469 lines
26 KiB
Python
469 lines
26 KiB
Python
import gradio as gr
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import os
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import shutil
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from chains.local_doc_qa import LocalDocQA
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from configs.model_config import *
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import nltk
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from models.base import (BaseAnswer,
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AnswerResult,
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AnswerResultStream,
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AnswerResultQueueSentinelTokenListenerQueue)
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import models.shared as shared
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from models.loader.args import parser
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from models.loader import LoaderCheckPoint
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nltk.data.path = [NLTK_DATA_PATH] + nltk.data.path
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def get_vs_list():
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lst_default = ["新建知识库"]
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if not os.path.exists(VS_ROOT_PATH):
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return lst_default
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lst = os.listdir(VS_ROOT_PATH)
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if not lst:
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return lst_default
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lst.sort()
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return lst_default + lst
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vs_list = get_vs_list()
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embedding_model_dict_list = list(embedding_model_dict.keys())
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llm_model_dict_list = list(llm_model_dict.keys())
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local_doc_qa = LocalDocQA()
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flag_csv_logger = gr.CSVLogger()
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def get_answer(query, vs_path, history, mode, score_threshold=VECTOR_SEARCH_SCORE_THRESHOLD,
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vector_search_top_k=VECTOR_SEARCH_TOP_K, chunk_conent: bool = True,
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chunk_size=CHUNK_SIZE, streaming: bool = STREAMING):
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if mode == "Bing搜索问答":
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for resp, history in local_doc_qa.get_search_result_based_answer(
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query=query, chat_history=history, streaming=streaming):
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source = "\n\n"
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source += "".join(
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[f"""<details> <summary>出处 [{i + 1}] <a href="{doc.metadata["source"]}" target="_blank">{doc.metadata["source"]}</a> </summary>\n"""
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f"""{doc.page_content}\n"""
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f"""</details>"""
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for i, doc in
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enumerate(resp["source_documents"])])
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history[-1][-1] += source
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yield history, ""
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elif mode == "知识库问答" and vs_path is not None and os.path.exists(vs_path):
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for resp, history in local_doc_qa.get_knowledge_based_answer(
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query=query, vs_path=vs_path, chat_history=history, streaming=streaming):
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source = "\n\n"
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source += "".join(
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[f"""<details> <summary>出处 [{i + 1}] {os.path.split(doc.metadata["source"])[-1]}</summary>\n"""
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f"""{doc.page_content}\n"""
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f"""</details>"""
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for i, doc in
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enumerate(resp["source_documents"])])
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history[-1][-1] += source
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yield history, ""
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elif mode == "知识库测试":
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if os.path.exists(vs_path):
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resp, prompt = local_doc_qa.get_knowledge_based_conent_test(query=query, vs_path=vs_path,
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score_threshold=score_threshold,
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vector_search_top_k=vector_search_top_k,
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chunk_conent=chunk_conent,
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chunk_size=chunk_size)
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if not resp["source_documents"]:
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yield history + [[query,
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"根据您的设定，没有匹配到任何内容，请确认您设置的知识相关度 Score 阈值是否过小或其他参数是否正确。"]], ""
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else:
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source = "\n".join(
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[
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f"""<details open> <summary>【知识相关度 Score】：{doc.metadata["score"]} - 【出处{i + 1}】： {os.path.split(doc.metadata["source"])[-1]} </summary>\n"""
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f"""{doc.page_content}\n"""
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f"""</details>"""
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for i, doc in
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enumerate(resp["source_documents"])])
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history.append([query, "以下内容为知识库中满足设置条件的匹配结果：\n\n" + source])
