222 lines
9.6 KiB
Python
222 lines
9.6 KiB
Python
import streamlit as st
|
||
from streamlit_chatbox import *
|
||
from typing import List, Dict
|
||
from datetime import datetime
|
||
|
||
from .utils import *
|
||
from dev_opsgpt.utils import *
|
||
from dev_opsgpt.chat.search_chat import SEARCH_ENGINES
|
||
|
||
chat_box = ChatBox(
|
||
assistant_avatar="../sources/imgs/devops-chatbot2.png"
|
||
)
|
||
|
||
GLOBAL_EXE_CODE_TEXT = ""
|
||
|
||
|
||
def get_messages_history(history_len: int) -> List[Dict]:
|
||
def filter(msg):
|
||
'''
|
||
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
|
||
'''
|
||
content = [x._content for x in msg["elements"] if x._output_method in ["markdown", "text"]]
|
||
return {
|
||
"role": msg["role"],
|
||
"content": content[0] if content else "",
|
||
}
|
||
|
||
history = chat_box.filter_history(100000, filter) # workaround before upgrading streamlit-chatbox.
|
||
user_count = 0
|
||
i = 1
|
||
for i in range(1, len(history) + 1):
|
||
if history[-i]["role"] == "user":
|
||
user_count += 1
|
||
if user_count >= history_len:
|
||
break
|
||
return history[-i:]
|
||
|
||
|
||
def dialogue_page(api: ApiRequest):
|
||
global GLOBAL_EXE_CODE_TEXT
|
||
chat_box.init_session()
|
||
|
||
with st.sidebar:
|
||
# TODO: 对话模型与会话绑定
|
||
def on_mode_change():
|
||
mode = st.session_state.dialogue_mode
|
||
text = f"已切换到 {mode} 模式。"
|
||
if mode == "知识库问答":
|
||
cur_kb = st.session_state.get("selected_kb")
|
||
if cur_kb:
|
||
text = f"{text} 当前知识库： `{cur_kb}`。"
|
||
st.toast(text)
|
||
# sac.alert(text, description="descp", type="success", closable=True, banner=True)
|
||
|
||
dialogue_mode = st.selectbox("请选择对话模式",
|
||
["LLM 对话",
|
||
"知识库问答",
|
||
"搜索引擎问答",
|
||
],
|
||
on_change=on_mode_change,
|
||
key="dialogue_mode",
|
||
)
|
||
history_len = st.number_input("历史对话轮数：", 0, 10, 3)
|
||
|
||
# todo: support history len
|
||
|
||
def on_kb_change():
|
||
st.toast(f"已加载知识库： {st.session_state.selected_kb}")
|
||
|
||
if dialogue_mode == "知识库问答":
|
||
with st.expander("知识库配置", True):
|
||
kb_list = api.list_knowledge_bases(no_remote_api=True)
|
||
selected_kb = st.selectbox(
|
||
"请选择知识库：",
|
||
kb_list,
|
||
on_change=on_kb_change,
|
||
key="selected_kb",
|
||
)
|
||
kb_top_k = st.number_input("匹配知识条数：", 1, 20, 3)
|
||
score_threshold = st.number_input("知识匹配分数阈值：", 0.0, float(SCORE_THRESHOLD), float(SCORE_THRESHOLD), float(SCORE_THRESHOLD//100))
|
||
# chunk_content = st.checkbox("关联上下文", False, disabled=True)
|
||
# chunk_size = st.slider("关联长度：", 0, 500, 250, disabled=True)
|
||
elif dialogue_mode == "搜索引擎问答":
|
||
with st.expander("搜索引擎配置", True):
|
||
search_engine = st.selectbox("请选择搜索引擎", SEARCH_ENGINES.keys(), 0)
|
||
se_top_k = st.number_input("匹配搜索结果条数：", 1, 20, 3)
|
||
|
||
code_interpreter_on = st.toggle("开启代码解释器")
|
||
code_exec_on = st.toggle("自动执行代码")
|
||
|
||
# Display chat messages from history on app rerun
|
||
|
||
chat_box.output_messages()
|
||
|
||
chat_input_placeholder = "请输入对话内容，换行请使用Ctrl+Enter "
|
||
code_text = "" or GLOBAL_EXE_CODE_TEXT
|
||
codebox_res = None
|
||
|
||
if prompt := st.chat_input(chat_input_placeholder, key="prompt"):
|
||
history = get_messages_history(history_len)
|
||
chat_box.user_say(prompt)
|
||
if dialogue_mode == "LLM 对话":
|
||
chat_box.ai_say("正在思考...")
