change arxiv download attempt url order

* Update bridge_all.py

* Update bridge_chatgpt.py

* Update bridge_chatgpt.py

* Update bridge_all.py

* Update bridge_all.py

.github/workflows/build-with-jittorllms.yml

@@ -1,44 +0,0 @@
-# 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 }}

.github/workflows/build-with-latex-arm.yml

@@ -1,14 +1,14 @@
 # https://docs.github.com/en/actions/publishing-packages/publishing-docker-images#publishing-images-to-github-packages
-name: build-with-all-capacity-beta
+name: build-with-latex-arm
 
 on:
   push:
     branches:
-      - 'master'
+      - "master"
 
 env:
   REGISTRY: ghcr.io
-  IMAGE_NAME: ${{ github.repository }}_with_all_capacity_beta
+  IMAGE_NAME: ${{ github.repository }}_with_latex_arm
 
 jobs:
   build-and-push-image:
@@ -18,11 +18,17 @@ jobs:
       packages: write
 
     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
-        uses: actions/checkout@v3
+        uses: actions/checkout@v4
 
       - name: Log in to the Container registry
-        uses: docker/login-action@v2
+        uses: docker/login-action@v3
         with:
           registry: ${{ env.REGISTRY }}
           username: ${{ github.actor }}
@@ -35,10 +41,11 @@ jobs:
           images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME }}
 
       - name: Build and push Docker image
-        uses: docker/build-push-action@v4
+        uses: docker/build-push-action@v6
         with:
           context: .
           push: true
-          file: docs/GithubAction+AllCapacityBeta
+          platforms: linux/arm64
+          file: docs/GithubAction+NoLocal+Latex+Arm
           tags: ${{ steps.meta.outputs.tags }}
-          labels: ${{ steps.meta.outputs.labels }}
+          labels: ${{ steps.meta.outputs.labels }}

.gitignore

@@ -160,3 +160,4 @@ test.*
 temp.*
 objdump*
 *.min.*.js
+TODO

README.md

@@ -1,5 +1,6 @@
 > [!IMPORTANT]
-> 2024.6.1: 版本3.80加入插件二级菜单功能（详见wiki）  
+> 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  
+> 2024.10.8: 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)  
 > 2024.3.11: 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。

check_proxy.py

@@ -1,3 +1,4 @@
+from loguru import logger
 
 def check_proxy(proxies, return_ip=False):
     import requests
@@ -19,14 +20,14 @@ def check_proxy(proxies, return_ip=False):
         else:
             result = f"代理配置 {proxies_https}, 代理数据解析失败：{data}"
         if not return_ip:
-            print(result)
+            logger.warning(result)
             return result
         else:
             return ip
     except:
         result = f"代理配置 {proxies_https}, 代理所在地查询超时，代理可能无效"
         if not return_ip:
-            print(result)
+            logger.warning(result)
             return result
         else:
             return ip
@@ -82,25 +83,25 @@ def patch_and_restart(path):
     import sys
     import time
     import glob
-    from shared_utils.colorful import print亮黄, print亮绿, print亮红
+    from shared_utils.colorful import log亮黄, log亮绿, log亮红
     # if not using config_private, move origin config.py as config_private.py
     if not os.path.exists('config_private.py'):
-        print亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
+        log亮黄('由于您没有设置config_private.py私密配置，现将您的现有配置移动至config_private.py以防止配置丢失，',
               '另外您可以随时在history子文件夹下找回旧版的程序。')
         shutil.copyfile('config.py', 'config_private.py')
     path_new_version = glob.glob(path + '/*-master')[0]
     dir_util.copy_tree(path_new_version, './')
-    print亮绿('代码已经更新，即将更新pip包依赖……')
-    for i in reversed(range(5)): time.sleep(1); print(i)
+    log亮绿('代码已经更新，即将更新pip包依赖……')
+    for i in reversed(range(5)): time.sleep(1); log亮绿(i)
     try:
         import subprocess
         subprocess.check_call([sys.executable, '-m', 'pip', 'install', '-r', 'requirements.txt'])
     except:
-        print亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
-    print亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
-    print(' ------------------------------ -----------------------------------')
-    for i in reversed(range(8)): time.sleep(1); print(i)
+        log亮红('pip包依赖安装出现问题，需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
+    log亮绿('更新完成，您可以随时在history子文件夹下找回旧版的程序，5s之后重启')
+    log亮红('假如重启失败，您可能需要手动安装新增的依赖库 `python -m pip install -r requirements.txt`，然后在用常规的`python main.py`的方式启动。')
+    log亮绿(' ------------------------------ -----------------------------------')
+    for i in reversed(range(8)): time.sleep(1); log亮绿(i)
     os.execl(sys.executable, sys.executable, *sys.argv)
 
 
@@ -135,9 +136,9 @@ def auto_update(raise_error=False):
             current_version = f.read()
             current_version = json.loads(current_version)['version']
         if (remote_version - current_version) >= 0.01-1e-5:
-            from shared_utils.colorful import print亮黄
-            print亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
-            print('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
+            from shared_utils.colorful import log亮黄
+            log亮黄(f'\n新版本可用。新版本:{remote_version}，当前版本:{current_version}。{new_feature}')
+            logger.info('（1）Github更新地址:\nhttps://github.com/binary-husky/chatgpt_academic\n')
             user_instruction = input('（2）是否一键更新代码（Y+回车=确认，输入其他/无输入+回车=不更新）？')
             if user_instruction in ['Y', 'y']:
                 path = backup_and_download(current_version, remote_version)
@@ -148,9 +149,9 @@ def auto_update(raise_error=False):
                     if raise_error:
                         from toolbox import trimmed_format_exc
                         msg += trimmed_format_exc()
-                    print(msg)
+                    logger.warning(msg)
             else:
-                print('自动更新程序：已禁用')
+                logger.info('自动更新程序：已禁用')
                 return
         else:
             return
@@ -159,10 +160,10 @@ def auto_update(raise_error=False):
         if raise_error:
             from toolbox import trimmed_format_exc
             msg += trimmed_format_exc()
-        print(msg)
+        logger.info(msg)
 
 def warm_up_modules():
-    print('正在执行一些模块的预热 ...')
+    logger.info('正在执行一些模块的预热 ...')
     from toolbox import ProxyNetworkActivate
     from request_llms.bridge_all import model_info
     with ProxyNetworkActivate("Warmup_Modules"):
@@ -172,7 +173,7 @@ def warm_up_modules():
         enc.encode("模块预热", disallowed_special=())
 
 def warm_up_vectordb():
-    print('正在执行一些模块的预热 ...')
+    logger.info('正在执行一些模块的预热 ...')
     from toolbox import ProxyNetworkActivate
     with ProxyNetworkActivate("Warmup_Modules"):
         import nltk

config.py

@@ -36,8 +36,11 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p
                     "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4-turbo-2024-04-09",
                     "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5",
                     "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo",
-                    "gemini-pro", "chatglm3"
+                    "gemini-1.5-pro", "chatglm3"
                     ]
+
+EMBEDDING_MODEL = "text-embedding-3-small"
+
 # --- --- --- ---
 # P.S. 其他可用的模型还包括
 # AVAIL_LLM_MODELS = [
@@ -50,12 +53,13 @@ AVAIL_LLM_MODELS = ["gpt-4-1106-preview", "gpt-4-turbo-preview", "gpt-4-vision-p
 #   "claude-3-haiku-20240307","claude-3-sonnet-20240229","claude-3-opus-20240229", "claude-2.1", "claude-instant-1.2",
 #   "moss", "llama2", "chatglm_onnx", "internlm", "jittorllms_pangualpha", "jittorllms_llama",
 #   "deepseek-chat" ,"deepseek-coder",
+#   "gemini-1.5-flash",
 #   "yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview",
 # ]
 # --- --- --- ---
-# 此外，您还可以在接入one-api/vllm/ollama时，
-# 使用"one-api-*","vllm-*","ollama-*"前缀直接使用非标准方式接入的模型，例如
-# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)"]
+# 此外，您还可以在接入one-api/vllm/ollama/Openroute时，
+# 使用"one-api-*","vllm-*","ollama-*","openrouter-*"前缀直接使用非标准方式接入的模型，例如
+# AVAIL_LLM_MODELS = ["one-api-claude-3-sonnet-20240229(max_token=100000)", "ollama-phi3(max_token=4096)","openrouter-openai/gpt-4o-mini","openrouter-openai/chatgpt-4o-latest"]
 # --- --- --- ---
 
