up

README.md

@@ -39,7 +39,7 @@ To translate this project to arbitary language with GPT, read and run [`multi_la
 
 功能（⭐= 近期新增功能） | 描述
 --- | ---
-⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | ⭐阿里达摩院[通义千问](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)
+⭐[接入新模型](https://github.com/binary-husky/gpt_academic/wiki/%E5%A6%82%E4%BD%95%E5%88%87%E6%8D%A2%E6%A8%A1%E5%9E%8B)！ | 百度[千帆](https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu)与文心一言, [通义千问](https://modelscope.cn/models/qwen/Qwen-7B-Chat/summary)，上海AI-Lab[书生](https://github.com/InternLM/InternLM)，讯飞[星火](https://xinghuo.xfyun.cn/)，[LLaMa2](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
 一键润色 | 支持一键润色、一键查找论文语法错误
 一键中英互译 | 一键中英互译
 一键代码解释 | 显示代码、解释代码、生成代码、给代码加注释
@@ -178,7 +178,7 @@ docker run --rm -it -e WEB_PORT=50923 -p 50923:50923 gpt-academic
 ```
 P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以直接使用docker-compose获取Latex功能（修改docker-compose.yml，保留方案4并删除其他方案）。
 
-2. ChatGPT + ChatGLM2 + MOSS（需要熟悉Docker）
+2. ChatGPT + ChatGLM2 + MOSS + LLAMA2 + 通义千问（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
 [![chatglm](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-chatglm.yml)
 
 ``` sh
@@ -186,7 +186,7 @@ P.S. 如果需要依赖Latex的插件功能，请见Wiki。另外，您也可以
 docker-compose up
 ```
 
-3. ChatGPT + LLAMA + 盘古 + RWKV（需要熟悉Docker）
+3. ChatGPT + LLAMA + 盘古 + RWKV（需要熟悉[Nvidia Docker](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html#installing-on-ubuntu-and-debian)运行时）
 [![jittorllms](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml/badge.svg?branch=master)](https://github.com/binary-husky/gpt_academic/actions/workflows/build-with-jittorllms.yml)
 
 ``` sh
@@ -313,6 +313,8 @@ Tip：不指定文件直接点击 `载入对话历史存档` 可以查看历史h
 
 ### II：版本:
 - version 3.5(Todo): 使用自然语言调用本项目的所有函数插件（高优先级）
+- version 3.49: 支持百度千帆平台和文心一言
+- version 3.48: 支持阿里达摩院通义千问，上海AI-Lab书生，讯飞星火
 - version 3.46: 支持完全脱手操作的实时语音对话
 - version 3.45: 支持自定义ChatGLM2微调模型
 - version 3.44: 正式支持Azure，优化界面易用性

app.py

@@ -4,7 +4,7 @@ def main():
     import subprocess, sys
     subprocess.check_call([sys.executable, '-m', 'pip', 'install', 'gradio-stable-fork'])
     import gradio as gr
-    if gr.__version__ not in ['3.28.3','3.32.3']: assert False, "需要特殊依赖，请务必用 pip install -r requirements.txt 指令安装依赖，详情信息见requirements.txt"
+    if gr.__version__ not in ['3.28.3','3.32.2']: assert False, "需要特殊依赖，请务必用 pip install -r requirements.txt 指令安装依赖，详情信息见requirements.txt"
     from request_llm.bridge_all import predict
     from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, load_chat_cookies, DummyWith
     # 建议您复制一个config_private.py放自己的秘密, 如API和代理网址, 避免不小心传github被别人看到

config.py

@@ -11,7 +11,11 @@
 API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
 
 
-# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改
+# [step 1]>> API_KEY = "sk-123456789xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx123456789"。极少数情况下，还需要填写组织（格式如org-123456789abcdefghijklmno的），请向下翻，找 API_ORG 设置项
+API_KEY = "此处填API密钥"    # 可同时填写多个API-KEY，用英文逗号分割，例如API_KEY = "sk-openaikey1,sk-openaikey2,fkxxxx-api2dkey3,azure-apikey4"
+
+
+# [step 2]>> 改为True应用代理，如果直接在海外服务器部署，此处不修改；如果使用本地或无地域限制的大模型时，此处也不需要修改
 USE_PROXY = False
 if USE_PROXY:
     """
@@ -69,7 +73,7 @@ MAX_RETRY = 2
 
 # OpenAI模型选择是（gpt4现在只对申请成功的人开放）
 LLM_MODEL = "gpt-3.5-turbo" # 可选 "chatglm"
-AVAIL_LLM_MODELS = ["newbing-free", "gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo"]
+AVAIL_LLM_MODELS = ["gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "api2d-gpt-3.5-turbo", "spark", "azure-gpt-3.5"]
 
 # ChatGLM(2) Finetune Model Path （如果使用ChatGLM2微调模型，需要把"chatglmft"加入AVAIL_LLM_MODELS中）
 ChatGLM_PTUNING_CHECKPOINT = "" # 例如"/home/hmp/ChatGLM2-6B/ptuning/output/6b-pt-128-1e-2/checkpoint-100"
@@ -147,3 +151,70 @@ ANTHROPIC_API_KEY = ""
 
