From 638c5104685b4571d60e48cfbbe7e9f0db550316 Mon Sep 17 00:00:00 2001
From: Stephen Hu <812791840@qq.com>
Date: Mon, 12 Jan 2026 11:07:11 +0800
Subject: [PATCH] refactor: introduce common normalize method in rerank base
 class (#12550)

### What problem does this PR solve?

introduce common normalize method in rerank base class

### Type of change

- [x] Refactoring
---
 rag/llm/rerank_model.py | 36 ++++++++++++++++++------------------
 1 file changed, 18 insertions(+), 18 deletions(-)

diff --git a/rag/llm/rerank_model.py b/rag/llm/rerank_model.py
index c876c0515..4dac44f2b 100644
--- a/rag/llm/rerank_model.py
+++ b/rag/llm/rerank_model.py
@@ -36,6 +36,22 @@ class Base(ABC):
     def similarity(self, query: str, texts: list):
         raise NotImplementedError("Please implement encode method!")
 
+    @staticmethod
+    def _normalize_rank(rank: np.ndarray) -> np.ndarray:
+        """
+        Normalize rank values to the range 0 to 1.
+        Avoids division by zero if all ranks are identical.
+        """
+        min_rank = np.min(rank)
+        max_rank = np.max(rank)
+
+        if not np.isclose(min_rank, max_rank, atol=1e-3):
+            rank = (rank - min_rank) / (max_rank - min_rank)
+        else:
+            rank = np.zeros_like(rank)
+
+        return rank
+
 
 class JinaRerank(Base):
     _FACTORY_NAME = "Jina"
@@ -121,15 +137,7 @@ class LocalAIRerank(Base):
         except Exception as _e:
             log_exception(_e, res)
 
-        # Normalize the rank values to the range 0 to 1
-        min_rank = np.min(rank)
-        max_rank = np.max(rank)
-
-        # Avoid division by zero if all ranks are identical
-        if not np.isclose(min_rank, max_rank, atol=1e-3):
-            rank = (rank - min_rank) / (max_rank - min_rank)
-        else:
-            rank = np.zeros_like(rank)
+        rank = Base._normalize_rank(rank)
 
         return rank, token_count
 
@@ -215,15 +223,7 @@ class OpenAI_APIRerank(Base):
         except Exception as _e:
             log_exception(_e, res)
 
-        # Normalize the rank values to the range 0 to 1
-        min_rank = np.min(rank)
-        max_rank = np.max(rank)
-
-        # Avoid division by zero if all ranks are identical
-        if not np.isclose(min_rank, max_rank, atol=1e-3):
-            rank = (rank - min_rank) / (max_rank - min_rank)
-        else:
-            rank = np.zeros_like(rank)
+        rank = Base._normalize_rank(rank)
 
         return rank, token_count