516 lines
25 KiB
Python
516 lines
25 KiB
Python
import streamlit as st
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from streamlit_chatbox import *
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from typing import List, Dict
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from datetime import datetime
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from random import randint
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from .utils import *
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from loguru import logger
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from muagent.utils import *
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from muagent.tools import TOOL_SETS
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from muagent.chat.search_chat import SEARCH_ENGINES
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from muagent.connector import PHASE_LIST, PHASE_CONFIGS
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from muagent.service.service_factory import get_cb_details_by_cb_name
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from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_MODEL, embedding_model_dict, EMBEDDING_ENGINE, KB_ROOT_PATH, llm_model_dict
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chat_box = ChatBox(
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assistant_avatar="../sources/imgs/devops-chatbot2.png"
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)
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cur_dir = os.path.dirname(os.path.abspath(__file__))
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GLOBAL_EXE_CODE_TEXT = ""
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GLOBAL_MESSAGE = {"figures": {}, "final_contents": {}}
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import yaml
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# 加载YAML文件
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webui_yaml_filename = "webui_zh.yaml" if True else "webui_en.yaml"
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with open(os.path.join(cur_dir, f"yamls/{webui_yaml_filename}"), 'r', encoding="utf-8") as f:
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try:
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webui_configs = yaml.safe_load(f)
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except yaml.YAMLError as exc:
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print(exc)
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def get_messages_history(history_len: int, isDetailed=False) -> 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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content = content[0] if content else ""
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if isDetailed:
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for k, v in GLOBAL_MESSAGE["final_contents"].items():
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if k == content:
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content = v[-1]
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break
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for k, v in GLOBAL_MESSAGE["figures"].items():
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content = content.replace(v, k)
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return {
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"role": msg["role"],
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"content": content,
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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 upload2sandbox(upload_file, api: ApiRequest):
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if upload_file is None:
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res = {"msg": False}
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else:
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res = api.web_sd_upload(upload_file)
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def dialogue_page(api: ApiRequest):
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global GLOBAL_EXE_CODE_TEXT
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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 = webui_configs["dialogue"]["text_mode_swtich"] + f"{mode}"
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if mode == webui_configs["dialogue"]["mode"][1]:
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cur_kb = st.session_state.get("selected_kb")
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if cur_kb:
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text = text + webui_configs["dialogue"]["text_knowledgeBase_swtich"] + f'`{cur_kb}`'
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st.toast(text)
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dialogue_mode = st.selectbox(webui_configs["dialogue"]["mode_instruction"],
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webui_configs["dialogue"]["mode"],
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# ["LLM 对话",
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# "知识库问答",
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# "代码知识库问答",
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# "搜索引擎问答",
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# "Agent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(webui_configs["dialogue"]["history_length"], 0, 10, 3)
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def on_kb_change():
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st.toast(f"{webui_configs['dialogue']['text_loaded_kbase']}: {st.session_state.selected_kb}")
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def on_cb_change():
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st.toast(f"{webui_configs['dialogue']['text_loaded_cbase']}: {st.session_state.selected_cb}")
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cb_details = get_cb_details_by_cb_name(st.session_state.selected_cb)
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st.session_state['do_interpret'] = cb_details['do_interpret']
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#
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if "interpreter_file_key" not in st.session_state:
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st.session_state["interpreter_file_key"] = 0
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not_agent_qa = True
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interpreter_file = ""
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is_detailed = False
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if dialogue_mode == webui_configs["dialogue"]["mode"][1]:
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with st.expander(webui_configs["dialogue"]["kbase_expander_name"], 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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webui_configs["dialogue"]["kbase_selectbox_name"],
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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(
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webui_configs["dialogue"]["kbase_ninput_topk_name"], 1, 20, 3)
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score_threshold = st.number_input(
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webui_configs["dialogue"]["kbase_ninput_score_threshold_name"],
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0.0, float(SCORE_THRESHOLD), float(SCORE_THRESHOLD),
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float(SCORE_THRESHOLD//100))
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elif dialogue_mode == webui_configs["dialogue"]["mode"][2]:
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with st.expander(webui_configs["dialogue"]["cbase_expander_name"], True):
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cb_list = api.list_cb(no_remote_api=True)
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logger.debug('codebase_list={}'.format(cb_list))
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selected_cb = st.selectbox(
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webui_configs["dialogue"]["cbase_selectbox_name"],
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cb_list,
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on_change=on_cb_change,
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key="selected_cb",
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)
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# change do_interpret
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st.toast(f"{webui_configs['dialogue']['text_loaded_cbase']}: {st.session_state.selected_cb}")
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cb_details = get_cb_details_by_cb_name(st.session_state.selected_cb)
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st.session_state['do_interpret'] = cb_details['do_interpret']
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cb_code_limit = st.number_input(
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webui_configs["dialogue"]["cbase_ninput_topk_name"], 1, 20, 1)
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search_type_list = webui_configs["dialogue"]["cbase_search_type_v1"] if st.session_state['do_interpret'] == 'YES' \
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else webui_configs["dialogue"]["cbase_search_type_v2"]
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cb_search_type = st.selectbox(
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webui_configs["dialogue"]["cbase_selectbox_type_name"],
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search_type_list,
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key='cb_search_type'
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)
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elif dialogue_mode == webui_configs["dialogue"]["mode"][3]:
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with st.expander(webui_configs["dialogue"]["expander_search_name"], True):
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search_engine = st.selectbox(
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webui_configs["dialogue"]["selectbox_search_name"],
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SEARCH_ENGINES.keys(), 0)
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se_top_k = st.number_input(
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webui_configs["dialogue"]["ninput_search_topk_name"], 1, 20, 3)
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elif dialogue_mode == webui_configs["dialogue"]["mode"][4]:
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not_agent_qa = False
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with st.expander(webui_configs["dialogue"]["phase_expander_name"], True):
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choose_phase = st.selectbox(
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webui_configs["dialogue"]["phase_selectbox_name"], PHASE_LIST, 0)
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is_detailed = st.toggle(webui_configs["dialogue"]["phase_toggle_detailed_name"], False)
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tool_using_on = st.toggle(
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webui_configs["dialogue"]["phase_toggle_doToolUsing"],
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PHASE_CONFIGS[choose_phase].get("do_using_tool", False))
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tool_selects = []
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if tool_using_on:
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with st.expander("工具军火库", True):
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tool_selects = st.multiselect(
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webui_configs["dialogue"]["phase_multiselect_tools"],
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TOOL_SETS, ["WeatherInfo"])
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search_on = st.toggle(webui_configs["dialogue"]["phase_toggle_doSearch"],
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PHASE_CONFIGS[choose_phase].get("do_search", False))
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search_engine, top_k = None, 3
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if search_on:
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with st.expander(webui_configs["dialogue"]["expander_search_name"], True):
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search_engine = st.selectbox(
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webui_configs["dialogue"]["selectbox_search_name"],
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SEARCH_ENGINES.keys(), 0)
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se_top_k = st.number_input(
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webui_configs["dialogue"]["ninput_search_topk_name"], 1, 20, 3)
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doc_retrieval_on = st.toggle(
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webui_configs["dialogue"]["phase_toggle_doDocRetrieval"],
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PHASE_CONFIGS[choose_phase].get("do_doc_retrieval", False)
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)
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selected_kb, top_k, score_threshold = None, 3, 1.0
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if doc_retrieval_on:
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with st.expander(webui_configs["dialogue"]["kbase_expander_name"], 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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webui_configs["dialogue"]["kbase_selectbox_name"],
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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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top_k = st.number_input(
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webui_configs["dialogue"]["kbase_ninput_topk_name"], 1, 20, 3)
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score_threshold = st.number_input(
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webui_configs["dialogue"]["kbase_ninput_score_threshold_name"],
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0.0, float(SCORE_THRESHOLD), float(SCORE_THRESHOLD),
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float(SCORE_THRESHOLD//100))
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code_retrieval_on = st.toggle(
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webui_configs["dialogue"]["phase_toggle_doCodeRetrieval"],
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PHASE_CONFIGS[choose_phase].get("do_code_retrieval", False))
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selected_cb, top_k = None, 1
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cb_search_type = "tag"
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if code_retrieval_on:
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with st.expander(webui_configs["dialogue"]["cbase_expander_name"], True):
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cb_list = api.list_cb(no_remote_api=True)
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logger.debug('codebase_list={}'.format(cb_list))
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selected_cb = st.selectbox(
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webui_configs["dialogue"]["cbase_selectbox_name"],
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cb_list,
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on_change=on_cb_change,
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key="selected_cb",
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)
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# change do_interpret
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st.toast(f"{webui_configs['dialogue']['text_loaded_cbase']}: {st.session_state.selected_cb}")
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cb_details = get_cb_details_by_cb_name(st.session_state.selected_cb)
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st.session_state['do_interpret'] = cb_details['do_interpret']
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top_k = st.number_input(
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webui_configs["dialogue"]["cbase_ninput_topk_name"], 1, 20, 1)
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search_type_list = webui_configs["dialogue"]["cbase_search_type_v1"] if st.session_state['do_interpret'] == 'YES' \
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else webui_configs["dialogue"]["cbase_search_type_v2"]
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cb_search_type = st.selectbox(
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webui_configs["dialogue"]["cbase_selectbox_type_name"],
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search_type_list,
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key='cb_search_type'
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)
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with st.expander(webui_configs["sandbox"]["expander_name"], False):
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interpreter_file = st.file_uploader(
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webui_configs["sandbox"]["file_upload_name"],
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[i for ls in LOADER2EXT_DICT.values() for i in ls] + ["jpg", "png"],
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accept_multiple_files=False,
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key=st.session_state.interpreter_file_key,
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)
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files = api.web_sd_list_files()
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files = files["data"]
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download_file = st.selectbox(webui_configs["sandbox"]["selectbox_name"], files,
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key="download_file",)
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cols = st.columns(3)
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file_url, file_name = api.web_sd_download(download_file)
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if cols[0].button(webui_configs["sandbox"]["button_upload_name"],):
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upload2sandbox(interpreter_file, api)
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st.session_state["interpreter_file_key"] += 1
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interpreter_file = ""
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st.experimental_rerun()
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cols[1].download_button(webui_configs["sandbox"]["button_download_name"],
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file_url, file_name)
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if cols[2].button(webui_configs["sandbox"]["button_delete_name"],):
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api.web_sd_delete(download_file)
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# code_interpreter_on = st.toggle(
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# webui_configs["sandbox"]["toggle_doCodeInterpreter"]) and not_agent_qa
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# code_exec_on = st.toggle(webui_configs["sandbox"]["toggle_doAutoCodeExec"]) and not_agent_qa
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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 = webui_configs["chat"]["chat_placeholder"]
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code_text = "" or GLOBAL_EXE_CODE_TEXT
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codebox_res = None
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if prompt := st.chat_input(chat_input_placeholder, key="prompt"):
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upload2sandbox(interpreter_file, api)
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logger.debug(f"prompt: {prompt}")
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history = get_messages_history(history_len, is_detailed)
