diff --git a/pilot/app.py b/pilot/app.py
index b3f8d6ab1..7cb3aad7f 100644
--- a/pilot/app.py
+++ b/pilot/app.py
@@ -46,7 +46,7 @@ def get_similar(q):
 
     for doc in docs:
         dc, s = doc 
-        print(dc.page_content)
+        print(s)
         yield dc.page_content 
 
 if __name__ == "__main__":
diff --git a/pilot/configs/model_config.py b/pilot/configs/model_config.py
index a9436276f..4527fafe0 100644
--- a/pilot/configs/model_config.py
+++ b/pilot/configs/model_config.py
@@ -8,7 +8,7 @@ ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__fi
 MODEL_PATH = os.path.join(ROOT_PATH, "models")
 VECTORE_PATH = os.path.join(ROOT_PATH, "vector_store")
 LOGDIR = os.path.join(ROOT_PATH, "logs")
-DATASETS_DIR = os.path.join(ROOT_PATH, "datasets")
+DATASETS_DIR = os.path.join(ROOT_PATH, "pilot/datasets")
 
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 LLM_MODEL_CONFIG = {
diff --git a/pilot/server/webserver.py b/pilot/server/webserver.py
index 8154ea99d..5aa3c6526 100644
--- a/pilot/server/webserver.py
+++ b/pilot/server/webserver.py
@@ -12,9 +12,9 @@ import requests
 from urllib.parse import urljoin
 from pilot.configs.model_config import DB_SETTINGS
 from pilot.connections.mysql_conn import MySQLOperator
-from pilot.vector_store.extract_tovec import get_vector_storelist, load_knownledge_from_doc
+from pilot.vector_store.extract_tovec import get_vector_storelist, load_knownledge_from_doc, knownledge_tovec_st
 
-from pilot.configs.model_config import LOGDIR, VICUNA_MODEL_SERVER, LLM_MODEL
+from pilot.configs.model_config import LOGDIR, VICUNA_MODEL_SERVER, LLM_MODEL, DATASETS_DIR
 
 from pilot.conversation import (
     default_conversation,
@@ -50,7 +50,7 @@ priority = {
 
 def get_simlar(q):
     
-    docsearch = load_knownledge_from_doc()
+    docsearch = knownledge_tovec_st(os.path.join(DATASETS_DIR, "plan.md"))
     docs = docsearch.similarity_search_with_score(q, k=1)
 
     contents = [dc.page_content for dc, _ in docs]
@@ -160,7 +160,7 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
         yield (state, state.to_gradio_chatbot()) + (no_change_btn,) * 5
         return
 
-    query = state.messages[-2][1]
+   
     if len(state.messages) == state.offset + 2:
         # 第一轮对话需要加入提示Prompt 
         
@@ -168,15 +168,25 @@ def http_bot(state, db_selector, temperature, max_new_tokens, request: gr.Reques
         new_state = conv_templates[template_name].copy()
         new_state.conv_id = uuid.uuid4().hex
         
+        query = state.messages[-2][1]
+
         # prompt 中添加上下文提示
         if db_selector:
             new_state.append_message(new_state.roles[0], gen_sqlgen_conversation(dbname) + query)
+            new_state.append_message(new_state.roles[1], None)
+            state = new_state
+        else:
+            new_state.append_message(new_state.roles[0], query)
+            new_state.append_message(new_state.roles[1], None)
+            state = new_state
 
-        new_state.append_message(new_state.roles[1], None)
-        state = new_state
-    
-    if not db_selector: 
-        state.append_message(new_state.roles[0], get_simlar(query) + query)
+    # try: 
+    #     if not db_selector: 
+    #         sim_q = get_simlar(query)
+    #         print("********vector similar info*************: ", sim_q)
+    #         state.append_message(new_state.roles[0], sim_q + query)
+    # except Exception as e:
+    #     print(e)
 
     prompt = state.get_prompt()
     
diff --git a/pilot/vector_store/extract_tovec.py b/pilot/vector_store/extract_tovec.py
index 223ff90c8..8badf6fed 100644
--- a/pilot/vector_store/extract_tovec.py
+++ b/pilot/vector_store/extract_tovec.py
@@ -50,17 +50,15 @@ def load_knownledge_from_doc():
     from pilot.configs.model_config import LLM_MODEL_CONFIG
     embeddings = HuggingFaceEmbeddings(model_name=LLM_MODEL_CONFIG["sentence-transforms"])
 
-    docs = []
     files = os.listdir(DATASETS_DIR)
     for file in files:
         if not os.path.isdir(file): 
-            with open(file, "r") as f:
-                doc = f.read()
-                docs.append(docs)    
+            filename = os.path.join(DATASETS_DIR, file)
+            with open(filename, "r") as f:
+                 knownledge = f.read()
 
-    print(doc)
     text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_owerlap=0)
-    texts = text_splitter.split_text("\n".join(docs))
+    texts = text_splitter.split_text(knownledge)
     docsearch = Chroma.from_texts(texts, embeddings, metadatas=[{"source": str(i)} for i in range(len(texts))],
                                 persist_directory=os.path.join(VECTORE_PATH, ".vectore"))
     return docsearch