mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 13:23:16 +08:00
65592a45c3
新功能：
- 支持在线 Embeddings：zhipu-api, qwen-api, minimax-api, qianfan-api
- API 增加 /other/embed_texts 接口
- init_database.py 增加 --embed-model 参数，可以指定使用的嵌入模型（本地或在线均可）
- 对于 FAISS 知识库，支持多向量库，默认位置：{KB_PATH}/vector_store/{embed_model}
- Lite 模式支持所有知识库相关功能。此模式下最主要的限制是：
- 不能使用本地 LLM 和 Embeddings 模型
- 知识库不支持 PDF 文件
- init_database.py 重建知识库时不再默认情况数据库表，增加 clear-tables 参数手动控制。
- API 和 WEBUI 中 score_threshold 参数范围改为 [0, 2]，以更好的适应在线嵌入模型
问题修复：
- API 中 list_config_models 会删除 ONLINE_LLM_MODEL 中的敏感信息，导致第二轮API请求错误
开发者：
- 统一向量库的识别：以（kb_name,embed_model）为判断向量库唯一性的依据，避免 FAISS 知识库缓存加载逻辑错误
- KBServiceFactory.get_service_by_name 中添加 default_embed_model 参数，用于在构建新知识库时设置 embed_model
- 优化 kb_service 中 Embeddings 操作：
- 统一加载接口： server.utils.load_embeddings，利用全局缓存避免各处 Embeddings 传参
- 统一文本嵌入接口：server.knowledge_base.kb_service.base.[embed_texts, embed_documents]
- 重写 normalize 函数，去除对 scikit-learn/scipy 的依赖
302 lines
13 KiB
Python
302 lines
13 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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import os
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from configs import (TEMPERATURE, HISTORY_LEN, PROMPT_TEMPLATES,
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DEFAULT_KNOWLEDGE_BASE, DEFAULT_SEARCH_ENGINE, SUPPORT_AGENT_MODEL)
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from typing import List, Dict
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chat_box = ChatBox(
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assistant_avatar=os.path.join(
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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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def dialogue_page(api: ApiRequest, is_lite: bool = False):
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if not chat_box.chat_inited:
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default_model = api.get_default_llm_model()[0]
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st.toast(
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f"欢迎使用 [`Langchain-Chatchat`](https://github.com/chatchat-space/Langchain-Chatchat) ! \n\n"
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f"当前运行的模型`{default_model}`, 您可以开始提问了."
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)
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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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dialogue_modes = ["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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"自定义Agent问答",
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]
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dialogue_mode = st.selectbox("请选择对话模式：",
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dialogue_modes,
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index=0,
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on_change=on_mode_change,
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key="dialogue_mode",
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)
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def on_llm_change():
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if llm_model:
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config = api.get_model_config(llm_model)
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if not config.get("online_api"): # 只有本地model_worker可以切换模型
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st.session_state["prev_llm_model"] = llm_model
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st.session_state["cur_llm_model"] = st.session_state.llm_model
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def llm_model_format_func(x):
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if x in running_models:
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return f"{x} (Running)"
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return x
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running_models = list(api.list_running_models())
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available_models = []
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config_models = api.list_config_models()
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worker_models = list(config_models.get("worker", {})) # 仅列出在FSCHAT_MODEL_WORKERS中配置的模型
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for m in worker_models:
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if m not in running_models and m != "default":
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available_models.append(m)
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for k, v in config_models.get("online", {}).items(): # 列出ONLINE_MODELS中直接访问的模型
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if not v.get("provider") and k not in running_models:
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available_models.append(k)
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llm_models = running_models + available_models
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index = llm_models.index(st.session_state.get("cur_llm_model", api.get_default_llm_model()[0]))
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llm_model = st.selectbox("选择LLM模型：",
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llm_models,
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index,
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format_func=llm_model_format_func,
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on_change=on_llm_change,
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key="llm_model",
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)
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if (st.session_state.get("prev_llm_model") != llm_model
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and not is_lite
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and not llm_model in config_models.get("online", {})
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and not llm_model in config_models.get("langchain", {})
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and llm_model not in running_models):
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with st.spinner(f"正在加载模型： {llm_model}，请勿进行操作或刷新页面"):
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prev_model = st.session_state.get("prev_llm_model")
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r = api.change_llm_model(prev_model, llm_model)
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if msg := check_error_msg(r):
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st.error(msg)
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elif msg := check_success_msg(r):
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st.success(msg)
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st.session_state["prev_llm_model"] = llm_model
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index_prompt = {
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"LLM 对话": "llm_chat",
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"自定义Agent问答": "agent_chat",
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"搜索引擎问答": "search_engine_chat",
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"知识库问答": "knowledge_base_chat",
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}
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prompt_templates_kb_list = list(PROMPT_TEMPLATES[index_prompt[dialogue_mode]].keys())
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prompt_template_name = prompt_templates_kb_list[0]
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if "prompt_template_select" not in st.session_state:
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st.session_state.prompt_template_select = prompt_templates_kb_list[0]
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def prompt_change():
