镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 06:26:47 +00:00
first_version
这个提交包含在：
lbykkkk
@@ -1,7 +1,12 @@
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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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import os
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from typing import List
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from llama_index.core import Document
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from shared_utils.fastapi_server import validate_path_safety
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from crazy_functions.crazy_utils import input_clipping, request_gpt_model_in_new_thread_with_ui_alive
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from toolbox import CatchException, update_ui, get_log_folder, update_ui_lastest_msg
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from toolbox import report_exception
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from crazy_functions.rag_fns.rag_file_support import extract_text
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VECTOR_STORE_TYPE = "Milvus"
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if VECTOR_STORE_TYPE == "Milvus":
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@@ -21,33 +26,109 @@ 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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def handle_document_upload(files: List[str], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
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"""
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Handles document uploads by extracting text and adding it to the vector store.
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# 1. we retrieve rag worker from global context
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Args:
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files (List[str]): List of file paths to process.
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llm_kwargs: Language model keyword arguments.
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plugin_kwargs: Plugin keyword arguments.
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chatbot: Chatbot instance.
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history: Chat history.
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system_prompt: System prompt.
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user_request: User request.
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"""
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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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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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)
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for file_path in files:
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try:
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validate_path_safety(file_path, user_name)
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text = extract_text(file_path)
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document = Document(text=text, metadata={"source": file_path})
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rag_worker.add_documents_to_vector_store([document])
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chatbot.append([f"上传文件: {os.path.basename(file_path)}", "文件已成功添加到知识库。"])
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except Exception as e:
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report_exception(chatbot, history, a=f"处理文件: {file_path}", b=str(e))
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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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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"""
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Handles RAG-based Q&A, including special commands and document uploads.
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Args:
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txt (str): User input text.
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llm_kwargs: Language model keyword arguments.
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plugin_kwargs: Plugin keyword arguments.
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chatbot: Chatbot instance.
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history: Chat history.
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system_prompt: System prompt.
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user_request: User request.
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"""
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# Define commands
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CLEAR_VECTOR_DB_CMD = "清空向量数据库"
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UPLOAD_DOCUMENT_CMD = "上传文档"
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# 1. 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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)
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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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# 2. Handle special commands
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if txt.startswith(UPLOAD_DOCUMENT_CMD):
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# Extract file paths from the user input
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# Assuming the user inputs file paths separated by commas after the command
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file_paths = txt[len(UPLOAD_DOCUMENT_CMD):].strip().split(',')
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file_paths = [path.strip() for path in file_paths if path.strip()]
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if not file_paths:
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report_exception(chatbot, history, a="上传文档", b="未提供任何文件路径。")
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yield from update_ui(chatbot=chatbot, history=history)
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return
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chatbot.append([txt, f'正在处理上传的文档 ({current_context}) ...'])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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yield from handle_document_upload(file_paths, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
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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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elif txt == CLEAR_VECTOR_DB_CMD:
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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_vector_store()
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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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# 2. clip history to reduce token consumption
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# 2-1. reduce chat round
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# 3. Normal Q&A processing
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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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# 4. Clip history to reduce token consumption
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txt_origin = txt
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if len(history) > MAX_HISTORY_ROUND * 2:
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@@ -55,41 +136,48 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
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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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# 5. 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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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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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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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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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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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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# 6. 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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# 7. Query language model
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if len(chatbot) != 0:
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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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inputs=prompt,
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inputs_show_user=i_say,
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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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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# 8. Remember Q&A
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yield from update_ui_lastest_msg(
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model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...',
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chatbot, history, delay=0.5
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)
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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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# 9. Final UI Update
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yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip)
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