mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-02-03 05:03:12 +08:00
372 lines
17 KiB
Python
372 lines
17 KiB
Python
import base64
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import uuid
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import os
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import re
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import time
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from typing import List, Dict
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import streamlit as st
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from streamlit_antd_components.utils import ParseItems
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import openai
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from streamlit_chatbox import *
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from streamlit_modal import Modal
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from datetime import datetime
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from chatchat.configs import (LLM_MODEL_CONFIG, SUPPORT_AGENT_MODELS, MODEL_PLATFORMS)
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from chatchat.server.callback_handler.agent_callback_handler import AgentStatus
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from chatchat.server.utils import MsgType, get_config_models
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from chatchat.server.utils import get_tool_config
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from chatchat.webui_pages.utils import *
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from chatchat.webui_pages.dialogue.utils import process_files
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img_dir = (Path(__file__).absolute().parent.parent.parent)
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chat_box = ChatBox(
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assistant_avatar=os.path.join(
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img_dir,
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"img",
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"chatchat_icon_blue_square_v2.png"
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)
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)
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def get_messages_history(history_len: int, content_in_expander: bool = False) -> List[Dict]:
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'''
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返回消息历史。
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content_in_expander控制是否返回expander元素中的内容，一般导出的时候可以选上，传入LLM的history不需要
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'''
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def filter(msg):
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content = [x for x in msg["elements"] if x._output_method in ["markdown", "text"]]
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if not content_in_expander:
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content = [x for x in content if not x._in_expander]
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content = [x.content for x in content]
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return {
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"role": msg["role"],
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"content": "\n\n".join(content),
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}
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return chat_box.filter_history(history_len=history_len, filter=filter)
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@st.cache_data
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def upload_temp_docs(files, _api: ApiRequest) -> str:
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'''
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将文件上传到临时目录，用于文件对话
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返回临时向量库ID
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'''
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return _api.upload_temp_docs(files).get("data", {}).get("id")
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def parse_command(text: str, modal: Modal) -> bool:
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'''
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检查用户是否输入了自定义命令，当前支持：
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/new {session_name}。如果未提供名称，默认为“会话X”
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/del {session_name}。如果未提供名称，在会话数量>1的情况下，删除当前会话。
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/clear {session_name}。如果未提供名称，默认清除当前会话
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/stop {session_name}。如果未提供名称，默认停止当前会话
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/help。查看命令帮助
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返回值：输入的是命令返回True，否则返回False
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'''
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if m := re.match(r"/([^\s]+)\s*(.*)", text):
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cmd, name = m.groups()
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name = name.strip()
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conv_names = chat_box.get_chat_names()
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if cmd == "help":
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modal.open()
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elif cmd == "new":
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if not name:
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i = 1
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while True:
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name = f"会话{i}"
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if name not in conv_names:
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break
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i += 1
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if name in st.session_state["conversation_ids"]:
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st.error(f"该会话名称 “{name}” 已存在")
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time.sleep(1)
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else:
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st.session_state["conversation_ids"][name] = uuid.uuid4().hex
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st.session_state["cur_conv_name"] = name
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elif cmd == "del":
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name = name or st.session_state.get("cur_conv_name")
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if len(conv_names) == 1:
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st.error("这是最后一个会话，无法删除")
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time.sleep(1)
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elif not name or name not in st.session_state["conversation_ids"]:
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st.error(f"无效的会话名称：“{name}”")
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time.sleep(1)
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else:
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st.session_state["conversation_ids"].pop(name, None)
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chat_box.del_chat_name(name)
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st.session_state["cur_conv_name"] = ""
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elif cmd == "clear":
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chat_box.reset_history(name=name or None)
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return True
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return False
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def dialogue_page(api: ApiRequest, is_lite: bool = False):
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st.session_state.setdefault("conversation_ids", {})
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st.session_state["conversation_ids"].setdefault(chat_box.cur_chat_name, uuid.uuid4().hex)
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st.session_state.setdefault("file_chat_id", None)
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# 弹出自定义命令帮助信息
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modal = Modal("自定义命令", key="cmd_help", max_width="500")
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if modal.is_open():
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with modal.container():
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cmds = [x for x in parse_command.__doc__.split("\n") if x.strip().startswith("/")]
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st.write("\n\n".join(cmds))
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with st.sidebar:
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tab1, tab2 = st.tabs(["对话设置", "模型设置"])
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with tab1:
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use_agent = st.checkbox("启用Agent", True, help="请确保选择的模型具备Agent能力")
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# 选择工具
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tools = api.list_tools()
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if use_agent:
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selected_tools = st.multiselect("选择工具", list(tools), format_func=lambda x: tools[x]["title"])
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else:
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selected_tool = st.selectbox("选择工具", list(tools), format_func=lambda x: tools[x]["title"])
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selected_tools = [selected_tool]
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selected_tool_configs = {name: tool["config"] for name, tool in tools.items() if name in selected_tools}
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# 当不启用Agent时，手动生成工具参数
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# TODO: 需要更精细的控制控件