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yield history, ""
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else:
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yield history + [[query,
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"请选择知识库后进行测试，当前未选择知识库。"]], ""
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else:
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for answer_result in local_doc_qa.llm.generatorAnswer(prompt=query, history=history,
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streaming=streaming):
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resp = answer_result.llm_output["answer"]
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history = answer_result.history
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history[-1][-1] = resp + (
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"\n\n当前知识库为空，如需基于知识库进行问答，请先加载知识库后，再进行提问。" if mode == "知识库问答" else "")
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yield history, ""
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logger.info(f"flagging: username={FLAG_USER_NAME},query={query},vs_path={vs_path},mode={mode},history={history}")
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flag_csv_logger.flag([query, vs_path, history, mode], username=FLAG_USER_NAME)
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def init_model(llm_model: BaseAnswer = None):
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try:
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local_doc_qa.init_cfg(llm_model=llm_model)
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generator = local_doc_qa.llm.generatorAnswer("你好")
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for answer_result in generator:
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print(answer_result.llm_output)
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reply = """模型已成功加载，可以开始对话，或从右侧选择模式后开始对话"""
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logger.info(reply)
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return reply
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except Exception as e:
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logger.error(e)
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reply = """模型未成功加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
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if str(e) == "Unknown platform: darwin":
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logger.info("该报错可能因为您使用的是 macOS 操作系统，需先下载模型至本地后执行 Web UI，具体方法请参考项目 README 中本地部署方法及常见问题："
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" https://github.com/imClumsyPanda/langchain-ChatGLM")
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else:
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logger.info(reply)
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return reply
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def reinit_model(llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora, top_k, history):
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try:
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llm_model_ins = shared.loaderLLM(llm_model, no_remote_model, use_ptuning_v2)
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llm_model_ins.history_len = llm_history_len
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local_doc_qa.init_cfg(llm_model=llm_model_ins,
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embedding_model=embedding_model,
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top_k=top_k)
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model_status = """模型已成功重新加载，可以开始对话，或从右侧选择模式后开始对话"""
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logger.info(model_status)
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except Exception as e:
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logger.error(e)
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model_status = """模型未成功重新加载，请到页面左上角"模型配置"选项卡中重新选择后点击"加载模型"按钮"""
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logger.info(model_status)
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return history + [[None, model_status]]
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def get_vector_store(vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
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vs_path = os.path.join(VS_ROOT_PATH, vs_id)
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filelist = []
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if not os.path.exists(os.path.join(UPLOAD_ROOT_PATH, vs_id)):
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os.makedirs(os.path.join(UPLOAD_ROOT_PATH, vs_id))
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if local_doc_qa.llm and local_doc_qa.embeddings:
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if isinstance(files, list):
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for file in files:
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filename = os.path.split(file.name)[-1]
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shutil.move(file.name, os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
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filelist.append(os.path.join(UPLOAD_ROOT_PATH, vs_id, filename))
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vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(filelist, vs_path, sentence_size)
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else:
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vs_path, loaded_files = local_doc_qa.one_knowledge_add(vs_path, files, one_conent, one_content_segmentation,
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sentence_size)
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if len(loaded_files):
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file_status = f"已添加 {'、'.join([os.path.split(i)[-1] for i in loaded_files if i])} 内容至知识库，并已加载知识库，请开始提问"
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else:
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file_status = "文件未成功加载，请重新上传文件"
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else:
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file_status = "模型未完成加载，请先在加载模型后再导入文件"
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vs_path = None
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logger.info(file_status)
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return vs_path, None, history + [[None, file_status]]
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def change_vs_name_input(vs_id, history):
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if vs_id == "新建知识库":
