镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-08 15:36:48 +00:00
比较提交
1 次代码提交
papershare
...
frontier_w
| 作者 | SHA1 | 提交日期 | |
|---|---|---|---|
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7415d532d1 |
@@ -1,14 +1,14 @@
|
|||||||
# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
|
# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
|
||||||
name: build-with-latex-arm
|
name: build-with-all-capacity-beta
|
||||||
|
|
||||||
on:
|
on:
|
||||||
push:
|
push:
|
||||||
branches:
|
branches:
|
||||||
- "master"
|
- 'master'
|
||||||
|
|
||||||
env:
|
env:
|
||||||
REGISTRY: ghcr.io
|
REGISTRY: ghcr.io
|
||||||
IMAGE_NAME: ${{ github.repository }}_with_latex_arm
|
IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta
|
||||||
|
|
||||||
jobs:
|
jobs:
|
||||||
build-and-push-image:
|
build-and-push-image:
|
||||||
@@ -18,17 +18,11 @@ jobs:
|
|||||||
packages: write
|
packages: write
|
||||||
|
|
||||||
steps:
|
steps:
|
||||||
- name: Set up QEMU
|
|
||||||
uses: docker/setup-qemu-action@v3
|
|
||||||
|
|
||||||
- name: Set up Docker Buildx
|
|
||||||
uses: docker/setup-buildx-action@v3
|
|
||||||
|
|
||||||
- name: Checkout repository
|
- name: Checkout repository
|
||||||
uses: actions/checkout@v4
|
uses: actions/checkout@v3
|
||||||
|
|
||||||
- name: Log in to the Container registry
|
- name: Log in to the Container registry
|
||||||
uses: docker/login-action@v3
|
uses: docker/login-action@v2
|
||||||
with:
|
with:
|
||||||
registry: ${{ env.REGISTRY }}
|
registry: ${{ env.REGISTRY }}
|
||||||
username: ${{ github.actor }}
|
username: ${{ github.actor }}
|
||||||
@@ -41,11 +35,10 @@ jobs:
|
|||||||
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
||||||
|
|
||||||
- name: Build and push Docker image
|
- name: Build and push Docker image
|
||||||
uses: docker/build-push-action@v6
|
uses: docker/build-push-action@v4
|
||||||
with:
|
with:
|
||||||
context: .
|
context: .
|
||||||
push: true
|
push: true
|
||||||
platforms: linux/arm64
|
file: docs/GithubAction+AllCapacityBeta
|
||||||
file: docs/GithubAction+NoLocal+Latex+Arm
|
|
||||||
tags: ${{ steps.meta.outputs.tags }}
|
tags: ${{ steps.meta.outputs.tags }}
|
||||||
labels: ${{ steps.meta.outputs.labels }}
|
labels: ${{ steps.meta.outputs.labels }}
|
||||||
44
.github/workflows/build-with-jittorllms.yml
vendored
普通文件
44
.github/workflows/build-with-jittorllms.yml
vendored
普通文件
@@ -0,0 +1,44 @@
|
|||||||
|
# https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
|
||||||
|
name: build-with-jittorllms
|
||||||
|
|
||||||
|
on:
|
||||||
|
push:
|
||||||
|
branches:
|
||||||
|
- 'master'
|
||||||
|
|
||||||
|
env:
|
||||||
|
REGISTRY: ghcr.io
|
||||||
|
IMAGE_NAME: ${{ github.repository }}_jittorllms
|
||||||
|
|
||||||
|
jobs:
|
||||||
|
build-and-push-image:
|
||||||
|
runs-on: ubuntu-latest
|
||||||
|
permissions:
|
||||||
|
contents: read
|
||||||
|
packages: write
|
||||||
|
|
||||||
|
steps:
|
||||||
|
- name: Checkout repository
|
||||||
|
uses: actions/checkout@v3
|
||||||
|
|
||||||
|
- name: Log in to the Container registry
|
||||||
|
uses: docker/login-action@v2
|
||||||
|
with:
|
||||||
|
registry: ${{ env.REGISTRY }}
|
||||||
|
username: ${{ github.actor }}
|
||||||
|
password: ${{ secrets.GITHUB_TOKEN }}
|
||||||
|
|
||||||
|
- name: Extract metadata (tags, labels) for Docker
|
||||||
|
id: meta
|
||||||
|
uses: docker/metadata-action@v4
|
||||||
|
with:
|
||||||
|
images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
|
||||||
|
|
||||||
|
- name: Build and push Docker image
|
||||||
|
uses: docker/build-push-action@v4
|
||||||
|
with:
|
||||||
|
context: .