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tool_input = {}
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if not use_agent and len(selected_tools) == 1:
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with st.expander("工具参数", True):
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for k, v in tools[selected_tools[0]]["args"].items():
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if choices := v.get("choices", v.get("enum")):
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tool_input[k] = st.selectbox(v["title"], choices)
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else:
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if v["type"] == "integer":
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tool_input[k] = st.slider(v["title"], value=v.get("default"))
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elif v["type"] == "number":
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tool_input[k] = st.slider(v["title"], value=v.get("default"), step=0.1)
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else:
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tool_input[k] = st.text_input(v["title"], v.get("default"))
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uploaded_file = st.file_uploader("上传附件", accept_multiple_files=False)
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files_upload = process_files(files=[uploaded_file]) if uploaded_file else None
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with tab2:
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# 会话
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conv_names = list(st.session_state["conversation_ids"].keys())
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index = 0
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if st.session_state.get("cur_conv_name") in conv_names:
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index = conv_names.index(st.session_state.get("cur_conv_name"))
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conversation_name = st.selectbox("当前会话", conv_names, index=index)
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chat_box.use_chat_name(conversation_name)
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conversation_id = st.session_state["conversation_ids"][conversation_name]
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# 模型
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platforms = ["所有"] + [x["platform_name"] for x in MODEL_PLATFORMS]
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platform = st.selectbox("选择模型平台", platforms)
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llm_models = list(get_config_models(model_type="llm", platform_name=None if platform=="所有" else platform))
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llm_model = st.selectbox("选择LLM模型", llm_models)
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# 传入后端的内容
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chat_model_config = {key: {} for key in LLM_MODEL_CONFIG.keys()}
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for key in LLM_MODEL_CONFIG:
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if LLM_MODEL_CONFIG[key]:
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first_key = next(iter(LLM_MODEL_CONFIG[key]))
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chat_model_config[key][first_key] = LLM_MODEL_CONFIG[key][first_key]
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if llm_model is not None:
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chat_model_config['llm_model'][llm_model] = LLM_MODEL_CONFIG['llm_model'].get(llm_model, {})
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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Shift+Enter。输入/help查看自定义命令 "
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# def on_feedback(
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# feedback,
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# message_id: str = "",
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# history_index: int = -1,
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# ):
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# reason = feedback["text"]
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# score_int = chat_box.set_feedback(feedback=feedback, history_index=history_index)
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# api.chat_feedback(message_id=message_id,
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# score=score_int,
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# reason=reason)
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# st.session_state["need_rerun"] = True
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# feedback_kwargs = {
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# "feedback_type": "thumbs",
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# "optional_text_label": "欢迎反馈您打分的理由",
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# }
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if prompt := st.chat_input(chat_input_placeholder, key="prompt"):
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if parse_command(text=prompt, modal=modal):
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st.rerun()
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else:
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history = get_messages_history(
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chat_model_config["llm_model"].get(next(iter(chat_model_config["llm_model"])), {}).get("history_len", 1)
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)
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chat_box.user_say(prompt)
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if files_upload:
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if files_upload["images"]:
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st.markdown(f'<img src="data:image/jpeg;base64,{files_upload["images"][0]}" width="300">',
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unsafe_allow_html=True)
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elif files_upload["videos"]:
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st.markdown(
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f'<video width="400" height="300" controls><source src="data:video/mp4;base64,{files_upload["videos"][0]}" type="video/mp4"></video>',
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unsafe_allow_html=True)
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elif files_upload["audios"]:
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st.markdown(
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f'<audio controls><source src="data:audio/wav;base64,{files_upload["audios"][0]}" type="audio/wav"></audio>',
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unsafe_allow_html=True)
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chat_box.ai_say("正在思考...")
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text = ""
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started = False
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client = openai.Client(base_url=f"{api_address()}/chat", api_key="NONE")
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messages = history + [{"role": "user", "content": prompt}]
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tools = list(selected_tool_configs)
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if len(selected_tools) == 1:
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tool_choice = selected_tools[0]
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else:
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tool_choice = None
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# 如果 tool_input 中有空的字段，设为用户输入
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for k in tool_input:
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if tool_input[k] in [None, ""]:
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tool_input[k] = prompt
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extra_body = dict(
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metadata=files_upload,
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chat_model_config=chat_model_config,
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conversation_id=conversation_id,
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tool_input = tool_input,
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)
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for d in client.chat.completions.create(
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messages=messages,
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model=llm_model,
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stream=True,
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tools=tools,
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tool_choice=tool_choice,
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extra_body=extra_body,
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):
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# print("\n\n", d.status, "\n", d, "\n\n")
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message_id = d.message_id
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metadata = {
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"message_id": message_id,
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}
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# clear initial message
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if not started:
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chat_box.update_msg("", streaming=False)
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started = True
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if d.status == AgentStatus.error:
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st.error(d.choices[0].delta.content)
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elif d.status == AgentStatus.llm_start:
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chat_box.insert_msg("正在解读工具输出结果...")
