镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
比较提交
47 次代码提交
version3.9
...
papershare
| 作者 | SHA1 | 提交日期 | |
|---|---|---|---|
|
|
50a1ea83ef | ||
|
|
a9c86a7fb8 | ||
|
|
2b299cf579 | ||
|
|
310122f5a7 | ||
|
|
0121cacc84 | ||
|
|
c83bf214d0 | ||
|
|
e34c49dce5 | ||
|
|
f2dcd6ad55 | ||
|
|
42d9712f20 | ||
|
|
3890467c84 | ||
|
|
074b3c9828 | ||
|
|
b8e8457a01 | ||
|
|
2c93a24d7e | ||
|
|
e9af6ef3a0 | ||
|
|
5ae8981dbb | ||
|
|
7f0ffa58f0 | ||
|
|
5888d038aa | ||
|
|
ee8213e936 | ||
|
|
a57dcbcaeb | ||
|
|
b812392a9d | ||
|
|
fce4fa1ec7 | ||
|
|
d13f1e270c | ||
|
|
85cf3d08eb | ||
|
|
584e747565 | ||
|
|
02ba653c19 | ||
|
|
2d12b5b27d | ||
|
|
a4bcd262f9 | ||
|
|
748e31102f | ||
|
|
97eef45ab7 | ||
|
|
0c0e2acb9b | ||
|
|
9fba8e0142 | ||
|
|
7d7867fb64 | ||
|
|
7ea791d83a | ||
|
|
f9dbaa39fb | ||
|
|
bbc2288c5b | ||
|
|
64ab916838 | ||
|
|
8fe559da9f | ||
|
|
09fd22091a | ||
|
|
df717f8bba | ||
|
|
e296719b23 | ||
|
|
2f343179a2 | ||
|
|
4d9604f2e9 | ||
|
|
bbf9e9f868 | ||
|
|
aa1f967dd7 | ||
|
|
0d082327c8 | ||
|
|
80acd9c875 | ||
|
|
17cd4f8210 |
2
.github/workflows/build-with-latex-arm.yml
vendored
2
.github/workflows/build-with-latex-arm.yml
vendored
@@ -46,6 +46,6 @@ jobs:
|
||||
context: .
|
||||
push: true
|
||||
platforms: linux/arm64
|
||||
file: docs/GithubAction+NoLocal+Latex
|
||||
file: docs/GithubAction+NoLocal+Latex+Arm
|
||||
tags: ${{ steps.meta.outputs.tags }}
|
||||
labels: ${{ steps.meta.outputs.labels }}
|
||||
@@ -3,7 +3,7 @@ from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip
|
||||
from functools import partial
|
||||
from loguru import logger
|
||||
|
||||
import glob, os, requests, time, json, tarfile
|
||||
import glob, os, requests, time, json, tarfile, threading
|
||||
|
||||
pj = os.path.join
|
||||
ARXIV_CACHE_DIR = get_conf("ARXIV_CACHE_DIR")
|
||||
@@ -138,25 +138,43 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
|
||||
cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
|
||||
if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
|
||||
|
||||
url_tar = url_.replace('/abs/', '/e-print/')
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
|
||||
extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
|
||||
os.makedirs(translation_dir, exist_ok=True)
|
||||
|
||||
# <-------------- download arxiv source file ------------->
|
||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
|
||||
dst = pj(translation_dir, arxiv_id + '.tar')
|
||||
if os.path.exists(dst):
|
||||
yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history) # 刷新界面
|
||||
os.makedirs(translation_dir, exist_ok=True)
|
||||
# <-------------- download arxiv source file ------------->
|
||||
|
||||
def fix_url_and_download():
|
||||
# for url_tar in [url_.replace('/abs/', '/e-print/'), url_.replace('/abs/', '/src/')]:
|
||||
for url_tar in [url_.replace('/abs/', '/src/'), url_.replace('/abs/', '/e-print/')]:
|
||||
proxies = get_conf('proxies')
|
||||
r = requests.get(url_tar, proxies=proxies)
|
||||
if r.status_code == 200:
|
||||
with open(dst, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
return True
|
||||
return False
|
||||
|
||||
if os.path.exists(dst) and allow_cache:
|
||||
yield from update_ui_lastest_msg(f"调用缓存 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
||||
success = True
|
||||
else:
|
||||
yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history) # 刷新界面
|
||||
proxies = get_conf('proxies')
|
||||
r = requests.get(url_tar, proxies=proxies)
|
||||
with open(dst, 'wb+') as f:
|
||||
f.write(r.content)
|
||||
yield from update_ui_lastest_msg(f"开始下载 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
||||
success = fix_url_and_download()
|
||||
yield from update_ui_lastest_msg(f"下载完成 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
if not success:
|
||||
yield from update_ui_lastest_msg(f"下载失败 {arxiv_id}", chatbot=chatbot, history=history)
|
||||
raise tarfile.ReadError(f"论文下载失败 {arxiv_id}")
|
||||
|
||||
# <-------------- extract file ------------->
|
||||
yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history) # 刷新界面
|
||||
from toolbox import extract_archive
|
||||
extract_archive(file_path=dst, dest_dir=extract_dst)
|
||||
try:
|
||||
extract_archive(file_path=dst, dest_dir=extract_dst)
|
||||
except tarfile.ReadError:
|
||||
os.remove(dst)
|
||||
raise tarfile.ReadError(f"论文下载失败")
|
||||
return extract_dst, arxiv_id
|
||||
|
||||
|
||||
@@ -320,11 +338,17 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