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return gr.update(visible=True), gr.update(visible=True), gr.update(visible=False), None, history
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else:
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file_status = f"已加载知识库{vs_id}，请开始提问"
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return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), os.path.join(VS_ROOT_PATH,
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vs_id), history + [
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[None, file_status]]
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knowledge_base_test_mode_info = ("【注意】\n\n"
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"1. 您已进入知识库测试模式，您输入的任何对话内容都将用于进行知识库查询，"
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"并仅输出知识库匹配出的内容及相似度分值和及输入的文本源路径，查询的内容并不会进入模型查询。\n\n"
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"2. 知识相关度 Score 经测试，建议设置为 500 或更低，具体设置情况请结合实际使用调整。"
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"""3. 使用"添加单条数据"添加文本至知识库时，内容如未分段，则内容越多越会稀释各查询内容与之关联的score阈值。\n\n"""
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"4. 单条内容长度建议设置在100-150左右。\n\n"
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"5. 本界面用于知识入库及知识匹配相关参数设定，但当前版本中，"
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"本界面中修改的参数并不会直接修改对话界面中参数，仍需前往`configs/model_config.py`修改后生效。"
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"相关参数将在后续版本中支持本界面直接修改。")
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def change_mode(mode, history):
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if mode == "知识库问答":
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return gr.update(visible=True), gr.update(visible=False), history
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# + [[None, "【注意】：您已进入知识库问答模式，您输入的任何查询都将进行知识库查询，然后会自动整理知识库关联内容进入模型查询！！！"]]
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elif mode == "知识库测试":
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return gr.update(visible=True), gr.update(visible=True), [[None,
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knowledge_base_test_mode_info]]
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else:
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return gr.update(visible=False), gr.update(visible=False), history
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def change_chunk_conent(mode, label_conent, history):
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conent = ""
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if "chunk_conent" in label_conent:
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conent = "搜索结果上下文关联"
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elif "one_content_segmentation" in label_conent: # 这里没用上，可以先留着
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conent = "内容分段入库"
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if mode:
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return gr.update(visible=True), history + [[None, f"【已开启{conent}】"]]
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else:
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return gr.update(visible=False), history + [[None, f"【已关闭{conent}】"]]
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def add_vs_name(vs_name, vs_list, chatbot):
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if vs_name in vs_list:
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vs_status = "与已有知识库名称冲突，请重新选择其他名称后提交"
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chatbot = chatbot + [[None, vs_status]]
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return gr.update(visible=True), vs_list, gr.update(visible=True), gr.update(visible=True), gr.update(
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visible=False), chatbot
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else:
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vs_status = f"""已新增知识库"{vs_name}",将在上传文件并载入成功后进行存储。请在开始对话前，先完成文件上传。 """
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chatbot = chatbot + [[None, vs_status]]
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return gr.update(visible=True, choices=[vs_name] + vs_list, value=vs_name), [vs_name] + vs_list, gr.update(
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visible=False), gr.update(visible=False), gr.update(visible=True), chatbot
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block_css = """.importantButton {
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background: linear-gradient(45deg, #7e0570,#5d1c99, #6e00ff) !important;
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border: none !important;
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}
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.importantButton:hover {
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background: linear-gradient(45deg, #ff00e0,#8500ff, #6e00ff) !important;
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border: none !important;
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}"""
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webui_title = """
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# 🎉langchain-ChatGLM WebUI🎉
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👍 [https://github.com/imClumsyPanda/langchain-ChatGLM](https://github.com/imClumsyPanda/langchain-ChatGLM)
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"""
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default_vs = vs_list[0] if len(vs_list) > 1 else "为空"
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init_message = f"""欢迎使用 langchain-ChatGLM Web UI！
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请在右侧切换模式，目前支持直接与 LLM 模型对话或基于本地知识库问答。
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知识库问答模式，选择知识库名称后，即可开始问答，当前知识库{default_vs}，如有需要可以在选择知识库名称后上传文件/文件夹至知识库。
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知识库暂不支持文件删除，该功能将在后续版本中推出。
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"""
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# 初始化消息
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args = None
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args = parser.parse_args()
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args_dict = vars(args)
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shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
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llm_model_ins = shared.loaderLLM()
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llm_model_ins.set_history_len(LLM_HISTORY_LEN)
