543 lines
22 KiB
Python
543 lines
22 KiB
Python
import streamlit as st
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# from st_btn_select import st_btn_select
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import tempfile
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###### 从webui借用的代码 #####
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###### 做了少量修改 #####
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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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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(KB_ROOT_PATH):
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return lst_default
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lst = os.listdir(KB_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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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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# 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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answer_result_stream_result = local_doc_qa.llm_model_chain(
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{"prompt": query, "history": history, "streaming": streaming})
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for answer_result in answer_result_stream_result['answer_result_stream']:
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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: str = 'chat-glm-6b', embedding_model: str = 'text2vec'):
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local_doc_qa = LocalDocQA()
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# 初始化消息
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args = parser.parse_args()
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args_dict = vars(args)
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args_dict.update(model=llm_model)
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shared.loaderCheckPoint = LoaderCheckPoint(args_dict)
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llm_model_ins = shared.loaderLLM()
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try:
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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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answer_result_stream_result = local_doc_qa.llm_model_chain(
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{"prompt": "你好", "history": [], "streaming": False})
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for answer_result in answer_result_stream_result['answer_result_stream']:
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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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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 local_doc_qa
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# 暂未使用到，先保留
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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(local_doc_qa, vs_id, files, sentence_size, history, one_conent, one_content_segmentation):
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vs_path = os.path.join(KB_ROOT_PATH, vs_id, "vector_store")
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filelist = []
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if not os.path.exists(os.path.join(KB_ROOT_PATH, vs_id, "content")):
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os.makedirs(os.path.join(KB_ROOT_PATH, vs_id, "content"))
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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(
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KB_ROOT_PATH, vs_id, "content", filename))
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filelist.append(os.path.join(
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KB_ROOT_PATH, vs_id, "content", filename))
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vs_path, loaded_files = local_doc_qa.init_knowledge_vector_store(
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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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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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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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###### #####
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###### todo #####
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# 1. streamlit运行方式与一般web服务器不同，使用模块是无法实现单例模式的，所以shared和local_doc_qa都需要进行全局化处理。
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# 目前已经实现了local_doc_qa的全局化，后面要考虑shared。
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# 2. 当前local_doc_qa是一个全局变量，一方面：任何一个session对其做出修改，都会影响所有session的对话;另一方面，如何处理所有session的请求竞争也是问题。
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# 这个暂时无法避免，在配置普通的机器上暂时也无需考虑。
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# 3. 目前只包含了get_answer对应的参数，以后可以添加其他参数，如temperature。
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###### #####
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###### 配置项 #####
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class ST_CONFIG:
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user_bg_color = '#77ff77'
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user_icon = 'https://tse2-mm.cn.bing.net/th/id/OIP-C.LTTKrxNWDr_k74wz6jKqBgHaHa?w=203&h=203&c=7&r=0&o=5&pid=1.7'
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robot_bg_color = '#ccccee'
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robot_icon = 'https://ts1.cn.mm.bing.net/th/id/R-C.5302e2cc6f5c7c4933ebb3394e0c41bc?rik=z4u%2b7efba5Mgxw&riu=http%3a%2f%2fcomic-cons.xyz%2fwp-content%2fuploads%2fStar-Wars-avatar-icon-C3PO.png&ehk=kBBvCvpJMHPVpdfpw1GaH%2brbOaIoHjY5Ua9PKcIs%2bAc%3d&risl=&pid=ImgRaw&r=0'
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default_mode = '知识库问答'
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defalut_kb = ''
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###### #####
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class MsgType:
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'''
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目前仅支持文本类型的输入输出，为以后多模态模型预留图像、视频、音频支持。
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'''
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TEXT = 1
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IMAGE = 2
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VIDEO = 3
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AUDIO = 4
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class TempFile:
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'''
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为保持与get_vector_store的兼容性，需要将streamlit上传文件转化为其可以接受的方式
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'''
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def __init__(self, path):
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self.name = path
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def init_session():
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st.session_state.setdefault('history', [])
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# def get_query_params():
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# '''
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# 可以用url参数传递配置参数：llm_model, embedding_model, kb, mode。
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# 该参数将覆盖model_config中的配置。处于安全考虑，目前只支持kb和mode
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# 方便将固定的配置分享给特定的人。
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# '''
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# params = st.experimental_get_query_params()
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# return {k: v[0] for k, v in params.items() if v}
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def robot_say(msg, kb=''):
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st.session_state['history'].append(
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{'is_user': False, 'type': MsgType.TEXT, 'content': msg, 'kb': kb})
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def user_say(msg):
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st.session_state['history'].append(
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{'is_user': True, 'type': MsgType.TEXT, 'content': msg})
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def format_md(msg, is_user=False, bg_color='', margin='10%'):
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'''
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将文本消息格式化为markdown文本
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'''
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if is_user:
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bg_color = bg_color or ST_CONFIG.user_bg_color
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text = f'''
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<div style="background:{bg_color};
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margin-left:{margin};
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word-break:break-all;
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float:right;
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padding:2%;
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border-radius:2%;">
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{msg}
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</div>
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'''
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else:
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bg_color = bg_color or ST_CONFIG.robot_bg_color
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text = f'''
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<div style="background:{bg_color};
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margin-right:{margin};
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word-break:break-all;
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padding:2%;
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border-radius:2%;">
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{msg}
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</div>
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'''
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return text
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def message(msg,
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is_user=False,
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msg_type=MsgType.TEXT,
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icon='',
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bg_color='',
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margin='10%',
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kb='',
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):
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'''
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渲染单条消息。目前仅支持文本
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'''
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cols = st.columns([1, 10, 1])
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empty = cols[1].empty()
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if is_user:
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icon = icon or ST_CONFIG.user_icon
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bg_color = bg_color or ST_CONFIG.user_bg_color
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cols[2].image(icon, width=40)
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if msg_type == MsgType.TEXT:
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text = format_md(msg, is_user, bg_color, margin)
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empty.markdown(text, unsafe_allow_html=True)
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else:
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raise RuntimeError('only support text message now.')
