codefuse-chatbot/examples/webui/document.py

import streamlit as st
import os
import time
import traceback
from typing import Literal, Dict, Tuple
from st_aggrid import AgGrid, JsCode
from st_aggrid.grid_options_builder import GridOptionsBuilder
import pandas as pd

from .utils import *
from muagent.utils.path_utils import *
from muagent.service.service_factory import get_kb_details, get_kb_doc_details
from muagent.orm import table_init

from configs.model_config import (
    KB_ROOT_PATH, kbs_config, DEFAULT_VS_TYPE, WEB_CRAWL_PATH,
    EMBEDDING_DEVICE, EMBEDDING_ENGINE, EMBEDDING_MODEL, embedding_model_dict,
    llm_model_dict
)

# SENTENCE_SIZE = 100

cell_renderer = JsCode("""function(params) {if(params.value==true){return '✓'}else{return '×'}}""")


def config_aggrid(
        df: pd.DataFrame,
        columns: Dict[Tuple[str, str], Dict] = {},
        selection_mode: Literal["single", "multiple", "disabled"] = "single",
        use_checkbox: bool = False,
) -> GridOptionsBuilder:
    gb = GridOptionsBuilder.from_dataframe(df)
    gb.configure_column("No", width=40)
    for (col, header), kw in columns.items():
        gb.configure_column(col, header, wrapHeaderText=True, **kw)
    gb.configure_selection(
        selection_mode=selection_mode,
        use_checkbox=use_checkbox,
        # pre_selected_rows=st.session_state.get("selected_rows", [0]),
    )
    return gb


def file_exists(kb: str, selected_rows: List) -> Tuple[str, str]:
    '''
    check whether a doc file exists in local knowledge base folder.
    return the file's name and path if it exists.
    '''
    if selected_rows:
        file_name = selected_rows[0]["file_name"]
        file_path = get_file_path(kb, file_name, KB_ROOT_PATH)
        if os.path.isfile(file_path):
            return file_name, file_path
    return "", ""


def knowledge_page(
        api: ApiRequest, 
        embedding_model_dict: dict = embedding_model_dict, 
        kbs_config: dict = kbs_config, 
        embedding_model: str = EMBEDDING_MODEL, 
        default_vs_type: str = DEFAULT_VS_TYPE, 
        web_crawl_path: str = WEB_CRAWL_PATH
        ):
    # 判断表是否存在并进行初始化
    table_init()

    try:
        kb_list = {x["kb_name"]: x for x in get_kb_details(KB_ROOT_PATH)}
    except Exception as e:
        st.error("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。")
        st.stop()
    kb_names = list(kb_list.keys())

    if "selected_kb_name" in st.session_state and st.session_state["selected_kb_name"] in kb_names:
        selected_kb_index = kb_names.index(st.session_state["selected_kb_name"])
    else:
        selected_kb_index = 0

    def format_selected_kb(kb_name: str) -> str:
        if kb := kb_list.get(kb_name):
            return f"{kb_name} ({kb['vs_type']} @ {kb['embed_model']})"
        else:
            return kb_name

    selected_kb = st.selectbox(
        "请选择或新建知识库：",
        kb_names + ["新建知识库"],
        format_func=format_selected_kb,
        index=selected_kb_index
    )

    if selected_kb == "新建知识库":
        with st.form("新建知识库"):

            kb_name = st.text_input(
                "新建知识库名称",
                placeholder="新知识库名称，不支持中文命名",
                key="kb_name",
            )

            cols = st.columns(2)

            vs_types = list(kbs_config.keys())
            vs_type = cols[0].selectbox(
                "向量库类型",
                vs_types,
                index=vs_types.index(default_vs_type),
                key="vs_type",
            )

            embed_models = list(embedding_model_dict.keys())

            embed_model = cols[1].selectbox(
                "Embedding 模型",
                embed_models,
                index=embed_models.index(embedding_model),
                key="embed_model",
            )

            submit_create_kb = st.form_submit_button(
                "新建",
                # disabled=not bool(kb_name),
                use_container_width=True,
            )