 
@@ -295,7 +299,7 @@ ARXIV_CACHE_DIR = "gpt_log/arxiv_cache"
 
 # 除了连接OpenAI之外，还有哪些场合允许使用代理，请尽量不要修改
 WHEN_TO_USE_PROXY = ["Download_LLM", "Download_Gradio_Theme", "Connect_Grobid",
-                     "Warmup_Modules", "Nougat_Download", "AutoGen"]
+                     "Warmup_Modules", "Nougat_Download", "AutoGen", "Connect_OpenAI_Embedding"]
 
 
 # 启用插件热加载

core_functional.py

@@ -17,7 +17,7 @@ def get_core_functions():
                             text_show_english=
                                 r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, "
                                 r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. "
-                                r"Firstly, you should provide the polished paragraph. "
+                                r"Firstly, you should provide the polished paragraph (in English). "
                                 r"Secondly, you should list all your modification and explain the reasons to do so in markdown table.",
                             text_show_chinese=
                                 r"作为一名中文学术论文写作改进助理，你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性，"

crazy_functional.py

@@ -1,5 +1,6 @@
 from toolbox import HotReload  # HotReload 的意思是热更新，修改函数插件后，不需要重启程序，代码直接生效
 from toolbox import trimmed_format_exc
+from loguru import logger
 
 
 def get_crazy_functions():
@@ -421,8 +422,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     # try:
     #     from crazy_functions.联网的ChatGPT import 连接网络回答问题
@@ -452,8 +453,8 @@ def get_crazy_functions():
     #         }
     #     )
     # except:
-    #     print(trimmed_format_exc())
-    #     print("Load function plugin failed")
+    #     logger.error(trimmed_format_exc())
+    #     logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.SourceCode_Analyse import 解析任意code项目
@@ -471,8 +472,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.询问多个大语言模型 import 同时问询_指定模型
@@ -490,8 +491,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
 
 
@@ -512,8 +513,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.数学动画生成manim import 动画生成
@@ -530,8 +531,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.Markdown_Translate import Markdown翻译指定语言
@@ -549,8 +550,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.知识库问答 import 知识库文件注入
@@ -568,8 +569,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.知识库问答 import 读取知识库作答
@@ -587,8 +588,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("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
@@ -604,8 +605,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
 
     try:
@@ -627,8 +628,8 @@ def get_crazy_functions():
                 }
             )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.批量翻译PDF文档_NOUGAT import 批量翻译PDF文档
@@ -644,8 +645,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.函数动态生成 import 函数动态生成
@@ -661,8 +662,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.多智能体 import 多智能体终端
@@ -678,8 +679,8 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        logger.error("Load function plugin failed")
 
     try:
         from crazy_functions.互动小游戏 import 随机小游戏
@@ -695,8 +696,33 @@ def get_crazy_functions():
             }
         )
     except:
-        print(trimmed_format_exc())
-        print("Load function plugin failed")
+        logger.error(trimmed_format_exc())
+        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:
     #     from crazy_functions.高级功能函数模板 import 测试图表渲染
@@ -709,7 +735,7 @@ def get_crazy_functions():
     #         }
     #     })
     # except:
-    #     print(trimmed_format_exc())
+    #     logger.error(trimmed_format_exc())
     #     print('Load function plugin failed')
 
     # try:

crazy_functions/Conversation_To_File.py

@@ -171,7 +171,7 @@ def 载入对话历史存档(txt, llm_kwargs, plugin_kwargs, chatbot, history, s
     system_prompt   给gpt的静默提醒
     user_request    当前用户的请求信息（IP地址等）
     """
-    from .crazy_utils import get_files_from_everything
+    from crazy_functions.crazy_utils import get_files_from_everything
     success, file_manifest, _ = get_files_from_everything(txt, type='.html')
 
     if not success:

crazy_functions/Image_Generate.py

@@ -30,7 +30,7 @@ def gen_image(llm_kwargs, prompt, resolution="1024x1024", model="dall-e-2", qual
     if style is not None:
         data['style'] = style
     response = requests.post(url, headers=headers, json=data, proxies=proxies)
-    print(response.content)
+    # logger.info(response.content)
     try:
         image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
     except:
@@ -76,7 +76,7 @@ def edit_image(llm_kwargs, prompt, image_path, resolution="1024x1024", model="da
         }
 
     response = requests.post(url, headers=headers, files=files, proxies=proxies)
-    print(response.content)
+    # logger.info(response.content)
     try:
         image_url = json.loads(response.content.decode('utf8'))['data'][0]['url']
     except:

crazy_functions/Latex_Function.py

@@ -1,6 +1,8 @@
 from toolbox import update_ui, trimmed_format_exc, get_conf, get_log_folder, promote_file_to_downloadzone, check_repeat_upload, map_file_to_sha256
 from toolbox import CatchException, report_exception, update_ui_lastest_msg, zip_result, gen_time_str
 from functools import partial
+from loguru import logger
+
 import glob, os, requests, time, json, tarfile
 
 pj = os.path.join
@@ -136,25 +138,43 @@ def arxiv_download(chatbot, history, txt, allow_cache=True):
     cached_translation_pdf = check_cached_translation_pdf(arxiv_id)
     if cached_translation_pdf and allow_cache: return cached_translation_pdf, arxiv_id
 
-    url_tar = url_.replace('/abs/', '/e-print/')
-    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
     extract_dst = pj(ARXIV_CACHE_DIR, arxiv_id, 'extract')
-    os.makedirs(translation_dir, exist_ok=True)
-
-    # <-------------- download arxiv source file ------------->
+    translation_dir = pj(ARXIV_CACHE_DIR, arxiv_id, 'e-print')
     dst = pj(translation_dir, arxiv_id + '.tar')
-    if os.path.exists(dst):
-        yield from update_ui_lastest_msg("调用缓存", chatbot=chatbot, history=history)  # 刷新界面
+    os.makedirs(translation_dir, exist_ok=True)
+    # <-------------- download arxiv source file ------------->
+
+    def fix_url_and_download():
+        # for url_tar in [url_.replace('/abs/', '/e-print/'), url_.replace('/abs/', '/src/')]:
+        for url_tar in [url_.replace('/abs/', '/src/'), url_.replace('/abs/', '/e-print/')]:
+            proxies = get_conf('proxies')
+            r = requests.get(url_tar, proxies=proxies)
+            if r.status_code == 200:
+                with open(dst, 'wb+') as f:
+                    f.write(r.content)
+                return True
+        return False
+
+    if os.path.exists(dst) and allow_cache:
+        yield from update_ui_lastest_msg(f"调用缓存 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
+        success = True
     else:
-        yield from update_ui_lastest_msg("开始下载", chatbot=chatbot, history=history)  # 刷新界面
-        proxies = get_conf('proxies')
-        r = requests.get(url_tar, proxies=proxies)
-        with open(dst, 'wb+') as f:
-            f.write(r.content)
+        yield from update_ui_lastest_msg(f"开始下载 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
+        success = fix_url_and_download()
+        yield from update_ui_lastest_msg(f"下载完成 {arxiv_id}", chatbot=chatbot, history=history)  # 刷新界面
+
+
+    if not success:
+        yield from update_ui_lastest_msg(f"下载失败 {arxiv_id}", chatbot=chatbot, history=history)
+        raise tarfile.ReadError(f"论文下载失败 {arxiv_id}")
+
     # <-------------- extract file ------------->
-    yield from update_ui_lastest_msg("下载完成", chatbot=chatbot, history=history)  # 刷新界面
     from toolbox import extract_archive
-    extract_archive(file_path=dst, dest_dir=extract_dst)
+    try:
+        extract_archive(file_path=dst, dest_dir=extract_dst)
+    except tarfile.ReadError:
+        os.remove(dst)
+        raise tarfile.ReadError(f"论文下载失败")
     return extract_dst, arxiv_id
 