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chat_box.user_say(prompt)
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if dialogue_mode == webui_configs["dialogue"]["mode"][0]:
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chat_box.ai_say(webui_configs["chat"]["chatbox_saying"])
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text = ""
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r = api.chat_chat(
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prompt, history, no_remote_api=True,
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embed_model=EMBEDDING_MODEL, embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
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model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE,api_key=llm_model_dict[LLM_MODEL]["api_key"],
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api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
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llm_model=LLM_MODEL)
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for t in r:
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if error_msg := check_error_msg(t): # check whether error occured
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st.error(error_msg)
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break
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text += t["answer"]
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# text = replace_lt_gt(text)
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chat_box.update_msg(text)
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# logger.debug(f"text: {text}")
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# text = replace_lt_gt(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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# 判断是否存在代码, 并提高编辑功能，执行功能
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# code_text = api.codebox.decode_code_from_text(text)
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# GLOBAL_EXE_CODE_TEXT = code_text
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# if code_text and code_exec_on:
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# codebox_res = api.codebox_chat("```"+code_text+"```", do_code_exe=True)
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elif dialogue_mode == webui_configs["dialogue"]["mode"][4]:
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display_infos = [webui_configs["chat"]["chatbox_saying"]]
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if search_on:
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display_infos.append(Markdown("...", in_expander=True,
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title=webui_configs["chat"]["chatbox_search_result"]))
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if doc_retrieval_on:
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display_infos.append(Markdown("...", in_expander=True,
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title=webui_configs["chat"]["chatbox_doc_result"]))
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if code_retrieval_on:
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display_infos.append(Markdown("...", in_expander=True,
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title=webui_configs["chat"]["chatbox_code_result"]))
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chat_box.ai_say(display_infos)
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if 'history_node_list' in st.session_state:
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history_node_list: List[str] = st.session_state['history_node_list']
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else:
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history_node_list: List[str] = []
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input_kargs = {"query": prompt,
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"phase_name": choose_phase,
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"history": history,
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"doc_engine_name": selected_kb,
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"search_engine_name": search_engine,
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"code_engine_name": selected_cb,
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"cb_search_type": cb_search_type,
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"top_k": top_k,
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"score_threshold": score_threshold,
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"do_search": search_on,
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"do_doc_retrieval": doc_retrieval_on,
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"do_code_retrieval": code_retrieval_on,
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"do_tool_retrieval": False,
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"custom_phase_configs": {},
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"custom_chain_configs": {},
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"custom_role_configs": {},
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"choose_tools": tool_selects,
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"history_node_list": history_node_list,
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"isDetailed": is_detailed,
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"upload_file": interpreter_file,
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"embed_model": EMBEDDING_MODEL,
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"model_device": EMBEDDING_DEVICE,
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"embed_model_path": embedding_model_dict[EMBEDDING_MODEL],
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"embed_engine": EMBEDDING_ENGINE,
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"kb_root_path": KB_ROOT_PATH,
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"model_name": LLM_MODEL,
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"api_key": llm_model_dict[LLM_MODEL]["api_key"],
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"api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],
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}
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text = ""
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d = {"docs": []}
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for idx_count, d in enumerate(api.agent_achat(**input_kargs)):
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if error_msg := check_error_msg(d): # check whether error occured
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st.error(error_msg)