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else:
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icon = icon or ST_CONFIG.robot_icon
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bg_color = bg_color or ST_CONFIG.robot_bg_color
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cols[0].image(icon, width=40)
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if kb:
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cols[0].write(f'({kb})')
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if msg_type == MsgType.TEXT:
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text = format_md(msg, is_user, bg_color, margin)
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empty.markdown(text, unsafe_allow_html=True)
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else:
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raise RuntimeError('only support text message now.')
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return empty
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def output_messages(
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user_bg_color='',
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robot_bg_color='',
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user_icon='',
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robot_icon='',
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):
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with chat_box.container():
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last_response = None
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for msg in st.session_state['history']:
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bg_color = user_bg_color if msg['is_user'] else robot_bg_color
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icon = user_icon if msg['is_user'] else robot_icon
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empty = message(msg['content'],
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is_user=msg['is_user'],
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icon=icon,
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msg_type=msg['type'],
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bg_color=bg_color,
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kb=msg.get('kb', '')
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)
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if not msg['is_user']:
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last_response = empty
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return last_response
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@st.cache_resource(show_spinner=False, max_entries=1)
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def load_model(llm_model: str, embedding_model: str):
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'''
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对应init_model，利用streamlit cache避免模型重复加载
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'''
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local_doc_qa = init_model(llm_model, embedding_model)
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robot_say('模型已成功加载，可以开始对话，或从左侧选择模式后开始对话。\n请尽量不要刷新页面，以免模型出错或重复加载。')
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return local_doc_qa
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# @st.cache_data
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def answer(query, vs_path='', history=[], mode='', score_threshold=0,
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vector_search_top_k=5, chunk_conent=True, chunk_size=100, qa=None
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):
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'''
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对应get_answer，--利用streamlit cache缓存相同问题的答案--
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'''
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return get_answer(query, vs_path, history, mode, score_threshold,
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vector_search_top_k, chunk_conent, chunk_size)
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def load_vector_store(
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vs_id,
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files,
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sentence_size=100,
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history=[],
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one_conent=None,
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one_content_segmentation=None,
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):
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return get_vector_store(
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local_doc_qa,
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vs_id,
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files,
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sentence_size,
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history,
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one_conent,
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one_content_segmentation,
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)
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# main ui
