support Oracle Database storage

README.md

@@ -22,7 +22,7 @@ This repository hosts the code of LightRAG. The structure of this code is based
 </div>
 
 ## 🎉 News
-- [x] [2024.11.08]🎯📢You can [use Oracle Database 23ai for Storage](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py) now.
+- [x] [2024.11.08]🎯📢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] [2024.11.04]🎯📢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`.
 - [x] [2024.10.20]🎯📢We’ve added a new feature to LightRAG: Graph Visualization.

examples/lightrag_oracle_demo.py

@@ -116,7 +116,7 @@ async def main():
         modes = ["naive", "local", "global", "hybrid"]
         for mode in modes:
             print("="*20, mode, "="*20)
-            print(await rag.aquery("这个文章讲了什么?", param=QueryParam(mode=mode)))
+            print(await rag.aquery("What are the top themes in this story?", param=QueryParam(mode=mode)))
             print("-"*100, "\n")
 
     except Exception as e:

lightrag/lightrag.py

@@ -34,7 +34,6 @@ from .base import (
     QueryParam,
 )
 
-
 from .storage import (
     JsonKVStorage,
     NanoVectorDBStorage,
@@ -116,15 +115,7 @@ class LightRAG:
     llm_model_kwargs: dict = field(default_factory=dict)
 
     # storage
-
     vector_db_storage_cls_kwargs: dict = field(default_factory=dict)
-    # if DATABASE_TYPE is None:
-    #     key_string_value_json_storage_cls: Type[BaseKVStorage] = JsonKVStorage
-    #     vector_db_storage_cls: Type[BaseVectorStorage] = NanoVectorDBStorage
-    #     vector_db_storage_cls_kwargs: dict = field(default_factory=dict)
-    # elif DATABASE_TYPE == "oracle":        
-    #     key_string_value_json_storage_cls: Type[BaseKVStorage] = OracleKVStorage,
-    #     vector_db_storage_cls: Type[BaseVectorStorage] = OracleVectorDBStorage,
         
     enable_llm_cache: bool = True
 
@@ -145,10 +136,9 @@ class LightRAG:
         # @TODO: should move all storage setup here to leverage initial start params attached to self.
 
         self.key_string_value_json_storage_cls: Type[BaseKVStorage] = self._get_storage_class()[self.kv_storage]
-
+        self.vector_db_storage_cls: Type[BaseVectorStorage] = self._get_storage_class()[self.vector_storage]        
         self.graph_storage_cls: Type[BaseGraphStorage] = self._get_storage_class()[self.graph_storage]
 
-        self.vector_db_storage_cls: Type[BaseVectorStorage] = self._get_storage_class()[self.vector_storage]
         if not os.path.exists(self.working_dir):
             logger.info(f"Creating working directory {self.working_dir}")
             os.makedirs(self.working_dir)
@@ -210,14 +200,17 @@ class LightRAG:
 
     def _get_storage_class(self) -> Type[BaseGraphStorage]:
         return {
+            # kv storage
             "JsonKVStorage":JsonKVStorage,
             "OracleKVStorage":OracleKVStorage,
 
+            # vector storage
             "NanoVectorDBStorage":NanoVectorDBStorage,
             "OracleVectorDBStorage":OracleVectorDBStorage,
 
-            "Neo4JStorage": Neo4JStorage,
+            # graph storage
             "NetworkXStorage": NetworkXStorage,
+            "Neo4JStorage": Neo4JStorage,
             "OracleGraphStorage": OracleGraphStorage,
             # "ArangoDBStorage": ArangoDBStorage
         }