diff --git a/lightrag/constants.py b/lightrag/constants.py
index f98eb937..c9772167 100644
--- a/lightrag/constants.py
+++ b/lightrag/constants.py
@@ -19,11 +19,11 @@ DEFAULT_MAX_ENTITY_TOKENS = 10000
 DEFAULT_MAX_RELATION_TOKENS = 10000
 DEFAULT_MAX_TOTAL_TOKENS = 30000
 DEFAULT_HISTORY_TURNS = 0
-DEFAULT_ENABLE_RERANK = True
 DEFAULT_COSINE_THRESHOLD = 0.2
 DEFAULT_RELATED_CHUNK_NUMBER = 5
 
 # Rerank configuration defaults
+DEFAULT_ENABLE_RERANK = True
 DEFAULT_MIN_RERANK_SCORE = 0.0
 
 # Separator for graph fields
diff --git a/lightrag/utils.py b/lightrag/utils.py
index 621cddee..87e91b0f 100644
--- a/lightrag/utils.py
+++ b/lightrag/utils.py
@@ -1874,24 +1874,25 @@ async def process_chunks_unified(
     # 2. Filter by minimum rerank score if reranking is enabled
     if query_param.enable_rerank and unique_chunks:
         min_rerank_score = global_config.get("min_rerank_score", 0.5)
-        original_count = len(unique_chunks)
+        if min_rerank_score > 0.0:
+            original_count = len(unique_chunks)
 
-        # Filter chunks with score below threshold
-        filtered_chunks = []
-        for chunk in unique_chunks:
-            rerank_score = chunk.get("rerank_score", 1.0)  # Default to 1.0 if no score
-            if rerank_score >= min_rerank_score:
-                filtered_chunks.append(chunk)
+            # Filter chunks with score below threshold
+            filtered_chunks = []
+            for chunk in unique_chunks:
+                rerank_score = chunk.get("rerank_score", 1.0)  # Default to 1.0 if no score
+                if rerank_score >= min_rerank_score:
+                    filtered_chunks.append(chunk)
 
-        unique_chunks = filtered_chunks
-        filtered_count = original_count - len(unique_chunks)
+            unique_chunks = filtered_chunks
+            filtered_count = original_count - len(unique_chunks)
 
-        if filtered_count > 0:
-            logger.info(
-                f"Rerank filtering remained: {len(unique_chunks)} chunks (min rerank score: {min_rerank_score})"
-            )
-        if not unique_chunks:
-            return []
+            if filtered_count > 0:
+                logger.info(
+                    f"Rerank filtering remained: {len(unique_chunks)} chunks (min rerank score: {min_rerank_score})"
+                )
+            if not unique_chunks:
+                return []
 
     # 3. Apply chunk_top_k limiting if specified
     if query_param.chunk_top_k is not None and query_param.chunk_top_k > 0: