ # Args:
+ # chunk_ids (list[str]): List of chunk IDs to be dropped from storage
- Returns:
- True: if the cache drop successfully
- False: if the cache drop failed, or the operation is not supported
- """
+ # Returns:
+ # True: if the cache drop successfully
+ # False: if the cache drop failed, or the operation is not supported
+ # """
@dataclass
diff --git a/lightrag/kg/json_kv_impl.py b/lightrag/kg/json_kv_impl.py
index 2345e50f..fa819d4a 100644
--- a/lightrag/kg/json_kv_impl.py
+++ b/lightrag/kg/json_kv_impl.py
@@ -172,52 +172,52 @@ class JsonKVStorage(BaseKVStorage):
except Exception:
return False
- async def drop_cache_by_chunk_ids(self, chunk_ids: list[str] | None = None) -> bool:
- """Delete specific cache records from storage by chunk IDs
+ # async def drop_cache_by_chunk_ids(self, chunk_ids: list[str] | None = None) -> bool:
+ # """Delete specific cache records from storage by chunk IDs
- Importance notes for in-memory storage:
- 1. Changes will be persisted to disk during the next index_done_callback
- 2. update flags to notify other processes that data persistence is needed
+ # Importance notes for in-memory storage:
+ # 1. Changes will be persisted to disk during the next index_done_callback
+ # 2. update flags to notify other processes that data persistence is needed
- Args:
- chunk_ids (list[str]): List of chunk IDs to be dropped from storage
+ # Args:
+ # chunk_ids (list[str]): List of chunk IDs to be dropped from storage
- Returns:
- True: if the cache drop successfully
- False: if the cache drop failed
- """
- if not chunk_ids:
- return False
+ # Returns:
+ # True: if the cache drop successfully
+ # False: if the cache drop failed
+ # """
+ # if not chunk_ids:
+ # return False
- try:
- async with self._storage_lock:
- # Iterate through all cache modes to find entries with matching chunk_ids
- for mode_key, mode_data in list(self._data.items()):
- if isinstance(mode_data, dict):
- # Check each cached entry in this mode
- for cache_key, cache_entry in list(mode_data.items()):
- if (
- isinstance(cache_entry, dict)
- and cache_entry.get("chunk_id") in chunk_ids
- ):
- # Remove this cache entry
- del mode_data[cache_key]
- logger.debug(
- f"Removed cache entry {cache_key} for chunk {cache_entry.get('chunk_id')}"
- )
+ # try:
+ # async with self._storage_lock:
+ # # Iterate through all cache modes to find entries with matching chunk_ids
+ # for mode_key, mode_data in list(self._data.items()):
+ # if isinstance(mode_data, dict):
+ # # Check each cached entry in this mode
+ # for cache_key, cache_entry in list(mode_data.items()):
+ # if (
+ # isinstance(cache_entry, dict)
+ # and cache_entry.get("chunk_id") in chunk_ids
+ # ):
+ # # Remove this cache entry
+ # del mode_data[cache_key]
+ # logger.debug(
+ # f"Removed cache entry {cache_key} for chunk {cache_entry.get('chunk_id')}"
+ # )
- # If the mode is now empty, remove it entirely
- if not mode_data:
- del self._data[mode_key]
+ # # If the mode is now empty, remove it entirely
+ # if not mode_data:
+ # del self._data[mode_key]
- # Set update flags to notify persistence is needed
- await set_all_update_flags(self.namespace)
+ # # Set update flags to notify persistence is needed
+ # await set_all_update_flags(self.namespace)
- logger.info(f"Cleared cache for {len(chunk_ids)} chunk IDs")
- return True
- except Exception as e:
- logger.error(f"Error clearing cache by chunk IDs: {e}")
- return False
+ # logger.info(f"Cleared cache for {len(chunk_ids)} chunk IDs")
+ # return True
+ # except Exception as e:
+ # logger.error(f"Error clearing cache by chunk IDs: {e}")
+ # return False
async def drop(self) -> dict[str, str]:
"""Drop all data from storage and clean up resources
diff --git a/lightrag/kg/postgres_impl.py b/lightrag/kg/postgres_impl.py
index 465643f0..f9dabafb 100644
--- a/lightrag/kg/postgres_impl.py
+++ b/lightrag/kg/postgres_impl.py
@@ -106,6 +106,35 @@ class PostgreSQLDB:
):
pass
+ async def _migrate_llm_cache_add_chunk_id(self):
+ """Add chunk_id column to LIGHTRAG_LLM_CACHE table if it doesn't exist"""
+ try:
+ # Check if chunk_id column exists
+ check_column_sql = """
+ SELECT column_name
+ FROM information_schema.columns
+ WHERE table_name = 'lightrag_llm_cache'
+ AND column_name = 'chunk_id'
+ """
+
+ column_info = await self.query(check_column_sql)
+ if not column_info:
+ logger.info("Adding chunk_id column to LIGHTRAG_LLM_CACHE table")
+ add_column_sql = """
+ ALTER TABLE LIGHTRAG_LLM_CACHE
+ ADD COLUMN chunk_id VARCHAR(255) NULL
+ """
+ await self.execute(add_column_sql)
+ logger.info(
