Zdao032
269 lines
11 KiB
Python
269 lines
11 KiB
Python
"""
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===================================
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@Auther：WenZ
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@Company: BooWay
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@project：booway_dm
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===================================
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"""
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import streamlit as st
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from utils import judge_define_suffix, match_suffix, retrieve_relevant_software
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from dialogue_management import QuestionInfo, DialogType, DialogInfo, QuestionType, NLUInfo, ScenInfo, TalkInfo, \
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ChatRecord
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from utils import stop_word_processing
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import spacy
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import zh_core_web_sm, zh_core_web_md, zh_core_web_lg, zh_core_web_trf
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from utils import get_keywords, get_keywords_v2, get_keywords_v3
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from vector_load import interface_search
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# nlp_sm = zh_core_web_sm.load()
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# nlp_md = zh_core_web_md.load()
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# nlp_lg = zh_core_web_lg.load()
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nlp_trf = zh_core_web_trf.load()
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polite_words = {"你好", "您好", "请", "请问", "谢谢", "不客气", "麻烦", "打扰", "拜托", "辛苦", "劳驾"}
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from chains_ceshi import suffix_answers
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from chains_ceshi import Vertical_classification
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from chains_ceshi import intention_judge
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from chains_ceshi import domain_judge
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from chains_ceshi import judge_5W2H
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from chains_rewrite import software_name_rewrite
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from chains_rewrite import query_function_rewrite
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from chains_rewrite import operation_guidance_rewrite
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from chains_rewrite import troubleshooting_rewrite
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from chains_rewrite import access_rewrite
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from kg_management import retriever_txt_faiss1
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from kg_management import retriever_txt_faiss2
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from kg_management import retriever_txt_faiss3
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from kg_management import retriever_txt_faiss4
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from kg_management import retriever_txt_faiss5
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from kg_management import retriever_txt_faiss6
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from kg_management import retriever_txt_faiss7
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from kg_management import retriever_txt_faiss8
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from kg_management import retriever_txt_faiss9
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from kg_management import input_index_csv_path
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from kg_management import xizang_input_csv_path
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from kg_management import cuceng_input_csv_path
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from kg_management import jigai_input_csv_path
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from kg_management import process_domain_category
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domain_mapping = {
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'西藏造价软件Z1': (retriever_txt_faiss1, retriever_txt_faiss2, retriever_txt_faiss3, xizang_input_csv_path),
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'新型储能计价通C1': (retriever_txt_faiss4, retriever_txt_faiss5, retriever_txt_faiss6, cuceng_input_csv_path),
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'技改检修计价通T1': (retriever_txt_faiss7, retriever_txt_faiss8, retriever_txt_faiss9, jigai_input_csv_path)
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}
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chain_suffix_answers = suffix_answers() # 后缀名问题处理
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chain_vertical = Vertical_classification() # 垂直/开放分类
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chain_intention = intention_judge() # 意图分类
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chain_domain = domain_judge() # 领域分类
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chain_5W2H = judge_5W2H() # 5W2H分类
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chains_name_rewrite = software_name_rewrite() # 问题改写：软件名改写
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chain_function_rewrite = query_function_rewrite() # 问题改写：软件功能查询
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chain_guidance_rewrite = operation_guidance_rewrite() # 问题改写：软件操作指导
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chain_troubleshooting_rewrite = troubleshooting_rewrite() # 问题改写：软件故障排查类
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chain_access_rewrite = access_rewrite() # 问题改写：软件下载与安装
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from chains_rewrite import to_normal_rewrite
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from chains_rewrite import retrieval_rewrite
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chain_normal_rewrite = to_normal_rewrite()
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chain_retrieval_rewrite = retrieval_rewrite()
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# 同义词替换
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from utils import normalize_text
