diff --git a/packages/dbgpt-app/src/dbgpt_app/knowledge/service.py b/packages/dbgpt-app/src/dbgpt_app/knowledge/service.py
index 798facff9..2befe7f65 100644
--- a/packages/dbgpt-app/src/dbgpt_app/knowledge/service.py
+++ b/packages/dbgpt-app/src/dbgpt_app/knowledge/service.py
@@ -340,11 +340,14 @@ class KnowledgeService:
                 else 0.3
             )
 
-            if CFG.RERANK_MODEL is not None:
-                if top_k < int(CFG.RERANK_TOP_K) or top_k < 20:
+            app_config = CFG.SYSTEM_APP.config.configs.get("app_config")
+            rerank_top_k = app_config.rag.rerank_top_k
+
+            if app_config.models.rerankers:
+                if top_k < int(rerank_top_k) or top_k < 20:
                     # We use reranker, so if the top_k is less than 20,
                     # we need to set it to 20
-                    top_k = max(int(CFG.RERANK_TOP_K), 20)
+                    top_k = max(int(rerank_top_k), 20)
 
             knowledge_space_retriever = KnowledgeSpaceRetriever(
                 space_id=space.id, top_k=top_k, system_app=CFG.SYSTEM_APP
@@ -360,7 +363,8 @@ class KnowledgeService:
             )
 
             recall_top_k = int(doc_recall_test_request.recall_top_k)
-            if CFG.RERANK_MODEL is not None:
+
+            if app_config.models.rerankers:
                 rerank_embeddings = RerankEmbeddingFactory.get_instance(
                     CFG.SYSTEM_APP
                 ).create()
diff --git a/packages/dbgpt-core/src/dbgpt/agent/resource/knowledge.py b/packages/dbgpt-core/src/dbgpt/agent/resource/knowledge.py
index aa18d0b53..03f6a42ad 100644
--- a/packages/dbgpt-core/src/dbgpt/agent/resource/knowledge.py
+++ b/packages/dbgpt-core/src/dbgpt/agent/resource/knowledge.py
@@ -5,7 +5,10 @@ from typing import TYPE_CHECKING, Any, Dict, List, Optional, Tuple, Type
 
 import cachetools
 
+from dbgpt._private.config import Config
 from dbgpt.core import Chunk
+from dbgpt.rag.embedding.embedding_factory import RerankEmbeddingFactory
+from dbgpt.rag.retriever.rerank import RerankEmbeddingsRanker
 from dbgpt.util.cache_utils import cached
 
 from .base import Resource, ResourceParameters, ResourceType
@@ -14,6 +17,8 @@ if TYPE_CHECKING:
     from dbgpt.rag.retriever.base import BaseRetriever
     from dbgpt.storage.vector_store.filters import MetadataFilters
 
+CFG = Config()
+
 
 @dataclasses.dataclass
 class RetrieverResourceParameters(ResourceParameters):
@@ -32,6 +37,12 @@ class RetrieverResource(Resource[ResourceParameters]):
         """Create a new RetrieverResource."""
         self._name = name
         self._retriever = retriever
+        app_config = CFG.SYSTEM_APP.config.configs.get("app_config")
+        rerank_embeddings = RerankEmbeddingFactory.get_instance(CFG.SYSTEM_APP).create()
+        self.need_rerank = bool(app_config.models.rerankers)
+        self.reranker = RerankEmbeddingsRanker(
+            rerank_embeddings, topk=app_config.rag.rerank_top_k
+        )
 
     @property
     def name(self) -> str:
@@ -77,6 +88,9 @@ class RetrieverResource(Resource[ResourceParameters]):
         if not question:
             raise ValueError("Question is required for knowledge resource.")
         chunks = await self.retrieve(question)
+        if self.need_rerank and len(chunks) > 1:
+            chunks = self.reranker.rank(candidates_with_scores=chunks, query=question)
+
         content = "\n".join(
             [f"--{i}--:" + chunk.content for i, chunk in enumerate(chunks)]
         )
@@ -97,6 +111,9 @@ class RetrieverResource(Resource[ResourceParameters]):
         if not question:
             raise ValueError("Question is required for knowledge resource.")
         chunks = await self.retrieve(question)
+        if self.need_rerank and len(chunks) > 1:
+            chunks = self.reranker.rank(candidates_with_scores=chunks, query=question)
+
         prompt_template = """Resources-{name}:\n {content}"""
         prompt_template_zh = """资源-{name}:\n {content}"""
         if lang == "en":