mirror of
https://github.com/RYDE-WORK/Langchain-Chatchat.git
synced 2026-01-19 13:23:16 +08:00
65592a45c3
新功能：
- 支持在线 Embeddings：zhipu-api, qwen-api, minimax-api, qianfan-api
- API 增加 /other/embed_texts 接口
- init_database.py 增加 --embed-model 参数，可以指定使用的嵌入模型（本地或在线均可）
- 对于 FAISS 知识库，支持多向量库，默认位置：{KB_PATH}/vector_store/{embed_model}
- Lite 模式支持所有知识库相关功能。此模式下最主要的限制是：
- 不能使用本地 LLM 和 Embeddings 模型
- 知识库不支持 PDF 文件
- init_database.py 重建知识库时不再默认情况数据库表，增加 clear-tables 参数手动控制。
- API 和 WEBUI 中 score_threshold 参数范围改为 [0, 2]，以更好的适应在线嵌入模型
问题修复：
- API 中 list_config_models 会删除 ONLINE_LLM_MODEL 中的敏感信息，导致第二轮API请求错误
开发者：
- 统一向量库的识别：以（kb_name,embed_model）为判断向量库唯一性的依据，避免 FAISS 知识库缓存加载逻辑错误
- KBServiceFactory.get_service_by_name 中添加 default_embed_model 参数，用于在构建新知识库时设置 embed_model
- 优化 kb_service 中 Embeddings 操作：
- 统一加载接口： server.utils.load_embeddings，利用全局缓存避免各处 Embeddings 传参
- 统一文本嵌入接口：server.knowledge_base.kb_service.base.[embed_texts, embed_documents]
- 重写 normalize 函数，去除对 scikit-learn/scipy 的依赖
311 lines
12 KiB
Python
311 lines
12 KiB
Python
import streamlit as st
|
||
from webui_pages.utils import *
|
||
from st_aggrid import AgGrid, JsCode
|
||
from st_aggrid.grid_options_builder import GridOptionsBuilder
|
||
import pandas as pd
|
||
from server.knowledge_base.utils import get_file_path, LOADER_DICT
|
||
from server.knowledge_base.kb_service.base import get_kb_details, get_kb_file_details
|
||
from typing import Literal, Dict, Tuple
|
||
from configs import (kbs_config,
|
||
EMBEDDING_MODEL, DEFAULT_VS_TYPE,
|
||
CHUNK_SIZE, OVERLAP_SIZE, ZH_TITLE_ENHANCE)
|
||
from server.utils import list_embed_models, list_online_embed_models
|
||
import os
|
||
import time
|
||
|
||
|
||
# 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)
|
||
if os.path.isfile(file_path):
|
||
return file_name, file_path
|
||
return "", ""
|
||
|
||
|
||
def knowledge_base_page(api: ApiRequest, is_lite: bool = None):
|
||
try:
|
||
kb_list = {x["kb_name"]: x for x in get_kb_details()}
|
||
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
|
||
|
||
if "selected_kb_info" not in st.session_state:
|
||
st.session_state["selected_kb_info"] = ""
|
||
|
||
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",
|
||
)
|
||
kb_info = st.text_input(
|
||
"知识库简介",
|
||
placeholder="知识库简介，方便Agent查找",
|
||
key="kb_info",
|
||
)
|
||
|
||
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",
|
||
)
|
||
|
||
if is_lite:
|
||
embed_models = list_online_embed_models()
|
||
else:
|
||
embed_models = list_embed_models() + list_online_embed_models()
|
||
|
||
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,
|
||
)
|
||
st.toast(ret.get("msg", " "))
|
||
st.session_state["selected_kb_name"] = kb_name
|
||
st.session_state["selected_kb_info"] = kb_info
|
||
st.experimental_rerun()
|
||
|
||
elif selected_kb:
|
||
kb = selected_kb
|
||
st.session_state["selected_kb_info"] = kb_list[kb]['kb_info']
|
||
# 上传文件
|
||
files = st.file_uploader("上传知识文件：",
|
||
[i for ls in LOADER_DICT.values() for i in ls],
|
||
accept_multiple_files=True,
|
||
)
|
||
kb_info = st.text_area("请输入知识库介绍:", value=st.session_state["selected_kb_info"], max_chars=None, key=None,
|
||
help=None, on_change=None, args=None, kwargs=None)
|
||
|
||
if kb_info != st.session_state["selected_kb_info"]:
|
||
st.session_state["selected_kb_info"] = kb_info
|
||
api.update_kb_info(kb, kb_info)
|
||
|
||
# with st.sidebar:
|
||
with st.expander(
|
||
"文件处理配置",
|
||
expanded=True,
|
||
):
|
||
cols = st.columns(3)
|
||
chunk_size = cols[0].number_input("单段文本最大长度：", 1, 1000, CHUNK_SIZE)
|
||
chunk_overlap = cols[1].number_input("相邻文本重合长度：", 0, chunk_size, OVERLAP_SIZE)
|
||
cols[2].write("")
|
||
cols[2].write("")
|
||
zh_title_enhance = cols[2].checkbox("开启中文标题加强", ZH_TITLE_ENHANCE)
|
||
|
||
if st.button(
|
||
"添加文件到知识库",
|
||
# use_container_width=True,
|
||
disabled=len(files) == 0,
|
||
):
|
||
ret = api.upload_kb_docs(files,
|
||
knowledge_base_name=kb,
|
||
override=True,
|
||
chunk_size=chunk_size,
|
||
chunk_overlap=chunk_overlap,
|
||
zh_title_enhance=zh_title_enhance)
|
||
if msg := check_success_msg(ret):
|
||
st.toast(msg, icon="✔")
|
||
elif msg := check_error_msg(ret):
|
||
st.toast(msg, icon="✖")
|
||
|
||
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_file_details(kb))
|
||
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", "docs_count", "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"): {},
|
||
("docs_count", "文档数量"): {},
|
||
("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,
|
||
enable_enterprise_modules=False
|
||
)
|
||
|
||
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,
|
||
):
|
||
file_names = [row["file_name"] for row in selected_rows]
|
||
api.update_kb_docs(kb,
|
||
file_names=file_names,
|
||
chunk_size=chunk_size,
|
||
chunk_overlap=chunk_overlap,
|
||
zh_title_enhance=zh_title_enhance)
|
||
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,
|
||
):
|
||
file_names = [row["file_name"] for row in selected_rows]
|
||
api.delete_kb_docs(kb, file_names=file_names)
|
||
st.experimental_rerun()
|
||
|
||
if cols[3].button(
|
||
"从知识库中删除",
|
||
type="primary",
|
||
use_container_width=True,
|
||
):
|
||
file_names = [row["file_name"] for row in selected_rows]
|
||
api.delete_kb_docs(kb, file_names=file_names, delete_content=True)
|
||
st.experimental_rerun()
|
||
|
||
st.divider()
|
||
|
||
cols = st.columns(3)
|
||
|
||
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,
|
||
chunk_size=chunk_size,
|
||
chunk_overlap=chunk_overlap,
|
||
zh_title_enhance=zh_title_enhance):
|
||
if msg := check_error_msg(d):
|
||
st.toast(msg)
|
||
else:
|
||
empty.progress(d["finished"] / d["total"], d["msg"])
|
||
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()
|