 
@@ -178,7 +198,7 @@ def pdf2tex_project(pdf_file_path, plugin_kwargs):
 
         if response.ok:
             pdf_id = response.json()["pdf_id"]
-            print(f"PDF processing initiated. PDF ID: {pdf_id}")
+            logger.info(f"PDF processing initiated. PDF ID: {pdf_id}")
 
             # Step 2: Check processing status
             while True:
@@ -186,12 +206,12 @@ def pdf2tex_project(pdf_file_path, plugin_kwargs):
                 conversion_data = conversion_response.json()
 
                 if conversion_data["status"] == "completed":
-                    print("PDF processing completed.")
+                    logger.info("PDF processing completed.")
                     break
                 elif conversion_data["status"] == "error":
-                    print("Error occurred during processing.")
+                    logger.info("Error occurred during processing.")
                 else:
-                    print(f"Processing status: {conversion_data['status']}")
+                    logger.info(f"Processing status: {conversion_data['status']}")
                     time.sleep(5)  # wait for a few seconds before checking again
 
             # Step 3: Save results to local files
@@ -206,7 +226,7 @@ def pdf2tex_project(pdf_file_path, plugin_kwargs):
             output_path = os.path.join(output_dir, output_name)
             with open(output_path, "wb") as output_file:
                 output_file.write(response.content)
-            print(f"tex.zip file saved at: {output_path}")
+            logger.info(f"tex.zip file saved at: {output_path}")
 
             import zipfile
             unzip_dir = os.path.join(output_dir, file_name_wo_dot)
@@ -216,7 +236,7 @@ def pdf2tex_project(pdf_file_path, plugin_kwargs):
             return unzip_dir
 
         else:
-            print(f"Error sending PDF for processing. Status code: {response.status_code}")
+            logger.error(f"Error sending PDF for processing. Status code: {response.status_code}")
             return None
     else:
         from crazy_functions.pdf_fns.parse_pdf_via_doc2x import 解析PDF_DOC2X_转Latex

crazy_functions/Latex全文润色.py

@@ -1,6 +1,6 @@
 from toolbox import update_ui, trimmed_format_exc, promote_file_to_downloadzone, get_log_folder
 from toolbox import CatchException, report_exception, write_history_to_file, zip_folder
-
+from loguru import logger
 
 class PaperFileGroup():
     def __init__(self):
@@ -33,7 +33,7 @@ class PaperFileGroup():
                     self.sp_file_index.append(index)
                     self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
 
-        print('Segmentation: done')
+        logger.info('Segmentation: done')
     def merge_result(self):
         self.file_result = ["" for _ in range(len(self.file_paths))]
         for r, k in zip(self.sp_file_result, self.sp_file_index):
@@ -56,7 +56,7 @@ class PaperFileGroup():
 
 def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en', mode='polish'):
     import time, os, re
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 
 
     #  <-------- 读取Latex文件，删除其中的所有注释 ---------->
@@ -122,7 +122,7 @@ def 多文件润色(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
         pfg.write_result()
         pfg.zip_result()
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 
     #  <-------- 整理结果，退出 ---------->
     create_report_file_name = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime()) + f"-chatgpt.polish.md"

crazy_functions/Latex全文翻译.py

@@ -1,6 +1,6 @@
 from toolbox import update_ui, promote_file_to_downloadzone
 from toolbox import CatchException, report_exception, write_history_to_file
-fast_debug = False
+from loguru import logger
 
 class PaperFileGroup():
     def __init__(self):
@@ -33,11 +33,11 @@ class PaperFileGroup():
                     self.sp_file_index.append(index)
                     self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.tex")
 
-        print('Segmentation: done')
+        logger.info('Segmentation: done')
 
 def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
     import time, os, re
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 
     #  <-------- 读取Latex文件，删除其中的所有注释 ---------->
     pfg = PaperFileGroup()

crazy_functions/Markdown_Translate.py

@@ -1,4 +1,5 @@
-import glob, shutil, os, re, logging
+import glob, shutil, os, re
+from loguru import logger
 from toolbox import update_ui, trimmed_format_exc, gen_time_str
 from toolbox import CatchException, report_exception, get_log_folder
 from toolbox import write_history_to_file, promote_file_to_downloadzone
@@ -34,7 +35,7 @@ class PaperFileGroup():
                     self.sp_file_contents.append(segment)
                     self.sp_file_index.append(index)
                     self.sp_file_tag.append(self.file_paths[index] + f".part-{j}.md")
-        logging.info('Segmentation: done')
+        logger.info('Segmentation: done')
 
     def merge_result(self):
         self.file_result = ["" for _ in range(len(self.file_paths))]
@@ -51,7 +52,7 @@ class PaperFileGroup():
         return manifest
 
 def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, language='en'):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 
     #  <-------- 读取Markdown文件，删除其中的所有注释 ---------->
     pfg = PaperFileGroup()
@@ -106,7 +107,7 @@ def 多文件翻译(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
                 expected_f_name = plugin_kwargs['markdown_expected_output_path']
                 shutil.copyfile(output_file, expected_f_name)
     except:
-        logging.error(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 
     #  <-------- 整理结果，退出 ---------->
     create_report_file_name = gen_time_str() + f"-chatgpt.md"
@@ -126,7 +127,7 @@ def get_files_from_everything(txt, preference=''):
         proxies = get_conf('proxies')
         # 网络的远程文件
         if preference == 'Github':
-            logging.info('正在从github下载资源 ...')
+            logger.info('正在从github下载资源 ...')
             if not txt.endswith('.md'):
                 # Make a request to the GitHub API to retrieve the repository information
                 url = txt.replace("https://github.com/", "https://api.github.com/repos/") + '/readme'

crazy_functions/Rag_Interface.py

@@ -0,0 +1,92 @@
+from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
+from crazy_functions.crazy_utils import input_clipping
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
+RAG_WORKER_REGISTER = {}
+MAX_HISTORY_ROUND = 5
+MAX_CONTEXT_TOKEN_LIMIT = 4096
+REMEMBER_PREVIEW = 1000
+
+@CatchException
+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
+    user_name = chatbot.get_user()
+    checkpoint_dir = get_log_folder(user_name, plugin_name='experimental_rag')
+    if user_name in RAG_WORKER_REGISTER:
+        rag_worker = RAG_WORKER_REGISTER[user_name]
+    else:
+        rag_worker = RAG_WORKER_REGISTER[user_name] = LlamaIndexRagWorker(
+            user_name, 
+            llm_kwargs, 
+            checkpoint_dir=checkpoint_dir, 
+            auto_load_checkpoint=True)
+    current_context = f"{VECTOR_STORE_TYPE} @ {checkpoint_dir}"
+    tip = "提示：输入“清空向量数据库”可以清空RAG向量数据库"
+    if txt == "清空向量数据库":
+        chatbot.append([txt, f'正在清空 ({current_context}) ...'])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+        rag_worker.purge()
+        yield from update_ui_lastest_msg('已清空', chatbot, history, delay=0) # 刷新界面
+        return
+
+    chatbot.append([txt, f'正在召回知识 ({current_context}) ...'])
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+
+    # 2. clip history to reduce token consumption
+    #   2-1. reduce chat round
+    txt_origin = txt
+
+    if len(history) > MAX_HISTORY_ROUND * 2:
+        history = history[-(MAX_HISTORY_ROUND * 2):]
+    txt_clip, history, flags = input_clipping(txt, history, max_token_limit=MAX_CONTEXT_TOKEN_LIMIT, return_clip_flags=True)
+    input_is_clipped_flag = (flags["original_input_len"] != flags["clipped_input_len"])
+
+    #   2-2. if input is clipped, add input to vector store before retrieve
+    if input_is_clipped_flag:
+        yield from update_ui_lastest_msg('检测到长输入, 正在向量化 ...', chatbot, history, delay=0) # 刷新界面
+        # save input to vector store
+        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
+        if len(txt_origin) > REMEMBER_PREVIEW:
+            HALF = REMEMBER_PREVIEW//2
+            i_say_to_remember = txt[:HALF] + f" ...\n...(省略{len(txt_origin)-REMEMBER_PREVIEW}字)...\n... " + txt[-HALF:]
+            if (flags["original_input_len"] - flags["clipped_input_len"]) > HALF:
+                txt_clip = txt_clip  + f" ...\n...(省略{len(txt_origin)-len(txt_clip)-HALF}字)...\n... " + txt[-HALF:]
+            else:
+                pass
+            i_say = txt_clip
+        else:
+            i_say_to_remember = i_say = txt_clip
+    else:
+        i_say_to_remember = i_say = txt_clip
+
+    # 3. we search vector store and build prompts
+    nodes = rag_worker.retrieve_from_store_with_query(i_say)
+    prompt = rag_worker.build_prompt(query=i_say, nodes=nodes)
+
+    # 4. it is time to query llms
+    if len(chatbot) != 0: chatbot.pop(-1) # pop temp chat, because we are going to add them again inside `request_gpt_model_in_new_thread_with_ui_alive`
+    model_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+        inputs=prompt, inputs_show_user=i_say,
+        llm_kwargs=llm_kwargs, chatbot=chatbot, history=history,
+        sys_prompt=system_prompt,
+        retry_times_at_unknown_error=0
+    )
+
+    # 5. remember what has been asked / answered
+    yield from update_ui_lastest_msg(model_say + '</br></br>' + f'对话记忆中, 请稍等 ({current_context}) ...', chatbot, history, delay=0.5) # 刷新界面
+    rag_worker.remember_qa(i_say_to_remember, model_say)
+    history.extend([i_say, model_say])
+
+    yield from update_ui_lastest_msg(model_say, chatbot, history, delay=0, msg=tip) # 刷新界面

