比较提交

...

47 次代码提交

作者 SHA1 备注 提交日期
binary-husky
50a1ea83ef control whether to allow sharing translation results with GPTAC academic cloud. 2024-10-18 18:05:50 +00:00
binary-husky
a9c86a7fb8 pre 2024-10-18 14:16:24 +00:00
binary-husky
2b299cf579 Merge branch 'master' into frontier 2024-10-16 15:22:27 +00:00
wsg1873
310122f5a7 solve the concatenate error. (#2011) 2024-10-16 00:56:24 +08:00
binary-husky
0121cacc84 Merge branch 'master' into frontier 2024-10-15 09:10:36 +00:00
binary-husky
c83bf214d0 change arxiv download attempt url order 2024-10-15 09:09:24 +00:00
binary-husky
e34c49dce5 compat: deal with arxiv url change 2024-10-15 09:07:39 +00:00
binary-husky
f2dcd6ad55 compat: arxiv translation src shift 2024-10-15 09:06:57 +00:00
binary-husky
42d9712f20 Merge branch 'frontier' of github.com:binary-husky/chatgpt_academic into frontier 2024-10-15 08:24:01 +00:00
binary-husky
3890467c84 replace rm with rm -f 2024-10-15 07:32:29 +00:00
binary-husky
074b3c9828 explicitly declare default value 2024-10-15 06:41:12 +00:00
Nextstrain
b8e8457a01 关于o1系列模型无法正常请求的修复,多模型轮询KeyError: 'finish_reason'的修复 (#1992)
* Update bridge_all.py

* Update bridge_chatgpt.py

* Update bridge_chatgpt.py

* Update bridge_all.py

* Update bridge_all.py
2024-10-15 14:36:51 +08:00
binary-husky
2c93a24d7e fix dockerfile: try align python 2024-10-15 06:35:35 +00:00
binary-husky
e9af6ef3a0 fix: github action glitch 2024-10-15 06:32:47 +00:00
wsg1873
5ae8981dbb add the '/Fit' destination (#2009) 2024-10-14 22:50:56 +08:00
Boyin Liu
7f0ffa58f0 Boyin rag (#1983)
* first_version

* rag document support

* RAG interactive prompts added, issues resolved

* Resolve conflicts

* Resolve conflicts

* Resolve conflicts

* more file format support

* move import

* Resolve LlamaIndexRagWorker bug

* new resolve

* Address import  LlamaIndexRagWorker problem

* change import order

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>
2024-10-14 22:48:24 +08:00
binary-husky
5888d038aa move import 2024-10-13 16:17:10 +00:00
binary-husky
ee8213e936 Merge branch 'boyin_rag' into frontier 2024-10-13 16:12:51 +00:00
binary-husky
a57dcbcaeb Merge branch 'frontier' of github.com:binary-husky/chatgpt_academic into frontier 2024-10-13 08:26:06 +00:00
binary-husky
b812392a9d Merge branch 'master' into frontier 2024-10-13 08:25:47 +00:00
lbykkkk
fce4fa1ec7 more file format support 2024-10-12 18:25:33 +00:00
Boyin Liu
d13f1e270c Merge branch 'master' into boyin_rag 2024-10-11 22:31:07 +08:00
lbykkkk
85cf3d08eb Resolve conflicts 2024-10-11 22:29:56 +08:00
lbykkkk
584e747565 Resolve conflicts 2024-10-11 22:27:57 +08:00
lbykkkk
02ba653c19 Resolve conflicts 2024-10-11 22:21:53 +08:00
lbykkkk
2d12b5b27d RAG interactive prompts added, issues resolved 2024-10-11 01:06:17 +08:00
binary-husky
a4bcd262f9 Merge branch 'master' into frontier 2024-10-07 05:20:49 +00:00
Boyin Liu
748e31102f Merge branch 'master' into boyin_rag 2024-10-05 23:58:43 +08:00
binary-husky
97eef45ab7 Merge branch 'frontier' of github.com:binary-husky/chatgpt_academic into frontier 2024-10-01 11:59:14 +00:00
binary-husky
0c0e2acb9b remove logging extra 2024-10-01 11:57:47 +00:00
Ren Lifei
9fba8e0142 Added some modules to support openrouter (#1975)
* Added some modules for supporting openrouter model

Added some modules for supporting openrouter model

* Update config.py

* Update .gitignore

* Update bridge_openrouter.py

* Not changed actually

* Refactor logging in bridge_openrouter.py

---------

Co-authored-by: binary-husky <qingxu.fu@outlook.com>
2024-09-28 18:05:34 +08:00
binary-husky
7d7867fb64 remove comment 2024-09-23 15:16:13 +00:00
lbykkkk
7ea791d83a rag document support 2024-09-22 21:37:57 +08:00
binary-husky
f9dbaa39fb Merge branch 'frontier' of github.com:binary-husky/chatgpt_academic into frontier 2024-09-21 15:40:24 +00:00
binary-husky
bbc2288c5b relax llama index version 2024-09-21 15:40:10 +00:00
Steven Moder
64ab916838 fix: loguru argument error with proxy enabled (#1977) 2024-09-21 23:32:00 +08:00
binary-husky
8fe559da9f update translation matrix 2024-09-21 14:56:10 +00:00
binary-husky
09fd22091a fix: console output 2024-09-21 14:41:36 +00:00
lbykkkk
df717f8bba first_version 2024-09-20 00:06:59 +08:00
binary-husky
e296719b23 Merge branch 'purge_print' into frontier 2024-09-16 09:56:25 +00:00
binary-husky
2f343179a2 logging -> loguru: final stage 2024-09-15 15:51:51 +00:00
binary-husky
4d9604f2e9 update social helper 2024-09-15 15:16:36 +00:00
binary-husky
bbf9e9f868 logging -> loguru stage 4 2024-09-14 16:00:09 +00:00
binary-husky
aa1f967dd7 support o1-preview and o1-mini 2024-09-13 03:11:53 +00:00
binary-husky
0d082327c8 logging -> loguru: stage 3 2024-09-11 08:49:55 +00:00
binary-husky
80acd9c875 import loguru: stage 2 2024-09-11 08:18:01 +00:00
binary-husky
17cd4f8210 logging sys to loguru: stage 1 complete 2024-09-11 03:30:30 +00:00
共有 13 个文件被更改,包括 479 次插入197 次删除

查看文件

@@ -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 }}

查看文件

@@ -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)

查看文件

@@ -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="", 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)

查看文件

@@ -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)

查看文件

@@ -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

查看文件

@@ -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

查看文件

@@ -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()

查看文件

@@ -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

查看文件

@@ -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"]

查看文件

@@ -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"]

查看文件

@@ -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)

查看文件

@@ -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

查看文件

@@ -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")