diff --git a/comps/cores/proto/docarray.py b/comps/cores/proto/docarray.py
index 9854cfff7..d785c2445 100644
--- a/comps/cores/proto/docarray.py
+++ b/comps/cores/proto/docarray.py
@@ -58,6 +58,7 @@ class EmbedDoc1024(BaseDoc):
 class SearchedDoc(BaseDoc):
     retrieved_docs: DocList[TextDoc]
     initial_query: str
+    top_n: int = 1
 
     class Config:
         json_encoders = {np.ndarray: lambda x: x.tolist()}
diff --git a/comps/reranks/README.md b/comps/reranks/README.md
index ac3ab3f78..ecec38272 100644
--- a/comps/reranks/README.md
+++ b/comps/reranks/README.md
@@ -100,3 +100,12 @@ curl http://localhost:8000/v1/reranking \
   -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}]}' \
   -H 'Content-Type: application/json'
 ```
+
+You can add the parameter `top_n` to specify the return number of the reranker model, default value is 1.
+
+```bash
+curl http://localhost:8000/v1/reranking \
+  -X POST \
+  -d '{"initial_query":"What is Deep Learning?", "retrieved_docs": [{"text":"Deep Learning is not..."}, {"text":"Deep learning is..."}], "top_n":2}' \
+  -H 'Content-Type: application/json'
+```
diff --git a/comps/reranks/langchain/reranking_tei_xeon.py b/comps/reranks/langchain/reranking_tei_xeon.py
index 0bfe88fb4..40bd5a6a8 100644
--- a/comps/reranks/langchain/reranking_tei_xeon.py
+++ b/comps/reranks/langchain/reranking_tei_xeon.py
@@ -1,6 +1,7 @@
 # Copyright (C) 2024 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
+import heapq
 import json
 import os
 import re
@@ -40,9 +41,11 @@ def reranking(input: SearchedDoc) -> LLMParamsDoc:
     headers = {"Content-Type": "application/json"}
     response = requests.post(url, data=json.dumps(data), headers=headers)
     response_data = response.json()
-    best_response = max(response_data, key=lambda response: response["score"])
-    doc = input.retrieved_docs[best_response["index"]]
-    if doc.text and len(re.findall("[\u4E00-\u9FFF]", doc.text)) / len(doc.text) >= 0.3:
+    best_response_list = heapq.nlargest(input.top_n, response_data, key=lambda x: x["score"])
+    context_str = ""
+    for best_response in best_response_list:
+        context_str = context_str + " " + input.retrieved_docs[best_response["index"]].text
+    if context_str and len(re.findall("[\u4E00-\u9FFF]", context_str)) / len(context_str) >= 0.3:
         # chinese context
         template = "仅基于以下背景回答问题:\n{context}\n问题: {question}"
     else:
@@ -51,7 +54,7 @@ def reranking(input: SearchedDoc) -> LLMParamsDoc:
 Question: {question}
         """
     prompt = ChatPromptTemplate.from_template(template)
-    final_prompt = prompt.format(context=doc.text, question=input.initial_query)
+    final_prompt = prompt.format(context=context_str, question=input.initial_query)
     statistics_dict["opea_service@reranking_tgi_gaudi"].append_latency(time.time() - start, None)
     return LLMParamsDoc(query=final_prompt.strip())