-
Notifications
You must be signed in to change notification settings - Fork 0
/
deploy_gpt_academic.sh
executable file
·696 lines (649 loc) · 27.9 KB
/
deploy_gpt_academic.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
#!/bin/bash
set -e
# 定义颜色变量
RED='\033[0;31m'
GREEN='\033[0;32m'
YELLOW='\033[0;33m'
BLUE='\033[0;34m'
PURPLE='\033[0;35m'
CYAN='\033[0;36m'
NC='\033[0m' # No Color
# 创建项目目录并进入
function create_project_directory() {
echo -e "${BLUE}正在创建项目目录...${NC}"
if [ -d "gpt_academic" ]; then
echo -e "${YELLOW}项目目录已存在,直接进入...${NC}"
else
mkdir -p gpt_academic
fi
cd gpt_academic
echo -e "${GREEN}已进入项目目录: $(pwd)${NC}"
echo
}
# 显示GPT Academic的ASCII艺术字
function display_logo() {
echo -e "${PURPLE}=========================================================="
echo -e " GPT Academic Docker 部署脚本"
echo -e "==========================================================${NC}"
echo -e "${CYAN}GPT 学术优化 (GPT Academic)${NC}"
echo -e "${BLUE}项目地址:${NC}https://github.com/binary-husky/gpt_academic"
echo "---"
echo "脚本项目地址 https://github.com/Limesain/gpt_academic-deploy-script"
echo "当前部署脚本为 1.0 适用GPT Academic项目版本为 3.73 测试平台为 ubuntu 20.04"
echo
}
# 检查是否已部署GPT Academic
function check_existing_deployment() {
if docker ps -a --format '{{.Names}}' | grep -Eq "^gpt_academic"; then
echo -e "${YELLOW}检测到已有的 GPT Academic 部署,直接进入主菜单...${NC}"
main_menu
exit 0
fi
}
# 更新系统软件包
function update_system_packages() {
echo -e "${BLUE}请选择更新软件包的方式:${NC}"
echo "1. 仅更新软件包列表"
echo "2. 更新软件包列表并升级系统软件包"
echo "3. 更新软件包列表并升级所有软件包"
echo
while true; do
read -p "请输入选项编号 (1-3): " update_choice
case $update_choice in
1)
echo -e "${GREEN}正在更新软件包列表...${NC}"
sudo apt update
break
;;
2)
echo -e "${GREEN}正在更新软件包列表并升级系统软件包...${NC}"
sudo apt update && sudo apt upgrade -y --no-install-recommends
break
;;
3)
echo -e "${GREEN}正在更新软件包列表并升级所有软件包...${NC}"
sudo apt update && sudo apt upgrade -y
break
;;
*)
echo -e "${RED}无效的选择。请输入 1 到 3 之间的数字。${NC}"
;;
esac
done
echo -e "${GREEN}系统更新完成。${NC}"
echo
}
# 安装Docker及其依赖项
function install_docker() {
echo -e "${BLUE}正在安装 Docker 及其依赖项...${NC}"
sudo apt install apt-transport-https ca-certificates curl gnupg lsb-release -y
curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo gpg --dearmor -o /usr/share/keyrings/docker-archive-keyring.gpg
echo "deb [arch=amd64 signed-by=/usr/share/keyrings/docker-archive-keyring.gpg] https://download.docker.com/linux/ubuntu $(lsb_release -cs) stable" | sudo tee /etc/apt/sources.list.d/docker.list > /dev/null
sudo apt update
sudo apt install docker-ce docker-ce-cli containerd.io -y
}