 # 自定义API KEY格式
 CUSTOM_API_KEY_PATTERN = ""
+
+
+# HUGGINGFACE的TOKEN，下载LLAMA时起作用 https://huggingface.co/docs/hub/security-tokens
+HUGGINGFACE_ACCESS_TOKEN = "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"
+
+
+# GROBID服务器地址（填写多个可以均衡负载），用于高质量地读取PDF文档
+# 获取方法：复制以下空间https://huggingface.co/spaces/qingxu98/grobid，设为public，然后GROBID_URL = "https://(你的hf用户名如qingxu98)-(你的填写的空间名如grobid).hf.space"
+GROBID_URLS = [
+    "https://qingxu98-grobid.hf.space","https://qingxu98-grobid2.hf.space","https://qingxu98-grobid3.hf.space",
+    "https://shaocongma-grobid.hf.space","https://FBR123-grobid.hf.space",
+]
+
+
+
+"""
+在线大模型配置关联关系示意图
+│
+├── "gpt-3.5-turbo" 等openai模型
+│   ├── API_KEY
+│   ├── CUSTOM_API_KEY_PATTERN（不常用）
+│   ├── API_ORG（不常用）
+│   └── API_URL_REDIRECT（不常用）
+│
+├── "azure-gpt-3.5" 等azure模型
+│   ├── API_KEY
+│   ├── AZURE_ENDPOINT
+│   ├── AZURE_API_KEY
+│   ├── AZURE_ENGINE
+│   └── API_URL_REDIRECT
+│
+├── "spark" 星火认知大模型
+│   ├── XFYUN_APPID
+│   ├── XFYUN_API_SECRET
+│   └── XFYUN_API_KEY
+│
+├── "claude-1-100k" 等claude模型
+│   └── ANTHROPIC_API_KEY
+│
+├── "stack-claude"
+│   ├── SLACK_CLAUDE_BOT_ID
+│   └── SLACK_CLAUDE_USER_TOKEN
+│
+├── "qianfan" 百度千帆大模型库
+│   ├── BAIDU_CLOUD_QIANFAN_MODEL
+│   ├── BAIDU_CLOUD_API_KEY
+│   └── BAIDU_CLOUD_SECRET_KEY
+│
+├── "newbing" Newbing接口不再稳定，不推荐使用
+    ├── NEWBING_STYLE
+    └── NEWBING_COOKIES
+
+
+
+插件在线服务配置依赖关系示意图
+│
+├── 语音功能
+│   ├── ENABLE_AUDIO
+│   ├── ALIYUN_TOKEN
+│   ├── ALIYUN_APPKEY
+│   ├── ALIYUN_ACCESSKEY
+│   └── ALIYUN_SECRET
+│
+├── PDF文档精准解析
+│   └── GROBID_URLS
+
+"""

crazy_functional.py

@@ -24,6 +24,7 @@ def get_crazy_functions():
     from crazy_functions.对话历史存档 import 对话历史存档
     from crazy_functions.对话历史存档 import 载入对话历史存档
     from crazy_functions.对话历史存档 import 删除所有本地对话历史记录
+    from crazy_functions.辅助功能 import 清除缓存
     
     from crazy_functions.批量Markdown翻译 import Markdown英译中
     function_plugins = {
@@ -40,7 +41,12 @@ def get_crazy_functions():
             "AsButton":False,
             "Function": HotReload(删除所有本地对话历史记录)
         },
-        "[测试功能] 解析Jupyter Notebook文件": {
+        "清除所有缓存文件（请谨慎操作）": {
+            "Color": "stop",
+            "AsButton": False,  # 加入下拉菜单中
+            "Function": HotReload(清除缓存)
+        },
+        "解析Jupyter Notebook文件": {
             "Color": "stop",
             "AsButton":False,
             "Function": HotReload(解析ipynb文件),
@@ -328,7 +334,7 @@ def get_crazy_functions():
     try:
         from crazy_functions.Langchain知识库 import 知识库问答
         function_plugins.update({
-            "[功能尚不稳定] 构建知识库（请先上传文件素材）": {
+            "构建知识库（请先上传文件素材）": {
                 "Color": "stop",
                 "AsButton": False,
                 "AdvancedArgs": True,
@@ -342,7 +348,7 @@ def get_crazy_functions():
     try:
         from crazy_functions.Langchain知识库 import 读取知识库作答
         function_plugins.update({
-            "[功能尚不稳定] 知识库问答": {
+            "知识库问答": {
                 "Color": "stop",
                 "AsButton": False,
                 "AdvancedArgs": True,
@@ -352,6 +358,32 @@ def get_crazy_functions():
         })
     except:
         print('Load function plugin failed')
+        
+    try:
+        from crazy_functions.交互功能函数模板 import 交互功能模板函数
+        function_plugins.update({
+            "交互功能模板函数": {
+                "Color": "stop",
+                "AsButton": False,
+                "Function": HotReload(交互功能模板函数)
+            }
+        })
+    except:
+        print('Load function plugin failed')
+
+    # try:
+    #     from crazy_functions.chatglm微调工具 import 微调数据集生成
+    #     function_plugins.update({
+    #         "黑盒模型学习: 微调数据集生成 (先上传数据集)": {
+    #             "Color": "stop",
+    #             "AsButton": False,
+    #             "AdvancedArgs": True,
+    #             "ArgsReminder": "针对数据集输入（如 绿帽子*深蓝色衬衫*黑色运动裤）给出指令，例如您可以将以下命令复制到下方: --llm_to_learn=azure-gpt-3.5 --prompt_prefix='根据下面的服装类型提示，想象一个穿着者，对这个人外貌、身处的环境、内心世界、过去经历进行描写。要求：100字以内，用第二人称。' --system_prompt=''",
+    #             "Function": HotReload(微调数据集生成)
+    #         }
+    #     })
+    # except:
+    #     print('Load function plugin failed')
 
     try:
         from crazy_functions.Latex输出PDF结果 import Latex英文纠错加PDF对比
@@ -366,7 +398,7 @@ def get_crazy_functions():
         })
         from crazy_functions.Latex输出PDF结果 import Latex翻译中文并重新编译PDF
         function_plugins.update({
-            "Arixv翻译（输入arxivID）[需Latex]": {
+            "Arixv论文精细翻译（输入arxivID）[需Latex]": {
                 "Color": "stop",
                 "AsButton": False,
                 "AdvancedArgs": True,
@@ -377,7 +409,7 @@ def get_crazy_functions():
             }
         })
         function_plugins.update({
-            "本地论文翻译（上传Latex压缩包）[需Latex]": {
+            "本地Latex论文精细翻译（上传Latex项目）[需Latex]": {
                 "Color": "stop",
                 "AsButton": False,
                 "AdvancedArgs": True,

