revise milvus rag
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@@ -21,16 +21,25 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
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# 1. we retrieve rag worker from global context
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user_name = chatbot.get_user()
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checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
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if user_name in RAG_WORKER_REGISTER:
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rag_worker = RAG_WORKER_REGISTER[user_name]
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else:
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rag_worker = RAG_WORKER_REGISTER[user_name] = LlamaIndexRagWorker(
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user_name,
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llm_kwargs,
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checkpoint_dir=get_log_folder(user_name, plugin_name='experimental_rag'),
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checkpoint_dir=checkpoint_dir,
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auto_load_checkpoint=True)
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current_context = f"{VECTOR_STORE_TYPE} @ {checkpoint_dir}"
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tip = "提示:输入“清空向量数据库”可以清空RAG向量数据库"
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if txt == "清空向量数据库":
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chatbot.append([txt, f'正在清空 ({current_context}) ...'])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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rag_worker.purge()
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yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 刷新界面
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return
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chatbot.append([txt, '正在召回知识 ...'])
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chatbot.append([txt, f'正在召回知识 ({current_context}) ...'])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 2. clip history to reduce token consumption
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@@ -75,8 +84,8 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
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)
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# 5. remember what has been asked / answered
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yield from update_ui_lastest_msg(model_say + '</br></br>' + '对话记忆中, 请稍等 ...', chatbot, history, delay=0.5) # 刷新界面
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yield from update_ui_lastest_msg(model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...', chatbot, history, delay=0.5) # 刷新界面
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rag_worker.remember_qa(i_say_to_remember, model_say)
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history.extend([i_say, model_say])
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yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0) # 刷新界面
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yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip) # 刷新界面
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