|
||||
# <-------------- more requirements ------------->
|
||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||
no_cache = more_req.startswith("--no-cache")
|
||||
if no_cache: more_req.lstrip("--no-cache")
|
||||
|
||||
no_cache = ("--no-cache" in more_req)
|
||||
if no_cache: more_req = more_req.replace("--no-cache", "").strip()
|
||||
|
||||
allow_gptac_cloud_io = ("--allow-cloudio" in more_req) # 从云端下载翻译结果,以及上传翻译结果到云端
|
||||
if allow_gptac_cloud_io: more_req = more_req.replace("--allow-cloudio", "").strip()
|
||||
|
||||
allow_cache = not no_cache
|
||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||
|
||||
|
||||
# <-------------- check deps ------------->
|
||||
try:
|
||||
import glob, os, time, subprocess
|
||||
@@ -351,6 +375,20 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
return
|
||||
|
||||
# #################################################################
|
||||
if allow_gptac_cloud_io and arxiv_id:
|
||||
# 访问 GPTAC学术云,查询云端是否存在该论文的翻译版本
|
||||
from crazy_functions.latex_fns.latex_actions import check_gptac_cloud
|
||||
success, downloaded = check_gptac_cloud(arxiv_id, chatbot)
|
||||
if success:
|
||||
chatbot.append([
|
||||
f"检测到GPTAC云端存在翻译版本, 如果不满意翻译结果, 请禁用云端分享, 然后重新执行。",
|
||||
None
|
||||
])
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
return
|
||||
#################################################################
|
||||
|
||||
if os.path.exists(txt):
|
||||
project_folder = txt
|
||||
else:
|
||||
@@ -388,14 +426,21 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
|
||||
# <-------------- zip PDF ------------->
|
||||
zip_res = zip_result(project_folder)
|
||||
if success:
|
||||
if allow_gptac_cloud_io and arxiv_id:
|
||||
# 如果用户允许,我们将翻译好的arxiv论文PDF上传到GPTAC学术云
|
||||
from crazy_functions.latex_fns.latex_actions import upload_to_gptac_cloud_if_user_allow
|
||||
threading.Thread(target=upload_to_gptac_cloud_if_user_allow,
|
||||
args=(chatbot, arxiv_id), daemon=True).start()
|
||||
|
||||
chatbot.append((f"成功啦", '请查收结果(压缩包)...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history);
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
else:
|
||||
chatbot.append((f"失败了",
|
||||
'虽然PDF生成失败了, 但请查收结果(压缩包), 内含已经翻译的Tex文档, 您可以到Github Issue区, 用该压缩包进行反馈。如系统是Linux,请检查系统字体(见Github wiki) ...'))
|
||||
yield from update_ui(chatbot=chatbot, history=history);
|
||||
yield from update_ui(chatbot=chatbot, history=history)
|
||||
time.sleep(1) # 刷新界面
|
||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||
|
||||
|
||||
@@ -30,6 +30,9 @@ class Arxiv_Localize(GptAcademicPluginTemplate):
|
||||
default_value="", type="string").model_dump_json(), # 高级参数输入区,自动同步
|
||||
"allow_cache":
|
||||
ArgProperty(title="是否允许从缓存中调取结果", options=["允许缓存", "从头执行"], default_value="允许缓存", description="无", type="dropdown").model_dump_json(),
|
||||
"allow_cloudio":
|
||||
ArgProperty(title="是否允许向GPTAC学术云共享翻译结果", options=["允许", "禁止"], default_value="禁止", description="人人为我,我为人人", type="dropdown").model_dump_json(),
|
||||
|
||||
}
|
||||
return gui_definition
|
||||
|
||||
@@ -38,9 +41,14 @@ class Arxiv_Localize(GptAcademicPluginTemplate):
|
||||
执行插件
|
||||
"""
|
||||
allow_cache = plugin_kwargs["allow_cache"]
|
||||
allow_cloudio = plugin_kwargs["allow_cloudio"]
|
||||
advanced_arg = plugin_kwargs["advanced_arg"]
|
||||
|
||||
if allow_cache == "从头执行": plugin_kwargs["advanced_arg"] = "--no-cache " + plugin_kwargs["advanced_arg"]
|
||||
|
||||
# 从云端下载翻译结果,以及上传翻译结果到云端;人人为我,我为人人。
|
||||
if allow_cloudio == "允许": plugin_kwargs["advanced_arg"] = "--allow-cloudio " + plugin_kwargs["advanced_arg"]
|
||||
|
||||
yield from Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||
|
||||
|
||||
|
||||
@@ -1,4 +1,11 @@
|
||||
import os,glob
|
||||
from typing import List
|
||||
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
|
||||
from toolbox import report_exception
|
||||
from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
|
||||
from shared_utils.fastapi_server import validate_path_safety
|
||||
from crazy_functions.crazy_utils import input_clipping
|
||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||
|
||||
@@ -7,6 +14,37 @@ MAX_HISTORY_ROUND = 5
|
||||
MAX_CONTEXT_TOKEN_LIMIT = 4096
|
||||
REMEMBER_PREVIEW = 1000
|
||||
|
||||
@CatchException
|
||||
def handle_document_upload(files: List[str], llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker):
|
||||
"""
|
||||
Handles document uploads by extracting text and adding it to the vector store.