# 验证Docker安装
function verify_docker_installation() {
echo -e "${BLUE}正在验证 Docker 安装...${NC}"
if ! sudo systemctl is-active docker >/dev/null 2>&1; then
echo -e "${BLUE}正在启动 Docker 服务...${NC}"
sudo systemctl start docker
fi
if sudo systemctl is-active docker >/dev/null 2>&1; then
echo -e "${GREEN}Docker 安装成功并正在运行。${NC}"
else
echo -e "${RED}Docker 安装似乎出现了问题,它目前未在运行。${NC}"
echo -e "${RED}请检查上述步骤的输出,以查找并解决问题。${NC}"
exit 1
fi
echo
}
# 安装Docker Compose
function install_docker_compose() {
echo -e "${BLUE}正在安装 Docker Compose...${NC}"
sudo apt install docker-compose -y
docker-compose --version
echo
}
function create_project_directory() {
echo -e "${BLUE}正在创建项目目录...${NC}"
if [ -d "gpt_academic" ]; then
echo -e "${YELLOW}项目目录已存在,直接进入...${NC}"
cd gpt_academic
else
mkdir -p gpt_academic
cd gpt_academic
fi
echo -e "${GREEN}已进入项目目录: $(pwd)${NC}"
echo
}
# 选择部署方案
function select_deployment_scheme() {
echo -e "${BLUE}请选择您想要部署的方案:${NC}"
echo "1. 部署 chatgpt,azure,星火,千帆,claude 等在线大模型方案(默认方案)"
echo "2. 部署 ChatGLM、Qwen、MOSS 等本地模型方案"
echo "3. 部署 ChatGPT、LLAMA、盘古、RWKV 本地模型方案"
echo "4. 部署 ChatGPT + Latex 方案"
echo "5. 部署 ChatGPT + 语音助手方案"
echo "0. 部署项目的全部能力（包含 cuda 和 latex 的大型镜像）方案"
echo
while true; do
read -p "请输入方案编号 (0-5): " scheme_choice
case $scheme_choice in
0|1|2|3|4|5)
generate_compose_file $scheme_choice
break
;;
*)
echo -e "${RED}无效的选择。请输入 0 到 5 之间的数字。${NC}"
;;
esac
done
echo -e "${GREEN}docker-compose.yml 文件创建完成。${NC}"
echo
}
# 生成docker-compose.yml文件
function generate_compose_file() {
case $1 in
0)
image="ghcr.io/binary-husky/gpt_academic_with_all_capacity:master"
cat > docker-compose.yml <<EOL
version: '3'
services:
gpt_academic_full_capability:
image: $image
environment:
API_KEY: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
USE_PROXY: "False"
LLM_MODEL: "gpt-3.5-turbo-0125"
AVAIL_LLM_MODELS: '["gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo", "gemini-pro", "chatglm3", "one-api-claude-3-sonnet-20240229(max_token=100000)", "moss", "qwen-turbo", "qwen-plus", "qwen-max", "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", "api2d-gpt-3.5-turbo-16k", "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]'
BAIDU_CLOUD_API_KEY: ""
BAIDU_CLOUD_SECRET_KEY: ""
BAIDU_CLOUD_QIANFAN_MODEL: "ERNIE-Bot"
XFYUN_APPID: ""
XFYUN_API_SECRET: ""
XFYUN_API_KEY: ""
ENABLE_AUDIO: "False"
ALIYUN_APPKEY: ""
ALIYUN_TOKEN: ""
DEFAULT_WORKER_NUM: "3"
WEB_PORT: "-1"
ADD_WAIFU: "False"
THEME: "Default"
AVAIL_THEMES: '["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gstaff/Xkcd", "NoCrypt/Miku"]'
INIT_SYS_PROMPT: "Serve me as a writing and programming assistant."
