diff --git a/README.md b/README.md
index 6602f1d3..33d36ae1 100644
--- a/README.md
+++ b/README.md
@@ -498,6 +498,10 @@ pip install fastapi uvicorn pydantic
 2. Set up your environment variables:
 ```bash
 export RAG_DIR="your_index_directory"  # Optional: Defaults to "index_default"
+export OPENAI_BASE_URL="Your OpenAI API base URL"  # Optional: Defaults to "https://api.openai.com/v1"
+export OPENAI_API_KEY="Your OpenAI API key"  # Required
+export LLM_MODEL="Your LLM model" # Optional: Defaults to "gpt-4o-mini" 
+export EMBEDDING_MODEL="Your embedding model" # Optional: Defaults to "text-embedding-3-large" 
 ```
 
 3. Run the API server:
@@ -522,7 +526,8 @@ The API server provides the following endpoints:
 ```json
 {
     "query": "Your question here",
-    "mode": "hybrid"  // Can be "naive", "local", "global", or "hybrid"
+    "mode": "hybrid",  // Can be "naive", "local", "global", or "hybrid"
+    "only_need_context": true // Optional: Defaults to false, if true, only the referenced context will be returned, otherwise the llm answer will be returned
 }
 ```
 - **Example:**
diff --git a/examples/lightrag_api_openai_compatible_demo.py b/examples/lightrag_api_openai_compatible_demo.py
index 2cd262bb..20a05a5f 100644
--- a/examples/lightrag_api_openai_compatible_demo.py
+++ b/examples/lightrag_api_openai_compatible_demo.py
@@ -1,4 +1,4 @@
-from fastapi import FastAPI, HTTPException
+from fastapi import FastAPI, HTTPException, File, UploadFile
 from pydantic import BaseModel
 import os
 from lightrag import LightRAG, QueryParam
@@ -18,22 +18,28 @@ app = FastAPI(title="LightRAG API", description="API for RAG operations")
 # 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", "gpt-4o-mini")
+print(f"LLM_MODEL: {LLM_MODEL}")
+EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "text-embedding-3-large")
+print(f"EMBEDDING_MODEL: {EMBEDDING_MODEL}")
+EMBEDDING_MAX_TOKEN_SIZE = int(os.environ.get("EMBEDDING_MAX_TOKEN_SIZE", 8192))
+print(f"EMBEDDING_MAX_TOKEN_SIZE: {EMBEDDING_MAX_TOKEN_SIZE}")
+
 if not os.path.exists(WORKING_DIR):
     os.mkdir(WORKING_DIR)
 
+
 # LLM model function
 
 
 async def llm_model_func(
-    prompt, system_prompt=None, history_messages=[], **kwargs
+        prompt, system_prompt=None, history_messages=[], **kwargs
 ) -> str:
     return await openai_complete_if_cache(
-        "gpt-4o-mini",
+        LLM_MODEL,
         prompt,
         system_prompt=system_prompt,
         history_messages=history_messages,
-        api_key="YOUR_API_KEY",
-        base_url="YourURL/v1",
         **kwargs,
     )
 
@@ -44,37 +50,41 @@ async def llm_model_func(
 async def embedding_func(texts: list[str]) -> np.ndarray:
     return await openai_embedding(
         texts,
-        model="text-embedding-3-large",
-        api_key="YOUR_API_KEY",
-        base_url="YourURL/v1",
+        model=EMBEDDING_MODEL,
     )
 
 
+async def get_embedding_dim():
+    test_text = ["This is a test sentence."]
+    embedding = await embedding_func(test_text)
+    embedding_dim = embedding.shape[1]
+    print(f"{embedding_dim=}")
+    return embedding_dim
+
+
 # Initialize RAG instance
 rag = LightRAG(
     working_dir=WORKING_DIR,
     llm_model_func=llm_model_func,
-    embedding_func=EmbeddingFunc(
-        embedding_dim=3072, max_token_size=8192, func=embedding_func
-    ),
+    embedding_func=EmbeddingFunc(embedding_dim=asyncio.run(get_embedding_dim()),
+                                 max_token_size=EMBEDDING_MAX_TOKEN_SIZE,
+                                 func=embedding_func),
 )
 
+
 # Data models
 
 
 class QueryRequest(BaseModel):
     query: str
     mode: str = "hybrid"
+    only_need_context: bool = False
 
 
 class InsertRequest(BaseModel):
     text: str
 
 
-class InsertFileRequest(BaseModel):
-    file_path: str
-
-
 class Response(BaseModel):
     status: str
     data: Optional[str] = None
@@ -89,7 +99,8 @@ async def query_endpoint(request: QueryRequest):
     try:
         loop = asyncio.get_event_loop()
         result = await loop.run_in_executor(
-            None, lambda: rag.query(request.query, param=QueryParam(mode=request.mode))
+            None, lambda: rag.query(request.query,
+                                    param=QueryParam(mode=request.mode, only_need_context=request.only_need_context))
         )
         return Response(status="success", data=result)
     except Exception as e:
@@ -107,30 +118,22 @@ async def insert_endpoint(request: InsertRequest):
 
 
 @app.post("/insert_file", response_model=Response)
-async def insert_file(request: InsertFileRequest):
+async def insert_file(file: UploadFile = File(...)):
     try:
-        # Check if file exists
-        if not os.path.exists(request.file_path):
-            raise HTTPException(
-                status_code=404, detail=f"File not found: {request.file_path}"
-            )
-
+        file_content = await file.read()
         # Read file content
         try:
-            with open(request.file_path, "r", encoding="utf-8") as f:
-                content = f.read()
+            content = file_content.decode("utf-8")
         except UnicodeDecodeError:
             # If UTF-8 decoding fails, try other encodings
-            with open(request.file_path, "r", encoding="gbk") as f:
-                content = f.read()
-
+            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 {request.file_path} inserted successfully",
+            message=f"File content from {file.filename} inserted successfully",
         )
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))