+ "Successfully added chunk_id column to LIGHTRAG_LLM_CACHE table"
+ )
+ else:
+ logger.info(
+ "chunk_id column already exists in LIGHTRAG_LLM_CACHE table"
+ )
+ except Exception as e:
+ logger.warning(f"Failed to add chunk_id column to LIGHTRAG_LLM_CACHE: {e}")
+
async def _migrate_timestamp_columns(self):
"""Migrate timestamp columns in tables to timezone-aware types, assuming original data is in UTC time"""
# Tables and columns that need migration
@@ -203,6 +232,13 @@ class PostgreSQLDB:
logger.error(f"PostgreSQL, Failed to migrate timestamp columns: {e}")
# Don't throw an exception, allow the initialization process to continue
+ # Migrate LLM cache table to add chunk_id field if needed
+ try:
+ await self._migrate_llm_cache_add_chunk_id()
+ except Exception as e:
+ logger.error(f"PostgreSQL, Failed to migrate LLM cache chunk_id field: {e}")
+ # Don't throw an exception, allow the initialization process to continue
+
async def query(
self,
sql: str,
@@ -497,6 +533,7 @@ class PGKVStorage(BaseKVStorage):
"original_prompt": v["original_prompt"],
"return_value": v["return"],
"mode": mode,
+ "chunk_id": v.get("chunk_id"),
}
await self.db.execute(upsert_sql, _data)
@@ -2357,6 +2394,7 @@ TABLES = {
mode varchar(32) NOT NULL,
original_prompt TEXT,
return_value TEXT,
+ chunk_id VARCHAR(255) NULL,
create_time TIMESTAMP DEFAULT CURRENT_TIMESTAMP,
update_time TIMESTAMP,
CONSTRAINT LIGHTRAG_LLM_CACHE_PK PRIMARY KEY (workspace, mode, id)
@@ -2389,10 +2427,10 @@ SQL_TEMPLATES = {
chunk_order_index, full_doc_id, file_path
FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id=$2
""",
- "get_by_id_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode
+ "get_by_id_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode, chunk_id
FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND mode=$2
""",
- "get_by_mode_id_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode
+ "get_by_mode_id_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode, chunk_id
FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND mode=$2 AND id=$3
""",
"get_by_ids_full_docs": """SELECT id, COALESCE(content, '') as content
@@ -2402,7 +2440,7 @@ SQL_TEMPLATES = {
chunk_order_index, full_doc_id, file_path
FROM LIGHTRAG_DOC_CHUNKS WHERE workspace=$1 AND id IN ({ids})
""",
- "get_by_ids_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode
+ "get_by_ids_llm_response_cache": """SELECT id, original_prompt, COALESCE(return_value, '') as "return", mode, chunk_id
FROM LIGHTRAG_LLM_CACHE WHERE workspace=$1 AND mode= IN ({ids})
""",
"filter_keys": "SELECT id FROM {table_name} WHERE workspace=$1 AND id IN ({ids})",
@@ -2411,12 +2449,13 @@ SQL_TEMPLATES = {
ON CONFLICT (workspace,id) DO UPDATE
SET content = $2, update_time = CURRENT_TIMESTAMP
""",
- "upsert_llm_response_cache": """INSERT INTO LIGHTRAG_LLM_CACHE(workspace,id,original_prompt,return_value,mode)
- VALUES ($1, $2, $3, $4, $5)
+ "upsert_llm_response_cache": """INSERT INTO LIGHTRAG_LLM_CACHE(workspace,id,original_prompt,return_value,mode,chunk_id)
+ VALUES ($1, $2, $3, $4, $5, $6)
ON CONFLICT (workspace,mode,id) DO UPDATE
SET original_prompt = EXCLUDED.original_prompt,
return_value=EXCLUDED.return_value,
mode=EXCLUDED.mode,
+ chunk_id=EXCLUDED.chunk_id,
update_time = CURRENT_TIMESTAMP
""",
"upsert_chunk": """INSERT INTO LIGHTRAG_DOC_CHUNKS (workspace, id, tokens,
diff --git a/lightrag/lightrag.py b/lightrag/lightrag.py
index f1c3747b..d299080a 100644
--- a/lightrag/lightrag.py
+++ b/lightrag/lightrag.py
@@ -1710,17 +1710,17 @@ class LightRAG:
chunk_ids = set(related_chunks.keys())
logger.info(f"Found {len(chunk_ids)} chunks to delete")
- # 3. **OPTIMIZATION 1**: Clear LLM cache for related chunks
- logger.info("Clearing LLM cache for related chunks...")
- cache_cleared = await self.llm_response_cache.drop_cache_by_chunk_ids(
- list(chunk_ids)
- )
- if cache_cleared:
- logger.info(f"Successfully cleared cache for {len(chunk_ids)} chunks")
- else:
- logger.warning(
- "Failed to clear chunk cache or cache clearing not supported"
- )
+ # # 3. **OPTIMIZATION 1**: Clear LLM cache for related chunks
+ # logger.info("Clearing LLM cache for related chunks...")
+ # cache_cleared = await self.llm_response_cache.drop_cache_by_chunk_ids(
+ # list(chunk_ids)
+ # )
+ # if cache_cleared:
+ # logger.info(f"Successfully cleared cache for {len(chunk_ids)} chunks")
+ # else:
+ # logger.warning(
+ # "Failed to clear chunk cache or cache clearing not supported"
+ # )
# 4. Analyze entities and relationships that will be affected
entities_to_delete = set()