CHATBOT_HEIGHT: 1115
CODE_HIGHLIGHT: "True"
LAYOUT: "LEFT-RIGHT"
DARK_MODE: "True"
TIMEOUT_SECONDS: 30
MAX_RETRY: 2
DEFAULT_FN_GROUPS: '["对话", "编程", "学术", "智能体"]'
MULTI_QUERY_LLM_MODELS: "gpt-3.5-turbo&chatglm3"
QWEN_LOCAL_MODEL_SELECTION: "Qwen/Qwen-1_8B-Chat-Int8"
LOCAL_MODEL_DEVICE: "cuda"
LOCAL_MODEL_QUANT: "FP16"
CONCURRENT_COUNT: 100
AUTO_CLEAR_TXT: "False"
API_ORG: ""
SLACK_CLAUDE_BOT_ID: ""
SLACK_CLAUDE_USER_TOKEN: ""
AZURE_ENDPOINT: "https://你亲手写的api名称.openai.azure.com/"
AZURE_API_KEY: ""
AZURE_ENGINE: ""
AZURE_CFG_ARRAY: '{}'
ALIYUN_ACCESSKEY: ""
ALIYUN_SECRET: ""
MATHPIX_APPID: ""
MATHPIX_APPKEY: ""
CUSTOM_API_KEY_PATTERN: ""
GEMINI_API_KEY: ""
HUGGINGFACE_ACCESS_TOKEN: "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"
GROBID_URLS: '["https://qingxu98-grobid.hf.space","https://qingxu98-grobid2.hf.space","https://qingxu98-grobid3.hf.space", "https://qingxu98-grobid4.hf.space","https://qingxu98-grobid5.hf.space", "https://qingxu98-grobid6.hf.space", "https://qingxu98-grobid7.hf.space", "https://qingxu98-grobid8.hf.space"]'
ALLOW_RESET_CONFIG: "False"
AUTOGEN_USE_DOCKER: "False"
WHEN_TO_USE_PROXY: '["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid", "Warmup_Modules", "Nougat_Download", "AutoGen"]'
BLOCK_INVALID_APIKEY: "False"
PLUGIN_HOT_RELOAD: "False"
NUM_CUSTOM_BASIC_BTN: 4
API_URL_REDIRECT: '{}'
SSL_KEYFILE: ""
SSL_CERTFILE: ""
ZHIPUAI_API_KEY: ""
ZHIPUAI_MODEL: ""
ANTHROPIC_API_KEY: ""
MOONSHOT_API_KEY: ""
PATH_PRIVATE_UPLOAD: "private_upload"
PATH_LOGGING: "gpt_log"
network_mode: "host"
command: bash -c "python3 -u main.py"
runtime: nvidia
devices:
- /dev/nvidia0:/dev/nvidia0
EOL
;;
1)
image="ghcr.io/binary-husky/gpt_academic_nolocal:master"
cat > docker-compose.yml <<EOL
version: '3'
services:
gpt_academic:
image: $image
environment:
API_KEY: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
USE_PROXY: "False"
LLM_MODEL: "gpt-3.5-turbo-0125"
AVAIL_LLM_MODELS: '["gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo", "gemini-pro", "chatglm3", "one-api-claude-3-sonnet-20240229(max_token=100000)", "moss", "qwen-turbo", "qwen-plus", "qwen-max", "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", "api2d-gpt-3.5-turbo-16k", "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]'
PROXIES: '{"http": "socks5h://localhost:11284", "https": "socks5h://localhost:11284"}'
WEB_PORT: "-1"
ADD_WAIFU: "False"
network_mode: "host"
command: bash -c "python3 -u main.py"
EOL
;;
2)
image="ghcr.io/binary-husky/gpt_academic_chatglm_moss:master"
cat > docker-compose.yml <<EOL
version: '3'
services:
gpt_academic_with_chatglm:
image: $image
environment:
API_KEY: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
USE_PROXY: "False"
LLM_MODEL: "gpt-3.5-turbo-0125"
AVAIL_LLM_MODELS: '["gpt4-0125-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo", "gemini-pro", "chatglm3", "one-api-claude-3-sonnet-20240229(max_token=100000)", "moss", "qwen-turbo", "qwen-plus", "qwen-max", "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", "api2d-gpt-3.5-turbo-16k", "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]'
LOCAL_MODEL_DEVICE: "cuda"
QWEN_LOCAL_MODEL_SELECTION: "Qwen/Qwen-1_8B-Chat-Int8"
runtime: nvidia
devices:
- /dev/nvidia0:/dev/nvidia0
EOL
;;
3)
image="ghcr.io/binary-husky/gpt_academic_jittorllms:master"
cat > docker-compose.yml <<EOL
version: '3'
services:
