diff --git a/docs/rerank_integration.md b/docs/rerank_integration.md
index fdaebfa5..4e4d433f 100644
--- a/docs/rerank_integration.md
+++ b/docs/rerank_integration.md
@@ -1,36 +1,24 @@
-# Rerank Integration in LightRAG
+# Rerank Integration Guide
 
-This document explains how to configure and use the rerank functionality in LightRAG to improve retrieval quality.
+LightRAG supports reranking functionality to improve retrieval quality by re-ordering documents based on their relevance to the query. Reranking is now controlled per query via the `enable_rerank` parameter (default: True).
 
-## Overview
-
-Reranking is an optional feature that improves the quality of retrieved documents by re-ordering them based on their relevance to the query. This is particularly useful when you want higher precision in document retrieval across all query modes (naive, local, global, hybrid, mix).
-
-## Architecture
-
-The rerank integration follows a simplified design pattern:
-
-- **Single Function Configuration**: All rerank settings (model, API keys, top_k, etc.) are contained within the rerank function
-- **Async Processing**: Non-blocking rerank operations
-- **Error Handling**: Graceful fallback to original results
-- **Optional Feature**: Can be enabled/disabled via configuration
-- **Code Reuse**: Single generic implementation for Jina/Cohere compatible APIs
-
-## Configuration
+## Quick Start
 
 ### Environment Variables
 
-Set this variable in your `.env` file or environment:
+Set these variables in your `.env` file or environment for rerank model configuration:
 
 ```bash
-# Enable/disable reranking
-ENABLE_RERANK=True
+# Rerank model configuration (required when enable_rerank=True in queries)
+RERANK_MODEL=BAAI/bge-reranker-v2-m3
+RERANK_BINDING_HOST=https://api.your-provider.com/v1/rerank
+RERANK_BINDING_API_KEY=your_api_key_here
 ```
 
 ### Programmatic Configuration
 
 ```python
-from lightrag import LightRAG
+from lightrag import LightRAG, QueryParam
 from lightrag.rerank import custom_rerank, RerankModel
 
 # Method 1: Using a custom rerank function with all settings included
@@ -49,8 +37,19 @@ rag = LightRAG(
     working_dir="./rag_storage",
     llm_model_func=your_llm_func,
     embedding_func=your_embedding_func,
-    enable_rerank=True,
-    rerank_model_func=my_rerank_func,
+    rerank_model_func=my_rerank_func,  # Configure rerank function
+)
+
+# Query with rerank enabled (default)
+result = await rag.aquery(
+    "your query",
+    param=QueryParam(enable_rerank=True)  # Control rerank per query
+)
+
+# Query with rerank disabled
+result = await rag.aquery(
+    "your query",
+    param=QueryParam(enable_rerank=False)
 )
 
 # Method 2: Using RerankModel wrapper
@@ -67,9 +66,17 @@ rag = LightRAG(
     working_dir="./rag_storage",
     llm_model_func=your_llm_func,
     embedding_func=your_embedding_func,
-    enable_rerank=True,
     rerank_model_func=rerank_model.rerank,
 )
+
+# Control rerank per query
+result = await rag.aquery(
+    "your query",
+    param=QueryParam(
+        enable_rerank=True,  # Enable rerank for this query
+        chunk_top_k=5       # Number of chunks to keep after reranking
+    )
+)
 ```
 
 ## Supported Providers
@@ -164,7 +171,6 @@ async def main():
         working_dir="./rag_storage",
         llm_model_func=gpt_4o_mini_complete,
         embedding_func=openai_embedding,
-        enable_rerank=True,
         rerank_model_func=my_rerank_func,
     )
 
