镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 06:26:47 +00:00
92 行
4.2 KiB
Python
92 行
4.2 KiB
Python
from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
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from crazy_functions.crazy_utils import input_clipping
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from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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VECTOR_STORE_TYPE = "Milvus"
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if VECTOR_STORE_TYPE == "Simple":
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from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
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if VECTOR_STORE_TYPE == "Milvus":
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from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
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RAG_WORKER_REGISTER = {}
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MAX_HISTORY_ROUND = 5
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MAX_CONTEXT_TOKEN_LIMIT = 4096
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REMEMBER_PREVIEW = 1000
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@CatchException
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def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
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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=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, 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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# 2-1. reduce chat round
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txt_origin = txt
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if len(history) > MAX_HISTORY_ROUND * 2:
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history = history[-(MAX_HISTORY_ROUND * 2):]
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txt_clip, history, flags = input_clipping(txt, history, max_token_limit=MAX_CONTEXT_TOKEN_LIMIT, return_clip_flags=True)
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input_is_clipped_flag = (flags["original_input_len"] != flags["clipped_input_len"])
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# 2-2. if input is clipped, add input to vector store before retrieve
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if input_is_clipped_flag:
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yield from update_ui_lastest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0) # 刷新界面
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# save input to vector store
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rag_worker.add_text_to_vector_store(txt_origin)
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yield from update_ui_lastest_msg('向量化完成 ...', chatbot, history, delay=0) # 刷新界面
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if len(txt_origin) > REMEMBER_PREVIEW:
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HALF = REMEMBER_PREVIEW//2
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i_say_to_remember = txt[:HALF] + f" ...\n...(省略{len(txt_origin)-REMEMBER_PREVIEW}字)...\n... " + txt[-HALF:]
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if (flags["original_input_len"] - flags["clipped_input_len"]) > HALF:
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txt_clip = txt_clip + f" ...\n...(省略{len(txt_origin)-len(txt_clip)-HALF}字)...\n... " + txt[-HALF:]
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else:
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pass
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i_say = txt_clip
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else:
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i_say_to_remember = i_say = txt_clip
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else:
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i_say_to_remember = i_say = txt_clip
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# 3. we search vector store and build prompts
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nodes = rag_worker.retrieve_from_store_with_query(i_say)
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prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
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# 4. it is time to query llms
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if len(chatbot) != 0: chatbot.pop(-1) # pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`
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model_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
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inputs=prompt, inputs_show_user=i_say,
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llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
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sys_prompt=system_prompt,
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retry_times_at_unknown_error=0
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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>' + 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, msg=tip) # 刷新界面
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