crazy_functions/Social_Helper.py

@@ -0,0 +1,167 @@
+import pickle, os, random
+from toolbox import CatchException, update_ui, get_conf, get_log_folder, update_ui_lastest_msg
+from crazy_functions.crazy_utils import input_clipping
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.json_fns.select_tool import structure_output, select_tool
+from pydantic import BaseModel, Field
+from loguru import logger
+from typing import List
+
+
+SOCIAL_NETWOK_WORKER_REGISTER = {}
+
+class SocialNetwork():
+    def __init__(self):
+        self.people = []
+
+class SaveAndLoad():
+    def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
+        self.user_name = user_name
+        self.checkpoint_dir = checkpoint_dir
+        if auto_load_checkpoint:
+            self.social_network = self.load_from_checkpoint(checkpoint_dir)
+        else:
+            self.social_network = SocialNetwork()
+
+    def does_checkpoint_exist(self, checkpoint_dir=None):
+        import os, glob
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if not os.path.exists(checkpoint_dir): return False
+        if len(glob.glob(os.path.join(checkpoint_dir, "social_network.pkl"))) == 0: return False
+        return True
+
+    def save_to_checkpoint(self, checkpoint_dir=None):
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        with open(os.path.join(checkpoint_dir, 'social_network.pkl'), "wb+") as f:
+            pickle.dump(self.social_network, f)
+        return
+
+    def load_from_checkpoint(self, checkpoint_dir=None):
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
+            with open(os.path.join(checkpoint_dir, 'social_network.pkl'), "rb") as f:
+                social_network = pickle.load(f)
+                return social_network
+        else:
+            return SocialNetwork()
+
+
+class Friend(BaseModel):
+    friend_name: str = Field(description="name of a friend")
+    friend_description: str = Field(description="description of a friend (everything about this friend)")
+    friend_relationship: str = Field(description="The relationship with a friend (e.g. friend, family, colleague)")
+
+class FriendList(BaseModel):
+    friends_list: List[Friend] = Field(description="The list of friends")
+
+
+class SocialNetworkWorker(SaveAndLoad):
+    def ai_socail_advice(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
+        pass
+
+    def ai_remove_friend(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
+        pass
+
+    def ai_list_friends(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
+        pass
+
+    def ai_add_multi_friends(self, prompt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type):
+        friend, err_msg = structure_output(
+            txt=prompt,
+            prompt="根据提示, 解析多个联系人的身份信息\n\n",
+            err_msg=f"不能理解该联系人",
+            run_gpt_fn=run_gpt_fn,
+            pydantic_cls=FriendList
+        )
+        if friend.friends_list:
+            for f in friend.friends_list: 
+                self.add_friend(f)
+            msg = f"成功添加{len(friend.friends_list)}个联系人: {str(friend.friends_list)}"
+            yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=0)
+
+
+    def run(self, txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+        prompt = txt
+        run_gpt_fn = lambda inputs, sys_prompt: predict_no_ui_long_connection(inputs=inputs, llm_kwargs=llm_kwargs, history=[], sys_prompt=sys_prompt, observe_window=[])
+        self.tools_to_select = {
+            "SocialAdvice":{
+                "explain_to_llm": "如果用户希望获取社交指导，调用SocialAdvice生成一些社交建议",
+                "callback": self.ai_socail_advice,
+            },
+            "AddFriends":{
+                "explain_to_llm": "如果用户给出了联系人，调用AddMultiFriends把联系人添加到数据库",
+                "callback": self.ai_add_multi_friends,
+            },
+            "RemoveFriend":{
+                "explain_to_llm": "如果用户希望移除某个联系人，调用RemoveFriend",
+                "callback": self.ai_remove_friend,
+            },
+            "ListFriends":{
+                "explain_to_llm": "如果用户列举联系人，调用ListFriends",
+                "callback": self.ai_list_friends,
+            }
+        }
+
+        try:
+            Explaination = '\n'.join([f'{k}: {v["explain_to_llm"]}' for k, v in self.tools_to_select.items()])
+            class UserSociaIntention(BaseModel):
+                intention_type: str = Field(
+                    description=
+                        f"The type of user intention. You must choose from {self.tools_to_select.keys()}.\n\n" 
+                        f"Explaination:\n{Explaination}", 
+                    default="SocialAdvice"
+                )
+            pydantic_cls_instance, err_msg = select_tool(
+                prompt=txt,
+                run_gpt_fn=run_gpt_fn,
+                pydantic_cls=UserSociaIntention
+            )
+        except Exception as e:
+            yield from update_ui_lastest_msg(
+                lastmsg=f"无法理解用户意图 {err_msg}", 
+                chatbot=chatbot, 
+                history=history, 
+                delay=0
+            )
+            return
+
+        intention_type = pydantic_cls_instance.intention_type
+        intention_callback = self.tools_to_select[pydantic_cls_instance.intention_type]['callback']
+        yield from intention_callback(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, run_gpt_fn, intention_type)
+
+
+    def add_friend(self, friend):
+        # check whether the friend is already in the social network
+        for f in self.social_network.people:
+            if f.friend_name == friend.friend_name:
+                f.friend_description = friend.friend_description
+                f.friend_relationship = friend.friend_relationship
+                logger.info(f"Repeated friend, update info: {friend}")
+                return
+        logger.info(f"Add a new friend: {friend}")
+        self.social_network.people.append(friend)
+        return
+
+
+@CatchException
+def I人助手(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):
+
+    # 1. we retrieve worker from global context
+    user_name = chatbot.get_user()
+    checkpoint_dir=get_log_folder(user_name, plugin_name='experimental_rag')
+    if user_name in SOCIAL_NETWOK_WORKER_REGISTER:
+        social_network_worker = SOCIAL_NETWOK_WORKER_REGISTER[user_name]
+    else:
+        social_network_worker = SOCIAL_NETWOK_WORKER_REGISTER[user_name] = SocialNetworkWorker(
+            user_name, 
+            llm_kwargs, 
+            checkpoint_dir=checkpoint_dir, 
+            auto_load_checkpoint=True
+        )
+
+    # 2. save
+    yield from social_network_worker.run(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request)
+    social_network_worker.save_to_checkpoint(checkpoint_dir)
+    yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+

crazy_functions/SourceCode_Analyse.py

@@ -5,8 +5,8 @@ from crazy_functions.crazy_utils import input_clipping
 
 def 解析源代码新(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     import os, copy
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
-    from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+    from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 
     summary_batch_isolation = True
     inputs_array = []