gpt_academic_with_rwkv:
image: $image
environment:
API_KEY: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
USE_PROXY: "False"
LLM_MODEL: "gpt-3.5-turbo-0125"
AVAIL_LLM_MODELS: '["gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo", "gemini-pro", "chatglm3", "one-api-claude-3-sonnet-20240229(max_token=100000)", "moss", "qwen-turbo", "qwen-plus", "qwen-max", "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", "api2d-gpt-3.5-turbo-16k", "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]'
LOCAL_MODEL_DEVICE: "cuda"
CHATGLM_PTUNING_CHECKPOINT: ""
runtime: nvidia
devices:
- /dev/nvidia0:/dev/nvidia0
EOL
;;
4)
image="ghcr.io/binary-husky/gpt_academic_with_latex:master"
cat > docker-compose.yml <<EOL
version: '3'
services:
gpt_academic_with_latex:
image: $image
environment:
API_KEY: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
USE_PROXY: "False"
LLM_MODEL: "gpt-3.5-turbo-0125"
AVAIL_LLM_MODELS: '["gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo", "gemini-pro", "chatglm3", "one-api-claude-3-sonnet-20240229(max_token=100000)", "moss", "qwen-turbo", "qwen-plus", "qwen-max", "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", "api2d-gpt-3.5-turbo-16k", "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]'
network_mode: "host"
command: bash -c "python3 -u main.py"
EOL
;;
5)
image="ghcr.io/binary-husky/gpt_academic_audio_assistant:master"
cat > docker-compose.yml <<EOL
version: '3'
services:
gpt_academic_with_audio:
image: $image
environment:
API_KEY: "sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
USE_PROXY: "False"
LLM_MODEL: "gpt-3.5-turbo-0125"
AVAIL_LLM_MODELS: '["gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo", "gemini-pro", "chatglm3", "one-api-claude-3-sonnet-20240229(max_token=100000)", "moss", "qwen-turbo", "qwen-plus", "qwen-max", "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", "api2d-gpt-3.5-turbo-16k", "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]'
ENABLE_AUDIO: "True"
ALIYUN_APPKEY: ""
ALIYUN_TOKEN: ""
network_mode: "host"
command: bash -c "python3 -u main.py"
EOL
;;
esac
}
# 添加API_KEY
function add_api_key() {
read -p "是否添加 API_KEY? [y/n]: " add_api_key
if [[ $add_api_key =~ ^[Yy]$ ]]; then
read -p "请输入 API KEY（多个 API KEY 请用英文字符 , 分开）: " api_key
sed -i "s/API_KEY:.*/API_KEY: \"$api_key\"/g" docker-compose.yml
fi
}
# 重定向URL链接
function redirect_url() {
read -p "是否重定向 URL 链接? [y/n]: " redirect_url
if [[ $redirect_url =~ ^[Yy]$ ]]; then
read -p "请输入重定向链接（https://reverse-proxy-url/v1/chat/completions）: " redirect_link
sed -i "/environment:/a \ \ \ \ \ \ API_URL_REDIRECT: '{\"https://api.openai.com/v1/chat/completions\": \"$redirect_link\"}'" docker-compose.yml
fi
}
# 启动GPT Academic
function start_gpt_academic() {
echo -e "${BLUE}正在启动 GPT Academic...${NC}"
docker-compose up -d
sleep 5
check_container_status
echo
}
# 检查容器运行状态
function check_container_status() {
echo -e "${BLUE}正在检查容器运行状态...${NC}"
if docker-compose ps | grep -q "Up"; then
echo -e "${GREEN}GPT Academic 已成功启动。${NC}"
else
echo -e "${RED}GPT Academic 启动失败。请检查日志以查找问题。${NC}"
fi
echo
}
function view_logs() {
echo -e "${BLUE}正在查看 GPT Academic 的运行日志,按 ESC 返回主菜单...${NC}"
echo -e "${YELLOW}注意: 如果日志显示不完整,请尝试调整终端窗口大小。${NC}"
echo -e "${CYAN}========== GPT Academic 运行日志 ==========${NC}"
tput sc
docker-compose logs --no-log-prefix -f --tail 100 &
LOG_PID=$!