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text = f"已切换为 {prompt_template_name} 模板。"
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st.toast(text)
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prompt_template_select = st.selectbox(
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"请选择Prompt模板：",
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prompt_templates_kb_list,
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index=0,
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on_change=prompt_change,
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key="prompt_template_select",
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)
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prompt_template_name = st.session_state.prompt_template_select
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temperature = st.slider("Temperature：", 0.0, 1.0, TEMPERATURE, 0.05)
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history_len = st.number_input("历史对话轮数：", 0, 20, 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()
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index = 0
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if DEFAULT_KNOWLEDGE_BASE in kb_list:
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index = kb_list.index(DEFAULT_KNOWLEDGE_BASE)
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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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index=index,
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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, VECTOR_SEARCH_TOP_K)
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## Bge 模型会超过1
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score_threshold = st.slider("知识匹配分数阈值：", 0.0, 2.0, float(SCORE_THRESHOLD), 0.01)
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elif dialogue_mode == "搜索引擎问答":
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search_engine_list = api.list_search_engines()
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if DEFAULT_SEARCH_ENGINE in search_engine_list:
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index = search_engine_list.index(DEFAULT_SEARCH_ENGINE)
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else:
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index = search_engine_list.index("duckduckgo") if "duckduckgo" in search_engine_list else 0
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with st.expander("搜索引擎配置", True):
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search_engine = st.selectbox(
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label="请选择搜索引擎",
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options=search_engine_list,
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index=index,
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)
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se_top_k = st.number_input("匹配搜索结果条数：", 1, 20, SEARCH_ENGINE_TOP_K)
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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 "
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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,
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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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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
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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 == "自定义Agent问答":
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if not any(agent in llm_model for agent in SUPPORT_AGENT_MODEL):
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chat_box.ai_say([
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f"正在思考... \n\n <span style='color:red'>该模型并没有进行Agent对齐，请更换支持Agent的模型获得更好的体验！</span>\n\n\n",
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Markdown("...", in_expander=True, title="思考过程", state="complete"),
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])
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else:
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chat_box.ai_say([
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f"正在思考...",
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Markdown("...", in_expander=True, title="思考过程", state="complete"),
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])
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text = ""
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ans = ""
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for d in api.agent_chat(prompt,
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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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):
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try:
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d = json.loads(d)
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except:
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pass
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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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if chunk := d.get("answer"):
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text += chunk
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chat_box.update_msg(text, element_index=1)
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if chunk := d.get("final_answer"):
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ans += chunk
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chat_box.update_msg(ans, element_index=0)
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if chunk := d.get("tools"):
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text += "\n\n".join(d.get("tools", []))
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chat_box.update_msg(text, element_index=1)
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chat_box.update_msg(ans, element_index=0, streaming=False)
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chat_box.update_msg(text, element_index=1, streaming=False)
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elif dialogue_mode == "知识库问答":
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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", state="complete"),
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])
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text = ""
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for d in api.knowledge_base_chat(prompt,
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knowledge_base_name=selected_kb,
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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): # check whether error occured
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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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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", state="complete"),
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])
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text = ""
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for d in api.search_engine_chat(prompt,
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search_engine_name=search_engine,
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top_k=se_top_k,
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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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split_result=se_top_k > 1):
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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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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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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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