|
||||||
|
push: true
|
||||||
|
file: docs/GithubAction+JittorLLMs
|
||||||
|
tags: ${{ steps.meta.outputs.tags }}
|
||||||
|
labels: ${{ steps.meta.outputs.labels }}
|
||||||
@@ -1,6 +1,5 @@
|
|||||||
> [!IMPORTANT]
|
> [!IMPORTANT]
|
||||||
> 2024.10.10: 突发停电,紧急恢复了提供[whl包](https://drive.google.com/file/d/19U_hsLoMrjOlQSzYS3pzWX9fTzyusArP/view?usp=sharing)的文件服务器
|
> 2024.6.1: 版本3.80加入插件二级菜单功能(详见wiki)
|
||||||
> 2024.10.8: 版本3.90加入对llama-index的初步支持,版本3.80加入插件二级菜单功能(详见wiki)
|
|
||||||
> 2024.5.1: 加入Doc2x翻译PDF论文的功能,[查看详情](https://github.com/binary-husky/gpt_academic/wiki/Doc2x)
|
> 2024.5.1: 加入Doc2x翻译PDF论文的功能,[查看详情](https://github.com/binary-husky/gpt_academic/wiki/Doc2x)
|
||||||
> 2024.3.11: 全力支持Qwen、GLM、DeepseekCoder等中文大语言模型! SoVits语音克隆模块,[查看详情](https://www.bilibili.com/video/BV1Rp421S7tF/)
|
> 2024.3.11: 全力支持Qwen、GLM、DeepseekCoder等中文大语言模型! SoVits语音克隆模块,[查看详情](https://www.bilibili.com/video/BV1Rp421S7tF/)
|
||||||
> 2024.1.17: 安装依赖时,请选择`requirements.txt`中**指定的版本**。 安装命令:`pip install -r requirements.txt`。本项目完全开源免费,您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
|
> 2024.1.17: 安装依赖时,请选择`requirements.txt`中**指定的版本**。 安装命令:`pip install -r requirements.txt`。本项目完全开源免费,您可通过订阅[在线服务](https://github.com/binary-husky/gpt_academic/wiki/online)的方式鼓励本项目的发展。
|
||||||
|
|||||||
@@ -6,6 +6,7 @@ from loguru import logger
|
|||||||
def get_crazy_functions():
|
def get_crazy_functions():
|
||||||
from crazy_functions.读文章写摘要 import 读文章写摘要
|
from crazy_functions.读文章写摘要 import 读文章写摘要
|
||||||
from crazy_functions.生成函数注释 import 批量生成函数注释
|
from crazy_functions.生成函数注释 import 批量生成函数注释
|
||||||
|
from crazy_functions.Rag_Interface import Rag问答
|
||||||
from crazy_functions.SourceCode_Analyse import 解析项目本身
|
from crazy_functions.SourceCode_Analyse import 解析项目本身
|
||||||
from crazy_functions.SourceCode_Analyse import 解析一个Python项目
|
from crazy_functions.SourceCode_Analyse import 解析一个Python项目
|
||||||
from crazy_functions.SourceCode_Analyse import 解析一个Matlab项目
|
from crazy_functions.SourceCode_Analyse import 解析一个Matlab项目
|
||||||
@@ -51,6 +52,13 @@ def get_crazy_functions():
|
|||||||
from crazy_functions.SourceCode_Comment import 注释Python项目
|
from crazy_functions.SourceCode_Comment import 注释Python项目
|
||||||
|
|
||||||
function_plugins = {
|
function_plugins = {
|
||||||
|
"Rag智能召回": {
|
||||||
|
"Group": "对话",
|
||||||
|
"Color": "stop",
|
||||||
|
"AsButton": False,
|
||||||
|
"Info": "将问答数据记录到向量库中,作为长期参考。",
|
||||||
|
"Function": HotReload(Rag问答),
|
||||||
|
},
|
||||||
"虚空终端": {
|
"虚空终端": {
|
||||||
"Group": "对话|编程|学术|智能体",
|
"Group": "对话|编程|学术|智能体",
|
||||||
"Color": "stop",
|
"Color": "stop",
|
||||||
@@ -699,31 +707,6 @@ def get_crazy_functions():
|
|||||||
logger.error(trimmed_format_exc())
|
logger.error(trimmed_format_exc())
|
||||||
logger.error("Load function plugin failed")
|
logger.error("Load function plugin failed")
|
||||||
|
|
||||||
try:
|
|
||||||
from crazy_functions.Rag_Interface import Rag问答
|
|
||||||
|
|
||||||
function_plugins.update(
|
|
||||||
{
|
|
||||||
"Rag智能召回": {
|
|
||||||
"Group": "对话",
|
|
||||||
"Color": "stop",
|
|
||||||
"AsButton": False,
|
|
||||||
"Info": "将问答数据记录到向量库中,作为长期参考。",
|
|
||||||
"Function": HotReload(Rag问答),
|
|
||||||
},
|
|
||||||
}
|
|
||||||
)
|
|
||||||
except:
|
|
||||||
logger.error(trimmed_format_exc())
|
|
||||||
logger.error("Load function plugin failed")
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
# try:
|
# try:
|
||||||
# from crazy_functions.高级功能函数模板 import 测试图表渲染
|
# from crazy_functions.高级功能函数模板 import 测试图表渲染
|
||||||
# function_plugins.update({
|
# function_plugins.update({
|
||||||
|
|||||||
@@ -3,7 +3,7 @@ from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip
|
|||||||
from functools import partial
|
from functools import partial
|
||||||
from loguru import logger
|
from loguru import logger
|
||||||
|
|
||||||
import glob, os, requests, time, json, tarfile, threading
|
import glob, os, requests, time, json, tarfile
|
||||||
|
|
||||||
pj = os.path.join
|
pj = os.path.join
|
||||||
ARXIV_CACHE_DIR = get_conf("ARXIV_CACHE_DIR")
|
ARXIV_CACHE_DIR = get_conf("ARXIV_CACHE_DIR")
|
||||||
@@ -138,43 +138,25 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
|
|||||||
cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
|
cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
|
||||||
if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
|
if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
|
||||||
|
|
||||||
extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
|
url_tar = url_.replace('/abs/', '/e-print/')
|
||||||
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
|
translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
|
||||||
dst = pj(translation_dir, arxiv_id + '.tar')
|
extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
|
||||||
os.makedirs(translation_dir, exist_ok=True)
|
os.makedirs(translation_dir, exist_ok=True)
|
||||||
|
|
||||||
# <-------------- download arxiv source file ------------->
|
# <-------------- download arxiv source file ------------->
|
||||||
|
dst = pj(translation_dir, arxiv_id + '.tar')
|
||||||
def fix_url_and_download():
|
if os.path.exists(dst):
|
||||||
# for url_tar in [url_.replace('/abs/', '/e-print/'), url_.replace('/abs/', '/src/')]:
|
yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history) # 刷新界面
|
||||||
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:
|
else:
|
||||||
yield from update_ui_lastest_msg(f"开始下载 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history) # 刷新界面
|
||||||
success = fix_url_and_download()
|
proxies = get_conf('proxies')
|
||||||
yield from update_ui_lastest_msg(f"下载完成 {arxiv_id}", chatbot=chatbot, history=history) # 刷新界面
|
r = requests.get(url_tar, proxies=proxies)
|
||||||
|
with open(dst, 'wb+') as f:
|
||||||
|
f.write(r.content)
|
||||||
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 ------------->
|
# <-------------- 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
|
from toolbox import extract_archive
|
||||||
try:
|