crazy_functions/SourceCode_Comment.py

@@ -73,7 +73,7 @@ def 注释源代码(file_manifest, project_folder, llm_kwargs, plugin_kwargs, ch
         file_tree_struct.manifest[fp].compare_html = compare_html_path
         with open(compare_html_path, 'w', encoding='utf-8') as f:
             f.write(html_template)
-        print('done 1')
+        # print('done 1')
 
     chatbot.append([None, f"正在处理:"])
     futures = []

crazy_functions/agent_fns/echo_agent.py

@@ -1,4 +1,5 @@
 from crazy_functions.agent_fns.pipe import PluginMultiprocessManager, PipeCom
+from loguru import logger
 
 class EchoDemo(PluginMultiprocessManager):
     def subprocess_worker(self, child_conn):
@@ -16,4 +17,4 @@ class EchoDemo(PluginMultiprocessManager):
             elif msg.cmd == "terminate":
                 self.child_conn.send(PipeCom("done", ""))
                 break
-        print('[debug] subprocess_worker terminated')
+        logger.info('[debug] subprocess_worker terminated')

crazy_functions/agent_fns/pipe.py

@@ -1,5 +1,6 @@
 from toolbox import get_log_folder, update_ui, gen_time_str, get_conf, promote_file_to_downloadzone
 from crazy_functions.agent_fns.watchdog import WatchDog
+from loguru import logger
 import time, os
 
 class PipeCom:
@@ -47,7 +48,7 @@ class PluginMultiprocessManager:
     def terminate(self):
         self.p.terminate()
         self.alive = False
-        print("[debug] instance terminated")
+        logger.info("[debug] instance terminated")
 
     def subprocess_worker(self, child_conn):
         # ⭐⭐ run in subprocess

crazy_functions/agent_fns/python_comment_agent.py

@@ -1,10 +1,12 @@
-from toolbox import CatchException, update_ui
-from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from request_llms.bridge_all import predict_no_ui_long_connection
 import datetime
 import re
 import os
+from loguru import logger
 from textwrap import dedent
+from toolbox import CatchException, update_ui
+from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+
 # TODO: 解决缩进问题
 
 find_function_end_prompt = '''
@@ -355,7 +357,7 @@ class PythonCodeComment():
                     try:
                         successful, hint = self.verify_successful(next_batch, result)
                     except Exception as e:
-                        print('ignored exception:\n' + str(e))
+                        logger.error('ignored exception:\n' + str(e))
                         break
                     if successful:
                         break

crazy_functions/agent_fns/watchdog.py

@@ -1,4 +1,5 @@
 import threading, time
+from loguru import logger
 
 class WatchDog():
     def __init__(self, timeout, bark_fn, interval=3, msg="") -> None:
@@ -13,7 +14,7 @@ class WatchDog():
         while True:
             if self.kill_dog: break
             if time.time() - self.last_feed > self.timeout:
-                if len(self.msg) > 0: print(self.msg)
+                if len(self.msg) > 0: logger.info(self.msg)
                 self.bark_fn()
                 break
             time.sleep(self.interval)

crazy_functions/chatglm微调工具.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, update_ui, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 import datetime, json
 
 def fetch_items(list_of_items, batch_size):

crazy_functions/crazy_utils.py

@@ -1,10 +1,10 @@
-from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
-from shared_utils.char_visual_effect import scolling_visual_effect
-import threading
 import os
-import logging
+import threading
+from loguru import logger
+from shared_utils.char_visual_effect import scolling_visual_effect
+from toolbox import update_ui, get_conf, trimmed_format_exc, get_max_token, Singleton
 
-def input_clipping(inputs, history, max_token_limit):
+def input_clipping(inputs, history, max_token_limit, return_clip_flags=False):
     """
     当输入文本 + 历史文本超出最大限制时，采取措施丢弃一部分文本。
     输入：
@@ -20,17 +20,20 @@ def input_clipping(inputs, history, max_token_limit):
     enc = model_info["gpt-3.5-turbo"]['tokenizer']
     def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
 
+
     mode = 'input-and-history'
     # 当 输入部分的token占比 小于 全文的一半时，只裁剪历史
     input_token_num = get_token_num(inputs)
+    original_input_len = len(inputs)
     if input_token_num < max_token_limit//2:
         mode = 'only-history'
         max_token_limit = max_token_limit - input_token_num
 
     everything = [inputs] if mode == 'input-and-history' else ['']
     everything.extend(history)
-    n_token = get_token_num('\n'.join(everything))
+    full_token_num = n_token = get_token_num('\n'.join(everything))
     everything_token = [get_token_num(e) for e in everything]
+    everything_token_num = sum(everything_token)
     delta = max(everything_token) // 16 # 截断时的颗粒度
 
     while n_token > max_token_limit:
@@ -43,10 +46,24 @@ def input_clipping(inputs, history, max_token_limit):
 
     if mode == 'input-and-history':
         inputs = everything[0]
+        full_token_num = everything_token_num
     else:
-        pass
+        full_token_num = everything_token_num + input_token_num
+
     history = everything[1:]
-    return inputs, history
+
+    flags = {
+        "mode": mode,
+        "original_input_token_num": input_token_num,
+        "original_full_token_num": full_token_num,
+        "original_input_len": original_input_len,
+        "clipped_input_len": len(inputs),
+    }
+
+    if not return_clip_flags:
+        return inputs, history
+    else:
+        return inputs, history, flags
 
 def request_gpt_model_in_new_thread_with_ui_alive(
         inputs, inputs_show_user, llm_kwargs,
@@ -116,7 +133,7 @@ def request_gpt_model_in_new_thread_with_ui_alive(
             except:
                 # 【第三种情况】：其他错误：重试几次
                 tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
+                logger.error(tb_str)
                 mutable[0] += f"[Local Message] 警告，在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
                 if retry_op > 0:
                     retry_op -= 1
@@ -266,7 +283,7 @@ def request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
                 # 【第三种情况】：其他错误
                 if detect_timeout(): raise RuntimeError("检测到程序终止。")
                 tb_str = '```\n' + trimmed_format_exc() + '```'
-                print(tb_str)
+                logger.error(tb_str)
                 gpt_say += f"[Local Message] 警告，线程{index}在执行过程中遭遇问题, Traceback：\n\n{tb_str}\n\n"
                 if len(mutable[index][0]) > 0: gpt_say += "此线程失败前收到的回答：\n\n" + mutable[index][0]
                 if retry_op > 0:
@@ -361,7 +378,7 @@ def read_and_clean_pdf_text(fp):
     import fitz, copy
     import re
     import numpy as np
-    from shared_utils.colorful import print亮黄, print亮绿
+    # from shared_utils.colorful import print亮黄, print亮绿
     fc = 0  # Index 0 文本
     fs = 1  # Index 1 字体
     fb = 2  # Index 2 框框
@@ -578,7 +595,7 @@ class nougat_interface():
     def nougat_with_timeout(self, command, cwd, timeout=3600):
         import subprocess
         from toolbox import ProxyNetworkActivate
-        logging.info(f'正在执行命令 {command}')
+        logger.info(f'正在执行命令 {command}')
         with ProxyNetworkActivate("Nougat_Download"):
             process = subprocess.Popen(command, shell=False, cwd=cwd, env=os.environ)
         try:
@@ -586,7 +603,7 @@ class nougat_interface():
         except subprocess.TimeoutExpired:
             process.kill()
             stdout, stderr = process.communicate()
-            print("Process timed out!")
+            logger.error("Process timed out!")
             return False
         return True
 

crazy_functions/diagram_fns/file_tree.py

@@ -1,5 +1,6 @@
 import os
 from textwrap import indent
+from loguru import logger
 
 class FileNode:
     def __init__(self, name, build_manifest=False):
@@ -60,7 +61,7 @@ class FileNode:
             current_node.children.append(term)
 
     def print_files_recursively(self, level=0, code="R0"):
-        print('    '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
+        logger.info('    '*level + self.name + ' ' + str(self.is_leaf) + ' ' + str(self.level))
         for j, child in enumerate(self.children):
             child.print_files_recursively(level=level+1, code=code+str(j))
             self.parenting_ship.extend(child.parenting_ship)
@@ -123,4 +124,4 @@ if __name__ == "__main__":
         "用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器用于加载和分割文件中的文本的通用文件加载器",
         "包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类包含了用于构建和管理向量数据库的函数和类",
     ]
-    print(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))
+    logger.info(build_file_tree_mermaid_diagram(file_manifest, file_comments, "项目文件树"))

crazy_functions/json_fns/pydantic_io.py

@@ -24,8 +24,8 @@ class Actor(BaseModel):
     film_names: List[str] = Field(description="list of names of films they starred in")
 """
 