|
||||
"""
|
||||
from llama_index.core import Document
|
||||
from crazy_functions.rag_fns.rag_file_support import extract_text, supports_format
|
||||
user_name = chatbot.get_user()
|
||||
checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
|
||||
|
||||
for file_path in files:
|
||||
try:
|
||||
validate_path_safety(file_path, user_name)
|
||||
text = extract_text(file_path)
|
||||
if text is None:
|
||||
chatbot.append(
|
||||
[f"上传文件: {os.path.basename(file_path)}", f"文件解析失败,无法提取文本内容,请更换文件。失败原因可能为:1.文档格式过于复杂;2. 不支持的文件格式,支持的文件格式后缀有:" + ", ".join(supports_format)])
|
||||
else:
|
||||
chatbot.append(
|
||||
[f"上传文件: {os.path.basename(file_path)}", f"上传文件前50个字符为:{text[:50]}。"])
|
||||
document = Document(text=text, metadata={"source": file_path})
|
||||
rag_worker.add_documents_to_vector_store([document])
|
||||
chatbot.append([f"上传文件: {os.path.basename(file_path)}", "文件已成功添加到知识库。"])
|
||||
except Exception as e:
|
||||
report_exception(chatbot, history, a=f"处理文件: {file_path}", b=str(e))
|
||||
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
|
||||
|
||||
# Main Q&A function with document upload support
|
||||
@CatchException
|
||||
def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||
|
||||
@@ -23,28 +61,48 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
|
||||
# 1. we retrieve rag worker from global context
|
||||
user_name = chatbot.get_user()
|
||||
checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
|
||||
|
||||
if user_name in RAG_WORKER_REGISTER:
|
||||
rag_worker = RAG_WORKER_REGISTER[user_name]
|
||||
else:
|
||||
rag_worker = RAG_WORKER_REGISTER[user_name] = LlamaIndexRagWorker(
|
||||
user_name,
|
||||
llm_kwargs,
|
||||
checkpoint_dir=checkpoint_dir,
|
||||
auto_load_checkpoint=True)
|
||||
user_name,
|
||||
llm_kwargs,
|
||||
checkpoint_dir=checkpoint_dir,
|
||||
auto_load_checkpoint=True
|
||||
)
|
||||
|
||||
current_context = f"{VECTOR_STORE_TYPE} @ {checkpoint_dir}"
|
||||
tip = "提示:输入“清空向量数据库”可以清空RAG向量数据库"
|
||||
if txt == "清空向量数据库":
|
||||
chatbot.append([txt, f'正在清空 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
rag_worker.purge()
|
||||
yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 刷新界面
|
||||
|
||||
# 2. Handle special commands
|
||||
if os.path.exists(txt) and os.path.isdir(txt):
|
||||
project_folder = txt
|
||||
validate_path_safety(project_folder, chatbot.get_user())
|
||||
# Extract file paths from the user input
|
||||
# Assuming the user inputs file paths separated by commas after the command
|
||||
file_paths = [f for f in glob.glob(f'{project_folder}/**/*', recursive=True)]
|
||||
chatbot.append([txt, f'正在处理上传的文档 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
yield from handle_document_upload(file_paths, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request, rag_worker)
|
||||
return
|
||||
|
||||
chatbot.append([txt, f'正在召回知识 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
elif txt == "清空向量数据库":
|
||||
chatbot.append([txt, f'正在清空 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
rag_worker.purge_vector_store()
|
||||
yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 刷新界面
|
||||
return
|
||||
|
||||
# 2. clip history to reduce token consumption
|
||||
# 2-1. reduce chat round
|
||||
else:
|
||||
report_exception(chatbot, history, a=f"上传文件路径错误: {txt}", b="请检查并提供正确路径。")
|
||||
|
||||
# 3. Normal Q&A processing
|
||||
chatbot.append([txt, f'正在召回知识 ({current_context}) ...'])