extract_archive(file_path=dst, dest_dir=extract_dst)
|
||||||
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
|
return extract_dst, arxiv_id
|
||||||
|
|
||||||
|
|
||||||
@@ -338,17 +320,11 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
|
|||||||
# <-------------- more requirements ------------->
|
# <-------------- more requirements ------------->
|
||||||
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
|
||||||
more_req = plugin_kwargs.get("advanced_arg", "")
|
more_req = plugin_kwargs.get("advanced_arg", "")
|
||||||
|
no_cache = more_req.startswith("--no-cache")
|
||||||
no_cache = ("--no-cache" in more_req)
|
if no_cache: more_req.lstrip("--no-cache")
|
||||||
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
|
allow_cache = not no_cache
|
||||||
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
_switch_prompt_ = partial(switch_prompt, more_requirement=more_req)
|
||||||
|
|
||||||
|
|
||||||
# <-------------- check deps ------------->
|
# <-------------- check deps ------------->
|
||||||
try:
|
try:
|
||||||
import glob, os, time, subprocess
|
import glob, os, time, subprocess
|
||||||
@@ -375,20 +351,6 @@ 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
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
return
|
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):
|
if os.path.exists(txt):
|
||||||
project_folder = txt
|
project_folder = txt
|
||||||
else:
|
else:
|
||||||
@@ -426,21 +388,14 @@ def Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot,
|
|||||||
# <-------------- zip PDF ------------->
|
# <-------------- zip PDF ------------->
|
||||||
zip_res = zip_result(project_folder)
|
zip_res = zip_result(project_folder)
|
||||||
if success:
|
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)...'))
|
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
|
time.sleep(1) # 刷新界面
|
||||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||||
|
|
||||||
else:
|
else:
|
||||||
chatbot.append((f"失败了",
|
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) ...'))
|
'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
|
time.sleep(1) # 刷新界面
|
||||||
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
promote_file_to_downloadzone(file=zip_res, chatbot=chatbot)
|
||||||
|
|
||||||
|
|||||||
@@ -30,9 +30,6 @@ class Arxiv_Localize(GptAcademicPluginTemplate):
|
|||||||
default_value="", type="string").model_dump_json(), # 高级参数输入区,自动同步
|
default_value="", type="string").model_dump_json(), # 高级参数输入区,自动同步
|
||||||
"allow_cache":
|
"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(),
|
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
|
return gui_definition
|
||||||
|
|
||||||
@@ -41,14 +38,9 @@ class Arxiv_Localize(GptAcademicPluginTemplate):
|
|||||||
执行插件
|
执行插件
|
||||||
"""
|
"""
|
||||||
allow_cache = plugin_kwargs["allow_cache"]
|
allow_cache = plugin_kwargs["allow_cache"]
|
||||||
allow_cloudio = plugin_kwargs["allow_cloudio"]
|
|
||||||
advanced_arg = plugin_kwargs["advanced_arg"]
|
advanced_arg = plugin_kwargs["advanced_arg"]
|
||||||
|
|
||||||
if allow_cache == "从头执行": plugin_kwargs["advanced_arg"] = "--no-cache " + 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)
|
yield from Latex翻译中文并重新编译PDF(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@@ -1,108 +1,53 @@
|
|||||||
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 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 input_clipping
|
||||||
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||||||
|
|
||||||
|
VECTOR_STORE_TYPE = "Milvus"
|
||||||
|
|
||||||
|
if VECTOR_STORE_TYPE == "Milvus":
|
||||||
|
try:
|
||||||
|
from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
|
||||||
|
except:
|
||||||
|
VECTOR_STORE_TYPE = "Simple"
|
||||||
|
|
||||||
|
if VECTOR_STORE_TYPE == "Simple":
|
||||||
|
from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
|
||||||
|
|
||||||
|
|
||||||
RAG_WORKER_REGISTER = {}
|
RAG_WORKER_REGISTER = {}
|
||||||
|
|
||||||
MAX_HISTORY_ROUND = 5
|
MAX_HISTORY_ROUND = 5
|
||||||
MAX_CONTEXT_TOKEN_LIMIT = 4096
|
MAX_CONTEXT_TOKEN_LIMIT = 4096
|
||||||
REMEMBER_PREVIEW = 1000
|
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
|
@CatchException
|
||||||
def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
def Rag问答(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
|
||||||
|
|
||||||
# import vector store lib
|
|
||||||
VECTOR_STORE_TYPE = "Milvus"
|
|
||||||
if VECTOR_STORE_TYPE == "Milvus":
|
|
||||||
try:
|
|
||||||
from crazy_functions.rag_fns.milvus_worker import MilvusRagWorker as LlamaIndexRagWorker
|
|
||||||
except:
|
|
||||||
VECTOR_STORE_TYPE = "Simple"
|
|
||||||
if VECTOR_STORE_TYPE == "Simple":
|
|
||||||
from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
|
|
||||||
|
|
||||||
# 1. we retrieve rag worker from global context
|
# 1. we retrieve rag worker from global context
|
||||||
user_name = chatbot.get_user()
|
user_name = chatbot.get_user()
|
||||||
checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
|
checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
|
||||||
|
|
||||||
if user_name in RAG_WORKER_REGISTER:
|
if user_name in RAG_WORKER_REGISTER:
|
||||||
rag_worker = RAG_WORKER_REGISTER[user_name]
|
rag_worker = RAG_WORKER_REGISTER[user_name]
|
||||||
else:
|
else:
|
||||||
rag_worker = RAG_WORKER_REGISTER[user_name] = LlamaIndexRagWorker(
|
rag_worker = RAG_WORKER_REGISTER[user_name] = LlamaIndexRagWorker(
|
||||||
user_name,
|
user_name,
|
||||||
llm_kwargs,
|
llm_kwargs,
|
||||||
checkpoint_dir=checkpoint_dir,
|
checkpoint_dir=checkpoint_dir,
|
||||||
auto_load_checkpoint=True
|
auto_load_checkpoint=True)
|
||||||
)
|
|
||||||
|
|
||||||
current_context = f"{VECTOR_STORE_TYPE} @ {checkpoint_dir}"
|
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"
|
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":
|
||||||
# 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
|
|
||||||
|
|
||||||
elif txt == "清空向量数据库":
|
|
||||||
chatbot.append([txt, f'正在清空 ({current_context}) ...'])
