Remove Oracle storage implementation

README-zh.md

@@ -11,7 +11,6 @@
 - [X] [2024.12.31]🎯📢LightRAG现在支持[通过文档ID删除](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。
 - [X] [2024.11.25]🎯📢LightRAG现在支持无缝集成[自定义知识图谱](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#insert-custom-kg)，使用户能够用自己的领域专业知识增强系统。
 - [X] [2024.11.19]🎯📢LightRAG的综合指南现已在[LearnOpenCV](https://learnopencv.com/lightrag)上发布。非常感谢博客作者。
-- [X] [2024.11.12]🎯📢LightRAG现在支持[Oracle Database 23ai的所有存储类型（KV、向量和图）](https://github.com/HKUDS/LightRAG/blob/main/examples/lightrag_oracle_demo.py)。
 - [X] [2024.11.11]🎯📢LightRAG现在支持[通过实体名称删除实体](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#delete)。
 - [X] [2024.11.09]🎯📢推出[LightRAG Gui](https://lightrag-gui.streamlit.app)，允许您插入、查询、可视化和下载LightRAG知识。
 - [X] [2024.11.04]🎯📢现在您可以[使用Neo4J进行存储](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage)。
@@ -1037,9 +1036,10 @@ rag.clear_cache(modes=["local"])
 | **参数** | **类型** | **说明** | **默认值** |
 |--------------|----------|-----------------|-------------|
 | **working_dir** | `str` | 存储缓存的目录 | `lightrag_cache+timestamp` |
-| **kv_storage** | `str` | 文档和文本块的存储类型。支持的类型：`JsonKVStorage`、`OracleKVStorage` | `JsonKVStorage` |
+| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage`,`TiDBKVStorage` | `JsonKVStorage` |
-| **vector_storage** | `str` | 嵌入向量的存储类型。支持的类型：`NanoVectorDBStorage`、`OracleVectorDBStorage` | `NanoVectorDBStorage` |
+| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`TiDBVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` |
-| **graph_storage** | `str` | 图边和节点的存储类型。支持的类型：`NetworkXStorage`、`Neo4JStorage`、`OracleGraphStorage` | `NetworkXStorage` |
+| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage`,`GremlinStorage` | `NetworkXStorage` |
+| **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` |
 | **chunk_token_size** | `int` | 拆分文档时每个块的最大令牌大小 | `1200` |
 | **chunk_overlap_token_size** | `int` | 拆分文档时两个块之间的重叠令牌大小 | `100` |
 | **tiktoken_model_name** | `str` | 用于计算令牌数的Tiktoken编码器的模型名称 | `gpt-4o-mini` |

README.md

@@ -41,7 +41,6 @@
 - [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] [2024.11.19]🎯📢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] [2024.11.11]🎯📢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] [2024.11.04]🎯📢You can now [use Neo4J for Storage](https://github.com/HKUDS/LightRAG?tab=readme-ov-file#using-neo4j-for-storage).
@@ -1065,9 +1064,10 @@ Valid modes are:
 | **Parameter** | **Type** | **Explanation** | **Default** |
 |--------------|----------|-----------------|-------------|
 | **working_dir** | `str` | Directory where the cache will be stored | `lightrag_cache+timestamp` |
-| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`, `OracleKVStorage` | `JsonKVStorage` |
+| **kv_storage** | `str` | Storage type for documents and text chunks. Supported types: `JsonKVStorage`,`PGKVStorage`,`RedisKVStorage`,`MongoKVStorage`,`TiDBKVStorage` | `JsonKVStorage` |
-| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`, `OracleVectorDBStorage` | `NanoVectorDBStorage` |
+| **vector_storage** | `str` | Storage type for embedding vectors. Supported types: `NanoVectorDBStorage`,`PGVectorStorage`,`MilvusVectorDBStorage`,`ChromaVectorDBStorage`,`FaissVectorDBStorage`,`TiDBVectorDBStorage`,`MongoVectorDBStorage`,`QdrantVectorDBStorage` | `NanoVectorDBStorage` |
-| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`, `Neo4JStorage`, `OracleGraphStorage` | `NetworkXStorage` |
+| **graph_storage** | `str` | Storage type for graph edges and nodes. Supported types: `NetworkXStorage`,`Neo4JStorage`,`PGGraphStorage`,`AGEStorage`,`GremlinStorage` | `NetworkXStorage` |
+| **doc_status_storage** | `str` | Storage type for documents process status. Supported types: `JsonDocStatusStorage`,`PGDocStatusStorage`,`MongoDocStatusStorage` | `JsonDocStatusStorage` |
 | **chunk_token_size** | `int` | Maximum token size per chunk when splitting documents | `1200` |
 | **chunk_overlap_token_size** | `int` | Overlap token size between two chunks when splitting documents | `100` |
 | **tiktoken_model_name** | `str` | Model name for the Tiktoken encoder used to calculate token numbers | `gpt-4o-mini` |

config.ini.example

@@ -13,15 +13,6 @@ uri=redis://localhost:6379/1
 [qdrant]
 uri = http://localhost:16333
 
