mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 13:23:16 +08:00
9c525b7fa5
新功能： - 优化 PDF 文件的 OCR，过滤无意义的小图片 by @liunux4odoo #2525 - 支持 Gemini 在线模型 by @yhfgyyf #2630 - 支持 GLM4 在线模型 by @zRzRzRzRzRzRzR - elasticsearch更新https连接 by @xldistance #2390 - 增强对PPT、DOC知识库文件的OCR识别 by @596192804 #2013 - 更新 Agent 对话功能 by @zRzRzRzRzRzRzR - 每次创建对象时从连接池获取连接，避免每次执行方法时都新建连接 by @Lijia0 #2480 - 实现 ChatOpenAI 判断token有没有超过模型的context上下文长度 by @glide-the - 更新运行数据库报错和项目里程碑 by @zRzRzRzRzRzRzR #2659 - 更新配置文件/文档/依赖 by @imClumsyPanda @zRzRzRzRzRzRzR - 添加日文版 readme by @eltociear #2787 修复： - langchain 更新后，PGVector 向量库连接错误 by @HALIndex #2591 - Minimax's model worker 错误 by @xyhshen - ES库无法向量检索.添加mappings创建向量索引 by MSZheng20 #2688
368 lines
15 KiB
Python
368 lines
15 KiB
Python
import streamlit as st
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from webui_pages.utils import *
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from st_aggrid import AgGrid, JsCode
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from st_aggrid.grid_options_builder import GridOptionsBuilder
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import pandas as pd
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from server.knowledge_base.utils import get_file_path, LOADER_DICT
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from server.knowledge_base.kb_service.base import get_kb_details, get_kb_file_details
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from typing import Literal, Dict, Tuple
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from configs import (kbs_config,
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EMBEDDING_MODEL, DEFAULT_VS_TYPE,
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CHUNK_SIZE, OVERLAP_SIZE, ZH_TITLE_ENHANCE)
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from server.utils import list_embed_models, list_online_embed_models
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import os
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import time
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cell_renderer = JsCode("""function(params) {if(params.value==true){return '✓'}else{return '×'}}""")
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def config_aggrid(
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df: pd.DataFrame,
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columns: Dict[Tuple[str, str], Dict] = {},
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selection_mode: Literal["single", "multiple", "disabled"] = "single",
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use_checkbox: bool = False,
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) -> GridOptionsBuilder:
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gb = GridOptionsBuilder.from_dataframe(df)
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gb.configure_column("No", width=40)
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for (col, header), kw in columns.items():
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gb.configure_column(col, header, wrapHeaderText=True, **kw)
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gb.configure_selection(
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selection_mode=selection_mode,
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use_checkbox=use_checkbox,
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pre_selected_rows=st.session_state.get("selected_rows", [0]),
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)
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gb.configure_pagination(
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enabled=True,
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paginationAutoPageSize=False,
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paginationPageSize=10
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)
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return gb
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def file_exists(kb: str, selected_rows: List) -> Tuple[str, str]:
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"""
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check whether a doc file exists in local knowledge base folder.
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return the file's name and path if it exists.
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"""
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if selected_rows:
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file_name = selected_rows[0]["file_name"]
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file_path = get_file_path(kb, file_name)
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if os.path.isfile(file_path):
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return file_name, file_path
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return "", ""
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def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
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try:
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kb_list = {x["kb_name"]: x for x in get_kb_details()}
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except Exception as e:
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st.error(
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"获取知识库信息错误，请检查是否已按照 `README.md` 中 `4 知识库初始化与迁移` 步骤完成初始化或迁移，或是否为数据库连接错误。")
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st.stop()
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kb_names = list(kb_list.keys())
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if "selected_kb_name" in st.session_state and st.session_state["selected_kb_name"] in kb_names:
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selected_kb_index = kb_names.index(st.session_state["selected_kb_name"])
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else:
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selected_kb_index = 0
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if "selected_kb_info" not in st.session_state:
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st.session_state["selected_kb_info"] = ""
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def format_selected_kb(kb_name: str) -> str:
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if kb := kb_list.get(kb_name):
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return f"{kb_name} ({kb['vs_type']} @ {kb['embed_model']})"
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else:
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return kb_name
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selected_kb = st.selectbox(
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"请选择或新建知识库：",
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kb_names + ["新建知识库"],
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format_func=format_selected_kb,
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index=selected_kb_index
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)
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if selected_kb == "新建知识库":
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with st.form("新建知识库"):
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kb_name = st.text_input(
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"新建知识库名称",
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placeholder="新知识库名称，不支持中文命名",
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key="kb_name",
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)
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kb_info = st.text_input(
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"知识库简介",
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placeholder="知识库简介，方便Agent查找",
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key="kb_info",
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)
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cols = st.columns(2)
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vs_types = list(kbs_config.keys())
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vs_type = cols[0].selectbox(
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"向量库类型",
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vs_types,
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index=vs_types.index(DEFAULT_VS_TYPE),
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key="vs_type",
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)
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if is_lite:
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embed_models = list_online_embed_models()
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else:
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embed_models = list_embed_models() + list_online_embed_models()
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embed_model = cols[1].selectbox(
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"Embedding 模型",
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embed_models,
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index=embed_models.index(EMBEDDING_MODEL),
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key="embed_model",