crazy_functions/latex_fns/latex_toolbox.py

@@ -281,9 +281,12 @@ def rm_comments(main_file):
 def find_tex_file_ignore_case(fp):
     dir_name = os.path.dirname(fp)
     base_name = os.path.basename(fp)
+    # 如果输入的文件路径是正确的
+    if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
+    # 如果不正确，试着加上.tex后缀试试
     if not base_name.endswith('.tex'): base_name+='.tex'
     if os.path.exists(pj(dir_name, base_name)): return pj(dir_name, base_name)
-    # go case in-sensitive
+    # 如果还找不到，解除大小写限制，再试一次
     import glob
     for f in glob.glob(dir_name+'/*.tex'):
         base_name_s = os.path.basename(fp)

crazy_functions/pdf_fns/parse_pdf.py

@@ -0,0 +1,25 @@
+import requests
+import random
+from functools import lru_cache
+class GROBID_OFFLINE_EXCEPTION(Exception): pass
+
+def get_avail_grobid_url():
+    from toolbox import get_conf
+    GROBID_URLS, = get_conf('GROBID_URLS')
+    if len(GROBID_URLS) == 0: return None
+    try:
+        _grobid_url = random.choice(GROBID_URLS) # 随机负载均衡
+        if _grobid_url.endswith('/'): _grobid_url = _grobid_url.rstrip('/')
+        res = requests.get(_grobid_url+'/api/isalive')
+        if res.text=='true': return _grobid_url
+        else: return None
+    except:
+        return None
+
+@lru_cache(maxsize=32)
+def parse_pdf(pdf_path, grobid_url):
+    import scipdf   # pip install scipdf_parser
+    if grobid_url.endswith('/'): grobid_url = grobid_url.rstrip('/')
+    article_dict = scipdf.parse_pdf_to_dict(pdf_path, grobid_url=grobid_url)
+    return article_dict
+

crazy_functions/批量翻译PDF文档_多线程.py

@@ -1,15 +1,19 @@
 from toolbox import CatchException, report_execption, write_results_to_file
-from toolbox import update_ui, promote_file_to_downloadzone
+from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
+from toolbox import write_history_to_file, get_log_folder
 from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 from .crazy_utils import read_and_clean_pdf_text
+from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url
 from colorful import *
+import glob
+import os
+import math
 
 @CatchException
-def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt, web_port):
-    import glob
-    import os
+def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
 
+    disable_auto_promotion(chatbot)
     # 基本信息：功能、贡献者
     chatbot.append([
         "函数插件功能？",
@@ -30,20 +34,11 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_
     # 清空历史，以免输入溢出
     history = []
 
+    from .crazy_utils import get_files_from_everything
+    success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
     # 检测输入参数，如没有给定输入参数，直接退出
-    if os.path.exists(txt):
-        project_folder = txt
-    else:
-        if txt == "":
-            txt = '空空如也的输入栏'
-        report_execption(chatbot, history,
-                         a=f"解析项目: {txt}", b=f"找不到本地项目或无权访问: {txt}")
-        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        return
-
-    # 搜索需要处理的文件清单
-    file_manifest = [f for f in glob.glob(
-        f'{project_folder}/**/*.pdf', recursive=True)]
+    if not success:
+        if txt == "": txt = '空空如也的输入栏'
 
     # 如果没找到任何文件
     if len(file_manifest) == 0:
@@ -53,22 +48,130 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, sys_
         return
 
     # 开始正式执行任务
-    yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt)
+    grobid_url = get_avail_grobid_url()
+    if grobid_url is not None:
+        yield from 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url)
+    else:
+        yield from update_ui_lastest_msg("GROBID服务不可用，请检查config中的GROBID_URL。作为替代，现在将执行效果稍差的旧版代码。", chatbot, history, delay=3)
+        yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
 
 
-def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, sys_prompt):
-    import os
+def 解析PDF_基于GROBID(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, grobid_url):
     import copy
     import tiktoken
     TOKEN_LIMIT_PER_FRAGMENT = 1280
     generated_conclusion_files = []
     generated_html_files = []
+    DST_LANG = "中文"
     for index, fp in enumerate(file_manifest):
+        chatbot.append(["当前进度：", f"正在连接GROBID服务，请稍候: {grobid_url}\n如果等待时间过长，请修改config中的GROBID_URL，可修改成本地GROBID服务。"]); yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        article_dict = parse_pdf(fp, grobid_url)
+        print(article_dict)
+        prompt = "以下是一篇学术论文的基本信息:\n"
+        # title
+        title = article_dict.get('title', '无法获取 title'); prompt += f'title:{title}\n\n'
+        # authors
+        authors = article_dict.get('authors', '无法获取 authors'); prompt += f'authors:{authors}\n\n'
+        # abstract
+        abstract = article_dict.get('abstract', '无法获取 abstract'); prompt += f'abstract:{abstract}\n\n'
+        # command
+        prompt += f"请将题目和摘要翻译为{DST_LANG}。"
+        meta = [f'# Title:\n\n', title, f'# Abstract:\n\n', abstract ]
+
+        # 单线，获取文章meta信息
+        paper_meta_info = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            inputs=prompt,
+            inputs_show_user=prompt,
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot, history=[],
+            sys_prompt="You are an academic paper reader。",
+        )
+
+        # 多线，翻译
+        inputs_array = []
+        inputs_show_user_array = []
 