while true; do
tput rc
tput ed
echo -en "${YELLOW}按 ESC 返回主菜单...${NC}"
read -s -n1 key
if [[ $key == $'\e' ]]; then
kill $LOG_PID
break
fi
done
echo -e "${CYAN}========== 日志查看结束 ==========${NC}"
echo -e "${BLUE}按任意键返回主菜单...${NC}"
read -n1
clear
main_menu
}
# 备份配置文件
function backup_config() {
local backup_file="docker-compose.yml.bak"
cp docker-compose.yml $backup_file
echo -e "${GREEN}docker-compose.yml 已备份为 $backup_file。${NC}"
}
# 还原配置文件
function restore_config() {
local backup_file="docker-compose.yml.bak"
if [ -f "$backup_file" ]; then
mv $backup_file docker-compose.yml
echo -e "${GREEN}docker-compose.yml 已从备份中还原。${NC}"
else
echo -e "${YELLOW}未找到 docker-compose.yml 的备份文件。${NC}"
fi
}
# 修改API密钥
function modify_api_key() {
echo -e "${BLUE}API 密钥修改选项:${NC}"
echo "1. 查看已添加的 API KEY"
echo "2. 添加 API KEY（增量添加）"
echo "3. 添加 API KEY（覆盖添加）"
echo "4. 删除所有 API KEY"
echo "0. 返回上一级菜单"
echo
while true; do
read -p "请输入选项编号 (0-4): " api_key_choice
case $api_key_choice in
1)
echo -e "${BLUE}已添加的 API KEY:${NC}"
grep -oP 'API_KEY: "\K[^"]+' docker-compose.yml
;;
2)
read -p "请输入要添加的 API KEY: " new_api_key
current_api_key=$(grep -oP 'API_KEY: "\K[^"]+' docker-compose.yml)
updated_api_key="$current_api_key,$new_api_key"
sed -i "s/API_KEY:.*/API_KEY: \"$updated_api_key\"/g" docker-compose.yml
echo -e "${GREEN}API KEY 已添加。${NC}"
;;
3)
read -p "请输入新的 API KEY（多个 API KEY 请用英文字符 , 分开）: " new_api_key
sed -i "s/API_KEY:.*/API_KEY: \"$new_api_key\"/g" docker-compose.yml
echo -e "${GREEN}API KEY 已更新。${NC}"
;;
4)
sed -i "s/API_KEY:.*/API_KEY: \"\"/g" docker-compose.yml
echo -e "${GREEN}所有 API KEY 已删除。${NC}"
;;
0)
config_menu
break
;;
*)
echo -e "${RED}无效的选择。请输入 0 到 4 之间的数字。${NC}"
;;
esac
done
}
# 修改多线程或请求速率
function modify_worker_num() {
current_worker_num=$(grep -oP 'DEFAULT_WORKER_NUM: \K\d+' docker-compose.yml)
if [[ $current_worker_num -eq 3 ]]; then
rate_limit="每分钟3次(Free trial users)"
else
rate_limit="每分钟3500次(Pay-as-you-go users)"
fi
echo -e "${BLUE}当前多线程数量: $current_worker_num, 对应的速率限制: $rate_limit${NC}"
read -p "请输入新的多线程数量: " new_worker_num
if [[ $new_worker_num =~ ^[0-9]+$ ]]; then
sed -i "s/DEFAULT_WORKER_NUM:.*/DEFAULT_WORKER_NUM: $new_worker_num/g" docker-compose.yml
echo -e "${GREEN}多线程数量已更新为 $new_worker_num。${NC}"
if [[ $new_worker_num -le 3 ]]; then
echo -e "${YELLOW}请注意,当前设置的多线程数量对应的速率限制为每分钟3次(Free trial users)。${NC}"
else