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text = d.choices[0].delta.content or ""
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elif d.status == AgentStatus.llm_new_token:
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text += d.choices[0].delta.content or ""
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chat_box.update_msg(text.replace("\n", "\n\n"), streaming=True, metadata=metadata)
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elif d.status == AgentStatus.llm_end:
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text += d.choices[0].delta.content or ""
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chat_box.update_msg(text.replace("\n", "\n\n"), streaming=False, metadata=metadata)
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# tool 的输出与 llm 输出重复了
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# elif d.status == AgentStatus.tool_start:
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# formatted_data = {
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# "Function": d.choices[0].delta.tool_calls[0].function.name,
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# "function_input": d.choices[0].delta.tool_calls[0].function.arguments,
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# }
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# formatted_json = json.dumps(formatted_data, indent=2, ensure_ascii=False)
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# text = """\n```{}\n```\n""".format(formatted_json)
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# chat_box.insert_msg( # TODO: insert text directly not shown
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# Markdown(text, title="Function call", in_expander=True, expanded=True, state="running"))
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# elif d.status == AgentStatus.tool_end:
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# tool_output = d.choices[0].delta.tool_calls[0].tool_output
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# if d.message_type == MsgType.IMAGE:
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# for url in json.loads(tool_output).get("images", []):
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# url = f"{api.base_url}/media/{url}"
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# chat_box.insert_msg(Image(url))
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# chat_box.update_msg(expanded=False, state="complete")
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# else:
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# text += """\n```\nObservation:\n{}\n```\n""".format(tool_output)
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# chat_box.update_msg(text, streaming=False, expanded=False, state="complete")
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elif d.status == AgentStatus.agent_finish:
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text = d.choices[0].delta.content or ""
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chat_box.update_msg(text.replace("\n", "\n\n"))
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elif d.status == None: # not agent chat
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if getattr(d, "is_ref", False):
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chat_box.insert_msg(Markdown(d.choices[0].delta.content or "", in_expander=True, state="complete", title="参考资料"))
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chat_box.insert_msg("")
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else:
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text += d.choices[0].delta.content or ""
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chat_box.update_msg(text.replace("\n", "\n\n"), streaming=True, metadata=metadata)
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chat_box.update_msg(text, streaming=False, metadata=metadata)
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if os.path.exists("tmp/image.jpg"):
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with open("tmp/image.jpg", "rb") as image_file:
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encoded_string = base64.b64encode(image_file.read()).decode()
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img_tag = f'<img src="data:image/jpeg;base64,{encoded_string}" width="300">'
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st.markdown(img_tag, unsafe_allow_html=True)
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os.remove("tmp/image.jpg")
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# chat_box.show_feedback(**feedback_kwargs,
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# key=message_id,
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# on_submit=on_feedback,
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# kwargs={"message_id": message_id, "history_index": len(chat_box.history) - 1})
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# elif dialogue_mode == "文件对话":
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# if st.session_state["file_chat_id"] is None:
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# st.error("请先上传文件再进行对话")
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# st.stop()
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# chat_box.ai_say([
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# f"正在查询文件 `{st.session_state['file_chat_id']}` ...",
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# Markdown("...", in_expander=True, title="文件匹配结果", state="complete"),
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# ])
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# text = ""
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# for d in api.file_chat(prompt,
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# knowledge_id=st.session_state["file_chat_id"],
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# top_k=kb_top_k,
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# score_threshold=score_threshold,
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# history=history,
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# model=llm_model,
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# prompt_name=prompt_template_name,
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# temperature=temperature):
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# if error_msg := check_error_msg(d):
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# st.error(error_msg)
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# elif chunk := d.get("answer"):
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# text += chunk
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# chat_box.update_msg(text, element_index=0)
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# chat_box.update_msg(text, element_index=0, streaming=False)
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# chat_box.update_msg("\n\n".join(d.get("docs", [])), element_index=1, streaming=False)
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if st.session_state.get("need_rerun"):
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st.session_state["need_rerun"] = False
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st.rerun()
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now = datetime.now()
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with tab1:
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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.rerun()
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warning_placeholder = st.empty()
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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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# st.write(chat_box.history)
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