-[oracle]
-dsn = localhost:1521/XEPDB1
-user = your_username
-password = your_password
-config_dir = /path/to/oracle/config
-wallet_location = /path/to/wallet  # 可选
-wallet_password = your_wallet_password  # 可选
-workspace = default  # 可选,默认为default
-
 [tidb]
 host = localhost
 port = 4000

env.example

@@ -109,16 +109,6 @@ LIGHTRAG_VECTOR_STORAGE=NanoVectorDBStorage
 LIGHTRAG_GRAPH_STORAGE=NetworkXStorage
 LIGHTRAG_DOC_STATUS_STORAGE=JsonDocStatusStorage
 
-### Oracle Database Configuration
-ORACLE_DSN=localhost:1521/XEPDB1
-ORACLE_USER=your_username
-ORACLE_PASSWORD='your_password'
-ORACLE_CONFIG_DIR=/path/to/oracle/config
-#ORACLE_WALLET_LOCATION=/path/to/wallet
-#ORACLE_WALLET_PASSWORD='your_password'
-### separating all data from difference Lightrag instances(deprecating)
-#ORACLE_WORKSPACE=default
-
 ### TiDB Configuration
 TIDB_HOST=localhost
 TIDB_PORT=4000

examples/lightrag_api_oracle_demo.py

@@ -1,267 +0,0 @@
-from fastapi import FastAPI, HTTPException, File, UploadFile
-from fastapi import Query
-from contextlib import asynccontextmanager
-from pydantic import BaseModel
-from typing import Optional, Any
-
-import sys
-import os
-
-
-from pathlib import Path
-
-import asyncio
-import nest_asyncio
-from lightrag import LightRAG, QueryParam
-from lightrag.llm.openai import openai_complete_if_cache, openai_embed
-from lightrag.utils import EmbeddingFunc
-import numpy as np
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-
-print(os.getcwd())
-script_directory = Path(__file__).resolve().parent.parent
-sys.path.append(os.path.abspath(script_directory))
-
-
-# Apply nest_asyncio to solve event loop issues
-nest_asyncio.apply()
-
-DEFAULT_RAG_DIR = "index_default"
-
-
-# We use OpenAI compatible API to call LLM on Oracle Cloud
-# More docs here https://github.com/jin38324/OCI_GenAI_access_gateway
-BASE_URL = "http://xxx.xxx.xxx.xxx:8088/v1/"
-APIKEY = "ocigenerativeai"
-
-# Configure working directory
-WORKING_DIR = os.environ.get("RAG_DIR", f"{DEFAULT_RAG_DIR}")
-print(f"WORKING_DIR: {WORKING_DIR}")
-LLM_MODEL = os.environ.get("LLM_MODEL", "cohere.command-r-plus-08-2024")
-print(f"LLM_MODEL: {LLM_MODEL}")
-EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "cohere.embed-multilingual-v3.0")
-print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
-EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 512))
-print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
-
-if not os.path.exists(WORKING_DIR):
-    os.mkdir(WORKING_DIR)
-
-os.environ["ORACLE_USER"] = ""
-os.environ["ORACLE_PASSWORD"] = ""
-os.environ["ORACLE_DSN"] = ""
-os.environ["ORACLE_CONFIG_DIR"] = "path_to_config_dir"
-os.environ["ORACLE_WALLET_LOCATION"] = "path_to_wallet_location"
-os.environ["ORACLE_WALLET_PASSWORD"] = "wallet_password"
-os.environ["ORACLE_WORKSPACE"] = "company"
-
-
-async def llm_model_func(
-    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
-) -> str:
-    return await openai_complete_if_cache(
-        LLM_MODEL,
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        api_key=APIKEY,
-        base_url=BASE_URL,
-        **kwargs,
-    )
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    return await openai_embed(
-        texts,
-        model=EMBEDDING_MODEL,
-        api_key=APIKEY,
-        base_url=BASE_URL,
-    )
-
-
-async def get_embedding_dim():
-    test_text = ["This is a test sentence."]
-    embedding = await embedding_func(test_text)
-    embedding_dim = embedding.shape[1]
-    return embedding_dim
-
-
-async def init():
-    # Detect embedding dimension
-    embedding_dimension = await get_embedding_dim()
-    print(f"Detected embedding dimension: {embedding_dimension}")
-    # Create Oracle DB connection
-    # The `config` parameter is the connection configuration of Oracle DB
-    # More docs here https://python-oracledb.readthedocs.io/en/latest/user_guide/connection_handling.html
-    # We storage data in unified tables, so we need to set a `workspace` parameter to specify which docs we want to store and query
-    # Below is an example of how to connect to Oracle Autonomous Database on Oracle Cloud
-
-    # Initialize LightRAG
-    # We use Oracle DB as the KV/vector/graph storage
-    rag = LightRAG(
-        enable_llm_cache=False,
-        working_dir=WORKING_DIR,
-        chunk_token_size=512,
-        llm_model_func=llm_model_func,
-        embedding_func=EmbeddingFunc(
-            embedding_dim=embedding_dimension,