echo -e "${YELLOW}请注意,当前设置的多线程数量对应的速率限制为每分钟3500次(Pay-as-you-go users)。${NC}"
fi
read -p "是否立即重启 GPT Academic 以应用新的设置? [y/n]: " restart_choice
if [[ $restart_choice =~ ^[Yy]$ ]]; then
echo -e "${GREEN}正在重新启动 GPT Academic...${NC}"
docker-compose down
docker-compose up -d
echo -e "${GREEN}GPT Academic 已重新启动,新的多线程设置已生效。${NC}"
else
echo -e "${YELLOW}新的多线程设置将在下次重启 GPT Academic 时生效。${NC}"
fi
else
echo -e "${RED}无效的输入。请输入一个数字。${NC}"
fi
}
# 修改其他配置
function modify_other_config() {
echo -e "${BLUE}请选择要修改的其他配置:${NC}"
echo "1. 代理设置"
echo "2. 使用的 LLM 模型"
echo "3. 可用的 LLM 模型列表"
echo "0. 返回上一级菜单"
echo
while true; do
read -p "请输入选项编号 (0-3): " other_config_choice
case $other_config_choice in
1)
read -p "是否使用代理? (True/False): " use_proxy
read -p "请输入代理地址和端口 (例如: socks5h://localhost:11284): " proxy_url
sed -i "s/USE_PROXY:.*/USE_PROXY: \"$use_proxy\"/g" docker-compose.yml
sed -i "s#PROXIES:.*#PROXIES: '{\"http\": \"$proxy_url\", \"https\": \"$proxy_url\"}'#g" docker-compose.yml
echo -e "${GREEN}代理设置已更新。${NC}"
;;
2)
read -p "请输入要使用的 LLM 模型: " llm_model
sed -i "s/LLM_MODEL:.*/LLM_MODEL: \"$llm_model\"/g" docker-compose.yml
echo -e "${GREEN}LLM 模型已更新。${NC}"
;;
3)
echo -e "${BLUE}可用的 LLM 模型列表:${NC}"
echo '["gpt-4-0125-preview", "gpt-4-turbo-preview", "gpt-4-vision-preview", "gpt-3.5-turbo-0125", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-3-turbo", "gemini-pro", "chatglm3", "one-api-claude-3-sonnet-20240229(max_token=100000)", "moss", "qwen-turbo", "qwen-plus", "qwen-max", "zhipuai", "qianfan", "deepseekcoder", "llama2", "qwen-local", "gpt-3.5-turbo-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-random", "api2d-gpt-3.5-turbo", "api2d-gpt-3.5-turbo-16k", "spark", "sparkv2", "sparkv3", "chatglm_onnx", "claude-1-100k", "claude-2", "internlm", "jittorllms_pangualpha", "jittorllms_llama"]'
read -p "请输入要使用的 LLM 模型列表（用引号和逗号分隔）: " llm_models
sed -i "s/AVAIL_LLM_MODELS:.*/AVAIL_LLM_MODELS: '$llm_models'/g" docker-compose.yml
echo -e "${GREEN}可用的 LLM 模型列表已更新。${NC}"
;;
0)
config_menu
break
;;
*)
echo -e "${RED}无效的选择。请输入 0 到 3 之间的数字。${NC}"
;;
esac
done
}
# 配置菜单
function config_menu() {
echo -e "${BLUE}请选择要修改的配置:${NC}"
echo "1. API 密钥"
echo "2. 多线程或请求速率"
echo "3. 其他配置"
echo "0. 返回主菜单"
echo
while true; do
read -p "请输入选项编号 (0-3): " config_choice
case $config_choice in
1)
modify_api_key
break
;;
2)
modify_worker_num
break
;;
3)
modify_other_config
break
;;
0)
main_menu
break