+        # get_token_num
+        from request_llm.bridge_all import model_info
+        enc = model_info[llm_kwargs['llm_model']]['tokenizer']
+        def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
+        from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
+
+        def break_down(txt):
+            raw_token_num = get_token_num(txt)
+            if raw_token_num <= TOKEN_LIMIT_PER_FRAGMENT:
+                return [txt]
+            else:
+                # raw_token_num > TOKEN_LIMIT_PER_FRAGMENT
+                # find a smooth token limit to achieve even seperation
+                count = int(math.ceil(raw_token_num / TOKEN_LIMIT_PER_FRAGMENT))
+                token_limit_smooth = raw_token_num // count + count
+                return breakdown_txt_to_satisfy_token_limit_for_pdf(txt, get_token_fn=get_token_num, limit=token_limit_smooth)
+
+        for section in article_dict.get('sections'):
+            if len(section['text']) == 0: continue
+            section_frags = break_down(section['text'])
+            for i, fragment in enumerate(section_frags):
+                heading = section['heading']
+                if len(section_frags) > 1: heading += f'Part-{i+1}'
+                inputs_array.append(
+                    f"你需要翻译{heading}章节，内容如下: \n\n{fragment}"
+                )
+                inputs_show_user_array.append(
+                    f"# {heading}\n\n{fragment}"
+                )
+
+        gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
+            inputs_array=inputs_array,
+            inputs_show_user_array=inputs_show_user_array,
+            llm_kwargs=llm_kwargs,
+            chatbot=chatbot,
+            history_array=[meta for _ in inputs_array],
+            sys_prompt_array=[
+                "请你作为一个学术翻译，负责把学术论文准确翻译成中文。注意文章中的每一句话都要翻译。" for _ in inputs_array],
+        )
+        res_path = write_history_to_file(meta +  ["# Meta Translation" , paper_meta_info] + gpt_response_collection, file_basename=None, file_fullname=None)
+        promote_file_to_downloadzone(res_path, rename_file=os.path.basename(fp)+'.md', chatbot=chatbot)
+        generated_conclusion_files.append(res_path)
+
+        ch = construct_html() 
+        orig = ""
+        trans = ""
+        gpt_response_collection_html = copy.deepcopy(gpt_response_collection)
+        for i,k in enumerate(gpt_response_collection_html): 
+            if i%2==0:
+                gpt_response_collection_html[i] = inputs_show_user_array[i//2]
+            else:
+                gpt_response_collection_html[i] = gpt_response_collection_html[i]
+
+        final = ["", "", "一、论文概况",  "", "Abstract", paper_meta_info,  "二、论文翻译",  ""]
+        final.extend(gpt_response_collection_html)
+        for i, k in enumerate(final): 
+            if i%2==0:
+                orig = k
+            if i%2==1:
+                trans = k
+                ch.add_row(a=orig, b=trans)
+        create_report_file_name = f"{os.path.basename(fp)}.trans.html"
+        html_file = ch.save_file(create_report_file_name)
+        generated_html_files.append(html_file)
+        promote_file_to_downloadzone(html_file, rename_file=os.path.basename(html_file), chatbot=chatbot)
+
+    chatbot.append(("给出输出文件清单", str(generated_conclusion_files + generated_html_files)))
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+
+def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
+    import copy
+    TOKEN_LIMIT_PER_FRAGMENT = 1280
+    generated_conclusion_files = []
+    generated_html_files = []
+    for index, fp in enumerate(file_manifest):
         # 读取PDF文件
         file_content, page_one = read_and_clean_pdf_text(fp)
         file_content = file_content.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
         page_one = str(page_one).encode('utf-8', 'ignore').decode()      # avoid reading non-utf8 chars
+
         # 递归地切割PDF文件
         from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
         from request_llm.bridge_all import model_info
@@ -140,8 +243,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
                     trans = k
                     ch.add_row(a=orig, b=trans)
             create_report_file_name = f"{os.path.basename(fp)}.trans.html"
-            ch.save_file(create_report_file_name)
-            generated_html_files.append(f'./gpt_log/{create_report_file_name}')
+            generated_html_files.append(ch.save_file(create_report_file_name))
         except:
             from toolbox import trimmed_format_exc
             print('writing html result failed:', trimmed_format_exc())
@@ -202,6 +304,6 @@ class construct_html():
 
 
     def save_file(self, file_name):
-        with open(f'./gpt_log/{file_name}', 'w', encoding='utf8') as f:
+        with open(os.path.join(get_log_folder(), file_name), 'w', encoding='utf8') as f:
             f.write(self.html_string.encode('utf-8', 'ignore').decode())
-
+        return os.path.join(get_log_folder(), file_name)

crazy_functions/辅助功能.py

@@ -0,0 +1,43 @@
+# encoding: utf-8
+# @Time   : 2023/4/19
+# @Author : Spike
+# @Descr   :
+from toolbox import update_ui
+from toolbox import CatchException, report_execption, write_results_to_file, get_log_folder
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+
+@CatchException
+def 猜你想问(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    if txt:
+        show_say = txt
+        prompt = txt+'\n回答完问题后，再列出用户可能提出的三个问题。'
+    else:
+        prompt = history[-1]+"\n分析上述回答，再列出用户可能提出的三个问题。"
+        show_say = '分析上述回答，再列出用户可能提出的三个问题。'
+    gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=prompt,
+        inputs_show_user=show_say,
+        llm_kwargs=llm_kwargs,
+        chatbot=chatbot,
+        history=history,
+        sys_prompt=system_prompt
+    )
+    chatbot[-1] = (show_say, gpt_say)
+    history.extend([show_say, gpt_say])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+
+@CatchException
+def 清除缓存(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
+    chatbot.append(['清除本地缓存数据', '执行中. 删除 gpt_log & private_upload'])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面
+
+    import shutil, os
+    gpt_log_dir = os.path.join(os.path.dirname(__file__), '..', 'gpt_log')
+    private_upload_dir = os.path.join(os.path.dirname(__file__), '..', 'private_upload')
+    shutil.rmtree(gpt_log_dir, ignore_errors=True)
+    shutil.rmtree(private_upload_dir, ignore_errors=True)
+
+    chatbot.append(['清除本地缓存数据', '执行完成'])
+    yield from update_ui(chatbot=chatbot, history=history)  # 刷新界面

docker-compose.yml

@@ -16,6 +16,7 @@ services:
       AVAIL_LLM_MODELS:         '    ["gpt-3.5-turbo", "api2d-gpt-3.5-turbo", "gpt-4", "api2d-gpt-4", "newbing"]                    '
       WEB_PORT:                 '    22303                                                                                          '
       ADD_WAIFU:                '    True                                                                                           '
+      # THEME:                    '    Chuanhu-Small-and-Beautiful                                                                    '
       # DEFAULT_WORKER_NUM:       '    10                                                                                             '
       # AUTHENTICATION:           '    [("username", "passwd"), ("username2", "passwd2")]                                             '
 