-            max_token_size=512,
-            func=embedding_func,
-        ),
-        graph_storage="OracleGraphStorage",
-        kv_storage="OracleKVStorage",
-        vector_storage="OracleVectorDBStorage",
-    )
-
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-
-    return rag
-
-
-# Extract and Insert into LightRAG storage
-# with open("./dickens/book.txt", "r", encoding="utf-8") as f:
-#   await rag.ainsert(f.read())
-
-# # Perform search in different modes
-# modes = ["naive", "local", "global", "hybrid"]
-# for mode in modes:
-#     print("="*20, mode, "="*20)
-#     print(await rag.aquery("这篇文档是关于什么内容的?", param=QueryParam(mode=mode)))
-#     print("-"*100, "\n")
-
-# Data models
-
-
-class QueryRequest(BaseModel):
-    query: str
-    mode: str = "hybrid"
-    only_need_context: bool = False
-    only_need_prompt: bool = False
-
-
-class DataRequest(BaseModel):
-    limit: int = 100
-
-
-class InsertRequest(BaseModel):
-    text: str
-
-
-class Response(BaseModel):
-    status: str
-    data: Optional[Any] = None
-    message: Optional[str] = None
-
-
-# API routes
-
-rag = None
-
-
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    global rag
-    rag = await init()
-    print("done!")
-    yield
-
-
-app = FastAPI(
-    title="LightRAG API", description="API for RAG operations", lifespan=lifespan
-)
-
-
-@app.post("/query", response_model=Response)
-async def query_endpoint(request: QueryRequest):
-    # try:
-    # loop = asyncio.get_event_loop()
-    if request.mode == "naive":
-        top_k = 3
-    else:
-        top_k = 60
-    result = await rag.aquery(
-        request.query,
-        param=QueryParam(
-            mode=request.mode,
-            only_need_context=request.only_need_context,
-            only_need_prompt=request.only_need_prompt,
-            top_k=top_k,
-        ),
-    )
-    return Response(status="success", data=result)
-    # except Exception as e:
-    #     raise HTTPException(status_code=500, detail=str(e))
-
-
-@app.get("/data", response_model=Response)
-async def query_all_nodes(type: str = Query("nodes"), limit: int = Query(100)):
-    if type == "nodes":
-        result = await rag.chunk_entity_relation_graph.get_all_nodes(limit=limit)
-    elif type == "edges":
-        result = await rag.chunk_entity_relation_graph.get_all_edges(limit=limit)
-    elif type == "statistics":
-        result = await rag.chunk_entity_relation_graph.get_statistics()
-    return Response(status="success", data=result)
-
-
-@app.post("/insert", response_model=Response)
-async def insert_endpoint(request: InsertRequest):
-    try:
-        loop = asyncio.get_event_loop()
-        await loop.run_in_executor(None, lambda: rag.insert(request.text))
-        return Response(status="success", message="Text inserted successfully")
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@app.post("/insert_file", response_model=Response)
-async def insert_file(file: UploadFile = File(...)):
-    try:
-        file_content = await file.read()
-        # Read file content
-        try:
-            content = file_content.decode("utf-8")
-        except UnicodeDecodeError:
-            # If UTF-8 decoding fails, try other encodings
-            content = file_content.decode("gbk")
-        # Insert file content
-        loop = asyncio.get_event_loop()
-        await loop.run_in_executor(None, lambda: rag.insert(content))
-
-        return Response(
-            status="success",
-            message=f"File content from {file.filename} inserted successfully",
-        )
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@app.get("/health")
-async def health_check():
-    return {"status": "healthy"}
-
-
-if __name__ == "__main__":
-    import uvicorn
-
-    uvicorn.run(app, host="127.0.0.1", port=8020)
-
-# Usage example
-# To run the server, use the following command in your terminal:
-# python lightrag_api_openai_compatible_demo.py
-
-# Example requests:
-# 1. Query:
-# curl -X POST "http://127.0.0.1:8020/query" -H "Content-Type: application/json" -d '{"query": "your query here", "mode": "hybrid"}'
-
-# 2. Insert text:
-# curl -X POST "http://127.0.0.1:8020/insert" -H "Content-Type: application/json" -d '{"text": "your text here"}'
-
-# 3. Insert file:
-# curl -X POST "http://127.0.0.1:8020/insert_file" -H "Content-Type: multipart/form-data" -F "file=@path/to/your/file.txt"
-
-
-# 4. Health check:
-# curl -X GET "http://127.0.0.1:8020/health"

