update knowledge_base and dialogue page of webui

README.md

@@ -361,11 +361,11 @@ CUDA_VISIBLE_DEVICES=0,1 python startup.py -a
 
 - Web UI 对话界面：
 
-  ![img](img/webui_0813_0.png)
+![img](img/webui_0915_0.png)
 
 - Web UI 知识库管理页面：
 
-  ![](img/webui_0813_1.png)
+![](img/webui_0915_1.png)
 
 ---
 

README_en.md

@@ -300,10 +300,13 @@ The API, chat interface of WebUI, and knowledge management interface of WebUI ar
 
 2. Chat Interface of WebUI
 
-![img](img/webui_0813_0.png)
+- Dialogue interface of WebUI
+
+![img](img/webui_0915_0.png)
 
 - Knowledge management interface of WebUI
-  ![img](img/webui_0813_1.png)
+
+![img](img/webui_0915_1.png)
 
 ### 6 Luanching API Service or WebUI step-by-step
 

configs/__init__.py

@@ -1,4 +1,4 @@
 from .model_config import *
 from .server_config import *
 
-VERSION = "v0.2.4-preview"
+VERSION = "v0.2.4"

configs/model_config.py.example

@@ -203,13 +203,13 @@ DEFAULT_VS_TYPE = "faiss"
 CACHED_VS_NUM = 1
 
 # 知识库匹配向量数量
-VECTOR_SEARCH_TOP_K = 5
+VECTOR_SEARCH_TOP_K = 3
 
 # 知识库匹配相关度阈值，取值范围在0-1之间，SCORE越小，相关度越高，取到1相当于不筛选，建议设置在0.5左右
 SCORE_THRESHOLD = 1
 
 # 搜索引擎匹配结题数量
-SEARCH_ENGINE_TOP_K = 5
+SEARCH_ENGINE_TOP_K = 3
 
 # nltk 模型存储路径
 NLTK_DATA_PATH = os.path.join(os.path.dirname(os.path.dirname(__file__)), "nltk_data")

webui_pages/dialogue/dialogue.py

@@ -60,6 +60,7 @@ def dialogue_page(api: ApiRequest):
                                       "知识库问答",
                                       "搜索引擎问答",
                                       ],
+                                     index=1,
                                      on_change=on_mode_change,
                                      key="dialogue_mode",
                                      )
@@ -95,7 +96,7 @@ def dialogue_page(api: ApiRequest):
                 r = api.change_llm_model(st.session_state.get("prev_llm_model"), llm_model)
         st.session_state["cur_llm_model"] = llm_model
 
-        temperature = st.number_input("Temperature：", 0.0, 1.0, TEMPERATURE, 0.05)
+        temperature = st.slider("Temperature：", 0.0, 1.0, TEMPERATURE, 0.05)
         history_len = st.number_input("历史对话轮数：", 0, 10, HISTORY_LEN)
 
         def on_kb_change():
@@ -111,7 +112,7 @@ def dialogue_page(api: ApiRequest):
                     key="selected_kb",
                 )
                 kb_top_k = st.number_input("匹配知识条数：", 1, 20, VECTOR_SEARCH_TOP_K)
-                score_threshold = st.number_input("知识匹配分数阈值：", 0.0, 1.0, float(SCORE_THRESHOLD), 0.01)
+                score_threshold = st.slider("知识匹配分数阈值：", 0.0, 1.0, float(SCORE_THRESHOLD), 0.01)
                 # chunk_content = st.checkbox("关联上下文", False, disabled=True)
                 # chunk_size = st.slider("关联长度：", 0, 500, 250, disabled=True)
         elif dialogue_mode == "搜索引擎问答":
@@ -158,7 +159,7 @@ def dialogue_page(api: ApiRequest):
                                              history=history,
                                              model=llm_model,
                                              temperature=temperature):
-                if error_msg := check_error_msg(d): # check whether error occured
+                if error_msg := check_error_msg(d):  # check whether error occured
                     st.error(error_msg)
                 elif chunk := d.get("answer"):
                     text += chunk
@@ -176,7 +177,7 @@ def dialogue_page(api: ApiRequest):
                                             top_k=se_top_k,
                                             model=llm_model,
                                             temperature=temperature):
-                if error_msg := check_error_msg(d): # check whether error occured
+                if error_msg := check_error_msg(d):  # check whether error occured
                     st.error(error_msg)
                 elif chunk := d.get("answer"):
                     text += chunk

webui_pages/knowledge_base/knowledge_base.py

@@ -142,7 +142,7 @@ def knowledge_base_page(api: ApiRequest):
         ):
             cols = st.columns(3)
             chunk_size = cols[0].number_input("单段文本最大长度：", 1, 1000, CHUNK_SIZE)
-            chunk_overlap = cols[1].number_input("相邻文本重合长度：", 0, 500, OVERLAP_SIZE)
+            chunk_overlap = cols[1].number_input("相邻文本重合长度：", 0, chunk_size, OVERLAP_SIZE)
             cols[2].write("")
             cols[2].write("")
             zh_title_enhance = cols[2].checkbox("开启中文标题加强", ZH_TITLE_ENHANCE)