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)
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submit_create_kb = st.form_submit_button(
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"新建",
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# disabled=not bool(kb_name),
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use_container_width=True,
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)
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if submit_create_kb:
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if not kb_name or not kb_name.strip():
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st.error(f"知识库名称不能为空！")
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elif kb_name in kb_list:
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st.error(f"名为 {kb_name} 的知识库已经存在！")
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else:
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ret = api.create_knowledge_base(
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knowledge_base_name=kb_name,
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vector_store_type=vs_type,
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embed_model=embed_model,
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)
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st.toast(ret.get("msg", " "))
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st.session_state["selected_kb_name"] = kb_name
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st.session_state["selected_kb_info"] = kb_info
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st.rerun()
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elif selected_kb:
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kb = selected_kb
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st.session_state["selected_kb_info"] = kb_list[kb]['kb_info']
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# 上传文件
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files = st.file_uploader("上传知识文件：",
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[i for ls in LOADER_DICT.values() for i in ls],
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accept_multiple_files=True,
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)
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kb_info = st.text_area("请输入知识库介绍:", value=st.session_state["selected_kb_info"], max_chars=None,
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key=None,
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help=None, on_change=None, args=None, kwargs=None)
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if kb_info != st.session_state["selected_kb_info"]:
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st.session_state["selected_kb_info"] = kb_info
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api.update_kb_info(kb, kb_info)
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# with st.sidebar:
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with st.expander(
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"文件处理配置",
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expanded=True,
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):
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cols = st.columns(3)
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chunk_size = cols[0].number_input("单段文本最大长度：", 1, 1000, CHUNK_SIZE)
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chunk_overlap = cols[1].number_input("相邻文本重合长度：", 0, chunk_size, OVERLAP_SIZE)
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cols[2].write("")
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cols[2].write("")
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zh_title_enhance = cols[2].checkbox("开启中文标题加强", ZH_TITLE_ENHANCE)
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if st.button(
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"添加文件到知识库",
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# use_container_width=True,
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disabled=len(files) == 0,
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):
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ret = api.upload_kb_docs(files,
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knowledge_base_name=kb,
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override=True,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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zh_title_enhance=zh_title_enhance)
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if msg := check_success_msg(ret):
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st.toast(msg, icon="✔")
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elif msg := check_error_msg(ret):
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st.toast(msg, icon="✖")
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st.divider()
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# 知识库详情
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# st.info("请选择文件，点击按钮进行操作。")
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doc_details = pd.DataFrame(get_kb_file_details(kb))
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selected_rows = []
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if not len(doc_details):
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st.info(f"知识库 `{kb}` 中暂无文件")
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else:
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st.write(f"知识库 `{kb}` 中已有文件:")
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st.info("知识库中包含源文件与向量库，请从下表中选择文件后操作")
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doc_details.drop(columns=["kb_name"], inplace=True)
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doc_details = doc_details[[
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"No", "file_name", "document_loader", "text_splitter", "docs_count", "in_folder", "in_db",
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]]
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doc_details["in_folder"] = doc_details["in_folder"].replace(True, "✓").replace(False, "×")
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doc_details["in_db"] = doc_details["in_db"].replace(True, "✓").replace(False, "×")
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gb = config_aggrid(
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doc_details,
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{
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("No", "序号"): {},
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("file_name", "文档名称"): {},
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# ("file_ext", "文档类型"): {},
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# ("file_version", "文档版本"): {},
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("document_loader", "文档加载器"): {},
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("docs_count", "文档数量"): {},
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("text_splitter", "分词器"): {},
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# ("create_time", "创建时间"): {},
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("in_folder", "源文件"): {"cellRenderer": cell_renderer},
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("in_db", "向量库"): {"cellRenderer": cell_renderer},
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},
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"multiple",
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)
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doc_grid = AgGrid(
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doc_details,
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gb.build(),
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columns_auto_size_mode="FIT_CONTENTS",
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theme="alpine",
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custom_css={
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"#gridToolBar": {"display": "none"},
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},
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allow_unsafe_jscode=True,
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enable_enterprise_modules=False
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)
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selected_rows = doc_grid.get("selected_rows", [])
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cols = st.columns(4)
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file_name, file_path = file_exists(kb, selected_rows)
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if file_path:
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with open(file_path, "rb") as fp:
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cols[0].download_button(
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"下载选中文档",
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fp,
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file_name=file_name,
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use_container_width=True, )
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else:
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cols[0].download_button(
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"下载选中文档",
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"",
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disabled=True,
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use_container_width=True, )
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st.write()
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# 将文件分词并加载到向量库中
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if cols[1].button(
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"重新添加至向量库" if selected_rows and (
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pd.DataFrame(selected_rows)["in_db"]).any() else "添加至向量库",
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disabled=not file_exists(kb, selected_rows)[0],
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use_container_width=True,
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):
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file_names = [row["file_name"] for row in selected_rows]
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api.update_kb_docs(kb,
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file_names=file_names,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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zh_title_enhance=zh_title_enhance)
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st.rerun()
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# 将文件从向量库中删除，但不删除文件本身。
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if cols[2].button(
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"从向量库删除",
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disabled=not (selected_rows and selected_rows[0]["in_db"]),
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use_container_width=True,
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):
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file_names = [row["file_name"] for row in selected_rows]
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api.delete_kb_docs(kb, file_names=file_names)
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st.rerun()
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if cols[3].button(
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"从知识库中删除",
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type="primary",
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use_container_width=True,
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):
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file_names = [row["file_name"] for row in selected_rows]
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api.delete_kb_docs(kb, file_names=file_names, delete_content=True)
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st.rerun()
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st.divider()
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cols = st.columns(3)
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if cols[0].button(
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"依据源文件重建向量库",
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help="无需上传文件，通过其它方式将文档拷贝到对应知识库content目录下，点击本按钮即可重建知识库。",
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use_container_width=True,
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type="primary",
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):
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with st.spinner("向量库重构中，请耐心等待，勿刷新或关闭页面。"):
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empty = st.empty()
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empty.progress(0.0, "")
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for d in api.recreate_vector_store(kb,
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chunk_size=chunk_size,
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chunk_overlap=chunk_overlap,
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zh_title_enhance=zh_title_enhance):
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if msg := check_error_msg(d):
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st.toast(msg)
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else:
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empty.progress(d["finished"] / d["total"], d["msg"])
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st.rerun()
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if cols[2].button(
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"删除知识库",
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use_container_width=True,
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):
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ret = api.delete_knowledge_base(kb)
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st.toast(ret.get("msg", " "))
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time.sleep(1)
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st.rerun()
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with st.sidebar:
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keyword = st.text_input("查询关键字")
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top_k = st.slider("匹配条数", 1, 100, 3)
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st.write("文件内文档列表。双击进行修改，在删除列填入 Y 可删除对应行。")
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docs = []
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df = pd.DataFrame([], columns=["seq", "id", "content", "source"])
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if selected_rows:
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file_name = selected_rows[0]["file_name"]
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docs = api.search_kb_docs(knowledge_base_name=selected_kb, file_name=file_name)
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data = [
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{"seq": i + 1, "id": x["id"], "page_content": x["page_content"], "source": x["metadata"].get("source"),
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"type": x["type"],
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"metadata": json.dumps(x["metadata"], ensure_ascii=False),
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"to_del": "",
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} for i, x in enumerate(docs)]
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df = pd.DataFrame(data)
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gb = GridOptionsBuilder.from_dataframe(df)
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gb.configure_columns(["id", "source", "type", "metadata"], hide=True)
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gb.configure_column("seq", "No.", width=50)
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gb.configure_column("page_content", "内容", editable=True, autoHeight=True, wrapText=True, flex=1,
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cellEditor="agLargeTextCellEditor", cellEditorPopup=True)
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gb.configure_column("to_del", "删除", editable=True, width=50, wrapHeaderText=True,
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cellEditor="agCheckboxCellEditor", cellRender="agCheckboxCellRenderer")
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gb.configure_selection()
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edit_docs = AgGrid(df, gb.build())
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if st.button("保存更改"):
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origin_docs = {
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x["id"]: {"page_content": x["page_content"], "type": x["type"], "metadata": x["metadata"]} for x in
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docs}
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changed_docs = []
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for index, row in edit_docs.data.iterrows():
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origin_doc = origin_docs[row["id"]]
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if row["page_content"] != origin_doc["page_content"]:
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if row["to_del"] not in ["Y", "y", 1]:
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changed_docs.append({
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"page_content": row["page_content"],
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"type": row["type"],
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"metadata": json.loads(row["metadata"]),
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})
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if changed_docs:
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if api.update_kb_docs(knowledge_base_name=selected_kb,
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file_names=[file_name],
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docs={file_name: changed_docs}):
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st.toast("更新文档成功")
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else:
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st.toast("更新文档失败")
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