;;
*)
echo -e "${RED}无效的选择。请输入 0 到 3 之间的数字。${NC}"
;;
esac
done
}
# 主菜单
function main_menu() {
echo -e "${PURPLE}=========================================================="
echo -e " GPT Academic 管理菜单"
echo -e "==========================================================${NC}"
echo -e "${BLUE}请选择一个选项:${NC}"
echo "1. 重新启动 GPT Academic"
echo "2. 修改配置"
echo "3. 查看运行日志"
echo "4. 备份配置文件"
echo "5. 还原配置文件"
echo "0. 退出"
echo
while true; do
read -p "请输入选项编号 (0-5): " menu_choice
case $menu_choice in
1)
echo -e "${GREEN}正在重新启动 GPT Academic...${NC}"
docker-compose down
start_gpt_academic
break
;;
2)
echo -e "${BLUE}进入配置修改菜单...${NC}"
backup_config
config_menu
read -p "配置已修改,是否立即重启 GPT Academic 以应用更改? [y/n]: " restart_choice
if [[ $restart_choice =~ ^[Yy]$ ]]; then
echo -e "${GREEN}正在重新启动 GPT Academic...${NC}"
docker-compose down
start_gpt_academic
else
echo -e "${YELLOW}配置已修改,但 GPT Academic 尚未重启。下次重启时更改将生效。${NC}"
fi
break
;;
3)
view_logs
break
;;
4)
backup_config
break
;;
5)
restore_config
read -p "配置已还原,是否立即重启 GPT Academic 以应用更改? [y/n]: " restart_choice
if [[ $restart_choice =~ ^[Yy]$ ]]; then
echo -e "${GREEN}正在重新启动 GPT Academic...${NC}"
docker-compose down
start_gpt_academic
else
echo -e "${YELLOW}配置已还原,但 GPT Academic 尚未重启。下次重启时更改将生效。${NC}"
fi
break
;;
0)
echo -e "${PURPLE}感谢您使用 GPT Academic 管理脚本,再见!${NC}"
exit 0
;;
*)
echo -e "${RED}无效的选择。请输入 0 到 5 之间的数字。${NC}"
;;
esac
done
}
# 主程序
function main() {
if [[ $1 == "menu" ]]; then
main_menu
else
create_project_directory
display_logo
check_existing_deployment
if [ $? -eq 0 ]; then
update_system_packages
install_docker
verify_docker_installation
install_docker_compose
select_deployment_scheme
add_api_key
redirect_url
start_gpt_academic
fi
fi
echo -e "${GREEN}GPT Academic 部署完成。脚本将在后台继续运行,监听 'gptadmin' 命令。${NC}"
echo -e "${GREEN}您可以随时输入 'gptadmin' 来打开管理菜单。${NC}"
}
# ...
# 监听 'gptadmin' 命令
function listen_for_gptadmin() {
while true; do
if [[ "$(ps -ocommand= -p $PPID)" == *"gptadmin"* ]]; then
main_menu
fi
sleep 1
done
}
# 定义 gptadmin 函数
gptadmin() {
bash "$0" menu
}
# 将 gptadmin 函数导出到当前 shell 会话
export -f gptadmin
# 运行主程序
main
# 在后台运行监听器
listen_for_gptadmin &
echo -e "${GREEN}GPT Academic 部署完成。脚本将在后台继续运行,监听 'gptadmin' 命令。${NC}"
echo -e "${GREEN}您可以随时输入 'gptadmin' 来打开管理菜单。${NC}"
echo -e "${YELLOW}请注意,如果你关闭当前终端会话,'gptadmin' 命令将不再可用。${NC}"
echo -e "${YELLOW}在新的终端会话中,你需要重新运行脚本来重新定义 'gptadmin' 命令。${NC}"