examples/lightrag_oracle_demo.py

@@ -1,141 +0,0 @@
-import sys
-import os
-from pathlib import Path
-import asyncio
-from lightrag import LightRAG, QueryParam
-from lightrag.llm.openai import openai_complete_if_cache, openai_embed
-from lightrag.utils import EmbeddingFunc
-import numpy as np
-from lightrag.kg.shared_storage import initialize_pipeline_status
-
-print(os.getcwd())
-script_directory = Path(__file__).resolve().parent.parent
-sys.path.append(os.path.abspath(script_directory))
-
-WORKING_DIR = "./dickens"
-
-# We use OpenAI compatible API to call LLM on Oracle Cloud
-# More docs here https://github.com/jin38324/OCI_GenAI_access_gateway
-BASE_URL = "http://xxx.xxx.xxx.xxx:8088/v1/"
-APIKEY = "ocigenerativeai"
-CHATMODEL = "cohere.command-r-plus"
-EMBEDMODEL = "cohere.embed-multilingual-v3.0"
-CHUNK_TOKEN_SIZE = 1024
-MAX_TOKENS = 4000
-
-if not os.path.exists(WORKING_DIR):
-    os.mkdir(WORKING_DIR)
-
-os.environ["ORACLE_USER"] = "username"
-os.environ["ORACLE_PASSWORD"] = "xxxxxxxxx"
-os.environ["ORACLE_DSN"] = "xxxxxxx_medium"
-os.environ["ORACLE_CONFIG_DIR"] = "path_to_config_dir"
-os.environ["ORACLE_WALLET_LOCATION"] = "path_to_wallet_location"
-os.environ["ORACLE_WALLET_PASSWORD"] = "wallet_password"
-os.environ["ORACLE_WORKSPACE"] = "company"
-
-
-async def llm_model_func(
-    prompt, system_prompt=None, history_messages=[], keyword_extraction=False, **kwargs
-) -> str:
-    return await openai_complete_if_cache(
-        CHATMODEL,
-        prompt,
-        system_prompt=system_prompt,
-        history_messages=history_messages,
-        api_key=APIKEY,
-        base_url=BASE_URL,
-        **kwargs,
-    )
-
-
-async def embedding_func(texts: list[str]) -> np.ndarray:
-    return await openai_embed(
-        texts,
-        model=EMBEDMODEL,
-        api_key=APIKEY,
-        base_url=BASE_URL,
-    )
-
-
-async def get_embedding_dim():
-    test_text = ["This is a test sentence."]
-    embedding = await embedding_func(test_text)
-    embedding_dim = embedding.shape[1]
-    return embedding_dim
-
-
-async def initialize_rag():
-    # Detect embedding dimension
-    embedding_dimension = await get_embedding_dim()
-    print(f"Detected embedding dimension: {embedding_dimension}")
-
-    # Initialize LightRAG
-    # We use Oracle DB as the KV/vector/graph storage
-    # You can add `addon_params={"example_number": 1, "language": "Simplfied Chinese"}` to control the prompt
-    rag = LightRAG(
-        # log_level="DEBUG",
-        working_dir=WORKING_DIR,
-        entity_extract_max_gleaning=1,
-        enable_llm_cache=True,
-        enable_llm_cache_for_entity_extract=True,
-        embedding_cache_config=None,  # {"enabled": True,"similarity_threshold": 0.90},
-        chunk_token_size=CHUNK_TOKEN_SIZE,
-        llm_model_max_token_size=MAX_TOKENS,
-        llm_model_func=llm_model_func,
-        embedding_func=EmbeddingFunc(
-            embedding_dim=embedding_dimension,
-            max_token_size=500,
-            func=embedding_func,
-        ),
-        graph_storage="OracleGraphStorage",
-        kv_storage="OracleKVStorage",
-        vector_storage="OracleVectorDBStorage",
-        addon_params={
-            "example_number": 1,
-            "language": "Simplfied Chinese",
-            "entity_types": ["organization", "person", "geo", "event"],
-            "insert_batch_size": 2,
-        },
-    )
-    await rag.initialize_storages()
-    await initialize_pipeline_status()
-
-    return rag
-
-
-async def main():
-    try:
-        # Initialize RAG instance
-        rag = await initialize_rag()
-
-        # Extract and Insert into LightRAG storage
-        with open(WORKING_DIR + "/docs.txt", "r", encoding="utf-8") as f:
-            all_text = f.read()
-            texts = [x for x in all_text.split("\n") if x]
-
-        # New mode use pipeline
-        await rag.apipeline_enqueue_documents(texts)
-        await rag.apipeline_process_enqueue_documents()
-
-        # Old method use ainsert
-        # await rag.ainsert(texts)
-
-        # Perform search in different modes
-        modes = ["naive", "local", "global", "hybrid"]
-        for mode in modes:
-            print("=" * 20, mode, "=" * 20)
-            print(
-                await rag.aquery(
-                    "What are the top themes in this story?",
-                    param=QueryParam(mode=mode),
-                )
-            )
-            print("-" * 100, "\n")
-
-    except Exception as e:
-        print(f"An error occurred: {e}")
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