-import json, re, logging
-
+import json, re
+from loguru import logger as logging
 
 PYDANTIC_FORMAT_INSTRUCTIONS = """The output should be formatted as a JSON instance that conforms to the JSON schema below.
 

crazy_functions/json_fns/select_tool.py

@@ -0,0 +1,26 @@
+from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
+
+def structure_output(txt, prompt, err_msg, run_gpt_fn, pydantic_cls):
+    gpt_json_io = GptJsonIO(pydantic_cls)
+    analyze_res = run_gpt_fn(
+        txt, 
+        sys_prompt=prompt + gpt_json_io.format_instructions
+    )
+    try:
+        friend = gpt_json_io.generate_output_auto_repair(analyze_res, run_gpt_fn)
+    except JsonStringError as e:
+        return None, err_msg
+
+    err_msg = ""
+    return friend, err_msg
+
+
+def select_tool(prompt, run_gpt_fn, pydantic_cls):
+    pydantic_cls_instance, err_msg = structure_output(
+        txt=prompt,
+        prompt="根据提示, 分析应该调用哪个工具函数\n\n",
+        err_msg=f"不能理解该联系人",
+        run_gpt_fn=run_gpt_fn,
+        pydantic_cls=pydantic_cls
+    )
+    return pydantic_cls_instance, err_msg

crazy_functions/latex_fns/latex_actions.py

@@ -1,15 +1,17 @@
+import os
+import re
+import shutil
+import numpy as np
+from loguru import logger
 from toolbox import update_ui, update_ui_lastest_msg, get_log_folder
 from toolbox import get_conf, promote_file_to_downloadzone
-from .latex_toolbox import PRESERVE, TRANSFORM
-from .latex_toolbox import set_forbidden_text, set_forbidden_text_begin_end, set_forbidden_text_careful_brace
-from .latex_toolbox import reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
-from .latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
-from .latex_toolbox import find_title_and_abs
-from .latex_pickle_io import objdump, objload
+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 reverse_forbidden_text_careful_brace, reverse_forbidden_text, convert_to_linklist, post_process
+from crazy_functions.latex_fns.latex_toolbox import fix_content, find_main_tex_file, merge_tex_files, compile_latex_with_timeout
+from crazy_functions.latex_fns.latex_toolbox import find_title_and_abs
+from crazy_functions.latex_fns.latex_pickle_io import objdump, objload
 
-import os, shutil
-import re
-import numpy as np
 
 pj = os.path.join
 
@@ -323,7 +325,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
         buggy_lines = [int(l) for l in buggy_lines]
         buggy_lines = sorted(buggy_lines)
         buggy_line = buggy_lines[0]-1
-        print("reversing tex line that has errors", buggy_line)
+        logger.warning("reversing tex line that has errors", buggy_line)
 
         # 重组，逆转出错的段落
         if buggy_line not in fixed_line:
@@ -337,7 +339,7 @@ def remove_buggy_lines(file_path, log_path, tex_name, tex_name_pure, n_fix, work
 
         return True, f"{tex_name_pure}_fix_{n_fix}", buggy_lines
     except:
-        print("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
+        logger.error("Fatal error occurred, but we cannot identify error, please download zip, read latex log, and compile manually.")
         return False, -1, [-1]
 
 
@@ -380,7 +382,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
 
             if mode!='translate_zh':
                 yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 使用latexdiff生成论文转化前后对比 ...', chatbot, history) # 刷新Gradio前端界面
-                print(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
+                logger.info(    f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex')
                 ok = compile_latex_with_timeout(f'latexdiff --encoding=utf8 --append-safecmd=subfile {work_folder_original}/{main_file_original}.tex  {work_folder_modified}/{main_file_modified}.tex --flatten > {work_folder}/merge_diff.tex', os.getcwd())
 
                 yield from update_ui_lastest_msg(f'尝试第 {n_fix}/{max_try} 次编译, 正在编译对比PDF ...', chatbot, history)   # 刷新Gradio前端界面
@@ -419,7 +421,7 @@ def 编译Latex(chatbot, history, main_file_original, main_file_modified, work_f
                         shutil.copyfile(concat_pdf, pj(work_folder, '..', 'translation', 'comparison.pdf'))
                     promote_file_to_downloadzone(concat_pdf, rename_file=None, chatbot=chatbot)  # promote file to web UI
                 except Exception as e:
-                    print(e)
+                    logger.error(e)
                     pass
             return True # 成功啦
         else:
@@ -465,4 +467,4 @@ def write_html(sp_file_contents, sp_file_result, chatbot, project_folder):
         promote_file_to_downloadzone(file=res, chatbot=chatbot)
     except:
         from toolbox import trimmed_format_exc
-        print('writing html result failed:', trimmed_format_exc())
+        logger.error('writing html result failed:', trimmed_format_exc())

crazy_functions/latex_fns/latex_toolbox.py

@@ -1,6 +1,8 @@
-import os, shutil
+import os
 import re
+import shutil
 import numpy as np
+from loguru import logger
 
 PRESERVE = 0
 TRANSFORM = 1
@@ -55,7 +57,7 @@ def post_process(root):
                     str_stack.append("{")
                 elif c == "}":
                     if len(str_stack) == 1:
-                        print("stack fix")
+                        logger.warning("fixing brace error")
                         return i
                     str_stack.pop(-1)
                 else:
@@ -601,7 +603,7 @@ def compile_latex_with_timeout(command, cwd, timeout=60):
     except subprocess.TimeoutExpired:
         process.kill()
         stdout, stderr = process.communicate()
-        print("Process timed out!")
+        logger.error("Process timed out (compile_latex_with_timeout)!")
         return False
     return True
 
@@ -642,6 +644,213 @@ def run_in_subprocess(func):
 
 
 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
+    from PyPDF2.generic import NameObject, TextStringObject, ArrayObject, FloatObject, NumberObject
+
+    Percent = 1
+    # 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,
+                )
+                if "/Annots" in new_page:
+                    annotations = new_page["/Annots"]
+                    for i, annot in enumerate(annotations):
+                        annot_obj = annot.get_object()
+
+                        # 检查注释类型是否是链接（/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）
+                            action = annot_obj.get("/A")
+                            if action:
+
+                                if "/S" in action and action["/S"] == "/GoTo":
+                                    # 内部链接：跳转到文档中的某个页面
+                                    dest = action.get("/D")  # 目标页或目标位置
+                                    # if dest and annot.idnum in page2_annot_id:
+                                    if dest in pdf2_reader.named_destinations:
+                                        # 获取原始文件中跳转信息，包括跳转页面
+                                        destination = pdf2_reader.named_destinations[
+                                            dest
+                                        ]
+                                        page_number = (
+                                            pdf2_reader.get_destination_page_number(
+                                                destination
+                                            )
+                                        )
+                                        # 更新跳转信息，跳转到对应的页面和，指定坐标 (100, 150)，缩放比例为 100%
+                                        # “/D”:[10,'/XYZ',100,100,0]
+                                        if destination.dest_array[1] == "/XYZ":
+                                            annot_obj["/A"].update(
+                                                {
+                                                    NameObject("/D"): ArrayObject(
+                                                        [
+                                                            NumberObject(page_number),
+                                                            destination.dest_array[1],
+                                                            FloatObject(
+                                                                destination.dest_array[
+                                                                    2
+                                                                ]
+                                                                + int(
+                                                                    page1.mediaBox.getWidth()
+                                                                )
+                                                            ),
+                                                            destination.dest_array[3],
+                                                            destination.dest_array[4],
+                                                        ]
+                                                    )  # 确保键和值是 PdfObject
+                                                }
+                                            )
+                                        else:
+                                            annot_obj["/A"].update(
+                                                {
+                                                    NameObject("/D"): ArrayObject(
+                                                        [
+                                                            NumberObject(page_number),
+                                                            destination.dest_array[1],
+                                                        ]
+                                                    )  # 确保键和值是 PdfObject
+                                                }
+                                            )
+
+                                        rect = annot_obj.get("/Rect")
+                                        # 更新点击坐标
+                                        rect = ArrayObject(
+                                            [
+                                                FloatObject(
+                                                    rect[0]
+                                                    + int(page1.mediaBox.getWidth())
+                                                ),
+                                                rect[1],
+                                                FloatObject(
+                                                    rect[2]
+                                                    + int(page1.mediaBox.getWidth())
+                                                ),
+                                                rect[3],
+                                            ]
+                                        )
+                                        annot_obj.update(
+                                            {
+                                                NameObject(
+                                                    "/Rect"
+                                                ): rect  # 确保键和值是 PdfObject
+                                            }
+                                        )
+                                    # if dest and annot.idnum in page1_annot_id:
+                                    if dest in pdf1_reader.named_destinations:
+
+                                        # 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
+                                    uri = action.get("/URI")
+                output_writer.addPage(new_page)
+            # Save the merged PDF file
+            with open(output_path, "wb") as output_file:
+                output_writer.write(output_file)
+
+
+def _merge_pdfs_legacy(pdf1_path, pdf2_path, output_path):
     import PyPDF2  # PyPDF2这个库有严重的内存泄露问题，把它放到子进程中运行，从而方便内存的释放
 
