diff --git a/api/app/core/rag/nlp/search.py b/api/app/core/rag/nlp/search.py
index 1f696c98..65fbd9cb 100644
--- a/api/app/core/rag/nlp/search.py
+++ b/api/app/core/rag/nlp/search.py
@@ -28,7 +28,9 @@ from app.core.rag.common.float_utils import get_float
 from app.core.rag.common.constants import PAGERANK_FLD, TAG_FLD
 from app.core.rag.llm.chat_model import Base
 from app.core.rag.llm.embedding_model import OpenAIEmbed
+import logging
 
+logger = logging.getLogger(__name__)
 
 def knowledge_retrieval(
         query: str,
@@ -62,7 +64,15 @@ def knowledge_retrieval(
         merge_strategy = config.get("merge_strategy", "weight")
         reranker_id = config.get("reranker_id")
         reranker_top_k = config.get("reranker_top_k", 1024)
-        use_graph = config.get("use_graph", "false").lower() == "true"
+        # use_graph = config.get("use_graph", "false").lower() == "true"
+
+        use_graph_value = config.get("use_graph", False)
+        if isinstance(use_graph_value, bool):
+            use_graph = use_graph_value
+        elif isinstance(use_graph_value, str):
+            use_graph = use_graph_value.lower() in ("true", "1", "yes")
+        else:
+            use_graph = False
 
         file_names_filter = []
         if user_ids:
@@ -159,13 +169,29 @@ def knowledge_retrieval(
 
         # Use the specified reranker for re-ranking
         if reranker_id:
-            return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
-        # use graph
+            try:
+                return rerank(db=db, reranker_id=reranker_id, query=query, docs=all_results, top_k=reranker_top_k)
+            except Exception as rerank_error:
+                # If reranker fails, log warning and continue with original results
+                logger.warning(
+                    "Reranker failed, falling back to original results",
+                    extra={
+                        "reranker_id": reranker_id,
+                        "query": query,
+                        "doc_count": len(all_results),
+                        "error": str(rerank_error),
+                    },
+                )
+
         if use_graph:
-            from app.core.rag.common.settings import kg_retriever
-            doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
-            if doc:
-                all_results.insert(0, doc)
+            try:
+                from app.core.rag.common.settings import kg_retriever
+                doc = kg_retriever.retrieval(question=query, workspace_ids=workspace_ids, kb_ids=kb_ids, emb_mdl=embedding_model, llm=chat_model)
+                if doc:
+                    all_results.insert(0, doc)
+            except Exception as graph_error:
+                print(f"Failed to retrieve from knowledge graph: {str(graph_error)}")
+        
         return all_results
 
     except Exception as e: