diff --git a/examples/lightrag_api_oracle_demo..py b/examples/lightrag_api_oracle_demo..py
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+
+from fastapi import FastAPI, HTTPException, File, UploadFile
+from contextlib import asynccontextmanager
+from pydantic import BaseModel
+from typing import Optional
+
+import sys, os
+print(os.getcwd())
+from pathlib import Path
+script_directory = Path(__file__).resolve().parent.parent
+sys.path.append(os.path.abspath(script_directory))
+
+import asyncio
+import nest_asyncio
+from lightrag import LightRAG, QueryParam
+from lightrag.llm import openai_complete_if_cache, openai_embedding
+from lightrag.utils import EmbeddingFunc
+import numpy as np
+from datetime import datetime
+
+from lightrag.kg.oracle_impl import OracleDB
+
+
+# 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")
+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)
+
+async def llm_model_func(
+    prompt, system_prompt=None, history_messages=[], **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_embedding(
+        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
+
+
+    oracle_db = OracleDB(config={
+        "user":"",
+        "password":"",
+        "dsn":"",
+        "config_dir":"",
+        "wallet_location":"",
+        "wallet_password":"",
+        "workspace":""
+        }  # specify which docs you want to store and query               
+        )
+    
+    # Check if Oracle DB tables exist, if not, tables will be created
+    await oracle_db.check_tables()
+    # 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"
+        )
+
+    # Setthe KV/vector/graph storage's `db` property, so all operation will use same connection pool
+    rag.graph_storage_cls.db = oracle_db
+    rag.key_string_value_json_storage_cls.db = oracle_db
+    rag.vector_db_storage_cls.db = oracle_db
+
+    return rag
+
+# Data models
+
+
+class QueryRequest(BaseModel):
+    query: str
+    mode: str = "hybrid"
+    only_need_context: bool = False
+
+
+class InsertRequest(BaseModel):
+    text: str
+
+
+class Response(BaseModel):
+    status: str
+    data: Optional[str] = None
+    message: Optional[str] = None
+
+
+# API routes
+
+rag = None  # 定义为全局对象
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    global rag
+    rag = await init()  # 在应用启动时初始化 `rag`
+    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()
+        result = await rag.aquery(
+                request.query,
+                param=QueryParam(
+                    mode=request.mode, only_need_context=request.only_need_context
+                ),
+            )
+        return Response(status="success", data=result)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@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="0.0.0.0", 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: application/json" -d '{"file_path": "path/to/your/file.txt"}'
+
+# 4. Health check:
+# curl -X GET "http://127.0.0.1:8020/health"
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