        if submit_create_kb:
            if not kb_name or not kb_name.strip():
                st.error(f"知识库名称不能为空！")
            elif kb_name in kb_list:
                st.error(f"名为 {kb_name} 的知识库已经存在！")
            else:
                ret = api.create_knowledge_base(
                    knowledge_base_name=kb_name,
                    vector_store_type=vs_type,
                    embed_model=embed_model,
                    embed_engine=EMBEDDING_ENGINE,
                    embedding_device= EMBEDDING_DEVICE,
                    embed_model_path=embedding_model_dict[embed_model],
                    api_key=llm_model_dict[LLM_MODEL]["api_key"],
                    api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                )
                st.toast(ret.get("msg", " "))
                st.session_state["selected_kb_name"] = kb_name
                st.experimental_rerun()

    elif selected_kb:
        kb = selected_kb

        # 上传文件
        # sentence_size = st.slider("文本入库分句长度限制", 1, 1000, SENTENCE_SIZE, disabled=True)
        files = st.file_uploader("上传知识文件",
                                 [i for ls in LOADER2EXT_DICT.values() for i in ls],
                                 accept_multiple_files=True,
                                 )

        if st.button(
                "添加文件到知识库",
                # help="请先上传文件，再点击添加",
                # use_container_width=True,
                disabled=len(files) == 0,
        ):
            data = [{"file": f, "knowledge_base_name": kb, "not_refresh_vs_cache": True, "embed_model": EMBEDDING_MODEL,
            "embed_model_path": embedding_model_dict[EMBEDDING_MODEL],
            "model_device": EMBEDDING_DEVICE,
            "embed_engine": EMBEDDING_ENGINE,
            "api_key": llm_model_dict[LLM_MODEL]["api_key"],
            "api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],
            }
                    for f in files]
            data[-1]["not_refresh_vs_cache"]=False
            for k in data:
                pass
                ret = api.upload_kb_doc(**k)
                if msg := check_success_msg(ret):
                    st.toast(msg, icon="✔")
                elif msg := check_error_msg(ret):
                    st.toast(msg, icon="✖")
            st.session_state.files = []

        base_url = st.text_input(
            "待获取内容的URL地址",
            placeholder="请填写正确可打开的URL地址",
            key="base_url",
        )


        if st.button(
            "添加URL内容到知识库",
            disabled= base_url is None or base_url=="",
            ):
            filename = base_url.replace("https://", " ").\
                replace("http://", " ").replace("/", " ").\
                replace("?", " ").replace("=", " ").replace(".", " ").strip()
            html_name = "_".join(filename.split(" ",) + ["html.jsonl"])
            text_name = "_".join(filename.split(" ",) + ["text.jsonl"])
            html_path = os.path.join(web_crawl_path, html_name,)
            text_path = os.path.join(web_crawl_path, text_name,)
            # if not os.path.exists(text_dir) or :
            st.toast(base_url)
            st.toast(html_path)
            st.toast(text_path)
            res = api.web_crawl(
                base_url=base_url,
                html_dir=html_path,
                text_dir=text_path,
                do_dfs = False,
                reptile_lib="requests",
                method="get",
                time_sleep=2,
                )
            
            if res["status"] == 200:
                st.toast(res["response"], icon="✔")
                data = [{"file": text_path, "filename": text_name, "knowledge_base_name": kb, "not_refresh_vs_cache": False,
                         "embed_model": EMBEDDING_MODEL,
                        "embed_model_path": embedding_model_dict[EMBEDDING_MODEL],
                        "model_device": EMBEDDING_DEVICE,
                        "embed_engine": EMBEDDING_ENGINE,
                        "api_key": llm_model_dict[LLM_MODEL]["api_key"],
                        "api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],}]
                for k in data:
                    ret = api.upload_kb_doc(**k)
                    logger.info(ret)
                    if msg := check_success_msg(ret):
                        st.toast(msg, icon="✔")
                    elif msg := check_error_msg(ret):
                        st.toast(msg, icon="✖")
                st.session_state.files = []
            else:
                st.toast(res["response"], icon="✖")

            if os.path.exists(html_path):
                os.remove(html_path)

        st.divider()