|
chatbot.append([txt, f'正在清空 ({current_context}) ...'])
|
||||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
rag_worker.purge_vector_store()
|
rag_worker.purge()
|
||||||
yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 刷新界面
|
yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 刷新界面
|
||||||
return
|
return
|
||||||
|
|
||||||
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}) ...'])
|
chatbot.append([txt, f'正在召回知识 ({current_context}) ...'])
|
||||||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||||||
|
|
||||||
# 4. Clip history to reduce token consumption
|
# 2. clip history to reduce token consumption
|
||||||
|
# 2-1. reduce chat round
|
||||||
txt_origin = txt
|
txt_origin = txt
|
||||||
|
|
||||||
if len(history) > MAX_HISTORY_ROUND * 2:
|
if len(history) > MAX_HISTORY_ROUND * 2:
|
||||||
@@ -110,47 +55,41 @@ 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)
|
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"])
|
input_is_clipped_flag = (flags["original_input_len"] != flags["clipped_input_len"])
|
||||||
|
|
||||||
# 5. If input is clipped, add input to vector store before retrieve
|
# 2-2. if input is clipped, add input to vector store before retrieve
|
||||||
if input_is_clipped_flag:
|
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
|
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
|
# save input to vector store
|
||||||
rag_worker.add_text_to_vector_store(txt_origin)
|
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:
|
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:]
|
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:
|
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:]
|
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
|
||||||
else:
|
else:
|
||||||
i_say_to_remember = i_say = txt_clip
|
i_say_to_remember = i_say = txt_clip
|
||||||
else:
|
else:
|
||||||
i_say_to_remember = i_say = txt_clip
|
i_say_to_remember = i_say = txt_clip
|
||||||
|
|
||||||
# 6. Search vector store and build prompts
|
# 3. we search vector store and build prompts
|
||||||
nodes = rag_worker.retrieve_from_store_with_query(i_say)
|
nodes = rag_worker.retrieve_from_store_with_query(i_say)
|
||||||
prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
|
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(
|
model_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
|
||||||
inputs=prompt,
|
inputs=prompt, inputs_show_user=i_say,
|
||||||
inputs_show_user=i_say,
|
llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
|
||||||
llm_kwargs=llm_kwargs,
|
|
||||||
chatbot=chatbot,
|
|
||||||
history=history,
|
|
||||||
sys_prompt=system_prompt,
|
sys_prompt=system_prompt,
|
||||||
retry_times_at_unknown_error=0
|
retry_times_at_unknown_error=0
|
||||||
)
|
)
|
||||||
|
|
||||||
# 8. Remember Q&A
|
# 5. remember what has been asked / answered
|
||||||
yield from update_ui_lastest_msg(
|
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
|
||||||
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)
|
rag_worker.remember_qa(i_say_to_remember, model_say)
|
||||||
history.extend([i_say, model_say])
|
history.extend([i_say, model_say])
|
||||||
|
|
||||||
# 9. Final UI Update
|
yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip) # 刷新界面
|
||||||
yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip)
|
|
||||||
|
|||||||
@@ -3,7 +3,7 @@ import re
|
|||||||
import shutil
|
import shutil
|
||||||
import numpy as np
|
import numpy as np
|
||||||
from loguru import logger
|
from loguru import logger
|
||||||
from toolbox import update_ui, update_ui_lastest_msg, get_log_folder, gen_time_str
|
from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
|
||||||
from toolbox import get_conf, promote_file_to_downloadzone
|
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 PRESERVE, TRANSFORM
|
||||||
from crazy_functions.latex_fns.latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
from crazy_functions.latex_fns.latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
|
||||||
@@ -423,9 +423,6 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
|
|||||||
except Exception as e:
|
except Exception as e:
|
||||||
logger.error(e)
|
logger.error(e)
|
||||||
pass
|
pass
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
return True # 成功啦
|
return True # 成功啦
|
||||||
else:
|
else:
|
||||||
if n_fix>=max_try: break
|
if n_fix>=max_try: break
|
||||||
@@ -471,66 +468,3 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
|
|||||||
except:
|