@@ -28,7 +29,7 @@ services:
 
 
 ### ===================================================
-### 【方案二】 如果需要运行ChatGLM本地模型
+### 【方案二】 如果需要运行ChatGLM + Qwen + MOSS等本地模型
 ### ===================================================
 version: '3'
 services:
@@ -36,11 +37,11 @@ services:
     image: ghcr.io/binary-husky/gpt_academic_chatglm_moss:master  # (Auto Built by Dockerfile: docs/Dockerfile+ChatGLM)
     environment:
       # 请查阅 `config.py` 以查看所有的配置信息
-      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx,fkxxxxxx-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx  '
+      API_KEY:                  '    sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx                                            '
       USE_PROXY:                '    True                                                                                           '
       proxies:                  '    { "http": "socks5h://localhost:10880", "https": "socks5h://localhost:10880", }                 '
       LLM_MODEL:                '    gpt-3.5-turbo                                                                                  '
-      AVAIL_LLM_MODELS:         '    ["chatglm", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"]                                       '
+      AVAIL_LLM_MODELS:         '    ["chatglm", "qwen", "moss", "gpt-3.5-turbo", "gpt-4", "newbing"]                               '
       LOCAL_MODEL_DEVICE:       '    cuda                                                                                           '
       DEFAULT_WORKER_NUM:       '    10                                                                                             '
       WEB_PORT:                 '    12303                                                                                          '
@@ -57,6 +58,10 @@ services:
     command: >
       bash -c "python3 -u main.py"
 
+    # P.S. 通过对 command 进行微调，可以便捷地安装额外的依赖
+    # command: >
+    #   bash -c "pip install -r request_llm/requirements_qwen.txt && python3 -u main.py"
+
 ### ===================================================
 ### 【方案三】 如果需要运行ChatGPT + LLAMA + 盘古 + RWKV本地模型
 ### ===================================================

docs/GithubAction+ChatGLM+Moss

@@ -18,6 +18,7 @@ WORKDIR /gpt/gpt_academic
 RUN git clone https://github.com/OpenLMLab/MOSS.git request_llm/moss
 RUN python3 -m pip install -r requirements.txt
 RUN python3 -m pip install -r request_llm/requirements_moss.txt
+RUN python3 -m pip install -r request_llm/requirements_qwen.txt
 RUN python3 -m pip install -r request_llm/requirements_chatglm.txt
 RUN python3 -m pip install -r request_llm/requirements_newbing.txt
 

request_llm/bridge_all.py

@@ -19,6 +19,12 @@ from .bridge_chatgpt import predict as chatgpt_ui
 from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
 from .bridge_chatglm import predict as chatglm_ui
 
+from .bridge_chatglm import predict_no_ui_long_connection as chatglm_noui
+from .bridge_chatglm import predict as chatglm_ui
+
+from .bridge_qianfan import predict_no_ui_long_connection as qianfan_noui
+from .bridge_qianfan import predict as qianfan_ui
+
 colors = ['#FF00FF', '#00FFFF', '#FF0000', '#990099', '#009999', '#990044']
 
 class LazyloadTiktoken(object):
@@ -165,7 +171,14 @@ model_info = {
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
-
+    "qianfan": {
+        "fn_with_ui": qianfan_ui,
+        "fn_without_ui": qianfan_noui,
+        "endpoint": None,
+        "max_token": 2000,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
 }
 
 # -=-=-=-=-=-=- 以下部分是新加入的模型，可能附带额外依赖 -=-=-=-=-=-=-
@@ -361,7 +374,7 @@ if "chatgpt_website" in AVAIL_LLM_MODELS:   # 接入一些逆向工程https://gi
             "chatgpt_website": {
                 "fn_with_ui": chatgpt_website_ui,
                 "fn_without_ui": chatgpt_website_noui,
-                "endpoint": None,
+                "endpoint": openai_endpoint,
                 "max_token": 4096,
                 "tokenizer": tokenizer_gpt35,
                 "token_cnt": get_token_num_gpt35,
@@ -385,6 +398,22 @@ if "spark" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
         })
     except:
         print(trimmed_format_exc())
+if "llama2" in AVAIL_LLM_MODELS:   # llama2
+    try:
+        from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui
+        from .bridge_llama2 import predict as llama2_ui
+        model_info.update({
+            "llama2": {
+                "fn_with_ui": llama2_ui,
+                "fn_without_ui": llama2_noui,
+                "endpoint": None,
+                "max_token": 4096,
+                "tokenizer": tokenizer_gpt35,
+                "token_cnt": get_token_num_gpt35,
+            }
+        })
+    except:
+        print(trimmed_format_exc())
 
 
 

request_llm/bridge_chatgpt.py

@@ -177,14 +177,13 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 yield from update_ui(chatbot=chatbot, history=history, msg="非Openai官方接口返回了错误:" + chunk.decode()) # 刷新界面
                 return
             