        # 知识库详情
        # st.info("请选择文件，点击按钮进行操作。")
        doc_details = pd.DataFrame(get_kb_doc_details(kb, KB_ROOT_PATH))
        if not len(doc_details):
            st.info(f"知识库 `{kb}` 中暂无文件")
        else:
            st.write(f"知识库 `{kb}` 中已有文件:")
            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")
            doc_details.drop(columns=["kb_name"], inplace=True)
            doc_details = doc_details[[
                "No", "file_name", "document_loader", "text_splitter", "in_folder", "in_db",
            ]]
            # doc_details["in_folder"] = doc_details["in_folder"].replace(True, "✓").replace(False, "×")
            # doc_details["in_db"] = doc_details["in_db"].replace(True, "✓").replace(False, "×")
            gb = config_aggrid(
                doc_details,
                {
                    ("No", "序号"): {},
                    ("file_name", "文档名称"): {},
                    # ("file_ext", "文档类型"): {},
                    # ("file_version", "文档版本"): {},
                    ("document_loader", "文档加载器"): {},
                    ("text_splitter", "分词器"): {},
                    # ("create_time", "创建时间"): {},
                    ("in_folder", "源文件"): {"cellRenderer": cell_renderer},
                    ("in_db", "向量库"): {"cellRenderer": cell_renderer},
                },
                "multiple",
            )

            doc_grid = AgGrid(
                doc_details,
                gb.build(),
                columns_auto_size_mode="FIT_CONTENTS",
                theme="alpine",
                custom_css={
                    "#gridToolBar": {"display": "none"},
                },
                allow_unsafe_jscode=True
            )

            selected_rows = doc_grid.get("selected_rows", [])

            cols = st.columns(4)
            file_name, file_path = file_exists(kb, selected_rows)
            if file_path:
                with open(file_path, "rb") as fp:
                    cols[0].download_button(
                        "下载选中文档",
                        fp,
                        file_name=file_name,
                        use_container_width=True, )
            else:
                cols[0].download_button(
                    "下载选中文档",
                    "",
                    disabled=True,
                    use_container_width=True, )

            st.write()
            # 将文件分词并加载到向量库中
            if cols[1].button(
                    "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",
                    disabled=not file_exists(kb, selected_rows)[0],
                    use_container_width=True,
            ):
                for row in selected_rows:
                    api.update_kb_doc(kb, row["file_name"], 
                                      embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
                                      embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
                                      model_device=EMBEDDING_DEVICE,
                                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
                                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                                      )
                st.experimental_rerun()

            # 将文件从向量库中删除，但不删除文件本身。
            if cols[2].button(
                    "从向量库删除",
                    disabled=not (selected_rows and selected_rows[0]["in_db"]),
                    use_container_width=True,
            ):
                for row in selected_rows:
                    api.delete_kb_doc(kb, row["file_name"],
                                      embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
                                      embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
                                      model_device=EMBEDDING_DEVICE,
                                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
                                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
                st.experimental_rerun()

            if cols[3].button(
                    "从知识库中删除",
                    type="primary",
                    use_container_width=True,
            ):
                for row in selected_rows:
                    ret = api.delete_kb_doc(kb, row["file_name"], True,
                                      embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
                                      embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
                                      model_device=EMBEDDING_DEVICE,
                                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
                                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
                    st.toast(ret.get("msg", " "))
                st.experimental_rerun()

        st.divider()

        cols = st.columns(3)

        # todo: freezed
        if cols[0].button(
                "依据源文件重建向量库",
                # 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。",
                use_container_width=True,
                type="primary",
        ):
            with st.spinner("向量库重构中，请耐心等待，勿刷新或关闭页面。"):
                empty = st.empty()
                empty.progress(0.0, "")
                for d in api.recreate_vector_store(
                    kb, vs_type=default_vs_type, embed_model=embedding_model, embedding_device=EMBEDDING_DEVICE,
                      embed_model_path=embedding_model_dict[embedding_model], embed_engine=EMBEDDING_ENGINE,
                      api_key=llm_model_dict[LLM_MODEL]["api_key"],
                      api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
                    ):
                    if msg := check_error_msg(d):
                        st.toast(msg)
                    else:
                        empty.progress(d["finished"] / d["total"], f"正在处理： {d['doc']}")
                st.experimental_rerun()

        if cols[2].button(
                "删除知识库",
                use_container_width=True,
        ):
            ret = api.delete_knowledge_base(kb,)
            st.toast(ret.get("msg", " "))
            time.sleep(1)
            st.experimental_rerun()