|
||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||
|
||||
# 4. Clip history to reduce token consumption
|
||||
txt_origin = txt
|
||||
|
||||
if len(history) > MAX_HISTORY_ROUND * 2:
|
||||
@@ -52,41 +110,47 @@ def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, u
|
||||
txt_clip, history, flags = input_clipping(txt, history, max_token_limit=MAX_CONTEXT_TOKEN_LIMIT, return_clip_flags=True)
|
||||
input_is_clipped_flag = (flags["original_input_len"] != flags["clipped_input_len"])
|
||||
|
||||
# 2-2. if input is clipped, add input to vector store before retrieve
|
||||
# 5. If input is clipped, add input to vector store before retrieve
|
||||
if input_is_clipped_flag:
|
||||
yield from update_ui_lastest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0) # 刷新界面
|
||||
# save input to vector store
|
||||
yield from update_ui_lastest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0) # 刷新界面
|
||||
# Save input to vector store
|
||||
rag_worker.add_text_to_vector_store(txt_origin)
|
||||
yield from update_ui_lastest_msg('向量化完成 ...', chatbot, history, delay=0) # 刷新界面
|
||||
yield from update_ui_lastest_msg('向量化完成 ...', chatbot, history, delay=0) # 刷新界面
|
||||
|
||||
if len(txt_origin) > REMEMBER_PREVIEW:
|
||||
HALF = REMEMBER_PREVIEW//2
|
||||
HALF = REMEMBER_PREVIEW // 2
|
||||
i_say_to_remember = txt[:HALF] + f" ...\n...(省略{len(txt_origin)-REMEMBER_PREVIEW}字)...\n... " + txt[-HALF:]
|
||||
if (flags["original_input_len"] - flags["clipped_input_len"]) > HALF:
|
||||
txt_clip = txt_clip + f" ...\n...(省略{len(txt_origin)-len(txt_clip)-HALF}字)...\n... " + txt[-HALF:]
|
||||
else:
|
||||
pass
|
||||
i_say = txt_clip
|
||||
txt_clip = txt_clip + f" ...\n...(省略{len(txt_origin)-len(txt_clip)-HALF}字)...\n... " + txt[-HALF:]
|
||||
else:
|
||||
i_say_to_remember = i_say = txt_clip
|
||||
else:
|
||||
i_say_to_remember = i_say = txt_clip
|
||||
|
||||
# 3. we search vector store and build prompts
|
||||
# 6. Search vector store and build prompts
|
||||
nodes = rag_worker.retrieve_from_store_with_query(i_say)
|
||||
prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
|
||||
# 7. Query language model
|
||||
if len(chatbot) != 0:
|
||||
chatbot.pop(-1) # Pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`
|
||||
|
||||
# 4. it is time to query llms
|
||||
if len(chatbot) != 0: chatbot.pop(-1) # pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`
|
||||
model_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||
inputs=prompt, inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||
inputs=prompt,
|
||||
inputs_show_user=i_say,
|
||||
llm_kwargs=llm_kwargs,
|
||||
chatbot=chatbot,
|
||||
history=history,
|
||||
sys_prompt=system_prompt,
|
||||
retry_times_at_unknown_error=0
|
||||
)
|
||||
|
||||
# 5. remember what has been asked / answered
|
||||
yield from update_ui_lastest_msg(model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...', chatbot, history, delay=0.5) # 刷新界面
|
||||
# 8. Remember Q&A
|
||||
yield from update_ui_lastest_msg(
|
||||
model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...',
|
||||
chatbot, history, delay=0.5
|
||||
)
|
||||
rag_worker.remember_qa(i_say_to_remember, model_say)
|
||||
history.extend([i_say, model_say])
|
||||
|
||||
yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip) # 刷新界面
|
||||
# 9. Final UI Update
|
||||
yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip)
|
||||
@@ -3,7 +3,7 @@ import re
|
||||
import shutil
|
||||
import numpy as np
|
||||
from loguru import logger
|
||||
from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
|
||||
from toolbox import update_ui, update_ui_lastest_msg, get_log_folder, gen_time_str
|
||||
from toolbox import get_conf, promote_file_to_downloadzone
|
||||
from crazy_functions.latex_fns.latex_toolbox import PRESERVE, TRANSFORM
|
||||
from crazy_functions.latex_fns.latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
||||
@@ -423,6 +423,9 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
||||
except Exception as e:
|
||||
logger.error(e)
|
||||
pass
|
||||
|
||||
|
||||
|
||||
return True # 成功啦
|
||||
else:
|
||||
if n_fix>=max_try: break
|
||||
@@ -468,3 +471,66 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
|
||||
except:
|
||||
from toolbox import trimmed_format_exc
|
||||
logger.error('writing html result failed:', trimmed_format_exc())
|
||||
|
||||
|
||||
def upload_to_gptac_cloud_if_user_allow(chatbot, arxiv_id):
|
||||
try:
|
||||
# 如果用户允许,我们将arxiv论文PDF上传到GPTAC学术云
|
||||
from toolbox import map_file_to_sha256
|
||||
# 检查是否顺利,如果没有生成预期的文件,则跳过
|
||||
is_result_good = False
|
||||