lightrag/api/README-zh.md

@@ -291,11 +291,10 @@ LightRAG 使用 4 种类型的存储用于不同目的：
 
 ```
 JsonKVStorage    JsonFile(默认)
-MongoKVStorage   MogonDB
-RedisKVStorage   Redis
-TiDBKVStorage    TiDB
 PGKVStorage      Postgres
-OracleKVStorage  Oracle
+RedisKVStorage   Redis
+MongoKVStorage   MogonDB
+TiDBKVStorage    TiDB
 ```
 
 * GRAPH_STORAGE 支持的实现名称
@@ -303,25 +302,21 @@ OracleKVStorage  Oracle
 ```
 NetworkXStorage      NetworkX(默认)
 Neo4JStorage         Neo4J
-MongoGraphStorage    MongoDB
+PGGraphStorage       Postgres
-TiDBGraphStorage     TiDB
 AGEStorage           AGE
 GremlinStorage       Gremlin
-PGGraphStorage       Postgres
-OracleGraphStorage   Postgres
 ```
 
 * VECTOR_STORAGE 支持的实现名称
 
 ```
 NanoVectorDBStorage         NanoVector(默认)
+PGVectorStorage             Postgres
 MilvusVectorDBStorge        Milvus
 ChromaVectorDBStorage       Chroma
-TiDBVectorDBStorage         TiDB
-PGVectorStorage             Postgres
 FaissVectorDBStorage        Faiss
 QdrantVectorDBStorage       Qdrant
-OracleVectorDBStorage       Oracle
+TiDBVectorDBStorage         TiDB
 MongoVectorDBStorage        MongoDB
 ```
 

lightrag/api/README.md

@@ -302,11 +302,10 @@ Each storage type have servals implementations:
 
 ```
 JsonKVStorage    JsonFile(default)
-MongoKVStorage   MogonDB
-RedisKVStorage   Redis
-TiDBKVStorage    TiDB
 PGKVStorage      Postgres
-OracleKVStorage  Oracle
+RedisKVStorage   Redis
+MongoKVStorage   MogonDB
+TiDBKVStorage    TiDB
 ```
 
 * GRAPH_STORAGE supported implement-name
@@ -314,25 +313,21 @@ OracleKVStorage  Oracle
 ```
 NetworkXStorage      NetworkX(defualt)
 Neo4JStorage         Neo4J
-MongoGraphStorage    MongoDB
+PGGraphStorage       Postgres
-TiDBGraphStorage     TiDB
 AGEStorage           AGE
 GremlinStorage       Gremlin
-PGGraphStorage       Postgres
-OracleGraphStorage   Postgres
 ```
 
