diff --git a/examples/rag_pipeline.py b/examples/rag_pipeline.py
index 7dbca35a6..5b716ce03 100644
--- a/examples/rag_pipeline.py
+++ b/examples/rag_pipeline.py
@@ -18,13 +18,13 @@
 )
 from metagpt.utils.exceptions import handle_exception
 
+LLM_TIP = "If you not sure, just answer I don't know."
+
 DOC_PATH = EXAMPLE_DATA_PATH / "rag/writer.txt"
-QUESTION = "What are key qualities to be a good writer?"
+QUESTION = f"What are key qualities to be a good writer? {LLM_TIP}"
 
 TRAVEL_DOC_PATH = EXAMPLE_DATA_PATH / "rag/travel.txt"
-TRAVEL_QUESTION = "What does Bob like?"
-
-LLM_TIP = "If you not sure, just answer I don't know."
+TRAVEL_QUESTION = f"What does Bob like? {LLM_TIP}"
 
 
 class Player(BaseModel):
@@ -40,21 +40,21 @@ def rag_key(self) -> str:
 
 
 class RAGExample:
-    """Show how to use RAG.
-
-    Default engine use LLM Reranker, if the answer from the LLM is incorrect, may encounter `IndexError: list index out of range`.
-    """
+    """Show how to use RAG."""
 
-    def __init__(self, engine: SimpleEngine = None):
+    def __init__(self, engine: SimpleEngine = None, use_llm_ranker: bool = True):
         self._engine = engine
+        self._use_llm_ranker = use_llm_ranker
 
     @property
     def engine(self):
         if not self._engine:
+            ranker_configs = [LLMRankerConfig()] if self._use_llm_ranker else None
+
             self._engine = SimpleEngine.from_docs(
                 input_files=[DOC_PATH],
                 retriever_configs=[FAISSRetrieverConfig()],
-                ranker_configs=[LLMRankerConfig()],
+                ranker_configs=ranker_configs,
             )
         return self._engine
 
@@ -105,7 +105,7 @@ async def add_docs(self):
         """
         self._print_title("Add Docs")
 
-        travel_question = f"{TRAVEL_QUESTION}{LLM_TIP}"
+        travel_question = f"{TRAVEL_QUESTION}"
         travel_filepath = TRAVEL_DOC_PATH
 
         logger.info("[Before add docs]")
@@ -240,8 +240,14 @@ async def _retrieve_and_print(self, question):
 
 
 async def main():
-    """RAG pipeline."""
-    e = RAGExample()
+    """RAG pipeline.
+
+    Note:
+    1. If `use_llm_ranker` is True, then it will use LLM Reranker to get better result, but it is not always guaranteed that the output will be parseable for reranking,
+       prefer `gpt-4-turbo`, otherwise might encounter `IndexError: list index out of range` or `ValueError: invalid literal for int() with base 10`.
+    """
+    e = RAGExample(use_llm_ranker=False)
+
     await e.run_pipeline()
     await e.add_docs()
     await e.add_objects()
diff --git a/metagpt/rag/benchmark/base.py b/metagpt/rag/benchmark/base.py
index c1fd297d9..b5d265b35 100644
--- a/metagpt/rag/benchmark/base.py
+++ b/metagpt/rag/benchmark/base.py
@@ -121,7 +121,7 @@ def mean_reciprocal_rank(self, nodes: list[NodeWithScore], reference_docs: list[
                     return mrr_sum
 
         return mrr_sum
-        
+
     async def semantic_similarity(self, response: str, reference: str) -> float:
         result = await self.evaluator.aevaluate(
             response=response,
diff --git a/metagpt/rag/factories/ranker.py b/metagpt/rag/factories/ranker.py
index b75745a1f..7abda162a 100644
--- a/metagpt/rag/factories/ranker.py
+++ b/metagpt/rag/factories/ranker.py
@@ -8,11 +8,11 @@
 from metagpt.rag.rankers.object_ranker import ObjectSortPostprocessor
 from metagpt.rag.schema import (
     BaseRankerConfig,
+    BGERerankConfig,
+    CohereRerankConfig,
     ColbertRerankConfig,
     LLMRankerConfig,
     ObjectRankerConfig,
-    CohereRerankConfig,
-    BGERerankConfig
 )
 
 
@@ -60,13 +60,15 @@ def _create_cohere_rerank(self, config: CohereRerankConfig, **kwargs) -> LLMRera
 
     def _create_bge_rerank(self, config: BGERerankConfig, **kwargs) -> LLMRerank:
         try:
-            from llama_index.postprocessor.flag_embedding_reranker import FlagEmbeddingReranker
+            from llama_index.postprocessor.flag_embedding_reranker import (
+                FlagEmbeddingReranker,
+            )
         except ImportError:
             raise ImportError(
                 "`llama-index-postprocessor-flag-embedding-reranker` package not found, please run `pip install llama-index-postprocessor-flag-embedding-reranker`"
             )
         return FlagEmbeddingReranker(**config.model_dump())
-        
+
     def _create_object_ranker(self, config: ObjectRankerConfig, **kwargs) -> LLMRerank:
         return ObjectSortPostprocessor(**config.model_dump())
 
diff --git a/metagpt/rag/retrievers/bm25_retriever.py b/metagpt/rag/retrievers/bm25_retriever.py
index 241820cf4..dc75d87b0 100644
--- a/metagpt/rag/retrievers/bm25_retriever.py
+++ b/metagpt/rag/retrievers/bm25_retriever.py
@@ -40,8 +40,10 @@ def add_nodes(self, nodes: list[BaseNode], **kwargs) -> None:
         self._corpus = [self._tokenizer(node.get_content()) for node in self._nodes]
         self.bm25 = BM25Okapi(self._corpus)
 
-        self._index.insert_nodes(nodes, **kwargs)
+        if self._index:
+            self._index.insert_nodes(nodes, **kwargs)
 
     def persist(self, persist_dir: str, **kwargs) -> None:
         """Support persist."""
-        self._index.storage_context.persist(persist_dir)
+        if self._index:
+            self._index.storage_context.persist(persist_dir)