for file_path in chatbot._cookies.get("files_to_promote", []):
|
||||
if file_path.endswith('translate_zh.pdf'):
|
||||
is_result_good = True
|
||||
if not is_result_good:
|
||||
return
|
||||
# 上传文件
|
||||
for file_path in chatbot._cookies.get("files_to_promote", []):
|
||||
align_name = None
|
||||
# normalized name
|
||||
for name in ['translate_zh.pdf', 'comparison.pdf']:
|
||||
if file_path.endswith(name): align_name = name
|
||||
# if match any align name
|
||||
if align_name:
|
||||
logger.info(f'Uploading to GPTAC cloud as the user has set `allow_cloud_io`: {file_path}')
|
||||
with open(file_path, 'rb') as f:
|
||||
import requests
|
||||
url = 'https://cloud-2.agent-matrix.com/upload'
|
||||
files = {'file': (align_name, f, 'application/octet-stream')}
|
||||
data = {
|
||||
'arxiv_id': arxiv_id,
|
||||
'file_hash': map_file_to_sha256(file_path),
|
||||
}
|
||||
resp = requests.post(url=url, files=files, data=data, timeout=30)
|
||||
logger.info(f'Uploading terminate ({resp.status_code})`: {file_path}')
|
||||
except:
|
||||
# 如果上传失败,不会中断程序,因为这是次要功能
|
||||
pass
|
||||
|
||||
def check_gptac_cloud(arxiv_id, chatbot):
|
||||
import requests
|
||||
success = False
|
||||
downloaded = []
|
||||
try:
|
||||
for pdf_target in ['translate_zh.pdf', 'comparison.pdf']:
|
||||
url = 'https://cloud-2.agent-matrix.com/paper_exist'
|
||||
data = {
|
||||
'arxiv_id': arxiv_id,
|
||||
'name': pdf_target,
|
||||
}
|
||||
resp = requests.post(url=url, data=data)
|
||||
cache_hit_result = resp.text.strip('"')
|
||||
if cache_hit_result.startswith("http"):
|
||||
url = cache_hit_result
|
||||
logger.info(f'Downloading from GPTAC cloud: {url}')
|
||||
resp = requests.get(url=url, timeout=30)
|
||||
target = os.path.join(get_log_folder(plugin_name='gptac_cloud'), gen_time_str(), pdf_target)
|
||||
os.makedirs(os.path.dirname(target), exist_ok=True)
|
||||
with open(target, 'wb') as f:
|
||||
f.write(resp.content)
|
||||
new_path = promote_file_to_downloadzone(target, chatbot=chatbot)
|
||||
success = True
|
||||
downloaded.append(new_path)
|
||||
except:
|
||||
pass
|
||||
return success, downloaded
|
||||
|
||||
@@ -697,15 +697,6 @@ def _merge_pdfs_ng(pdf1_path, pdf2_path, output_path):
|
||||
),
|
||||
0,
|
||||
)
|
||||
if "/Annots" in page1:
|
||||
page1_annot_id = [annot.idnum for annot in page1["/Annots"]]
|
||||
else:
|
||||
page1_annot_id = []
|
||||
|
||||
if "/Annots" in page2:
|
||||
page2_annot_id = [annot.idnum for annot in page2["/Annots"]]
|
||||
else:
|
||||
page2_annot_id = []
|
||||
if "/Annots" in new_page:
|
||||
annotations = new_page["/Annots"]
|
||||
for i, annot in enumerate(annotations):
|
||||
@@ -720,114 +711,148 @@ def _merge_pdfs_ng(pdf1_path, pdf2_path, output_path):
|
||||
if "/S" in action and action["/S"] == "/GoTo":
|
||||
# 内部链接:跳转到文档中的某个页面
|
||||
dest = action.get("/D") # 目标页或目标位置
|
||||
if dest and annot.idnum in page2_annot_id:
|
||||
# 获取原始文件中跳转信息,包括跳转页面
|
||||
destination = pdf2_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
page_number = (
|
||||
pdf2_reader.get_destination_page_number(
|
||||
destination
|
||||
)
|
||||
)
|
||||
# 更新跳转信息,跳转到对应的页面和,指定坐标 (100, 150),缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[2]
|
||||
+ int(
|
||||
page1.mediaBox.getWidth()
|
||||
)
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
rect = annot_obj.get("/Rect")
|
||||
# 更新点击坐标
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(
|
||||
rect[0]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[1],
|
||||
FloatObject(
|
||||
rect[2]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[3],
|
||||
# if dest and annot.idnum in page2_annot_id:
|
||||
# if dest in pdf2_reader.named_destinations:
|
||||
if dest and page2.annotations:
|
||||
if annot in page2.annotations:
|
||||
# 获取原始文件中跳转信息,包括跳转页面
|
||||
destination = pdf2_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
if dest and annot.idnum in page1_annot_id:
|
||||
# 获取原始文件中跳转信息,包括跳转页面
|
||||
destination = pdf1_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
page_number = (
|
||||
pdf1_reader.get_destination_page_number(
|
||||
destination
|
||||
page_number = (
|
||||
pdf2_reader.get_destination_page_number(
|
||||
destination
|
||||
)
|
||||
)
|
||||
)
|
||||
# 更新跳转信息,跳转到对应的页面和,指定坐标 (100, 150),缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[2]
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
rect = annot_obj.get("/Rect")
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(rect[0]),
|
||||
rect[1],
|
||||
FloatObject(rect[2]),
|
||||
rect[3],
|
||||