except:
|
||||||
from toolbox import trimmed_format_exc
|
from toolbox import trimmed_format_exc
|
||||||
logger.error('writing html result failed:', 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
|
|
||||||
|
|||||||
@@ -644,17 +644,8 @@ def run_in_subprocess(func):
|
|||||||
|
|
||||||
|
|
||||||
def _merge_pdfs(pdf1_path, pdf2_path, output_path):
|
def _merge_pdfs(pdf1_path, pdf2_path, output_path):
|
||||||
try:
|
|
||||||
logger.info("Merging PDFs using _merge_pdfs_ng")
|
|
||||||
_merge_pdfs_ng(pdf1_path, pdf2_path, output_path)
|
|
||||||
except:
|
|
||||||
logger.info("Merging PDFs using _merge_pdfs_legacy")
|
|
||||||
_merge_pdfs_legacy(pdf1_path, pdf2_path, output_path)
|
|
||||||
|
|
||||||
|
|
||||||
def _merge_pdfs_ng(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
|
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
|
||||||
from PyPDF2.generic import NameObject, TextStringObject, ArrayObject, FloatObject, NumberObject
|
from PyPDF2.generic import NameObject, TextStringObject,ArrayObject,FloatObject,NumberObject
|
||||||
|
|
||||||
Percent = 1
|
Percent = 1
|
||||||
# raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
|
# raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
|
||||||
@@ -697,206 +688,65 @@ def _merge_pdfs_ng(pdf1_path, pdf2_path, output_path):
|
|||||||
),
|
),
|
||||||
0,
|
0,
|
||||||
)
|
)
|
||||||
if "/Annots" in new_page:
|
if '/Annots' in page1:
|
||||||
annotations = new_page["/Annots"]
|
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):
|
for i, annot in enumerate(annotations):
|
||||||
annot_obj = annot.get_object()
|
annot_obj = annot.get_object()
|
||||||
|
|
||||||
# 检查注释类型是否是链接(/Link)
|
# 检查注释类型是否是链接(/Link)
|
||||||
if annot_obj.get("/Subtype") == "/Link":
|
if annot_obj.get('/Subtype') == '/Link':
|
||||||
# 检查是否为内部链接跳转(/GoTo)或外部URI链接(/URI)
|
# 检查是否为内部链接跳转(/GoTo)或外部URI链接(/URI)
|
||||||
action = annot_obj.get("/A")
|
action = annot_obj.get('/A')
|
||||||
if action:
|
if action:
|
||||||
|
|
||||||
if "/S" in action and action["/S"] == "/GoTo":
|
if '/S' in action and action['/S'] == '/GoTo':
|
||||||
# 内部链接:跳转到文档中的某个页面
|
# 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
|
dest = action.get('/D') # 目标页或目标位置
|
||||||
# if dest and annot.idnum in page2_annot_id:
|
if dest and annot.idnum in page2_annot_id:
|
||||||
# if dest in pdf2_reader.named_destinations:
|
# 获取原始文件中跳转信息,包括跳转页面
|
||||||
if dest and page2.annotations:
|
destination = pdf2_reader.named_destinations[dest]
|
||||||
if annot in page2.annotations:
|
page_number = pdf2_reader.get_destination_page_number(destination)
|
||||||
# 获取原始文件中跳转信息,包括跳转页面
|
#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%
|
||||||
destination = pdf2_reader.named_destinations[
|
#“/D”:[10,'/XYZ',100,100,0]
|
||||||
dest
|
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
|
||||||
page_number = (
|
})
|
||||||
pdf2_reader.get_destination_page_number(
|
rect = annot_obj.get('/Rect')
|
||||||
destination
|
# 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] ])
|
||||||
# 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%
|
annot_obj.update({
|
||||||
# “/D”:[10,'/XYZ',100,100,0]
|
NameObject("/Rect"): rect # 确保键和值是 PdfObject
|
||||||
if destination.dest_array[1] == "/XYZ":
|
})
|
||||||
annot_obj["/A"].update(
|
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
|
||||||
NameObject("/D"): ArrayObject(
|
destination = pdf1_reader.named_destinations[dest]
|
||||||
[
|
page_number = pdf1_reader.get_destination_page_number(destination)
|
||||||
NumberObject(page_number),
|
#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%
|
||||||
destination.dest_array[1],
|
#“/D”:[10,'/XYZ',100,100,0]
|
||||||
FloatObject(
|
annot_obj['/A'].update({
|
||||||
destination.dest_array[
|
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
|
||||||
2
|
})
|
||||||
]
|
rect = annot_obj.get('/Rect')
|
||||||
+ int(
|
rect = ArrayObject([FloatObject(rect[0]),rect[1],
|
||||||
page1.mediaBox.getWidth()
|
FloatObject(rect[2]),rect[3] ])
|
||||||
)
|
annot_obj.update({
|
||||||
),
|
NameObject("/Rect"): rect # 确保键和值是 PdfObject
|
||||||
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")
|
elif '/S' in action and action['/S'] == '/URI':
|
||||||
# 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
|
|
||||||
]
|
|
||||||
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
|
# 外部链接:跳转到某个URI
|
||||||
uri = action.get("/URI")
|
uri = action.get('/URI')
|
||||||
output_writer.addPage(new_page)