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logger.debug(f"d: {d['answer']}")
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text = d["answer"]
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for text_length in range(0, len(text)+1, 10):
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chat_box.update_msg(text[:text_length+10], element_index=0, streaming=True)
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GLOBAL_MESSAGE.setdefault("final_contents", {}).setdefault(d.get("answer", ""), []).append(d.get("final_content", ""))
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for k, v in d["figures"].items():
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if k in text:
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img_html = "\n<img src='data:image/png;base64,{}' class='img-fluid'>\n".format(v)
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text = text.replace(k, img_html).replace(".png", "")
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GLOBAL_MESSAGE.setdefault("figures", {}).setdefault(k, v)
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chat_box.update_msg(text, element_index=0, streaming=False, state="complete") # 更新最终的字符串，去除光标
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if search_on:
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chat_box.update_msg(f"{webui_configs['chat']['chatbox_search_result']}:\n\n" + "\n\n".join(d["search_docs"]), element_index=search_on, streaming=False, state="complete")
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if doc_retrieval_on:
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chat_box.update_msg(f"{webui_configs['chat']['chatbox_doc_result']}:\n\n" + "\n\n".join(d["db_docs"]), element_index=search_on+doc_retrieval_on, streaming=False, state="complete")
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if code_retrieval_on:
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chat_box.update_msg(f"{webui_configs['chat']['chatbox_code_result']}:\n\n" + "\n\n".join(d["code_docs"]),
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element_index=search_on+doc_retrieval_on+code_retrieval_on, streaming=False, state="complete")
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history_node_list.extend([node[0] for node in d.get("related_nodes", [])])
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history_node_list = list(set(history_node_list))
|
||
st.session_state['history_node_list'] = history_node_list
|
||
elif dialogue_mode == webui_configs["dialogue"]["mode"][1]:
|
||
history = get_messages_history(history_len)
|
||
chat_box.ai_say([
|
||
f"{webui_configs['chat']['chatbox_doc_querying']} `{selected_kb}` ...",
|
||
Markdown("...", in_expander=True, title=webui_configs['chat']['chatbox_doc_result']),
|
||
])
|
||
text = ""
|
||
d = {"docs": []}
|
||
for idx_count, d in enumerate(
|
||
api.knowledge_base_chat(
|
||
prompt, selected_kb, kb_top_k, score_threshold, history,
|
||
embed_model=EMBEDDING_MODEL, embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
|
||
model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL,
|
||
api_key=llm_model_dict[LLM_MODEL]["api_key"],
|
||
api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
|
||
)
|
||
):
|
||
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("{webui_configs['chat']['chatbox_doc_result']}: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
|
||
|
||
elif dialogue_mode == webui_configs["dialogue"]["mode"][2]:
|
||
logger.info('prompt={}'.format(prompt))
|
||
logger.info('history={}'.format(history))
|
||
if 'history_node_list' in st.session_state:
|
||
api.codeChat.history_node_list = st.session_state['history_node_list']
|
||
|
||
chat_box.ai_say([
|
||
f"{webui_configs['chat']['chatbox_code_querying']} `{selected_cb}` ...",
|
||
Markdown("...", in_expander=True, title=webui_configs['chat']['chatbox_code_result']),
|
||
])
|
||
text = ""
|
||
d = {"codes": []}
|
||
|
||
for idx_count, d in enumerate(api.code_base_chat(query=prompt, code_base_name=selected_cb,
|
||
code_limit=cb_code_limit, history=history,
|
||
cb_search_type=cb_search_type,
|
||
no_remote_api=True, embed_model=EMBEDDING_MODEL,
|
||
embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
|
||
embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL,
|
||
api_key=llm_model_dict[LLM_MODEL]["api_key"],
|
||
api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
|
||
)):
|
||
if error_msg := check_error_msg(d):
|
||
st.error(error_msg)
|
||
text += d["answer"]
|
||
if idx_count % 10 == 0:
|
||
# text = replace_lt_gt(text)
|
||
chat_box.update_msg(text, element_index=0)
|
||
|
||
# postprocess
|
||
logger.debug(f"d={d}")
|
||
text = replace_lt_gt(text)
|
||
chat_box.update_msg(text, element_index=0, streaming=False) # 更新最终的字符串，去除光标
|
||
logger.debug('text={}'.format(text))
|
||
chat_box.update_msg("\n".join(d["codes"]), element_index=1, streaming=False, state="complete")
|
||
|
||
# session state update
|
||
# st.session_state['history_node_list'] = api.codeChat.history_node_list
|
||
|
||
elif dialogue_mode == webui_configs["dialogue"]["mode"][3]:
|
||
chat_box.ai_say([
|
||
webui_configs['chat']['chatbox_searching'],
|
||
Markdown("...", in_expander=True, title=webui_configs['chat']['chatbox_search_result']),
|
||
])
|
||
text = ""
|
||
d = {"docs": []}
|
||
for idx_count, d in enumerate(
|
||
api.search_engine_chat(
|
||
prompt, search_engine, se_top_k, history, embed_model=EMBEDDING_MODEL,
|
||
embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
|
||
model_device=EMBEDDING_DEVICE, embed_engine=EMBEDDING_ENGINE, llm_model=LLM_MODEL,
|
||
api_key=llm_model_dict[LLM_MODEL]["api_key"],
|
||
api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
|
||
):
|
||
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(f"{webui_configs['chat']['chatbox_search_result']}: \n\n".join(d["docs"]), element_index=1, streaming=False, state="complete")
|
||
|
||
# 将上传文件清空
|
||
st.session_state["interpreter_file_key"] += 1
|
||
st.experimental_rerun()
|
||
|
||
now = datetime.now()
|
||
with st.sidebar:
|
||
|
||
cols = st.columns(2)
|
||
export_btn = cols[0]
|
||
if cols[1].button(
|
||
webui_configs['export']['button_clear_conversation_name'],
|
||
use_container_width=True,
|
||
):
|
||
chat_box.reset_history()
|
||
GLOBAL_EXE_CODE_TEXT = ""
|
||
if 'history_node_list' in st.session_state:
|
||
st.session_state['history_node_list'] = []
|
||
st.experimental_rerun()
|
||
|
||
export_btn.download_button(
|
||
webui_configs['export']['download_button_export_name'],
|
||
"".join(chat_box.export2md()),
|
||
file_name=f"{now:%Y-%m-%d %H.%M}_conversations.md",
|
||
mime="text/markdown",
|
||
use_container_width=True,
|
||
)
|