|
||
text = ""
|
||
r = api.chat_chat(prompt, history)
|
||
for t in r:
|
||
if error_msg := check_error_msg(t): # check whether error occured
|
||
st.error(error_msg)
|
||
break
|
||
text += t["answer"]
|
||
chat_box.update_msg(text)
|
||
logger.debug(f"text: {text}")
|
||
chat_box.update_msg(text, streaming=False) # 更新最终的字符串，去除光标
|
||
# 判断是否存在代码, 并提高编辑功能，执行功能
|
||
code_text = api.codebox.decode_code_from_text(text)
|
||
GLOBAL_EXE_CODE_TEXT = code_text
|
||
if code_text and code_exec_on:
|
||
codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
|
||
|
||
elif dialogue_mode == "知识库问答":
|
||
history = get_messages_history(history_len)
|
||
chat_box.ai_say([
|
||
f"正在查询知识库 `{selected_kb}` ...",
|
||
Markdown("...", in_expander=True, title="知识库匹配结果"),
|
||
])
|
||
text = ""
|
||
for idx_count, d in enumerate(api.knowledge_base_chat(prompt, selected_kb, kb_top_k, score_threshold, history)):
|
||
if error_msg := check_error_msg(d): # check whether error occured
|
||
st.error(error_msg)
|
||
text += d["answer"]
|
||
if idx_count%10 == 0:
|
||
chat_box.update_msg(text, element_index=0)
|
||
# chat_box.update_msg("知识库匹配结果: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
|
||
chat_box.update_msg(text, element_index=0, streaming=False) # 更新最终的字符串，去除光标
|
||
chat_box.update_msg("知识库匹配结果: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
|
||
# 判断是否存在代码, 并提高编辑功能，执行功能
|
||
code_text = api.codebox.decode_code_from_text(text)
|
||
GLOBAL_EXE_CODE_TEXT = code_text
|
||
if code_text and code_exec_on:
|
||
codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
|
||
elif dialogue_mode == "搜索引擎问答":
|
||
chat_box.ai_say([
|
||
f"正在执行 `{search_engine}` 搜索...",
|
||
Markdown("...", in_expander=True, title="网络搜索结果"),
|
||
])
|
||
text = ""
|
||
d = {"docs": []}
|
||
for d in api.search_engine_chat(prompt, search_engine, se_top_k):
|
||
if error_msg := check_error_msg(d): # check whether error occured
|
||
st.error(error_msg)
|
||
text += d["answer"]
|
||
if idx_count%10 == 0:
|
||
chat_box.update_msg(text, element_index=0)
|
||
# chat_box.update_msg("搜索匹配结果: \n\n".join(d["docs"]), element_index=1, streaming=False)
|
||
chat_box.update_msg(text, element_index=0, streaming=False) # 更新最终的字符串，去除光标
|
||
chat_box.update_msg("搜索匹配结果: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
|
||
# 判断是否存在代码, 并提高编辑功能，执行功能
|
||
code_text = api.codebox.decode_code_from_text(text)
|
||
GLOBAL_EXE_CODE_TEXT = code_text
|
||
if code_text and code_exec_on:
|
||
codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
|
||
|
||
if code_interpreter_on:
|
||
with st.expander("代码编辑执行器", False):
|
||
code_part = st.text_area("代码片段", code_text, key="code_text")
|
||
cols = st.columns(2)
|
||
if cols[0].button(
|
||
"修改对话",
|
||
use_container_width=True,
|
||
):
|
||
code_text = code_part
|
||
GLOBAL_EXE_CODE_TEXT = code_text
|
||
st.toast("修改对话成功")
|
||
|
||
if cols[1].button(
|
||
"执行代码",
|
||
use_container_width=True
|
||
):
|
||
if code_text:
|
||
codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
|
||
st.toast("正在执行代码")
|
||
else:
|
||
st.toast("code 不能为空")
|
||
|
||
#TODO 这段信息会被记录到history里
|
||
if codebox_res is not None and codebox_res.code_exe_status != 200:
|
||
st.toast(f"{codebox_res.code_exe_response}")
|
||
|
||
if codebox_res is not None and codebox_res.code_exe_status == 200:
|
||
st.toast(f"codebox_chajt {codebox_res}")
|
||
chat_box.ai_say(Markdown(code_text, in_expander=True, title="code interpreter", unsafe_allow_html=True), )
|
||
if codebox_res.code_exe_type == "image/png":
|
||
base_text = f"```\n{code_text}\n```\n\n"
|
||
img_html = "<img src='data:image/png;base64,{}' class='img-fluid'>".format(
|
||
codebox_res.code_exe_response
|
||
)
|
||
chat_box.update_msg(base_text + img_html, streaming=False, state="complete")
|
||
else:
|
||
chat_box.update_msg('```\n'+code_text+'\n```'+"\n\n"+'```\n'+codebox_res.code_exe_response+'\n```',
|
||
streaming=False, state="complete")
|
||
|
||
now = datetime.now()
|
||
with st.sidebar:
|
||
|
||
cols = st.columns(2)
|
||
export_btn = cols[0]
|
||
if cols[1].button(
|
||
"清空对话",
|
||
use_container_width=True,
|
||
):
|
||
chat_box.reset_history()
|
||
GLOBAL_EXE_CODE_TEXT = ""
|
||
st.experimental_rerun()
|
||
|
||
export_btn.download_button(
|
||
"导出记录",
|
||
"".join(chat_box.export2md()),
|
||
file_name=f"{now:%Y-%m-%d %H.%M}_对话记录.md",
|
||
mime="text/markdown",
|
||
use_container_width=True,
|
||
)
|