     Percent = 0.95

crazy_functions/live_audio/aliyunASR.py

@@ -1,5 +1,6 @@
-import time, logging, json, sys, struct
+import time, json, sys, struct
 import numpy as np
+from loguru import logger as logging
 from scipy.io.wavfile import WAVE_FORMAT
 
 def write_numpy_to_wave(filename, rate, data, add_header=False):
@@ -106,18 +107,14 @@ def is_speaker_speaking(vad, data, sample_rate):
 class AliyunASR():
 
     def test_on_sentence_begin(self, message, *args):
-        # print("test_on_sentence_begin:{}".format(message))
         pass
 
     def test_on_sentence_end(self, message, *args):
-        # print("test_on_sentence_end:{}".format(message))
         message = json.loads(message)
         self.parsed_sentence = message['payload']['result']
         self.event_on_entence_end.set()
-        # print(self.parsed_sentence)
 
     def test_on_start(self, message, *args):
-        # print("test_on_start:{}".format(message))
         pass
 
     def test_on_error(self, message, *args):
@@ -129,13 +126,11 @@ class AliyunASR():
         pass
 
     def test_on_result_chg(self, message, *args):
-        # print("test_on_chg:{}".format(message))
         message = json.loads(message)
         self.parsed_text = message['payload']['result']
         self.event_on_result_chg.set()
 
     def test_on_completed(self, message, *args):
-        # print("on_completed:args=>{} message=>{}".format(args, message))
         pass
 
     def audio_convertion_thread(self, uuid):
@@ -248,14 +243,14 @@ class AliyunASR():
 
         try:
             response = client.do_action_with_exception(request)
-            print(response)
+            logging.info(response)
             jss = json.loads(response)
             if 'Token' in jss and 'Id' in jss['Token']:
                 token = jss['Token']['Id']
                 expireTime = jss['Token']['ExpireTime']
-                print("token = " + token)
-                print("expireTime = " + str(expireTime))
+                logging.info("token = " + token)
+                logging.info("expireTime = " + str(expireTime))
         except Exception as e:
-            print(e)
+            logging.error(e)
 
         return token

crazy_functions/pdf_fns/breakdown_txt.py

@@ -1,4 +1,5 @@
 from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout
+from loguru import logger
 
 def force_breakdown(txt, limit, get_token_fn):
     """ 当无法用标点、空行分割时，我们用最暴力的方法切割
@@ -76,7 +77,7 @@ def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=F
             remain_txt_to_cut = post
             remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage)
             process = fin_len/total_len
-            print(f'正在文本切分 {int(process*100)}%')
+            logger.info(f'正在文本切分 {int(process*100)}%')
             if len(remain_txt_to_cut.strip()) == 0:
                 break
     return res
@@ -119,7 +120,7 @@ if __name__ == '__main__':
     for i in range(5):
         file_content += file_content
 
-    print(len(file_content))
+    logger.info(len(file_content))
     TOKEN_LIMIT_PER_FRAGMENT = 2500
     res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT)
 

crazy_functions/pdf_fns/parse_pdf_legacy.py

@@ -5,6 +5,7 @@ from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_
 from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 from crazy_functions.crazy_utils import read_and_clean_pdf_text
 from shared_utils.colorful import *
+from loguru import logger
 import os
 
 def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
@@ -93,7 +94,7 @@ def 解析PDF_简单拆解(file_manifest, project_folder, llm_kwargs, plugin_kwa
             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())
+            logger.error('writing html result failed:', trimmed_format_exc())
 
     # 准备文件的下载
     for pdf_path in generated_conclusion_files:

crazy_functions/rag_fns/llama_index_worker.py

@@ -0,0 +1,130 @@
+import llama_index
+import os
+import atexit
+from loguru import logger
+from typing import List
+from llama_index.core import Document
+from llama_index.core.schema import TextNode
+from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
+from shared_utils.connect_void_terminal import get_chat_default_kwargs
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
+from llama_index.core.ingestion import run_transformations
+from llama_index.core import PromptTemplate
+from llama_index.core.response_synthesizers import TreeSummarize
+
+DEFAULT_QUERY_GENERATION_PROMPT = """\
+Now, you have context information as below:
+---------------------
+{context_str}
+---------------------
+Answer the user request below (use the context information if necessary, otherwise you can ignore them):
+---------------------
+{query_str}
+"""
+
+QUESTION_ANSWER_RECORD = """\
+{{
+    "type": "This is a previous conversation with the user",
+    "question": "{question}",
+    "answer": "{answer}",
+}}
+"""
+
+
+class SaveLoad():
+
+    def does_checkpoint_exist(self, checkpoint_dir=None):
+        import os, glob
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if not os.path.exists(checkpoint_dir): return False
+        if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
+        return True
+
+    def save_to_checkpoint(self, checkpoint_dir=None):
+        logger.info(f'saving vector store to: {checkpoint_dir}')
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
+
+    def load_from_checkpoint(self, checkpoint_dir=None):
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
+            logger.info('loading checkpoint from disk')
+            from llama_index.core import StorageContext, load_index_from_storage
+            storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
+            self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
+            return self.vs_index
+        else:
+            return self.create_new_vs()
+
+    def create_new_vs(self):
+        return GptacVectorStoreIndex.default_vector_store(embed_model=self.embed_model)
+
+    def purge(self):
+        import shutil
+        shutil.rmtree(self.checkpoint_dir, ignore_errors=True)
+        self.vs_index = self.create_new_vs()
+
+
+class LlamaIndexRagWorker(SaveLoad):
+    def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
+        self.debug_mode = True
+        self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
+        self.user_name = user_name
+        self.checkpoint_dir = checkpoint_dir
+        if auto_load_checkpoint:
+            self.vs_index = self.load_from_checkpoint(checkpoint_dir)
+        else:
+            self.vs_index = self.create_new_vs(checkpoint_dir)
+        atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
+
+    def assign_embedding_model(self):
+        pass
+
+    def inspect_vector_store(self):
+        # This function is for debugging
+        self.vs_index.storage_context.index_store.to_dict()
+        docstore = self.vs_index.storage_context.docstore.docs
+        vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
+        logger.info('\n++ --------inspect_vector_store begin--------')
+        logger.info(vector_store_preview)
+        logger.info('oo --------inspect_vector_store end--------')
+        return vector_store_preview
+
+    def add_documents_to_vector_store(self, document_list):
+        documents = [Document(text=t) for t in document_list]
+        documents_nodes = run_transformations(
+                        documents,  # type: ignore
+                        self.vs_index._transformations,
+                        show_progress=True
+                    )
+        self.vs_index.insert_nodes(documents_nodes)
+        if self.debug_mode: self.inspect_vector_store()
+
+    def add_text_to_vector_store(self, text):
+        node = TextNode(text=text)
+        documents_nodes = run_transformations(
+                        [node],
+                        self.vs_index._transformations,
+                        show_progress=True
+                    )
+        self.vs_index.insert_nodes(documents_nodes)
+        if self.debug_mode: self.inspect_vector_store()
+
+    def remember_qa(self, question, answer):
+        formatted_str = QUESTION_ANSWER_RECORD.format(question=question, answer=answer)
+        self.add_text_to_vector_store(formatted_str)
+
+    def retrieve_from_store_with_query(self, query):
+        if self.debug_mode: self.inspect_vector_store()
+        retriever = self.vs_index.as_retriever()
+        return retriever.retrieve(query)
+
+    def build_prompt(self, query, nodes):
+        context_str = self.generate_node_array_preview(nodes)
+        return DEFAULT_QUERY_GENERATION_PROMPT.format(context_str=context_str, query_str=query)
+        
+    def generate_node_array_preview(self, nodes):
+        buf = "\n".join(([f"(No.{i+1} | score {n.score:.3f}): {n.text}" for i, n in enumerate(nodes)]))
+        if self.debug_mode: logger.info(buf)
+        return buf