# 更新跳转信息,跳转到对应的页面和,指定坐标 (100, 150),缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
if destination.dest_array[1] == "/XYZ":
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[
|
||||
2
|
||||
]
|
||||
+ int(
|
||||
page1.mediaBox.getWidth()
|
||||
)
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
else:
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
rect = annot_obj.get("/Rect")
|
||||
# 更新点击坐标
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(
|
||||
rect[0]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[1],
|
||||
FloatObject(
|
||||
rect[2]
|
||||
+ int(page1.mediaBox.getWidth())
|
||||
),
|
||||
rect[3],
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
# if dest and annot.idnum in page1_annot_id:
|
||||
# if dest in pdf1_reader.named_destinations:
|
||||
if dest and page1.annotations:
|
||||
if annot in page1.annotations:
|
||||
# 获取原始文件中跳转信息,包括跳转页面
|
||||
destination = pdf1_reader.named_destinations[
|
||||
dest
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
page_number = (
|
||||
pdf1_reader.get_destination_page_number(
|
||||
destination
|
||||
)
|
||||
)
|
||||
# 更新跳转信息,跳转到对应的页面和,指定坐标 (100, 150),缩放比例为 100%
|
||||
# “/D”:[10,'/XYZ',100,100,0]
|
||||
if destination.dest_array[1] == "/XYZ":
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
FloatObject(
|
||||
destination.dest_array[
|
||||
2
|
||||
]
|
||||
),
|
||||
destination.dest_array[3],
|
||||
destination.dest_array[4],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
else:
|
||||
annot_obj["/A"].update(
|
||||
{
|
||||
NameObject("/D"): ArrayObject(
|
||||
[
|
||||
NumberObject(page_number),
|
||||
destination.dest_array[1],
|
||||
]
|
||||
) # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
rect = annot_obj.get("/Rect")
|
||||
rect = ArrayObject(
|
||||
[
|
||||
FloatObject(rect[0]),
|
||||
rect[1],
|
||||
FloatObject(rect[2]),
|
||||
rect[3],
|
||||
]
|
||||
)
|
||||
annot_obj.update(
|
||||
{
|
||||
NameObject(
|
||||
"/Rect"
|
||||
): rect # 确保键和值是 PdfObject
|
||||
}
|
||||
)
|
||||
|
||||
elif "/S" in action and action["/S"] == "/URI":
|
||||
# 外部链接:跳转到某个URI
|
||||
uri = action.get("/URI")
|
||||
|
||||
output_writer.addPage(new_page)
|
||||
# Save the merged PDF file
|
||||
with open(output_path, "wb") as output_file:
|
||||
output_writer.write(output_file)
|
||||
|
||||
|
||||
|
||||
def _merge_pdfs_legacy(pdf1_path, pdf2_path, output_path):
|
||||
import PyPDF2 # PyPDF2这个库有严重的内存泄露问题,把它放到子进程中运行,从而方便内存的释放
|
||||
|
||||
|
||||
@@ -1,17 +1,13 @@
|
||||
import llama_index
|
||||
import os
|
||||
import atexit
|
||||
from loguru import logger
|
||||
from typing import List
|
||||
|
||||
from llama_index.core import Document
|
||||
from llama_index.core.schema import TextNode
|
||||
from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
|
||||
from shared_utils.connect_void_terminal import get_chat_default_kwargs
|
||||
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
|
||||
from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
|
||||
from llama_index.core.ingestion import run_transformations
|
||||
from llama_index.core import PromptTemplate
|
||||
from llama_index.core.response_synthesizers import TreeSummarize
|
||||
from llama_index.core.schema import TextNode
|
||||
|
||||
from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
|
||||
from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
|
||||
|
||||
DEFAULT_QUERY_GENERATION_PROMPT = """\
|
||||
Now, you have context information as below:
|
||||
@@ -63,7 +59,7 @@ class SaveLoad():
|
||||
def purge(self):
|
||||
import shutil
|
||||
shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
|
||||
self.vs_index = self.create_new_vs()
|
||||
self.vs_index = self.create_new_vs(self.checkpoint_dir)
|
||||
|
||||
|
||||
class LlamaIndexRagWorker(SaveLoad):
|
||||
@@ -75,7 +71,7 @@ class LlamaIndexRagWorker(SaveLoad):
|
||||
if auto_load_checkpoint:
|
||||
self.vs_index = self.load_from_checkpoint(checkpoint_dir)
|
||||
else:
|
||||
self.vs_index = self.create_new_vs(checkpoint_dir)
|
||||
self.vs_index = self.create_new_vs()
|
||||
atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
|
||||
|
||||
def assign_embedding_model(self):
|
||||
@@ -91,40 +87,52 @@ class LlamaIndexRagWorker(SaveLoad):
|
||||
logger.info('oo --------inspect_vector_store end--------')
|
||||
return vector_store_preview
|
||||
|
||||
def add_documents_to_vector_store(self, document_list):
|
||||
documents = [Document(text=t) for t in document_list]
|
||||
def add_documents_to_vector_store(self, document_list: List[Document]):
|
||||
"""
|
||||
Adds a list of Document objects to the vector store after processing.