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st.set_page_config(webui_title, layout='wide')
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init_session()
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# params = get_query_params()
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# llm_model = params.get('llm_model', LLM_MODEL)
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# embedding_model = params.get('embedding_model', EMBEDDING_MODEL)
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with st.spinner(f'正在加载模型({LLM_MODEL} + {EMBEDDING_MODEL})，请耐心等候...'):
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local_doc_qa = load_model(LLM_MODEL, EMBEDDING_MODEL)
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def use_kb_mode(m):
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return m in ['知识库问答', '知识库测试']
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# sidebar
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modes = ['LLM 对话', '知识库问答', 'Bing搜索问答', '知识库测试']
|
||
with st.sidebar:
|
||
def on_mode_change():
|
||
m = st.session_state.mode
|
||
robot_say(f'已切换到"{m}"模式')
|
||
if m == '知识库测试':
|
||
robot_say(knowledge_base_test_mode_info)
|
||
|
||
index = 0
|
||
try:
|
||
index = modes.index(ST_CONFIG.default_mode)
|
||
except:
|
||
pass
|
||
mode = st.selectbox('对话模式', modes, index,
|
||
on_change=on_mode_change, key='mode')
|
||
|
||
with st.expander('模型配置', '知识' not in mode):
|
||
with st.form('model_config'):
|
||
index = 0
|
||
try:
|
||
index = llm_model_dict_list.index(LLM_MODEL)
|
||
except:
|
||
pass
|
||
llm_model = st.selectbox('LLM模型', llm_model_dict_list, index)
|
||
|
||
no_remote_model = st.checkbox('加载本地模型', False)
|
||
use_ptuning_v2 = st.checkbox('使用p-tuning-v2微调过的模型', False)
|
||
use_lora = st.checkbox('使用lora微调的权重', False)
|
||
try:
|
||
index = embedding_model_dict_list.index(EMBEDDING_MODEL)
|
||
except:
|
||
pass
|
||
embedding_model = st.selectbox(
|
||
'Embedding模型', embedding_model_dict_list, index)
|
||
|
||
btn_load_model = st.form_submit_button('重新加载模型')
|
||
if btn_load_model:
|
||
local_doc_qa = load_model(llm_model, embedding_model)
|
||
|
||
if mode in ['知识库问答', '知识库测试']:
|
||
vs_list = get_vs_list()
|
||
vs_list.remove('新建知识库')
|
||
|
||
def on_new_kb():
|
||
name = st.session_state.kb_name
|
||
if name in vs_list:
|
||
st.error(f'名为“{name}”的知识库已存在。')
|
||
else:
|
||
vs_list.append(name)
|
||
st.session_state.vs_path = name
|
||
|
||
def on_vs_change():
|
||
robot_say(f'已加载知识库： {st.session_state.vs_path}')
|
||
with st.expander('知识库配置', True):
|
||
cols = st.columns([12, 10])
|
||
kb_name = cols[0].text_input(
|
||
'新知识库名称', placeholder='新知识库名称', label_visibility='collapsed')
|
||
if 'kb_name' not in st.session_state:
|
||
st.session_state.kb_name = kb_name
|
||
cols[1].button('新建知识库', on_click=on_new_kb)
|
||
vs_path = st.selectbox(
|
||
'选择知识库', vs_list, on_change=on_vs_change, key='vs_path')
|
||
|
||
st.text('')
|
||
|
||
score_threshold = st.slider(
|
||
'知识相关度阈值', 0, 1000, VECTOR_SEARCH_SCORE_THRESHOLD)
|
||
top_k = st.slider('向量匹配数量', 1, 20, VECTOR_SEARCH_TOP_K)
|
||
history_len = st.slider(
|
||
'LLM对话轮数', 1, 50, LLM_HISTORY_LEN) # 也许要跟知识库分开设置
|
||
# local_doc_qa.llm.set_history_len(history_len)
|
||
chunk_conent = st.checkbox('启用上下文关联', False)
|
||
st.text('')
|
||
# chunk_conent = st.checkbox('分割文本', True) # 知识库文本分割入库
|
||
chunk_size = st.slider('上下文关联长度', 1, 1000, CHUNK_SIZE)
|
||
sentence_size = st.slider('文本入库分句长度限制', 1, 1000, SENTENCE_SIZE)
|
||
files = st.file_uploader('上传知识文件',
|
||
['docx', 'txt', 'md', 'csv', 'xlsx', 'pdf'],
|
||
accept_multiple_files=True)
|
||
if st.button('添加文件到知识库'):
|
||
temp_dir = tempfile.mkdtemp()
|
||
file_list = []
|
||
for f in files:
|
||
file = os.path.join(temp_dir, f.name)
|
||
with open(file, 'wb') as fp:
|
||
fp.write(f.getvalue())
|
||
file_list.append(TempFile(file))
|
||
_, _, history = load_vector_store(
|
||
vs_path, file_list, sentence_size, [], None, None)
|
||
st.session_state.files = []
|
||
|
||
|
||
# main body
|
||
chat_box = st.empty()
|
||
|
||
with st.form('my_form', clear_on_submit=True):
|
||
cols = st.columns([8, 1])
|
||
question = cols[0].text_input(
|
||
'temp', key='input_question', label_visibility='collapsed')
|
||
|
||
def on_send():
|
||
q = st.session_state.input_question
|
||
if q:
|
||
user_say(q)
|
||
|
||
if mode == 'LLM 对话':
|
||
robot_say('正在思考...')
|
||
last_response = output_messages()
|
||
for history, _ in answer(q,
|
||
history=[],
|
||
mode=mode):
|
||
last_response.markdown(
|
||
format_md(history[-1][-1], False),
|
||
unsafe_allow_html=True
|
||
)
|
||
elif use_kb_mode(mode):
|
||
robot_say('正在思考...', vs_path)
|
||
last_response = output_messages()
|
||
for history, _ in answer(q,
|
||
vs_path=os.path.join(
|
||
KB_ROOT_PATH, vs_path, "vector_store"),
|
||
history=[],
|
||
mode=mode,
|
||
score_threshold=score_threshold,
|
||
vector_search_top_k=top_k,
|
||
chunk_conent=chunk_conent,
|
||
chunk_size=chunk_size):
|
||
last_response.markdown(
|
||
format_md(history[-1][-1], False, 'ligreen'),
|
||
unsafe_allow_html=True
|
||
)
|
||
else:
|
||
robot_say('正在思考...')
|
||
last_response = output_messages()
|
||
st.session_state['history'][-1]['content'] = history[-1][-1]
|
||
submit = cols[1].form_submit_button('发送', on_click=on_send)
|
||
|
||
output_messages()
|
||
|
||
# st.write(st.session_state['history'])
|