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synonym_dict = {
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"西藏造价软件Z1": ["西藏造价Z1", "Z1软件", "西藏Z1", "西藏工程造价Z1", "西藏造价通Z1", "Z1", "西藏",
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"西藏造价z1", "z1软件", "西藏z1", "西藏工程造价z1", "西藏造价通z1", "z1", ],
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"新型储能计价通C1": ["储能计价通C1", "C1储能计价", "新型储能C1", "储能计价C1", "新型储能计价通", "C1", "储能",
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"储能计价通c1", "c1储能计价", "新型储能c1", "储能计价c1", "新型储能计价通", "c1"],
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"技改检修计价通T1": ["技改检修T1", "T1检修计价", "技改计价通T1", "技改检修计价通", "T1技改检修", "T1", "技改",
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"技改检修t1", "t1检修计价", "技改计价通t1", "技改检修计价通", "t1技改检修", "t1"],
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"费率": ["费费率"],
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"下载": ["获取", "安装", "下载下来", "装上"]
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}
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import random
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reponse_prompts = [
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"🤔 Booway软件助手：请问您指的是哪个软件？",
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"🤔 Booway软件助手：请提供软件名称，以便更好地帮助您。",
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"🤔 Booway软件助手：请问您使用的是什么软件？",
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"🤔 Booway软件助手：请告诉我您要查询的软件名称。",
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"🤔 Booway软件助手：请问是哪款软件？"
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]
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import streamlit as st
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import random
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st.set_page_config(page_title="Booway 助手", layout="wide")
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# st.title("🤖 Booway 助手")
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# 助手简介
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st.markdown("""
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# 🤖 Booway 软件助手
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欢迎使用 **booway 软件助手**，这是一个用于协助用户进行电力造价软件相关问题咨询的智能系统。
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**目前可咨询软件为：**
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- 西藏造价软件Z1
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- 新型储能计价通C1
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- 技改检修计价通T1
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- 后缀名文件咨询
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**使用方法：**
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直接在下方输入你的问题，例如：
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- 你好，想问下储能的C1那个软件。初设的基本预备费费率想调整一下,但是没有找到能调整的地方
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- 如何把西藏老定额工程升级成西藏Z1的新定额工程
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- 储能软件勾选了卸车，总价不变呢
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- bjgx用什么软件打开的？
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- 设备运杂费率怎么设置 (多轮测试)
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- 你好，初设的基本预备费费率想调整一下,但是没有找到能调整的地方（多轮测试）
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**注意：多轮对话**
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- ~~目前多轮对话中 当机器人询问用户什么软件，则必须是以上软件名字~~
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如果你输入的是闲聊内容，系统将提示仅内测用户可用。
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""")
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input_str = st.text_input("🦉 用户：", "")
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if input_str:
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if judge_define_suffix(input_str):
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nlu_info = NLUInfo(vertical_category="软件咨询")
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nlu_info.intent_category = "查询功能"
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nlu_info.domain_category = "后缀名查询"
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suffix_name = match_suffix(input_str)
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nlu_info.retrieve_keywords = suffix_name
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suffix_to_software = retrieve_relevant_software(suffix_name)
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if isinstance(suffix_to_software, int):
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st.info("Booway 助手：未查到相关知识")
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elif isinstance(suffix_to_software, str):
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query_rewrite = f"{suffix_name}是什么文件？用什么软件打开？"
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nlu_info.rewrite = query_rewrite
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query_kg = suffix_to_software
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result = chain_suffix_answers.invoke({"query": input_str, "kg": query_kg})
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st.subheader("识别出的NLU信息")
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st.json({"垂直分类": nlu_info.vertical_category,
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"意图分类": nlu_info.intent_category,
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"领域分类": nlu_info.domain_category,
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"问题分类": nlu_info.question_type,
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"检索语义": nlu_info.retrieve_keywords,
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"改写结果": nlu_info.rewrite,
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"检索回答": result})
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elif isinstance(suffix_to_software, list):
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suffix_to_software_str = '\n'.join(suffix_to_software)
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query_rewrite = f"{suffix_name}是什么文件？用什么软件打开？"
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nlu_info.rewrite = query_rewrite
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query_kg = suffix_to_software_str
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result = chain_suffix_answers.invoke({"query": input_str, "kg": query_kg})
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st.subheader("识别出的NLU信息")
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st.json({"垂直分类": nlu_info.vertical_category,
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"意图分类": nlu_info.intent_category,