|
||||
"""
|
||||
documents = document_list
|
||||
documents_nodes = run_transformations(
|
||||
documents, # type: ignore
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
documents, # type: ignore
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
self.vs_index.insert_nodes(documents_nodes)
|
||||
if self.debug_mode: self.inspect_vector_store()
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
|
||||
def add_text_to_vector_store(self, text):
|
||||
def add_text_to_vector_store(self, text: str):
|
||||
node = TextNode(text=text)
|
||||
documents_nodes = run_transformations(
|
||||
[node],
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
[node],
|
||||
self.vs_index._transformations,
|
||||
show_progress=True
|
||||
)
|
||||
self.vs_index.insert_nodes(documents_nodes)
|
||||
if self.debug_mode: self.inspect_vector_store()
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
|
||||
def remember_qa(self, question, answer):
|
||||
formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
|
||||
self.add_text_to_vector_store(formatted_str)
|
||||
|
||||
def retrieve_from_store_with_query(self, query):
|
||||
if self.debug_mode: self.inspect_vector_store()
|
||||
if self.debug_mode:
|
||||
self.inspect_vector_store()
|
||||
retriever = self.vs_index.as_retriever()
|
||||
return retriever.retrieve(query)
|
||||
|
||||
def build_prompt(self, query, nodes):
|
||||
context_str = self.generate_node_array_preview(nodes)
|
||||
return DEFAULT_QUERY_GENERATION_PROMPT.format(context_str=context_str, query_str=query)
|
||||
|
||||
|
||||
def generate_node_array_preview(self, nodes):
|
||||
buf = "\n".join(([f"(No.{i+1} | score {n.score:.3f}): {n.text}" for i, n in enumerate(nodes)]))
|
||||
if self.debug_mode: logger.info(buf)
|
||||
return buf
|
||||
|
||||
def purge_vector_store(self):
|
||||
"""
|
||||
Purges the current vector store and creates a new one.
|
||||
"""
|
||||
self.purge()
|
||||
@@ -0,0 +1,22 @@
|
||||
import os
|
||||
from llama_index.core import SimpleDirectoryReader
|
||||
|
||||
supports_format = ['.csv', '.docx', '.epub', '.ipynb', '.mbox', '.md', '.pdf', '.txt', '.ppt',
|
||||
'.pptm', '.pptx']
|
||||
|
||||
|
||||
# 修改后的 extract_text 函数,结合 SimpleDirectoryReader 和自定义解析逻辑
|
||||
def extract_text(file_path):
|
||||
_, ext = os.path.splitext(file_path.lower())
|
||||
|
||||
# 使用 SimpleDirectoryReader 处理它支持的文件格式
|
||||
if ext in supports_format:
|
||||
try:
|
||||
reader = SimpleDirectoryReader(input_files=[file_path])
|
||||
documents = reader.load_data()
|
||||
if len(documents) > 0:
|
||||
return documents[0].text
|
||||
except Exception as e:
|
||||
pass
|
||||
|
||||
return None
|
||||
@@ -3,19 +3,33 @@
|
||||
# - 2 构建 docker build -t gpt-academic-nolocal-latex -f docs/GithubAction+NoLocal+Latex .
|
||||
# - 3 运行 docker run -v /home/fuqingxu/arxiv_cache:/root/arxiv_cache --rm -it --net=host gpt-academic-nolocal-latex
|
||||
|
||||
FROM menghuan1918/ubuntu_uv_ctex:latest
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
SHELL ["/bin/bash", "-c"]
|
||||
FROM fuqingxu/python311_texlive_ctex:latest
|
||||
ENV PATH "$PATH:/usr/local/texlive/2022/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2023/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2024/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2025/bin/x86_64-linux"
|
||||
ENV PATH "$PATH:/usr/local/texlive/2026/bin/x86_64-linux"
|
||||
|
||||
# 指定路径
|
||||
WORKDIR /gpt
|
||||
|
||||
RUN pip3 install openai numpy arxiv rich
|
||||
RUN pip3 install colorama Markdown pygments pymupdf
|
||||
RUN pip3 install python-docx pdfminer
|
||||
RUN pip3 install nougat-ocr
|
||||
|
||||
# 装载项目文件
|
||||
COPY . .