 * VECTOR_STORAGE supported implement-name
 
 ```
 NanoVectorDBStorage         NanoVector(default)
-MilvusVectorDBStorage       Milvus
-ChromaVectorDBStorage       Chroma
-TiDBVectorDBStorage         TiDB
 PGVectorStorage             Postgres
+MilvusVectorDBStorge        Milvus
+ChromaVectorDBStorage       Chroma
 FaissVectorDBStorage        Faiss
 QdrantVectorDBStorage       Qdrant
-OracleVectorDBStorage       Oracle
+TiDBVectorDBStorage         TiDB
 MongoVectorDBStorage        MongoDB
 ```
 

lightrag/kg/__init__.py

@@ -6,7 +6,6 @@ STORAGE_IMPLEMENTATIONS = {
             "RedisKVStorage",
             "TiDBKVStorage",
             "PGKVStorage",
-            "OracleKVStorage",
         ],
         "required_methods": ["get_by_id", "upsert"],
     },
@@ -19,7 +18,6 @@ STORAGE_IMPLEMENTATIONS = {
             "AGEStorage",
             "GremlinStorage",
             "PGGraphStorage",
-            # "OracleGraphStorage",
         ],
         "required_methods": ["upsert_node", "upsert_edge"],
     },
@@ -32,7 +30,6 @@ STORAGE_IMPLEMENTATIONS = {
             "PGVectorStorage",
             "FaissVectorDBStorage",
             "QdrantVectorDBStorage",
-            "OracleVectorDBStorage",
             "MongoVectorDBStorage",
         ],
         "required_methods": ["query", "upsert"],
@@ -41,7 +38,6 @@ STORAGE_IMPLEMENTATIONS = {
         "implementations": [
             "JsonDocStatusStorage",
             "PGDocStatusStorage",
-            "PGDocStatusStorage",
             "MongoDocStatusStorage",
         ],
         "required_methods": ["get_docs_by_status"],
@@ -56,12 +52,6 @@ STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = {
     "RedisKVStorage": ["REDIS_URI"],
     "TiDBKVStorage": ["TIDB_USER", "TIDB_PASSWORD", "TIDB_DATABASE"],
     "PGKVStorage": ["POSTGRES_USER", "POSTGRES_PASSWORD", "POSTGRES_DATABASE"],
-    "OracleKVStorage": [
-        "ORACLE_DSN",
-        "ORACLE_USER",
-        "ORACLE_PASSWORD",
-        "ORACLE_CONFIG_DIR",
-    ],
     # Graph Storage Implementations
     "NetworkXStorage": [],
     "Neo4JStorage": ["NEO4J_URI", "NEO4J_USERNAME", "NEO4J_PASSWORD"],
@@ -78,12 +68,6 @@ STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = {
         "POSTGRES_PASSWORD",
         "POSTGRES_DATABASE",
     ],
-    "OracleGraphStorage": [
-        "ORACLE_DSN",
-        "ORACLE_USER",
-        "ORACLE_PASSWORD",
-        "ORACLE_CONFIG_DIR",
-    ],
     # Vector Storage Implementations
     "NanoVectorDBStorage": [],
     "MilvusVectorDBStorage": [],
@@ -92,12 +76,6 @@ STORAGE_ENV_REQUIREMENTS: dict[str, list[str]] = {
     "PGVectorStorage": ["POSTGRES_USER", "POSTGRES_PASSWORD", "POSTGRES_DATABASE"],
     "FaissVectorDBStorage": [],
     "QdrantVectorDBStorage": ["QDRANT_URL"],  # QDRANT_API_KEY has default value None
-    "OracleVectorDBStorage": [
-        "ORACLE_DSN",
-        "ORACLE_USER",
-        "ORACLE_PASSWORD",
-        "ORACLE_CONFIG_DIR",
-    ],
     "MongoVectorDBStorage": [],
     # Document Status Storage Implementations
     "JsonDocStatusStorage": [],
@@ -112,9 +90,6 @@ STORAGES = {
     "NanoVectorDBStorage": ".kg.nano_vector_db_impl",
     "JsonDocStatusStorage": ".kg.json_doc_status_impl",
     "Neo4JStorage": ".kg.neo4j_impl",
-    "OracleKVStorage": ".kg.oracle_impl",
-    "OracleGraphStorage": ".kg.oracle_impl",
-    "OracleVectorDBStorage": ".kg.oracle_impl",
     "MilvusVectorDBStorage": ".kg.milvus_impl",
     "MongoKVStorage": ".kg.mongo_impl",
     "MongoDocStatusStorage": ".kg.mongo_impl",