140 lines
5.4 KiB
Python
140 lines
5.4 KiB
Python
import streamlit as st
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from webui_pages.utils import *
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from streamlit_chatbox import *
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from datetime import datetime
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from server.chat.search_engine_chat import SEARCH_ENGINES
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from typing import List, Dict
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chat_box = ChatBox()
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def get_messages_history(history_len: int) -> List[Dict]:
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def filter(msg):
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'''
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针对当前简单文本对话，只返回每条消息的第一个element的内容
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'''
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content = [x._content for x in msg["elements"] if x._output_method in ["markdown", "text"]]
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return {
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"role": msg["role"],
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"content": content[0] if content else "",
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}
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history = chat_box.filter_history(100000, filter) # workaround before upgrading streamlit-chatbox.
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user_count = 0
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i = 1
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for i in range(1, len(history) + 1):
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if history[-i]["role"] == "user":
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user_count += 1
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if user_count >= history_len:
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break
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return history[-i:]
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def dialogue_page(api: ApiRequest):
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chat_box.init_session()
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with st.sidebar:
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# TODO: 对话模型与会话绑定
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def on_mode_change():
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mode = st.session_state.dialogue_mode
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text = f"已切换到 {mode} 模式。"
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if mode == "知识库问答":
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cur_kb = st.session_state.get("selected_kb")
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if cur_kb:
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text = f"{text} 当前知识库： `{cur_kb}`。"
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st.toast(text)
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# sac.alert(text, description="descp", type="success", closable=True, banner=True)
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dialogue_mode = st.selectbox("请选择对话模式",
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["LLM 对话",
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"知识库问答",
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"搜索引擎问答",
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],
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on_change=on_mode_change,
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key="dialogue_mode",
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)
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history_len = st.number_input("历史对话轮数：", 0, 10, 3)
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# todo: support history len
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def on_kb_change():
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st.toast(f"已加载知识库： {st.session_state.selected_kb}")
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if dialogue_mode == "知识库问答":
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with st.expander("知识库配置", True):
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kb_list = api.list_knowledge_bases(no_remote_api=True)
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selected_kb = st.selectbox(
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"请选择知识库：",
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kb_list,
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on_change=on_kb_change,
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key="selected_kb",
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)
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kb_top_k = st.number_input("匹配知识条数：", 1, 20, 3)
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# score_threshold = st.slider("知识匹配分数阈值：", 0, 1, 0, disabled=True)
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# chunk_content = st.checkbox("关联上下文", False, disabled=True)
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# chunk_size = st.slider("关联长度：", 0, 500, 250, disabled=True)
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elif dialogue_mode == "搜索引擎问答":
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with st.expander("搜索引擎配置", True):
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search_engine = st.selectbox("请选择搜索引擎", SEARCH_ENGINES.keys(), 0)
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se_top_k = st.number_input("匹配搜索结果条数：", 1, 20, 3)
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# Display chat messages from history on app rerun
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chat_box.output_messages()
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chat_input_placeholder = "请输入对话内容，换行请使用Ctrl+Enter "
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if prompt := st.chat_input(chat_input_placeholder, key="prompt"):
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history = get_messages_history(history_len)
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chat_box.user_say(prompt)
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if dialogue_mode == "LLM 对话":
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chat_box.ai_say("正在思考...")
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text = ""
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r = api.chat_chat(prompt, history)
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for t in r:
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text += t
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chat_box.update_msg(text)
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chat_box.update_msg(text, streaming=False) # 更新最终的字符串，去除光标
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elif dialogue_mode == "知识库问答":
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history = get_messages_history(history_len)
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chat_box.ai_say([
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f"正在查询知识库 `{selected_kb}` ...",
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Markdown("...", in_expander=True, title="知识库匹配结果"),
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])
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text = ""
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for d in api.knowledge_base_chat(prompt, selected_kb, kb_top_k, history):
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text += d["answer"]
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chat_box.update_msg(text, 0)
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chat_box.update_msg("\n\n".join(d["docs"]), 1, streaming=False)
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chat_box.update_msg(text, 0, streaming=False)
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elif dialogue_mode == "搜索引擎问答":
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chat_box.ai_say([
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f"正在执行 `{search_engine}` 搜索...",
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Markdown("...", in_expander=True, title="网络搜索结果"),
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])
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text = ""
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for d in api.search_engine_chat(prompt, search_engine, se_top_k):
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text += d["answer"]
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chat_box.update_msg(text, 0)
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chat_box.update_msg("\n\n".join(d["docs"]), 1, streaming=False)
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chat_box.update_msg(text, 0, streaming=False)
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now = datetime.now()
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with st.sidebar:
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cols = st.columns(2)
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export_btn = cols[0]
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if cols[1].button(
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"清空对话",
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use_container_width=True,
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):
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chat_box.reset_history()
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st.experimental_rerun()
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export_btn.download_button(
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"导出记录",
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"".join(chat_box.export2md()),
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file_name=f"{now:%Y-%m-%d %H.%M}_对话记录.md",
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mime="text/markdown",
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use_container_width=True,
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)
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