diff --git a/docs/rerank_integration.md b/docs/rerank_integration.md
index 4e4d433f..0e6c5169 100644
--- a/docs/rerank_integration.md
+++ b/docs/rerank_integration.md
@@ -22,14 +22,14 @@ from lightrag import LightRAG, QueryParam
 from lightrag.rerank import custom_rerank, RerankModel
 
 # Method 1: Using a custom rerank function with all settings included
-async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
+async def my_rerank_func(query: str, documents: list, top_n: int = None, **kwargs):
     return await custom_rerank(
         query=query,
         documents=documents,
         model="BAAI/bge-reranker-v2-m3",
         base_url="https://api.your-provider.com/v1/rerank",
         api_key="your_api_key_here",
-        top_k=top_k or 10,  # Handle top_k within the function
+        top_n=top_n or 10,  # Handle top_n within the function
         **kwargs
     )
 
@@ -95,7 +95,7 @@ result = await custom_rerank(
     model="BAAI/bge-reranker-v2-m3",
     base_url="https://api.your-provider.com/v1/rerank",
     api_key="your_api_key_here",
-    top_k=10
+    top_n=10
 )
 ```
 
@@ -109,7 +109,7 @@ result = await jina_rerank(
     documents=documents,
     model="BAAI/bge-reranker-v2-m3",
     api_key="your_jina_api_key",
-    top_k=10
+    top_n=10
 )
 ```
 
@@ -123,7 +123,7 @@ result = await cohere_rerank(
     documents=documents,
     model="rerank-english-v2.0",
     api_key="your_cohere_api_key",
-    top_k=10
+    top_n=10
 )
 ```
 
@@ -141,7 +141,7 @@ Reranking is automatically applied at these key retrieval stages:
 | Parameter | Type | Default | Description |
 |-----------|------|---------|-------------|
 | `enable_rerank` | bool | False | Enable/disable reranking |
-| `rerank_model_func` | callable | None | Custom rerank function containing all configurations (model, API keys, top_k, etc.) |
+| `rerank_model_func` | callable | None | Custom rerank function containing all configurations (model, API keys, top_n, etc.) |
 
 ## Example Usage
 
@@ -154,14 +154,14 @@ from lightrag.llm.openai import gpt_4o_mini_complete, openai_embedding
 from lightrag.kg.shared_storage import initialize_pipeline_status
 from lightrag.rerank import jina_rerank
 
-async def my_rerank_func(query: str, documents: list, top_k: int = None, **kwargs):
+async def my_rerank_func(query: str, documents: list, top_n: int = None, **kwargs):
     """Custom rerank function with all settings included"""
     return await jina_rerank(
         query=query,
         documents=documents,
         model="BAAI/bge-reranker-v2-m3",
         api_key="your_jina_api_key_here",
-        top_k=top_k or 10,  # Default top_k if not provided
+        top_n=top_n or 10,  # Default top_n if not provided
         **kwargs
     )
 
@@ -186,7 +186,7 @@ async def main():
     # Query with rerank (automatically applied)
     result = await rag.aquery(
         "Your question here",
-        param=QueryParam(enable_rerank=True)  # This top_k is passed to rerank function
+        param=QueryParam(enable_rerank=True)  # This top_n is passed to rerank function
     )
 
     print(result)
@@ -212,7 +212,7 @@ async def test_rerank():
         model="BAAI/bge-reranker-v2-m3",
         base_url="https://api.your-provider.com/v1/rerank",
         api_key="your_api_key_here",
-        top_k=2
+        top_n=2
     )
 
     for doc in reranked:
@@ -221,11 +221,11 @@ async def test_rerank():
 
 ## Best Practices
 
-1. **Self-Contained Functions**: Include all necessary configurations (API keys, models, top_k handling) within your rerank function
+1. **Self-Contained Functions**: Include all necessary configurations (API keys, models, top_n handling) within your rerank function
 2. **Performance**: Use reranking selectively for better performance vs. quality tradeoff
 3. **API Limits**: Monitor API usage and implement rate limiting within your rerank function
 4. **Fallback**: Always handle rerank failures gracefully (returns original results)
-5. **Top-k Handling**: Handle top_k parameter appropriately within your rerank function
+5. **Top-n Handling**: Handle top_n parameter appropriately within your rerank function
 6. **Cost Management**: Consider rerank API costs in your budget planning
 
 ## Troubleshooting