|
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
|
|
||||||
|
|
||||||
Percent = 0.95
|
|
||||||
# raise RuntimeError('PyPDF2 has a serious memory leak problem, please use other tools to merge PDF files.')
|
|
||||||
# Open the first PDF file
|
|
||||||
with open(pdf1_path, "rb") as pdf1_file:
|
|
||||||
pdf1_reader = PyPDF2.PdfFileReader(pdf1_file)
|
|
||||||
# Open the second PDF file
|
|
||||||
with open(pdf2_path, "rb") as pdf2_file:
|
|
||||||
pdf2_reader = PyPDF2.PdfFileReader(pdf2_file)
|
|
||||||
# Create a new PDF file to store the merged pages
|
|
||||||
output_writer = PyPDF2.PdfFileWriter()
|
|
||||||
# Determine the number of pages in each PDF file
|
|
||||||
num_pages = max(pdf1_reader.numPages, pdf2_reader.numPages)
|
|
||||||
# Merge the pages from the two PDF files
|
|
||||||
for page_num in range(num_pages):
|
|
||||||
# Add the page from the first PDF file
|
|
||||||
if page_num < pdf1_reader.numPages:
|
|
||||||
page1 = pdf1_reader.getPage(page_num)
|
|
||||||
else:
|
|
||||||
page1 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
|
|
||||||
# Add the page from the second PDF file
|
|
||||||
if page_num < pdf2_reader.numPages:
|
|
||||||
page2 = pdf2_reader.getPage(page_num)
|
|
||||||
else:
|
|
||||||
page2 = PyPDF2.PageObject.createBlankPage(pdf1_reader)
|
|
||||||
# Create a new empty page with double width
|
|
||||||
new_page = PyPDF2.PageObject.createBlankPage(
|
|
||||||
width=int(
|
|
||||||
int(page1.mediaBox.getWidth())
|
|
||||||
+ int(page2.mediaBox.getWidth()) * Percent
|
|
||||||
),
|
|
||||||
height=max(page1.mediaBox.getHeight(), page2.mediaBox.getHeight()),
|
|
||||||
)
|
|
||||||
new_page.mergeTranslatedPage(page1, 0, 0)
|
|
||||||
new_page.mergeTranslatedPage(
|
|
||||||
page2,
|
|
||||||
int(
|
|
||||||
int(page1.mediaBox.getWidth())
|
|
||||||
- int(page2.mediaBox.getWidth()) * (1 - Percent)
|
|
||||||
),
|
|
||||||
0,
|
|
||||||
)
|
|
||||||
output_writer.addPage(new_page)
|
output_writer.addPage(new_page)
|
||||||
# Save the merged PDF file
|
# Save the merged PDF file
|
||||||
with open(output_path, "wb") as output_file:
|
with open(output_path, "wb") as output_file:
|
||||||
|
|||||||
@@ -1,13 +1,17 @@
|
|||||||
|
import llama_index
|
||||||
|
import os
|
||||||
import atexit
|
import atexit
|
||||||
from loguru import logger
|
from loguru import logger
|
||||||
from typing import List
|
from typing import List
|
||||||
|
|
||||||
from llama_index.core import Document
|
from llama_index.core import Document
|
||||||
from llama_index.core.ingestion import run_transformations
|
|
||||||
from llama_index.core.schema import TextNode
|
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
|
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
|
||||||
|
|
||||||
DEFAULT_QUERY_GENERATION_PROMPT = """\
|
DEFAULT_QUERY_GENERATION_PROMPT = """\
|
||||||
Now, you have context information as below:
|
Now, you have context information as below:
|
||||||
@@ -59,7 +63,7 @@ class SaveLoad():
|
|||||||
def purge(self):
|
def purge(self):
|
||||||
import shutil
|
import shutil
|
||||||
shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
|
shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
|
||||||
self.vs_index = self.create_new_vs(self.checkpoint_dir)
|
self.vs_index = self.create_new_vs()
|
||||||
|
|
||||||
|
|
||||||
class LlamaIndexRagWorker(SaveLoad):
|
class LlamaIndexRagWorker(SaveLoad):
|
||||||
@@ -71,7 +75,7 @@ class LlamaIndexRagWorker(SaveLoad):
|
|||||||
if auto_load_checkpoint:
|
if auto_load_checkpoint:
|
||||||
self.vs_index = self.load_from_checkpoint(checkpoint_dir)
|
self.vs_index = self.load_from_checkpoint(checkpoint_dir)
|
||||||
else:
|
else:
|
||||||
self.vs_index = self.create_new_vs()
|
self.vs_index = self.create_new_vs(checkpoint_dir)
|
||||||
atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
|
atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
|
||||||
|
|
||||||
def assign_embedding_model(self):
|
def assign_embedding_model(self):
|
||||||
@@ -87,52 +91,40 @@ class LlamaIndexRagWorker(SaveLoad):
|
|||||||
logger.info('oo --------inspect_vector_store end--------')
|
logger.info('oo --------inspect_vector_store end--------')
|
||||||
return vector_store_preview
|
return vector_store_preview
|
||||||
|
|
||||||
def add_documents_to_vector_store(self, document_list: List[Document]):
|
def add_documents_to_vector_store(self, document_list):
|
||||||
"""
|
documents = [Document(text=t) for t in document_list]
|
||||||
Adds a list of Document objects to the vector store after processing.
|
|
||||||
"""
|
|
||||||
documents = document_list
|
|
||||||
documents_nodes = run_transformations(
|
documents_nodes = run_transformations(
|
||||||
documents, # type: ignore
|
documents, # type: ignore
|
||||||
self.vs_index._transformations,
|
self.vs_index._transformations,
|
||||||
show_progress=True
|
show_progress=True
|
||||||
)
|
)
|
||||||
self.vs_index.insert_nodes(documents_nodes)
|
self.vs_index.insert_nodes(documents_nodes)
|
||||||
if self.debug_mode:
|
if self.debug_mode: self.inspect_vector_store()
|
||||||
self.inspect_vector_store()
|
|
||||||
|
|
||||||
def add_text_to_vector_store(self, text: str):
|
def add_text_to_vector_store(self, text):
|
||||||
node = TextNode(text=text)
|
node = TextNode(text=text)
|
||||||
documents_nodes = run_transformations(
|
documents_nodes = run_transformations(
|
||||||
[node],
|
[node],
|
||||||
self.vs_index._transformations,
|
self.vs_index._transformations,
|
||||||
show_progress=True
|
show_progress=True
|
||||||
)
|
)
|
||||||
self.vs_index.insert_nodes(documents_nodes)
|
self.vs_index.insert_nodes(documents_nodes)
|
||||||
if self.debug_mode:
|
if self.debug_mode: self.inspect_vector_store()
|
||||||
self.inspect_vector_store()
|
|
||||||
|
|
||||||
def remember_qa(self, question, answer):
|
def remember_qa(self, question, answer):
|
||||||
formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
|
formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
|
||||||
self.add_text_to_vector_store(formatted_str)
|
self.add_text_to_vector_store(formatted_str)
|
||||||
|
|
||||||
def retrieve_from_store_with_query(self, query):
|
def retrieve_from_store_with_query(self, query):
|
||||||
if self.debug_mode:
|
if self.debug_mode: self.inspect_vector_store()
|
||||||
self.inspect_vector_store()
|
|
||||||
retriever = self.vs_index.as_retriever()
|
retriever = self.vs_index.as_retriever()
|
||||||
return retriever.retrieve(query)
|
return retriever.retrieve(query)
|
||||||
|
|
||||||
def build_prompt(self, query, nodes):
|
def build_prompt(self, query, nodes):
|
||||||
context_str = self.generate_node_array_preview(nodes)
|
context_str = self.generate_node_array_preview(nodes)
|
||||||
return DEFAULT_QUERY_GENERATION_PROMPT.format(context_str=context_str, query_str=query)
|
return DEFAULT_QUERY_GENERATION_PROMPT.format(context_str=context_str, query_str=query)
|
||||||
|
|
||||||
def generate_node_array_preview(self, nodes):
|
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)]))
|
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)
|
if self.debug_mode: logger.info(buf)
|
||||||
return buf
|
return buf
|
||||||
|
|
||||||
def purge_vector_store(self):
|
|
||||||
"""
|
|
||||||
Purges the current vector store and creates a new one.