@@ -180,7 +186,7 @@ async def main():
     # Query with rerank (automatically applied)
     result = await rag.aquery(
         "Your question here",
-        param=QueryParam(mode="hybrid", top_k=5)  # This top_k is passed to rerank function
+        param=QueryParam(enable_rerank=True)  # This top_k is passed to rerank function
     )
 
     print(result)
diff --git a/examples/rerank_example.py b/examples/rerank_example.py
index e0e361a5..42b4dd38 100644
--- a/examples/rerank_example.py
+++ b/examples/rerank_example.py
@@ -9,7 +9,11 @@ Configuration Required:
 2. Set your embedding API key and base URL in embedding_func()
 3. Set your rerank API key and base URL in the rerank configuration
 4. Or use environment variables (.env file):
-   - ENABLE_RERANK=True
+   - RERANK_MODEL=your_rerank_model
+   - RERANK_BINDING_HOST=your_rerank_endpoint
+   - RERANK_BINDING_API_KEY=your_rerank_api_key
+
+Note: Rerank is now controlled per query via the 'enable_rerank' parameter (default: True)
 """
 
 import asyncio
@@ -83,8 +87,7 @@ async def create_rag_with_rerank():
             max_token_size=8192,
             func=embedding_func,
         ),
-        # Simplified Rerank Configuration
-        enable_rerank=True,
+        # Rerank Configuration - provide the rerank function
         rerank_model_func=my_rerank_func,
     )
 
@@ -120,7 +123,6 @@ async def create_rag_with_rerank_model():
             max_token_size=8192,
             func=embedding_func,
         ),
-        enable_rerank=True,
         rerank_model_func=rerank_model.rerank,
     )
 
@@ -130,9 +132,9 @@ async def create_rag_with_rerank_model():
     return rag
 
 
-async def test_rerank_with_different_topk():
+async def test_rerank_with_different_settings():
     """
-    Test rerank functionality with different top_k settings
+    Test rerank functionality with different enable_rerank settings
     """
     print("🚀 Setting up LightRAG with Rerank functionality...")
 
@@ -154,16 +156,41 @@ async def test_rerank_with_different_topk():
     print(f"\n🔍 Testing query: '{query}'")
     print("=" * 80)
 
-    # Test different top_k values to show parameter priority
-    top_k_values = [2, 5, 10]
+    # Test with rerank enabled (default)
+    print("\n📊 Testing with enable_rerank=True (default):")
+    result_with_rerank = await rag.aquery(
+        query,
+        param=QueryParam(
+            mode="naive",
+            top_k=10,
+            chunk_top_k=5,
+            enable_rerank=True,  # Explicitly enable rerank
+        ),
+    )
+    print(f"   Result length: {len(result_with_rerank)} characters")
+    print(f"   Preview: {result_with_rerank[:100]}...")
 
-    for top_k in top_k_values:
-        print(f"\n📊 Testing with QueryParam(top_k={top_k}):")
+    # Test with rerank disabled
+    print("\n📊 Testing with enable_rerank=False:")
+    result_without_rerank = await rag.aquery(
+        query,
+        param=QueryParam(
+            mode="naive",
+            top_k=10,
+            chunk_top_k=5,
+            enable_rerank=False,  # Disable rerank
+        ),
+    )
+    print(f"   Result length: {len(result_without_rerank)} characters")
+    print(f"   Preview: {result_without_rerank[:100]}...")
 
-        # Test naive mode with specific top_k
-        result = await rag.aquery(query, param=QueryParam(mode="naive", top_k=top_k))
-        print(f"   Result length: {len(result)} characters")
-        print(f"   Preview: {result[:100]}...")
+    # Test with default settings (enable_rerank defaults to True)
+    print("\n📊 Testing with default settings (enable_rerank defaults to True):")
+    result_default = await rag.aquery(
+        query, param=QueryParam(mode="naive", top_k=10, chunk_top_k=5)
+    )
+    print(f"   Result length: {len(result_default)} characters")
+    print(f"   Preview: {result_default[:100]}...")
 
 
 async def test_direct_rerank():
@@ -209,17 +236,21 @@ async def main():
     print("=" * 60)
 
     try:
-        # Test rerank with different top_k values
-        await test_rerank_with_different_topk()
+        # Test rerank with different enable_rerank settings
+        await test_rerank_with_different_settings()
 