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model_status = init_model(llm_model=llm_model_ins)
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default_theme_args = dict(
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font=["Source Sans Pro", 'ui-sans-serif', 'system-ui', 'sans-serif'],
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font_mono=['IBM Plex Mono', 'ui-monospace', 'Consolas', 'monospace'],
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)
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with gr.Blocks(css=block_css, theme=gr.themes.Default(**default_theme_args)) as demo:
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vs_path, file_status, model_status, vs_list = gr.State(
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os.path.join(VS_ROOT_PATH, vs_list[0]) if len(vs_list) > 1 else ""), gr.State(""), gr.State(
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model_status), gr.State(vs_list)
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gr.Markdown(webui_title)
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with gr.Tab("对话"):
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with gr.Row():
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with gr.Column(scale=10):
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chatbot = gr.Chatbot([[None, init_message], [None, model_status.value]],
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elem_id="chat-box",
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show_label=False).style(height=750)
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query = gr.Textbox(show_label=False,
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placeholder="请输入提问内容，按回车进行提交").style(container=False)
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with gr.Column(scale=5):
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mode = gr.Radio(["LLM 对话", "知识库问答", "Bing搜索问答"],
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label="请选择使用模式",
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value="知识库问答", )
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knowledge_set = gr.Accordion("知识库设定", visible=False)
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vs_setting = gr.Accordion("配置知识库")
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mode.change(fn=change_mode,
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inputs=[mode, chatbot],
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outputs=[vs_setting, knowledge_set, chatbot])
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with vs_setting:
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select_vs = gr.Dropdown(vs_list.value,
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label="请选择要加载的知识库",
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interactive=True,
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value=vs_list.value[0] if len(vs_list.value) > 0 else None
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)
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vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
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lines=1,
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interactive=True,
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visible=True)
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vs_add = gr.Button(value="添加至知识库选项", visible=True)
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file2vs = gr.Column(visible=False)
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with file2vs:
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# load_vs = gr.Button("加载知识库")
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gr.Markdown("向知识库中添加文件")
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sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
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label="文本入库分句长度限制",
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interactive=True, visible=True)
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with gr.Tab("上传文件"):
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files = gr.File(label="添加文件",
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file_types=['.txt', '.md', '.docx', '.pdf'],
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file_count="multiple",
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show_label=False)
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load_file_button = gr.Button("上传文件并加载知识库")
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with gr.Tab("上传文件夹"):
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folder_files = gr.File(label="添加文件",
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# file_types=['.txt', '.md', '.docx', '.pdf'],
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file_count="directory",
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show_label=False)
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load_folder_button = gr.Button("上传文件夹并加载知识库")
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vs_add.click(fn=add_vs_name,
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inputs=[vs_name, vs_list, chatbot],
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outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot])
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select_vs.change(fn=change_vs_name_input,
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inputs=[select_vs, chatbot],
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outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
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load_file_button.click(get_vector_store,
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show_progress=True,
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inputs=[select_vs, files, sentence_size, chatbot, vs_add, vs_add],
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outputs=[vs_path, files, chatbot], )
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load_folder_button.click(get_vector_store,
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show_progress=True,
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inputs=[select_vs, folder_files, sentence_size, chatbot, vs_add,
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vs_add],
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outputs=[vs_path, folder_files, chatbot], )
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flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
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query.submit(get_answer,
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[query, vs_path, chatbot, mode],