|
|
||||||
"""
|
|
||||||
self.purge()
|
|
||||||
@@ -1,22 +0,0 @@
|
|||||||
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
|
|
||||||
1
docs/Dockerfile+JittorLLM
普通文件
1
docs/Dockerfile+JittorLLM
普通文件
@@ -0,0 +1 @@
|
|||||||
|
# 此Dockerfile不再维护,请前往docs/GithubAction+JittorLLMs
|
||||||
@@ -0,0 +1,57 @@
|
|||||||
|
# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacity --network=host --build-arg http_proxy=http://localhost:10881 --build-arg https_proxy=http://localhost:10881 .
|
||||||
|
# docker build -t gpt-academic-all-capacity -f docs/GithubAction+AllCapacityBeta --network=host .
|
||||||
|
# docker run -it --net=host gpt-academic-all-capacity bash
|
||||||
|
|
||||||
|
# 从NVIDIA源,从而支持显卡(检查宿主的nvidia-smi中的cuda版本必须>=11.3)
|
||||||
|
FROM fuqingxu/11.3.1-runtime-ubuntu20.04-with-texlive:latest
|
||||||
|
|
||||||
|
# edge-tts需要的依赖,某些pip包所需的依赖
|
||||||
|
RUN apt update && apt install ffmpeg build-essential -y
|
||||||
|
|
||||||
|
# use python3 as the system default python
|
||||||
|
WORKDIR /gpt
|
||||||
|
RUN curl -sS https://bootstrap.pypa.io/get-pip.py | python3.8
|
||||||
|
|
||||||
|
# # 非必要步骤,更换pip源 (以下三行,可以删除)
|
||||||
|
# RUN echo '[global]' > /etc/pip.conf && \
|
||||||
|
# echo 'index-url = https://mirrors.aliyun.com/pypi/simple/' >> /etc/pip.conf && \
|
||||||
|
# echo 'trusted-host = mirrors.aliyun.com' >> /etc/pip.conf
|
||||||
|
|
||||||
|
# 下载pytorch
|
||||||
|
RUN python3 -m pip install torch torchvision --extra-index-url https://download.pytorch.org/whl/cu113
|
||||||
|
# 准备pip依赖
|
||||||
|
RUN python3 -m pip install openai numpy arxiv rich
|
||||||
|
RUN python3 -m pip install colorama Markdown pygments pymupdf
|
||||||
|
RUN python3 -m pip install python-docx moviepy pdfminer
|
||||||
|
RUN python3 -m pip install zh_langchain==0.2.1 pypinyin
|
||||||
|
RUN python3 -m pip install rarfile py7zr
|
||||||
|
RUN python3 -m pip install aliyun-python-sdk-core==2.13.3 pyOpenSSL webrtcvad scipy git+https://github.com/aliyun/alibabacloud-nls-python-sdk.git
|
||||||
|
# 下载分支
|
||||||
|
WORKDIR /gpt
|
||||||
|
RUN git clone --depth=1 https://github.com/binary-husky/gpt_academic.git
|
||||||
|
WORKDIR /gpt/gpt_academic
|
||||||
|
RUN git clone --depth=1 https://github.com/OpenLMLab/MOSS.git request_llms/moss
|
||||||
|
|
||||||
|
RUN python3 -m pip install -r requirements.txt
|
||||||
|
RUN python3 -m pip install -r request_llms/requirements_moss.txt
|
||||||
|
RUN python3 -m pip install -r request_llms/requirements_qwen.txt
|
||||||
|
RUN python3 -m pip install -r request_llms/requirements_chatglm.txt
|
||||||
|
RUN python3 -m pip install -r request_llms/requirements_newbing.txt
|
||||||
|
RUN python3 -m pip install nougat-ocr
|
||||||
|
|
||||||
|
|
||||||
|
# 预热Tiktoken模块
|
||||||
|
RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
|
||||||
|
|
||||||
|
# 安装知识库插件的额外依赖
|
||||||
|
RUN apt-get update && apt-get install libgl1 -y
|
||||||
|
RUN pip3 install transformers protobuf langchain sentence-transformers faiss-cpu nltk beautifulsoup4 bitsandbytes tabulate icetk --upgrade
|
||||||
|
RUN pip3 install unstructured[all-docs] --upgrade
|
||||||
|
RUN python3 -c 'from check_proxy import warm_up_vectordb; warm_up_vectordb()'
|
||||||
|
RUN rm -rf /usr/local/lib/python3.8/dist-packages/tests
|
||||||
|
|
||||||
|
|
||||||
|
# COPY .cache /root/.cache
|
||||||
|
# COPY config_private.py config_private.py
|
||||||
|
# 启动
|
||||||
|
CMD ["python3", "-u", "main.py"]
|
||||||
@@ -1,25 +0,0 @@
|
|||||||
# 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"]
|
|
||||||
@@ -256,8 +256,6 @@ model_info = {
|
|||||||
"max_token": 128000,
|
"max_token": 128000,
|
||||||
"tokenizer": tokenizer_gpt4,
|
"tokenizer": tokenizer_gpt4,
|
||||||
"token_cnt": get_token_num_gpt4,
|
"token_cnt": get_token_num_gpt4,
|
||||||
