Merge pull request #1693 from HKUDS/delete_doc

Feat: Add comprehensive document deletion feature with integrated knowledge graph reconstruction

Author: danielaskdd

README-zh.md

@@ -932,6 +932,94 @@ rag.insert_custom_kg(custom_kg)
 
 </details>
 
+## 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。
+
+<details>
+<summary> <b>删除实体</b> </summary>
+
+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
+
+</details>
+
+<details>
+<summary> <b>删除关系</b> </summary>
+
+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
+
+</details>
+
+<details>
+<summary> <b>通过文档ID删除</b> </summary>
+
+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。
+
+</details>
+
+<details>
+<summary> <b>删除注意事项</b> </summary>
+
+**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
+
+</details>
+
 ## 实体合并
 
 <details>

README.md

@@ -988,6 +988,89 @@ These operations maintain data consistency across both the graph database and ve
 
 </details>
 
+## Delete Functions
+
+LightRAG provides comprehensive deletion capabilities, allowing you to delete documents, entities, and relationships.
+
+<details>
+<summary> <b>Delete Entities</b> </summary>
+
+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
+
+</details>
+
+<details>
+<summary> <b>Delete Relations</b> </summary>
+
+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
+
+</details>
+
+<details>
+<summary> <b>Delete by Document ID</b> </summary>
+
+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.
+
+</details>
+
+**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
 
 <details>

lightrag/api/__init__.py

@@ -1 +1 @@
-__api_version__ = "0173"
+__api_version__ = "0174"

lightrag/api/lightrag_server.py

@@ -355,7 +355,13 @@ async def azure_openai_model_complete(
         )
 
     # Add routes
-    app.include_router(create_document_routes(rag, doc_manager, api_key))
+    app.include_router(
+        create_document_routes(
+            rag,
+            doc_manager,
+            api_key,
+        )
+    )
     app.include_router(create_query_routes(rag, api_key, args.top_k))
     app.include_router(create_graph_routes(rag, api_key))