378 lines
15 KiB
Python
378 lines
15 KiB
Python
import streamlit as st
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import os
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import time
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import traceback
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from typing import Literal, Dict, Tuple
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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 .utils import *
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from muagent.utils.path_utils import *
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from muagent.service.service_factory import get_kb_details, get_kb_doc_details
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from muagent.orm import table_init
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from configs.model_config import (
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KB_ROOT_PATH, kbs_config, DEFAULT_VS_TYPE, WEB_CRAWL_PATH,
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EMBEDDING_DEVICE, EMBEDDING_ENGINE, EMBEDDING_MODEL, embedding_model_dict,
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llm_model_dict
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)
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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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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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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, KB_ROOT_PATH)
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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_page(
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api: ApiRequest,
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embedding_model_dict: dict = embedding_model_dict,
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kbs_config: dict = kbs_config,
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embedding_model: str = EMBEDDING_MODEL,
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default_vs_type: str = DEFAULT_VS_TYPE,
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web_crawl_path: str = WEB_CRAWL_PATH
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):
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# 判断表是否存在并进行初始化
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table_init()
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try:
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kb_list = {x["kb_name"]: x for x in get_kb_details(KB_ROOT_PATH)}
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except Exception as e:
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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。")
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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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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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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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embed_models = list(embedding_model_dict.keys())
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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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embed_engine=EMBEDDING_ENGINE,
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embedding_device= EMBEDDING_DEVICE,
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embed_model_path=embedding_model_dict[embed_model],
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api_key=llm_model_dict[LLM_MODEL]["api_key"],
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api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
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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.experimental_rerun()
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elif selected_kb:
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kb = selected_kb
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# 上传文件
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# sentence_size = st.slider("文本入库分句长度限制", 1, 1000, SENTENCE_SIZE, disabled=True)
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files = st.file_uploader("上传知识文件",
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[i for ls in LOADER2EXT_DICT.values() for i in ls],
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accept_multiple_files=True,
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)
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if st.button(
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"添加文件到知识库",
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# help="请先上传文件，再点击添加",
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# use_container_width=True,
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disabled=len(files) == 0,
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):
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data = [{"file": f, "knowledge_base_name": kb, "not_refresh_vs_cache": True, "embed_model": EMBEDDING_MODEL,
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"embed_model_path": embedding_model_dict[EMBEDDING_MODEL],
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"model_device": EMBEDDING_DEVICE,
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"embed_engine": EMBEDDING_ENGINE,
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"api_key": llm_model_dict[LLM_MODEL]["api_key"],
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"api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],
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}
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for f in files]
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data[-1]["not_refresh_vs_cache"]=False
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for k in data:
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pass
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ret = api.upload_kb_doc(**k)
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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.session_state.files = []
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base_url = st.text_input(
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"待获取内容的URL地址",
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placeholder="请填写正确可打开的URL地址",
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key="base_url",
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)
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if st.button(
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"添加URL内容到知识库",
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disabled= base_url is None or base_url=="",
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):
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filename = base_url.replace("https://", " ").\
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replace("http://", " ").replace("/", " ").\
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replace("?", " ").replace("=", " ").replace(".", " ").strip()
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html_name = "_".join(filename.split(" ",) + ["html.jsonl"])
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text_name = "_".join(filename.split(" ",) + ["text.jsonl"])
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html_path = os.path.join(web_crawl_path, html_name,)
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text_path = os.path.join(web_crawl_path, text_name,)
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# if not os.path.exists(text_dir) or :
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st.toast(base_url)
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st.toast(html_path)
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st.toast(text_path)
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res = api.web_crawl(
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base_url=base_url,
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html_dir=html_path,
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text_dir=text_path,
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do_dfs = False,
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reptile_lib="requests",
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method="get",
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time_sleep=2,
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)
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if res["status"] == 200:
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st.toast(res["response"], icon="✔")
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data = [{"file": text_path, "filename": text_name, "knowledge_base_name": kb, "not_refresh_vs_cache": False,
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"embed_model": EMBEDDING_MODEL,
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"embed_model_path": embedding_model_dict[EMBEDDING_MODEL],
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"model_device": EMBEDDING_DEVICE,
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"embed_engine": EMBEDDING_ENGINE,
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"api_key": llm_model_dict[LLM_MODEL]["api_key"],
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"api_base_url": llm_model_dict[LLM_MODEL]["api_base_url"],}]
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for k in data:
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ret = api.upload_kb_doc(**k)
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logger.info(ret)
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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.session_state.files = []
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else:
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st.toast(res["response"], icon="✖")
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if os.path.exists(html_path):
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os.remove(html_path)
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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_doc_details(kb, KB_ROOT_PATH))
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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", "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"): {"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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)
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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 (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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for row in selected_rows:
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api.update_kb_doc(kb, row["file_name"],
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embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
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embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
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model_device=EMBEDDING_DEVICE,
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api_key=llm_model_dict[LLM_MODEL]["api_key"],
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api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
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)
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st.experimental_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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for row in selected_rows:
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api.delete_kb_doc(kb, row["file_name"],
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embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
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embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
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model_device=EMBEDDING_DEVICE,
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api_key=llm_model_dict[LLM_MODEL]["api_key"],
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api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
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st.experimental_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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for row in selected_rows:
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ret = api.delete_kb_doc(kb, row["file_name"], True,
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embed_engine=EMBEDDING_ENGINE,embed_model=EMBEDDING_MODEL,
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embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
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model_device=EMBEDDING_DEVICE,
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api_key=llm_model_dict[LLM_MODEL]["api_key"],
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api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],)
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st.toast(ret.get("msg", " "))
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st.experimental_rerun()
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st.divider()
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cols = st.columns(3)
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# todo: freezed
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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(
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kb, vs_type=default_vs_type, embed_model=embedding_model, embedding_device=EMBEDDING_DEVICE,
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embed_model_path=embedding_model_dict[embedding_model], embed_engine=EMBEDDING_ENGINE,
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api_key=llm_model_dict[LLM_MODEL]["api_key"],
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api_base_url=llm_model_dict[LLM_MODEL]["api_base_url"],
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):
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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"], 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.get("msg", " "))
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time.sleep(1)
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st.experimental_rerun()
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