-            # print(chunk.decode()[6:])
-            if is_head_of_the_stream and (r'"object":"error"' not in chunk.decode()):
+            chunk_decoded = chunk.decode()
+            if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"choices" not in chunk_decoded):
                 # 数据流的第一帧不携带content
                 is_head_of_the_stream = False; continue
             
             if chunk:
                 try:
-                    chunk_decoded = chunk.decode()
                     # 前者是API2D的结束条件，后者是OPENAI的结束条件
                     if ('data: [DONE]' in chunk_decoded) or (len(json.loads(chunk_decoded[6:])['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
@@ -192,7 +191,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                         break
                     # 处理数据流的主体
                     chunkjson = json.loads(chunk_decoded[6:])
-                    status_text = f"finish_reason: {chunkjson['choices'][0]['finish_reason']}"
+                    status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
                     # 如果这里抛出异常，一般是文本过长，详情见get_full_error的输出
                     gpt_replying_buffer = gpt_replying_buffer + json.loads(chunk_decoded[6:])['choices'][0]["delta"]["content"]
                     history[-1] = gpt_replying_buffer
@@ -216,7 +215,6 @@ def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
         history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
                                                max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
         chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
-                        # history = []    # 清除历史
     elif "does not exist" in error_msg:
         chatbot[-1] = (chatbot[-1][0], f"[Local Message] Model {llm_kwargs['llm_model']} does not exist. 模型不存在, 或者您没有获得体验资格.")
     elif "Incorrect API key" in error_msg:

request_llm/bridge_chatgpt_website.py

@@ -118,16 +118,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
     chatbot 为WebUI中显示的对话列表，修改它，然后yeild出去，可以直接修改对话界面内容
     additional_fn代表点击的哪个按钮，按钮见functional.py
     """
-    if is_any_api_key(inputs):
-        chatbot._cookies['api_key'] = inputs
-        chatbot.append(("输入已识别为openai的api_key", what_keys(inputs)))
-        yield from update_ui(chatbot=chatbot, history=history, msg="api_key已导入") # 刷新界面
-        return
-    elif not is_any_api_key(chatbot._cookies['api_key']):
-        chatbot.append((inputs, "缺少api_key。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。"))
-        yield from update_ui(chatbot=chatbot, history=history, msg="缺少api_key") # 刷新界面
-        return
-
     if additional_fn is not None:
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
@@ -245,14 +235,9 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
     if not is_any_api_key(llm_kwargs['api_key']):
         raise AssertionError("你提供了错误的API_KEY。\n\n1. 临时解决方案：直接在输入区键入api_key，然后回车提交。\n\n2. 长效解决方案：在config.py中配置。")
 
-    api_key = select_api_key(llm_kwargs['api_key'], llm_kwargs['llm_model'])
-
     headers = {
         "Content-Type": "application/json",
-        "Authorization": f"Bearer {api_key}"
     }
-    if API_ORG.startswith('org-'): headers.update({"OpenAI-Organization": API_ORG})
-    if llm_kwargs['llm_model'].startswith('azure-'): headers.update({"api-key": api_key})
 
     conversation_cnt = len(history) // 2
 

request_llm/bridge_llama2.py

@@ -0,0 +1,91 @@
+model_name = "LLaMA"
+cmd_to_install = "`pip install -r request_llm/requirements_chatglm.txt`"
+
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer
+from toolbox import update_ui, get_conf, ProxyNetworkActivate
+from multiprocessing import Process, Pipe
+from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns, SingletonLocalLLM
+from threading import Thread
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 Local Model
+# ------------------------------------------------------------------------------------------------------------------------
+@SingletonLocalLLM
+class GetONNXGLMHandle(LocalLLMHandle):
+
+    def load_model_info(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        self.model_name = model_name
+        self.cmd_to_install = cmd_to_install
+
+    def load_model_and_tokenizer(self):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        import os, glob
+        import os
+        import platform
+        huggingface_token, device = get_conf('HUGGINGFACE_ACCESS_TOKEN', 'LOCAL_MODEL_DEVICE')
+        assert len(huggingface_token) != 0, "没有填写 HUGGINGFACE_ACCESS_TOKEN"
+        with open(os.path.expanduser('~/.cache/huggingface/token'), 'w') as f:
+            f.write(huggingface_token)
+        model_id = 'meta-llama/Llama-2-7b-chat-hf'
+        with ProxyNetworkActivate():
+            self._tokenizer = AutoTokenizer.from_pretrained(model_id, use_auth_token=huggingface_token)
+            # use fp16
+            model = AutoModelForCausalLM.from_pretrained(model_id, use_auth_token=huggingface_token).eval()
+            if device.startswith('cuda'): model = model.half().to(device)
+            self._model = model
+
+            return self._model, self._tokenizer
+
+    def llm_stream_generator(self, **kwargs):
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
+        def adaptor(kwargs):
+            query = kwargs['query']
+            max_length = kwargs['max_length']
+            top_p = kwargs['top_p']
+            temperature = kwargs['temperature']
+            history = kwargs['history']
+            console_slience = kwargs.get('console_slience', True)
+            return query, max_length, top_p, temperature, history, console_slience
+        
+        def convert_messages_to_prompt(query, history):
+            prompt = ""
+            for a, b in history:
+                prompt += f"\n[INST]{a}[/INST]"
+                prompt += "\n{b}" + b
+            prompt += f"\n[INST]{query}[/INST]"
+            return prompt
+        
+        query, max_length, top_p, temperature, history, console_slience = adaptor(kwargs)
+        prompt = convert_messages_to_prompt(query, history)
+        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
+        # code from transformers.llama
+        streamer = TextIteratorStreamer(self._tokenizer)
+        # Run the generation in a separate thread, so that we can fetch the generated text in a non-blocking way.
+        inputs = self._tokenizer([prompt], return_tensors="pt")
+        prompt_tk_back = self._tokenizer.batch_decode(inputs['input_ids'])[0]
+
+        generation_kwargs = dict(inputs.to(self._model.device), streamer=streamer, max_new_tokens=max_length)
+        thread = Thread(target=self._model.generate, kwargs=generation_kwargs)
+        thread.start()
+        generated_text = ""
+        for new_text in streamer: 
+            generated_text += new_text
+            if not console_slience: print(new_text, end='')
+            yield generated_text.lstrip(prompt_tk_back).rstrip("</s>")
+        if not console_slience: print()
+        # =-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=--=-=-
+        
+    def try_to_import_special_deps(self, **kwargs):
+        # import something that will raise error if the user does not install requirement_*.txt
+        # 🏃‍♂️🏃‍♂️🏃‍♂️ 主进程执行
+        import importlib
+        importlib.import_module('transformers')
+
+
+# ------------------------------------------------------------------------------------------------------------------------
+# 🔌💻 GPT-Academic Interface
+# ------------------------------------------------------------------------------------------------------------------------
+predict_no_ui_long_connection, predict = get_local_llm_predict_fns(GetONNXGLMHandle, model_name)