|
||||
RUN /root/.cargo/bin/uv venv --seed \
|
||||
&& source .venv/bin/activate \
|
||||
&& /root/.cargo/bin/uv pip install openai numpy arxiv rich colorama Markdown pygments pymupdf python-docx pdfminer \
|
||||
&& /root/.cargo/bin/uv pip install -r requirements.txt \
|
||||
&& /root/.cargo/bin/uv clean
|
||||
|
||||
|
||||
# 安装依赖
|
||||
RUN pip3 install -r requirements.txt
|
||||
|
||||
# edge-tts需要的依赖
|
||||
RUN apt update && apt install ffmpeg -y
|
||||
|
||||
# 可选步骤,用于预热模块
|
||||
RUN .venv/bin/python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD [".venv/bin/python3", "-u", "main.py"]
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
|
||||
@@ -0,0 +1,25 @@
|
||||
# 此Dockerfile适用于“无本地模型”的环境构建,如果需要使用chatglm等本地模型,请参考 docs/Dockerfile+ChatGLM
|
||||
# - 1 修改 `config.py`
|
||||
# - 2 构建 docker build -t gpt-academic-nolocal-latex -f docs/GithubAction+NoLocal+Latex .
|
||||
# - 3 运行 docker run -v /home/fuqingxu/arxiv_cache:/root/arxiv_cache --rm -it --net=host gpt-academic-nolocal-latex
|
||||
|
||||
FROM menghuan1918/ubuntu_uv_ctex:latest
|
||||
ENV DEBIAN_FRONTEND=noninteractive
|
||||
SHELL ["/bin/bash", "-c"]
|
||||
WORKDIR /gpt
|
||||
COPY . .
|
||||
RUN /root/.cargo/bin/uv venv --seed \
|
||||
&& source .venv/bin/activate \
|
||||
&& /root/.cargo/bin/uv pip install openai numpy arxiv rich colorama Markdown pygments pymupdf python-docx pdfminer \
|
||||
&& /root/.cargo/bin/uv pip install -r requirements.txt \
|
||||
&& /root/.cargo/bin/uv clean
|
||||
|
||||
# 对齐python3
|
||||
RUN rm -f /usr/bin/python3 && ln -s /gpt/.venv/bin/python /usr/bin/python3
|
||||
RUN rm -f /usr/bin/python && ln -s /gpt/.venv/bin/python /usr/bin/python
|
||||
|
||||
# 可选步骤,用于预热模块
|
||||
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||
|
||||
# 启动
|
||||
CMD ["python3", "-u", "main.py"]
|
||||
@@ -1285,4 +1285,3 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot,
|
||||
|
||||
# 更新一下llm_kwargs的参数,否则会出现参数不匹配的问题
|
||||
yield from method(inputs, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream, additional_fn)
|
||||
|
||||
|
||||
@@ -202,10 +202,13 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
|
||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||||
raise RuntimeError("用户取消了程序。")
|
||||
else: raise RuntimeError("意外Json结构:"+delta)
|
||||
if json_data and json_data['finish_reason'] == 'content_filter':
|
||||
raise RuntimeError("由于提问含不合规内容被Azure过滤。")
|
||||
if json_data and json_data['finish_reason'] == 'length':
|
||||
|
||||
finish_reason = json_data.get('finish_reason', None) if json_data else None
|
||||
if finish_reason == 'content_filter':
|
||||
raise RuntimeError("由于提问含不合规内容被过滤。")
|
||||
if finish_reason == 'length':
|
||||
raise ConnectionAbortedError("正常结束,但显示Token不足,导致输出不完整,请削减单次输入的文本量。")
|
||||
|
||||
return result
|
||||
|
||||
|
||||
@@ -536,4 +539,3 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
|
||||
|
||||
return headers,payload
|
||||
|
||||
|
||||
|
||||
@@ -19,4 +19,8 @@ if __name__ == "__main__":
|
||||
plugin_test = importlib.import_module('test_utils').plugin_test
|
||||
|
||||
|
||||
plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2203.01927")
|
||||
# plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2203.01927")
|
||||
# plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="gpt_log/arxiv_cache/2203.01927/workfolder")
|
||||
# plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2410.05779")
|
||||
plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="gpt_log/default_user/workfolder")
|
||||
|
||||
|
||||
在新工单中引用
屏蔽一个用户