         # Test direct rerank
         await test_direct_rerank()
 
         print("\n✅ Example completed successfully!")
         print("\n💡 Key Points:")
-        print("   ✓ All rerank configurations are contained within rerank_model_func")
-        print("   ✓ Rerank improves document relevance ordering")
-        print("   ✓ Configure API keys within your rerank function")
+        print("   ✓ Rerank is now controlled per query via 'enable_rerank' parameter")
+        print("   ✓ Default value for enable_rerank is True")
+        print("   ✓ Rerank function is configured at LightRAG initialization")
+        print("   ✓ Per-query enable_rerank setting overrides default behavior")
+        print(
+            "   ✓ If enable_rerank=True but no rerank model is configured, a warning is issued"
+        )
         print("   ✓ Monitor API usage and costs when using rerank services")
 
     except Exception as e:
diff --git a/lightrag/rerank.py b/lightrag/rerank.py
index 59719bc9..297fa053 100644
--- a/lightrag/rerank.py
+++ b/lightrag/rerank.py
@@ -10,55 +10,58 @@ from .utils import logger
 
 class RerankModel(BaseModel):
     """
-    Pydantic model class for defining a custom rerank model.
-
-    This class provides a convenient wrapper for rerank functions, allowing you to
-    encapsulate all rerank configurations (API keys, model settings, etc.) in one place.
-
-    Attributes:
-        rerank_func (Callable[[Any], List[Dict]]): A callable function that reranks documents.
-            The function should take query and documents as input and return reranked results.
-        kwargs (Dict[str, Any]): A dictionary that contains the arguments to pass to the callable function.
-            This should include all necessary configurations such as model name, API key, base_url, etc.
+    Wrapper for rerank functions that can be used with LightRAG.
 
     Example usage:
-        Rerank model example with Jina:
-        ```python
-        rerank_model = RerankModel(
-            rerank_func=jina_rerank,
-            kwargs={
-                "model": "BAAI/bge-reranker-v2-m3",
-                "api_key": "your_api_key_here",
-                "base_url": "https://api.jina.ai/v1/rerank"
-            }
+    ```python
+    from lightrag.rerank import RerankModel, jina_rerank
+
+    # Create rerank model
+    rerank_model = RerankModel(
+        rerank_func=jina_rerank,
+        kwargs={
+            "model": "BAAI/bge-reranker-v2-m3",
+            "api_key": "your_api_key_here",
+            "base_url": "https://api.jina.ai/v1/rerank"
+        }
+    )
+
+    # Use in LightRAG
+    rag = LightRAG(
+        rerank_model_func=rerank_model.rerank,
+        # ... other configurations
+    )
+
+    # Query with rerank enabled (default)
+    result = await rag.aquery(
+        "your query",
+        param=QueryParam(enable_rerank=True)
+    )
+    ```
+
+    Or define a custom function directly:
+    ```python
+    async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
+        return await jina_rerank(
+            query=query,
+            documents=documents,
+            model="BAAI/bge-reranker-v2-m3",
+            api_key="your_api_key_here",
+            top_k=top_k or 10,
+            **kwargs
         )
 
-        # Use in LightRAG
-        rag = LightRAG(
-            enable_rerank=True,
-            rerank_model_func=rerank_model.rerank,
-            # ... other configurations
-        )
-        ```
+    rag = LightRAG(
+        rerank_model_func=my_rerank_func,
+        # ... other configurations
+    )
 
-        Or define a custom function directly:
-        ```python
-        async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
-            return await jina_rerank(
-                query=query,
-                documents=documents,
-                model="BAAI/bge-reranker-v2-m3",
-                api_key="your_api_key_here",
-                top_k=top_k or 10,
-                **kwargs
-            )
-
-        rag = LightRAG(
-            enable_rerank=True,
-            rerank_model_func=my_rerank_func,
-            # ... other configurations
-        )
-        ```
+    # Control rerank per query
+    result = await rag.aquery(
+        "your query",
+        param=QueryParam(enable_rerank=True)  # Enable rerank for this query
+    )
+    ```
     """
 
     rerank_func: Callable[[Any], List[Dict]]