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"领域分类": nlu_info.domain_category,
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"问题分类": nlu_info.question_type,
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"检索语义": nlu_info.retrieve_keywords,
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"改写结果": nlu_info.rewrite,
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"检索回答": result})
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else:
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# 多轮对话处理
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# 第一步：预处理输入
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input_str_stoped = stop_word_processing(input_str, nlp_trf, polite_words)
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input_str_syn = normalize_text(input_str_stoped, synonym_dict)
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# 第二步：调用分类链判断领域
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vertical_category = chain_domain.invoke(input_str_syn)
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# 第三步：若为未知领域，尝试引导补充软件名
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if vertical_category == "未知":
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# 初始化状态变量
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if "mt_input_done" not in st.session_state:
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st.session_state.mt_input_done = False
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if "mt_input_value" not in st.session_state:
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st.session_state.mt_input_value = ""
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if "mt_prompt" not in st.session_state:
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st.session_state.mt_prompt = random.choice(reponse_prompts)
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# 尚未完成补充输入
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if not st.session_state.mt_input_done:
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mt_conversation = st.text_input(
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f"{st.session_state.mt_prompt}", key="mt_input"
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)
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if mt_conversation:
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st.session_state.mt_input_value = mt_conversation
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st.session_state.mt_input_done = True
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st.rerun()
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else:
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st.stop() # 等待输入
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# 完成补充输入，重新获取 vertical_category
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mt_input_str = normalize_text(st.session_state.mt_input_value, synonym_dict)
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vertical_category = chain_domain.invoke(mt_input_str)
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# 第四步：NLU构建
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nlu_info = NLUInfo(vertical_category="软件咨询")
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nlu_info.domain_category = vertical_category
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# 第五步：改写问题并提取关键词
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input_str_name_rewrite = chains_name_rewrite.invoke({
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"query": input_str,
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"software_name": nlu_info.domain_category
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})
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input_str_rewrite = chain_normal_rewrite.invoke(input_str_name_rewrite)
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temp_retriever = get_keywords_v2(input_str_rewrite)
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nlu_info.question_type = chain_5W2H.invoke(input_str_rewrite)
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nlu_info.intent_category = chain_intention.invoke(input_str)
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# 第六步：调用检索器并重写查询
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retrievers, input_csv_path = (
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domain_mapping[nlu_info.domain_category][:3],
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domain_mapping[nlu_info.domain_category][3]
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)
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index_keywords = interface_search(temp_retriever, *retrievers)
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st.info(f"提取关键词：{index_keywords}")
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nlu_info.rewrite = chain_retrieval_rewrite.invoke({
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"query": input_str_rewrite,
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"question_type": nlu_info.question_type,
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"intention_type": nlu_info.intent_category,
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"keywords": index_keywords
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})
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nlu_info.retrieve_keywords = get_keywords_v3(nlu_info.rewrite)
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# 第七步：展示识别结果
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st.subheader("识别出的NLU信息")
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st.json({"垂直分类":nlu_info.vertical_category,
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"意图分类":nlu_info.intent_category,
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"领域分类":nlu_info.domain_category,
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"问题分类":nlu_info.question_type,
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"检索语义":nlu_info.retrieve_keywords,
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"改写结果":nlu_info.rewrite})
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for key in ["mt_input_done", "mt_input_value", "mt_prompt", "mt_input"]:
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if key in st.session_state:
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del st.session_state[key]
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# streamlit run streamlit_main.py --server.port 2335
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