request_llm/bridge_qianfan.py

@@ -0,0 +1,164 @@
+
+import time, requests, json
+from multiprocessing import Process, Pipe
+from functools import wraps
+from datetime import datetime, timedelta
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, get_conf
+
+model_name = '千帆大模型平台'
+timeout_bot_msg = '[Local Message] Request timeout. Network error.'
+
+def cache_decorator(timeout):
+    cache = {}
+    def decorator(func):
+        @wraps(func)
+        def wrapper(*args, **kwargs):
+            key = (func.__name__, args, frozenset(kwargs.items()))
+            # Check if result is already cached and not expired
+            if key in cache:
+                result, timestamp = cache[key]
+                if datetime.now() - timestamp < timedelta(seconds=timeout):
+                    return result
+
+            # Call the function and cache the result
+            result = func(*args, **kwargs)
+            cache[key] = (result, datetime.now())
+            return result
+        return wrapper
+    return decorator
+
+@cache_decorator(timeout=3600)
+def get_access_token():
+    """
+    使用 AK，SK 生成鉴权签名（Access Token）
+    :return: access_token，或是None(如果错误)
+    """
+    # if (access_token_cache is None) or (time.time() - last_access_token_obtain_time > 3600):
+    BAIDU_CLOUD_API_KEY, BAIDU_CLOUD_SECRET_KEY = get_conf('BAIDU_CLOUD_API_KEY', 'BAIDU_CLOUD_SECRET_KEY')
+
+    if len(BAIDU_CLOUD_SECRET_KEY) == 0: raise RuntimeError("没有配置BAIDU_CLOUD_SECRET_KEY")
+    if len(BAIDU_CLOUD_API_KEY) == 0: raise RuntimeError("没有配置BAIDU_CLOUD_API_KEY")
+
+    url = "https://aip.baidubce.com/oauth/2.0/token"
+    params = {"grant_type": "client_credentials", "client_id": BAIDU_CLOUD_API_KEY, "client_secret": BAIDU_CLOUD_SECRET_KEY}
+    access_token_cache = str(requests.post(url, params=params).json().get("access_token"))
+    return access_token_cache
+    # else:
+    #     return access_token_cache
+
+
+def generate_message_payload(inputs, llm_kwargs, history, system_prompt):
+    conversation_cnt = len(history) // 2
+    messages = [{"role": "user", "content": system_prompt}]
+    messages.append({"role": "assistant", "content": 'Certainly!'})
+    if conversation_cnt:
+        for index in range(0, 2*conversation_cnt, 2):
+            what_i_have_asked = {}
+            what_i_have_asked["role"] = "user"
+            what_i_have_asked["content"] = history[index]
+            what_gpt_answer = {}
+            what_gpt_answer["role"] = "assistant"
+            what_gpt_answer["content"] = history[index+1]
+            if what_i_have_asked["content"] != "":
+                if what_gpt_answer["content"] == "": continue
+                if what_gpt_answer["content"] == timeout_bot_msg: continue
+                messages.append(what_i_have_asked)
+                messages.append(what_gpt_answer)
+            else:
+                messages[-1]['content'] = what_gpt_answer['content']
+    what_i_ask_now = {}
+    what_i_ask_now["role"] = "user"
+    what_i_ask_now["content"] = inputs
+    messages.append(what_i_ask_now)
+    return messages
+
+
+def generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
+    BAIDU_CLOUD_QIANFAN_MODEL,  = get_conf('BAIDU_CLOUD_QIANFAN_MODEL')
+
+    url_lib = {
+        "ERNIE-Bot":            "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/completions" ,
+        "ERNIE-Bot-turbo":      "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/eb-instant"  ,
+        "BLOOMZ-7B":            "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/bloomz_7b1",
+
+        "Llama-2-70B-Chat":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_70b",
+        "Llama-2-13B-Chat":     "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_13b",
+        "Llama-2-7B-Chat":      "https://aip.baidubce.com/rpc/2.0/ai_custom/v1/wenxinworkshop/chat/llama_2_7b",
+    }
+
+    url = url_lib[BAIDU_CLOUD_QIANFAN_MODEL]
+
+    url += "?access_token=" + get_access_token()
+
+
+    payload = json.dumps({
+        "messages": generate_message_payload(inputs, llm_kwargs, history, system_prompt),
+        "stream": True
+    })
+    headers = {
+        'Content-Type': 'application/json'
+    }
+    response = requests.request("POST", url, headers=headers, data=payload, stream=True)
+    buffer = ""
+    for line in response.iter_lines():
+        if len(line) == 0: continue
+        try:
+            dec = line.decode().lstrip('data:')
+            dec = json.loads(dec)
+            incoming = dec['result']
+            buffer += incoming
+            yield buffer
+        except:
+            if ('error_code' in dec) and ("max length" in dec['error_msg']):
+                raise ConnectionAbortedError(dec['error_msg'])  # 上下文太长导致 token 溢出
+            elif ('error_code' in dec):
+                raise RuntimeError(dec['error_msg'])
+
+
+def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=[], console_slience=False):
+    """
+        ⭐多线程方法
+        函数的说明请见 request_llm/bridge_all.py
+    """
+    watch_dog_patience = 5
+    response = ""
+
+    for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, sys_prompt):
+        if len(observe_window) >= 1:
+            observe_window[0] = response
+        if len(observe_window) >= 2:
+            if (time.time()-observe_window[1]) > watch_dog_patience: raise RuntimeError("程序终止。")
+    return response
+
+def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
+    """
+        ⭐单线程方法
+        函数的说明请见 request_llm/bridge_all.py
+    """
+    chatbot.append((inputs, ""))
+
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    yield from update_ui(chatbot=chatbot, history=history)
+    # 开始接收回复
+    try:
+        for response in generate_from_baidu_qianfan(inputs, llm_kwargs, history, system_prompt):
+            chatbot[-1] = (inputs, response)
+            yield from update_ui(chatbot=chatbot, history=history)
+    except ConnectionAbortedError as e:
+        from .bridge_all import model_info
+        if len(history) >= 2: history[-1] = ""; history[-2] = "" # 清除当前溢出的输入：history[-2] 是本次输入, history[-1] 是本次输出
+        history = clip_history(inputs=inputs, history=history, tokenizer=model_info[llm_kwargs['llm_model']]['tokenizer'], 
+                    max_token_limit=(model_info[llm_kwargs['llm_model']]['max_token'])) # history至少释放二分之一
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Reduce the length. 本次输入过长, 或历史数据过长. 历史缓存数据已部分释放, 您可以请再次尝试. (若再次失败则更可能是因为输入过长.)")
+        yield from update_ui(chatbot=chatbot, history=history, msg="异常") # 刷新界面
+        return
+    
+    # 总结输出
+    response = f"[Local Message]: {model_name}响应异常 ..."
+    if response == f"[Local Message]: 等待{model_name}响应中 ...":
+        response = f"[Local Message]: {model_name}响应异常 ..."
+    history.extend([inputs, response])
+    yield from update_ui(chatbot=chatbot, history=history)

