164 lines
6.3 KiB
Python
164 lines
6.3 KiB
Python
# encoding: utf-8
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'''
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@author: 温进
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@file: code.py.py
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@time: 2023/10/23 下午5:31
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@desc:
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'''
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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 configs.model_config import embedding_model_dict, kbs_config, EMBEDDING_MODEL, DEFAULT_VS_TYPE, WEB_CRAWL_PATH
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from .utils import *
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from coagent.utils.path_utils import *
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from coagent.service.service_factory import get_cb_details, get_cb_details_by_cb_name
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from coagent.orm import table_init
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from configs.model_config import EMBEDDING_DEVICE, EMBEDDING_ENGINE, EMBEDDING_MODEL, embedding_model_dict,llm_model_dict
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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 file_exists(cb: str, selected_rows: List) -> Tuple[str, str]:
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'''
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check whether the dir exist in local file
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return the dir'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]["code_name"]
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file_path = get_file_path(cb, 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 code_page(api: ApiRequest):
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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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logger.info(get_cb_details())
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cb_list = {x["code_name"]: x for x in get_cb_details()}
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except Exception as e:
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logger.exception(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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cb_names = list(cb_list.keys())
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if "selected_cb_name" in st.session_state and st.session_state["selected_cb_name"] in cb_names:
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selected_cb_index = cb_names.index(st.session_state["selected_cb_name"])
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else:
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selected_cb_index = 0
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def format_selected_cb(cb_name: str) -> str:
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if cb := cb_list.get(cb_name):
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return f"{cb_name} ({cb['code_path']})"
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else:
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return cb_name
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selected_cb = st.selectbox(
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"请选择或新建代码知识库：",
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cb_names + ["新建代码知识库"],
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format_func=format_selected_cb,
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index=selected_cb_index
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)
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if selected_cb == "新建代码知识库":
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with st.form("新建代码知识库"):
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cb_name = st.text_input(
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"新建代码知识库名称",
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placeholder="新代码知识库名称，不支持中文命名",
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key="cb_name",
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)
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file = st.file_uploader("上传代码库 zip 文件",
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['.zip'],
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accept_multiple_files=False,
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)
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do_interpret = st.checkbox('**代码解读**', value=False, help='代码解读会针对每个代码文件通过 LLM 获取解释并且向量化存储。当代码文件较多时，\
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导入速度会变慢，且如果使用收费 API 的话可能会造成较大花费。如果要使用基于描述的代码问答模式，此项必须勾选', key='do_interpret')
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logger.info(f'do_interpret={do_interpret}')
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submit_create_kb = st.form_submit_button(
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"新建",
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use_container_width=True,
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)
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if submit_create_kb:
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# unzip file
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logger.info('files={}'.format(file))
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if not cb_name or not cb_name.strip():
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st.error(f"知识库名称不能为空！")
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elif cb_name in cb_list:
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st.error(f"名为 {cb_name} 的知识库已经存在！")
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elif file.type not in ['application/zip', 'application/x-zip-compressed']:
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logger.error(f"{file.type}")
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st.error('请先上传 zip 文件，再新建代码知识库')
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else:
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ret = api.create_code_base(
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cb_name,
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file,
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do_interpret,
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no_remote_api=True,
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embed_engine=EMBEDDING_ENGINE,
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embed_model=EMBEDDING_MODEL,
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embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
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embedding_device=EMBEDDING_DEVICE,
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llm_model=LLM_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_cb_name"] = cb_name
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st.experimental_rerun()
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elif selected_cb:
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cb = selected_cb
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# 知识库详情
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cb_details = get_cb_details_by_cb_name(cb)
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if not len(cb_details):
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st.info(f"代码知识库 `{cb}` 中暂无信息")
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else:
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logger.info(cb_details)
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st.write(f"代码知识库 `{cb}` 加载成功，中含有以下信息:")
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st.write('代码知识库 `{}` 代码文件数=`{}`'.format(cb_details['code_name'],
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cb_details.get('code_file_num', 'unknown')))
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st.write('代码知识库 `{}` 知识图谱节点数=`{}`'.format(cb_details['code_name'], cb_details['code_graph_node_num']))
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st.write('代码知识库 `{}` 是否进行代码解读=`{}`'.format(cb_details['code_name'], cb_details['do_interpret']))
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st.divider()
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cols = st.columns(3)
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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_code_base(cb,
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no_remote_api=True,
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embed_engine=EMBEDDING_ENGINE,
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embed_model=EMBEDDING_MODEL,
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embed_model_path=embedding_model_dict[EMBEDDING_MODEL],
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embedding_device=EMBEDDING_DEVICE,
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llm_model=LLM_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", "删除成功"))
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time.sleep(0.05)
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st.experimental_rerun()
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