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[chatbot, query])
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with gr.Tab("知识库测试 Beta"):
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with gr.Row():
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with gr.Column(scale=10):
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chatbot = gr.Chatbot([[None, knowledge_base_test_mode_info]],
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elem_id="chat-box",
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show_label=False).style(height=750)
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query = gr.Textbox(show_label=False,
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placeholder="请输入提问内容，按回车进行提交").style(container=False)
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with gr.Column(scale=5):
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mode = gr.Radio(["知识库测试"], # "知识库问答",
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label="请选择使用模式",
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value="知识库测试",
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visible=False)
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knowledge_set = gr.Accordion("知识库设定", visible=True)
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vs_setting = gr.Accordion("配置知识库", visible=True)
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mode.change(fn=change_mode,
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inputs=[mode, chatbot],
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outputs=[vs_setting, knowledge_set, chatbot])
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with knowledge_set:
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score_threshold = gr.Number(value=VECTOR_SEARCH_SCORE_THRESHOLD,
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label="知识相关度 Score 阈值，分值越低匹配度越高",
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precision=0,
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interactive=True)
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vector_search_top_k = gr.Number(value=VECTOR_SEARCH_TOP_K, precision=0,
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label="获取知识库内容条数", interactive=True)
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chunk_conent = gr.Checkbox(value=False,
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label="是否启用上下文关联",
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interactive=True)
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chunk_sizes = gr.Number(value=CHUNK_SIZE, precision=0,
|
||
label="匹配单段内容的连接上下文后最大长度",
|
||
interactive=True, visible=False)
|
||
chunk_conent.change(fn=change_chunk_conent,
|
||
inputs=[chunk_conent, gr.Textbox(value="chunk_conent", visible=False), chatbot],
|
||
outputs=[chunk_sizes, chatbot])
|
||
with vs_setting:
|
||
select_vs = gr.Dropdown(vs_list.value,
|
||
label="请选择要加载的知识库",
|
||
interactive=True,
|
||
value=vs_list.value[0] if len(vs_list.value) > 0 else None)
|
||
vs_name = gr.Textbox(label="请输入新建知识库名称，当前知识库命名暂不支持中文",
|
||
lines=1,
|
||
interactive=True,
|
||
visible=True)
|
||
vs_add = gr.Button(value="添加至知识库选项", visible=True)
|
||
file2vs = gr.Column(visible=False)
|
||
with file2vs:
|
||
# load_vs = gr.Button("加载知识库")
|
||
gr.Markdown("向知识库中添加单条内容或文件")
|
||
sentence_size = gr.Number(value=SENTENCE_SIZE, precision=0,
|
||
label="文本入库分句长度限制",
|
||
interactive=True, visible=True)
|
||
with gr.Tab("上传文件"):
|
||
files = gr.File(label="添加文件",
|
||
file_types=['.txt', '.md', '.docx', '.pdf'],
|
||
file_count="multiple",
|
||
show_label=False
|
||
)
|
||
load_file_button = gr.Button("上传文件并加载知识库")
|
||
with gr.Tab("上传文件夹"):
|
||
folder_files = gr.File(label="添加文件",
|
||
# file_types=['.txt', '.md', '.docx', '.pdf'],
|
||
file_count="directory",
|
||
show_label=False)
|
||
load_folder_button = gr.Button("上传文件夹并加载知识库")
|
||
with gr.Tab("添加单条内容"):
|
||
one_title = gr.Textbox(label="标题", placeholder="请输入要添加单条段落的标题", lines=1)
|
||
one_conent = gr.Textbox(label="内容", placeholder="请输入要添加单条段落的内容", lines=5)
|
||
one_content_segmentation = gr.Checkbox(value=True, label="禁止内容分句入库",
|
||
interactive=True)
|
||
load_conent_button = gr.Button("添加内容并加载知识库")
|
||
# 将上传的文件保存到content文件夹下,并更新下拉框
|
||
vs_add.click(fn=add_vs_name,
|
||
inputs=[vs_name, vs_list, chatbot],
|
||
outputs=[select_vs, vs_list, vs_name, vs_add, file2vs, chatbot])
|
||
select_vs.change(fn=change_vs_name_input,
|
||
inputs=[select_vs, chatbot],
|
||
outputs=[vs_name, vs_add, file2vs, vs_path, chatbot])
|
||
load_file_button.click(get_vector_store,
|
||
show_progress=True,
|
||
inputs=[select_vs, files, sentence_size, chatbot, vs_add, vs_add],
|
||
outputs=[vs_path, files, chatbot], )
|
||
load_folder_button.click(get_vector_store,
|
||
show_progress=True,
|
||
inputs=[select_vs, folder_files, sentence_size, chatbot, vs_add,
|
||
vs_add],
|
||
outputs=[vs_path, folder_files, chatbot], )
|
||
load_conent_button.click(get_vector_store,
|
||
show_progress=True,
|
||
inputs=[select_vs, one_title, sentence_size, chatbot,
|
||
one_conent, one_content_segmentation],
|
||
outputs=[vs_path, files, chatbot], )
|
||
flag_csv_logger.setup([query, vs_path, chatbot, mode], "flagged")
|
||
query.submit(get_answer,
|
||
[query, vs_path, chatbot, mode, score_threshold, vector_search_top_k, chunk_conent,
|
||
chunk_sizes],
|
||
[chatbot, query])
|
||
with gr.Tab("模型配置"):
|
||
llm_model = gr.Radio(llm_model_dict_list,
|
||
label="LLM 模型",
|
||
value=LLM_MODEL,
|
||
interactive=True)
|
||
no_remote_model = gr.Checkbox(shared.LoaderCheckPoint.no_remote_model,
|
||
label="加载本地模型",
|
||
interactive=True)
|
||
|
||
llm_history_len = gr.Slider(0, 10,
|
||
value=LLM_HISTORY_LEN,
|
||
step=1,
|
||
label="LLM 对话轮数",
|
||
interactive=True)
|
||
use_ptuning_v2 = gr.Checkbox(USE_PTUNING_V2,
|
||
label="使用p-tuning-v2微调过的模型",
|
||
interactive=True)
|
||
use_lora = gr.Checkbox(USE_LORA,
|
||
label="使用lora微调的权重",
|
||
interactive=True)
|
||
embedding_model = gr.Radio(embedding_model_dict_list,
|
||
label="Embedding 模型",
|
||
value=EMBEDDING_MODEL,
|
||
interactive=True)
|
||
top_k = gr.Slider(1, 20, value=VECTOR_SEARCH_TOP_K, step=1,
|
||
label="向量匹配 top k", interactive=True)
|
||
load_model_button = gr.Button("重新加载模型")
|
||
load_model_button.click(reinit_model, show_progress=True,
|
||
inputs=[llm_model, embedding_model, llm_history_len, no_remote_model, use_ptuning_v2, use_lora,
|
||
top_k, chatbot], outputs=chatbot)
|
||
|
||
(demo
|
||
.queue(concurrency_count=3)
|
||
.launch(server_name='0.0.0.0',
|
||
server_port=7860,
|
||
show_api=False,
|
||
share=False,
|
||
inbrowser=False)) |