update webui.knowledge_base layout
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imClumsyPanda
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@ -45,6 +45,8 @@
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参见 [版本更新日志](https://github.com/imClumsyPanda/langchain-ChatGLM/releases)。
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从`0.1.x`升级过来的用户请注意，在完成[“开发部署 3 设置配置项”](docs/INSTALL.md)之后，需要将现有知识库迁移到新格式，具体见[知识库初始化与迁移](docs/INSTALL.md#知识库初始化与迁移)。
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## 模型支持
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本项目中默认使用的 LLM 模型为 [THUDM/chatglm2-6b](https://huggingface.co/THUDM/chatglm2-6b)，默认使用的 Embedding 模型为 [moka-ai/m3e-base](https://huggingface.co/moka-ai/m3e-base) 为例。
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webui.py
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webui.py
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@ -19,7 +19,6 @@ if __name__ == "__main__":
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f"欢迎使用 [`Langchain-Chatglm`](https://github.com/chatchat-space/langchain-chatglm) ! \n\n"
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f"当前使用模型`{LLM_MODEL}`, 您可以开始提问了."
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)
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st.toast(" ")
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pages = {"对话": {"icon": "chat",
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"func": dialogue_page,
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@ -1,6 +1,6 @@
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import streamlit as st
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from webui_pages.utils import *
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from st_aggrid import AgGrid
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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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@ -10,6 +10,8 @@ from configs.model_config import embedding_model_dict, kbs_config, EMBEDDING_MOD
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# SENTENCE_SIZE = 100
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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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@ -33,10 +35,16 @@ def knowledge_base_page(api: ApiRequest):
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kb_list = get_kb_details()
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kb_names = [x["kb_name"] for x in kb_list]
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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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selected_kb = st.selectbox(
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"请选择知识库：",
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"请选择或新建知识库：",
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kb_list + ["新建知识库"],
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format_func=lambda s: f"{s['kb_name']} ({s['vs_type']} @ {s['embed_model']})" if type(s) != str else s,
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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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@ -85,8 +93,8 @@ def knowledge_base_page(api: ApiRequest):
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embed_model=embed_model,
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)
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st.toast(ret["msg"])
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# st.experimental_rerun()
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st.session_state["selected_kb_name"] = kb_name
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st.experimental_rerun()
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elif selected_kb:
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kb = selected_kb["kb_name"]
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@ -111,7 +119,7 @@ def knowledge_base_page(api: ApiRequest):
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if ret["code"] == 200:
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st.toast(ret["msg"], icon="✔")
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else:
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st.toast(ret["msg"], icon="❌")
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st.toast(ret["msg"], icon="✖")
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st.session_state.files = []
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st.divider()
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@ -123,22 +131,25 @@ def knowledge_base_page(api: ApiRequest):
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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", "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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("text_splitter", "分词器"): {},
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# ("create_time", "创建时间"): {},
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("in_folder", "源文件"): {},
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("in_db", "向量库"): {},
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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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@ -151,6 +162,7 @@ def knowledge_base_page(api: ApiRequest):
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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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)
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selected_rows = doc_grid.get("selected_rows", [])
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@ -172,9 +184,10 @@ def knowledge_base_page(api: ApiRequest):
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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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"添加至向量库", # "重新添加至向量库"
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"重新添加至向量库" if selected_rows and (pd.DataFrame(selected_rows)["in_db"]).any() else "添加至向量库",
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disabled=len(selected_rows) == 0,
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use_container_width=True,
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):
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@ -193,7 +206,7 @@ def knowledge_base_page(api: ApiRequest):
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st.experimental_rerun()
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if cols[3].button(
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"删除选中文档！",
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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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@ -213,17 +226,18 @@ def knowledge_base_page(api: ApiRequest):
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use_container_width=True,
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type="primary",
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):
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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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print(d)
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empty.progress(d["finished"] / d["total"], f"正在处理： {d['doc']}")
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empty.write("重建完毕")
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with st.spinner("向量库重构中"):
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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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print(d)
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empty.progress(d["finished"] / d["total"], f"正在处理： {d['doc']}")
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st.experimental_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["detail"][0]["msg"])
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st.experimental_rerun()
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st.toast(ret["msg"])
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