"openai_disable_system_prompt": True,
|
|
||||||
"openai_disable_stream": True,
|
|
||||||
},
|
},
|
||||||
"o1-mini": {
|
"o1-mini": {
|
||||||
"fn_with_ui": chatgpt_ui,
|
"fn_with_ui": chatgpt_ui,
|
||||||
@@ -266,8 +264,6 @@ model_info = {
|
|||||||
"max_token": 128000,
|
"max_token": 128000,
|
||||||
"tokenizer": tokenizer_gpt4,
|
"tokenizer": tokenizer_gpt4,
|
||||||
"token_cnt": get_token_num_gpt4,
|
"token_cnt": get_token_num_gpt4,
|
||||||
"openai_disable_system_prompt": True,
|
|
||||||
"openai_disable_stream": True,
|
|
||||||
},
|
},
|
||||||
|
|
||||||
"gpt-4-turbo": {
|
"gpt-4-turbo": {
|
||||||
@@ -1285,3 +1281,4 @@ 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
|
# 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)
|
yield from method(inputs, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, stream, additional_fn)
|
||||||
|
|
||||||
|
|||||||
@@ -202,13 +202,10 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
|
|||||||
if (time.time()-observe_window[1]) > watch_dog_patience:
|
if (time.time()-observe_window[1]) > watch_dog_patience:
|
||||||
raise RuntimeError("用户取消了程序。")
|
raise RuntimeError("用户取消了程序。")
|
||||||
else: raise RuntimeError("意外Json结构:"+delta)
|
else: raise RuntimeError("意外Json结构:"+delta)
|
||||||
|
if json_data and json_data['finish_reason'] == 'content_filter':
|
||||||
finish_reason = json_data.get('finish_reason', None) if json_data else None
|
raise RuntimeError("由于提问含不合规内容被Azure过滤。")
|
||||||
if finish_reason == 'content_filter':
|
if json_data and json_data['finish_reason'] == 'length':
|
||||||
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。")
|
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
|
return result
|
||||||
|
|
||||||
|
|
||||||
@@ -539,3 +536,4 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
|
|||||||
|
|
||||||
return headers,payload
|
return headers,payload
|
||||||
|
|
||||||
|
|
||||||
|
|||||||
@@ -2,15 +2,14 @@ https://public.agent-matrix.com/publish/gradio-3.32.10-py3-none-any.whl
|
|||||||
fastapi==0.110
|
fastapi==0.110
|
||||||
gradio-client==0.8
|
gradio-client==0.8
|
||||||
pypdf2==2.12.1
|
pypdf2==2.12.1
|
||||||
httpx<=0.25.2
|
|
||||||
zhipuai==2.0.1
|
zhipuai==2.0.1
|
||||||
tiktoken>=0.3.3
|
tiktoken>=0.3.3
|
||||||
requests[socks]
|
requests[socks]
|
||||||
pydantic==2.9.2
|
pydantic==2.5.2
|
||||||
|
llama-index~=0.10
|
||||||
protobuf==3.20
|
protobuf==3.20
|
||||||
transformers>=4.27.1,<4.42
|
transformers>=4.27.1,<4.42
|
||||||
scipdf_parser>=0.52
|
scipdf_parser>=0.52
|
||||||
spacy==3.7.4
|
|
||||||
anthropic>=0.18.1
|
anthropic>=0.18.1
|
||||||
python-markdown-math
|
python-markdown-math
|
||||||
pymdown-extensions
|
pymdown-extensions
|
||||||
@@ -33,14 +32,3 @@ loguru
|
|||||||
arxiv
|
arxiv
|
||||||
numpy
|
numpy
|
||||||
rich
|
rich
|
||||||
|
|
||||||
|
|
||||||
llama-index-core==0.10.68
|
|
||||||
llama-index-legacy==0.9.48
|
|
||||||
llama-index-readers-file==0.1.33
|
|
||||||
llama-index-readers-llama-parse==0.1.6
|
|
||||||
llama-index-embeddings-azure-openai==0.1.10
|
|
||||||
llama-index-embeddings-openai==0.1.10
|
|
||||||
llama-parse==0.4.9
|
|
||||||
mdit-py-plugins>=0.3.3
|
|
||||||
linkify-it-py==2.0.3
|
|
||||||
@@ -1,12 +0,0 @@
|
|||||||
"""
|
|
||||||
对项目中的各个插件进行测试。运行方法:直接运行 python tests/test_plugins.py
|
|
||||||
"""
|
|
||||||
|
|
||||||
import init_test
|
|
||||||
import os, sys
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
from test_utils import plugin_test
|
|
||||||
|
|
||||||
plugin_test(plugin='crazy_functions.数学动画生成manim->动画生成', main_input="A point moving along function culve y=sin(x), starting from x=0 and stop at x=4*\pi.")
|
|
||||||
@@ -19,8 +19,4 @@ if __name__ == "__main__":
|
|||||||
plugin_test = importlib.import_module('test_utils').plugin_test
|
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