update webui.py

chains/local_doc_qa.py

@@ -252,7 +252,7 @@ class LocalDocQA:
                     logger.info(f"{file} 未能成功加载")
         if len(docs) > 0:
             logger.info("文件加载完毕，正在生成向量库")
-            if vs_path and os.path.isdir(vs_path):
+            if vs_path and os.path.isdir(vs_path) and "index.faiss" in os.listdir(vs_path):
                 vector_store = load_vector_store(vs_path, self.embeddings)
                 vector_store.add_documents(docs)
                 torch_gc()

webui.py

@@ -50,18 +50,19 @@ def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCOR
                     enumerate(resp["source_documents"])])
             history[-1][-1] += source
             yield history, ""
-    elif mode == "知识库问答" and vs_path is not None and os.path.exists(vs_path) and "index.faiss" in os.listdir(vs_path):
-            for resp, history in local_doc_qa.get_knowledge_based_answer(
-                    query=query, vs_path=vs_path, chat_history=history, streaming=streaming):
-                source = "\n\n"
-                source += "".join(
-                    [f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
-                     f"""{doc.page_content}\n"""
-                     f"""</details>"""
-                     for i, doc in
-                     enumerate(resp["source_documents"])])
-                history[-1][-1] += source
-                yield history, ""
+    elif mode == "知识库问答" and vs_path is not None and os.path.exists(vs_path) and "index.faiss" in os.listdir(
+            vs_path):
+        for resp, history in local_doc_qa.get_knowledge_based_answer(
+                query=query, vs_path=vs_path, chat_history=history, streaming=streaming):
+            source = "\n\n"
+            source += "".join(
+                [f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
+                 f"""{doc.page_content}\n"""
+                 f"""</details>"""
+                 for i, doc in
+                 enumerate(resp["source_documents"])])
+            history[-1][-1] += source
+            yield history, ""
     elif mode == "知识库测试":
         if os.path.exists(vs_path):
             resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path,
@@ -124,7 +125,6 @@ def init_model():
 
 def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k,
                  history):
-
     try:
         llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
         llm_model_ins.history_len = llm_history_len
@@ -229,16 +229,17 @@ def add_vs_name(vs_name, chatbot):
         chatbot = chatbot + [[None, vs_status]]
         return gr.update(visible=True, choices=get_vs_list(), value=vs_name), gr.update(
             visible=False), gr.update(visible=False), gr.update(visible=True), chatbot
-            
+
+
 # 自动化加载固定文件间中文件
-def init_set_vector_store(content_dir,vs_id,history):
+def reinit_vector_store(vs_id, history):
     try:
         shutil.rmtree(VS_ROOT_PATH)
         vs_path = os.path.join(VS_ROOT_PATH, vs_id)
         sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
-                                                  label="文本入库分句长度限制",
-                                                  interactive=True, visible=True)
-        vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(content_dir, vs_path, sentence_size)
+                                  label="文本入库分句长度限制",
+                                  interactive=True, visible=True)
+        vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(UPLOAD_ROOT_PATH, vs_path, sentence_size)
         model_status = """知识库构建成功"""
     except Exception as e:
         logger.error(e)
@@ -487,8 +488,8 @@ with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as
                                 inputs=[llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2,
                                         use_lora, top_k, chatbot], outputs=chatbot)
         load_knowlege_button = gr.Button("重新构建知识库")
-        load_knowlege_button.click(init_set_vector_store, show_progress=True,
-                                   inputs=[UPLOAD_ROOT_PATH, select_vs,chatbot], outputs=chatbot)
+        load_knowlege_button.click(reinit_vector_store, show_progress=True,
+                                   inputs=[select_vs, chatbot], outputs=chatbot)
 (demo
  .queue(concurrency_count=3)
  .launch(server_name='0.0.0.0',