request_llm/local_llm_class.py

@@ -128,7 +128,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name):
 
         # chatglm 没有 sys_prompt 接口，因此把prompt加入 history
         history_feedin = []
-        history_feedin.append(["What can I do?", sys_prompt])
+        history_feedin.append([sys_prompt, "Certainly!"])
         for i in range(len(history)//2):
             history_feedin.append([history[2*i], history[2*i+1]] )
 
@@ -161,7 +161,7 @@ def get_local_llm_predict_fns(LLMSingletonClass, model_name):
 
         # 处理历史信息
         history_feedin = []
-        history_feedin.append(["What can I do?", system_prompt] )
+        history_feedin.append([system_prompt, "Certainly!"])
         for i in range(len(history)//2):
             history_feedin.append([history[2*i], history[2*i+1]] )
 

requirements.txt

@@ -17,5 +17,6 @@ openai
 numpy
 arxiv
 rich
-websocket-client
 pypdf2==2.12.1
+websocket-client
+scipdf_parser==0.3

tests/test_plugins.py

@@ -9,9 +9,9 @@ validate_path() # 返回项目根路径
 from tests.test_utils import plugin_test
 
 if __name__ == "__main__":
-    plugin_test(plugin='crazy_functions.命令行助手->命令行助手', main_input='查看当前的docker容器列表')
+    # plugin_test(plugin='crazy_functions.命令行助手->命令行助手', main_input='查看当前的docker容器列表')
 
-    plugin_test(plugin='crazy_functions.解析项目源代码->解析一个Python项目', main_input="crazy_functions/test_project/python/dqn")
+    # plugin_test(plugin='crazy_functions.解析项目源代码->解析一个Python项目', main_input="crazy_functions/test_project/python/dqn")
 
     # plugin_test(plugin='crazy_functions.解析项目源代码->解析一个C项目', main_input="crazy_functions/test_project/cpp/cppipc")
 
@@ -19,7 +19,7 @@ if __name__ == "__main__":
 
     # plugin_test(plugin='crazy_functions.批量Markdown翻译->Markdown中译英', main_input="README.md")
 
-    # plugin_test(plugin='crazy_functions.批量翻译PDF文档_多线程->批量翻译PDF文档', main_input="crazy_functions/test_project/pdf_and_word")
+    plugin_test(plugin='crazy_functions.批量翻译PDF文档_多线程->批量翻译PDF文档', main_input='crazy_functions/test_project/pdf_and_word/aaai.pdf')
 
     # plugin_test(plugin='crazy_functions.谷歌检索小助手->谷歌检索小助手', main_input="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG=")
     

tests/test_utils.py

@@ -22,10 +22,12 @@ def silence_stdout(func):
     def wrapper(*args, **kwargs):
         _original_stdout = sys.stdout
         sys.stdout = open(os.devnull, 'w')
+        sys.stdout.reconfigure(encoding='utf-8')
         for q in func(*args, **kwargs):
             sys.stdout = _original_stdout
             yield q
             sys.stdout = open(os.devnull, 'w')
+            sys.stdout.reconfigure(encoding='utf-8')
         sys.stdout.close()
         sys.stdout = _original_stdout
     return wrapper
@@ -35,6 +37,7 @@ def silence_stdout_fn(func):
     def wrapper(*args, **kwargs):
         _original_stdout = sys.stdout
         sys.stdout = open(os.devnull, 'w')
+        sys.stdout.reconfigure(encoding='utf-8')
         result = func(*args, **kwargs)
         sys.stdout.close()
         sys.stdout = _original_stdout

version

@@ -1,5 +1,5 @@
 {
-  "version": 3.48,
+  "version": 3.49,
   "show_feature": true,
-  "new_feature": "接入阿里通义千问、讯飞星火、上海AI-Lab书生 <-> 优化一键升级 <-> 提高arxiv翻译速度和成功率 <-> 支持自定义APIKEY格式 <-> 临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块 <-> 完善本地Latex矫错和翻译功能 <-> 增加gpt-3.5-16k的支持"
+  "new_feature": "支持借助GROBID实现PDF高精度翻译 <-> 接入百度千帆平台和文心一言 <-> 接入阿里通义千问、讯飞星火、上海AI-Lab书生 <-> 优化一键升级 <-> 提高arxiv翻译速度和成功率 <-> 支持自定义APIKEY格式 <-> 临时修复theme的文件丢失问题 <-> 新增实时语音对话插件（自动断句，脱手对话） <-> 支持加载自定义的ChatGLM2微调模型 <-> 动态ChatBot窗口高度 <-> 修复Azure接口的BUG <-> 完善多语言模块"
 }