crazy_functions/rag_fns/milvus_worker.py

@@ -0,0 +1,108 @@
+import llama_index
+import os
+import atexit
+from typing import List
+from loguru import logger
+from llama_index.core import Document
+from llama_index.core.schema import TextNode
+from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel
+from shared_utils.connect_void_terminal import get_chat_default_kwargs
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
+from crazy_functions.rag_fns.vector_store_index import GptacVectorStoreIndex
+from llama_index.core.ingestion import run_transformations
+from llama_index.core import PromptTemplate
+from llama_index.core.response_synthesizers import TreeSummarize
+from llama_index.core import StorageContext
+from llama_index.vector_stores.milvus import MilvusVectorStore
+from crazy_functions.rag_fns.llama_index_worker import LlamaIndexRagWorker
+
+DEFAULT_QUERY_GENERATION_PROMPT = """\
+Now, you have context information as below:
+---------------------
+{context_str}
+---------------------
+Answer the user request below (use the context information if necessary, otherwise you can ignore them):
+---------------------
+{query_str}
+"""
+
+QUESTION_ANSWER_RECORD = """\
+{{
+    "type": "This is a previous conversation with the user",
+    "question": "{question}",
+    "answer": "{answer}",
+}}
+"""
+
+
+class MilvusSaveLoad():
+
+    def does_checkpoint_exist(self, checkpoint_dir=None):
+        import os, glob
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if not os.path.exists(checkpoint_dir): return False
+        if len(glob.glob(os.path.join(checkpoint_dir, "*.json"))) == 0: return False
+        return True
+
+    def save_to_checkpoint(self, checkpoint_dir=None):
+        logger.info(f'saving vector store to: {checkpoint_dir}')
+        # if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        # self.vs_index.storage_context.persist(persist_dir=checkpoint_dir)
+
+    def load_from_checkpoint(self, checkpoint_dir=None):
+        if checkpoint_dir is None: checkpoint_dir = self.checkpoint_dir
+        if self.does_checkpoint_exist(checkpoint_dir=checkpoint_dir):
+            logger.info('loading checkpoint from disk')
+            from llama_index.core import StorageContext, load_index_from_storage
+            storage_context = StorageContext.from_defaults(persist_dir=checkpoint_dir)
+            try:
+                self.vs_index = load_index_from_storage(storage_context, embed_model=self.embed_model)
+                return self.vs_index
+            except:
+                return self.create_new_vs(checkpoint_dir)
+        else:
+            return self.create_new_vs(checkpoint_dir)
+
+    def create_new_vs(self, checkpoint_dir, overwrite=False):
+        vector_store = MilvusVectorStore(
+            uri=os.path.join(checkpoint_dir, "milvus_demo.db"), 
+            dim=self.embed_model.embedding_dimension(),
+            overwrite=overwrite
+        )
+        storage_context = StorageContext.from_defaults(vector_store=vector_store)
+        index = GptacVectorStoreIndex.default_vector_store(storage_context=storage_context, embed_model=self.embed_model)
+        return index
+
+    def purge(self):
+        self.vs_index = self.create_new_vs(self.checkpoint_dir, overwrite=True)
+
+class MilvusRagWorker(MilvusSaveLoad, LlamaIndexRagWorker):
+
+    def __init__(self, user_name, llm_kwargs, auto_load_checkpoint=True, checkpoint_dir=None) -> None:
+        self.debug_mode = True
+        self.embed_model = OpenAiEmbeddingModel(llm_kwargs)
+        self.user_name = user_name
+        self.checkpoint_dir = checkpoint_dir
+        if auto_load_checkpoint:
+            self.vs_index = self.load_from_checkpoint(checkpoint_dir)
+        else:
+            self.vs_index = self.create_new_vs(checkpoint_dir)
+        atexit.register(lambda: self.save_to_checkpoint(checkpoint_dir))
+
+    def inspect_vector_store(self):
+        # This function is for debugging
+        try:
+            self.vs_index.storage_context.index_store.to_dict()
+            docstore = self.vs_index.storage_context.docstore.docs
+            if not docstore.items():
+                raise ValueError("cannot inspect")
+            vector_store_preview = "\n".join([ f"{_id} | {tn.text}" for _id, tn in docstore.items() ])
+        except:
+            dummy_retrieve_res: List["NodeWithScore"] = self.vs_index.as_retriever().retrieve(' ')
+            vector_store_preview = "\n".join(
+                [f"{node.id_} | {node.text}" for node in dummy_retrieve_res]
+            )
+        logger.info('\n++ --------inspect_vector_store begin--------')
+        logger.info(vector_store_preview)
+        logger.info('oo --------inspect_vector_store end--------')
+        return vector_store_preview

crazy_functions/rag_fns/vector_store_index.py

@@ -0,0 +1,58 @@
+from llama_index.core import VectorStoreIndex
+from typing import Any,  List, Optional
+
+from llama_index.core.callbacks.base import CallbackManager
+from llama_index.core.schema import TransformComponent
+from llama_index.core.service_context import ServiceContext
+from llama_index.core.settings import (
+    Settings,
+    callback_manager_from_settings_or_context,
+    transformations_from_settings_or_context,
+)
+from llama_index.core.storage.storage_context import StorageContext
+
+
+class GptacVectorStoreIndex(VectorStoreIndex):
+    
+    @classmethod
+    def default_vector_store(
+        cls,
+        storage_context: Optional[StorageContext] = None,
+        show_progress: bool = False,
+        callback_manager: Optional[CallbackManager] = None,
+        transformations: Optional[List[TransformComponent]] = None,
+        # deprecated
+        service_context: Optional[ServiceContext] = None,
+        embed_model = None,
+        **kwargs: Any,
+    ):
+        """Create index from documents.
+
+        Args:
+            documents (Optional[Sequence[BaseDocument]]): List of documents to
+                build the index from.
+
+        """
+        storage_context = storage_context or StorageContext.from_defaults()
+        docstore = storage_context.docstore
+        callback_manager = (
+            callback_manager
+            or callback_manager_from_settings_or_context(Settings, service_context)
+        )
+        transformations = transformations or transformations_from_settings_or_context(
+            Settings, service_context
+        )
+
+        with callback_manager.as_trace("index_construction"):
+
+            return cls(
+                nodes=[],
+                storage_context=storage_context,
+                callback_manager=callback_manager,
+                show_progress=show_progress,
+                transformations=transformations,
+                service_context=service_context,
+                embed_model=embed_model,
+                **kwargs,
+            )
+

crazy_functions/vector_fns/vector_database.py

@@ -1,16 +1,17 @@
 # From project chatglm-langchain
 
-import threading
-from toolbox import Singleton
 import os
-import shutil
 import os
 import uuid
 import tqdm
+import shutil
+import threading
+import numpy as np
+from toolbox import Singleton
+from loguru import logger
 from langchain.vectorstores import FAISS
 from langchain.docstore.document import Document
 from typing import List, Tuple
-import numpy as np
 from crazy_functions.vector_fns.general_file_loader import load_file
 
 embedding_model_dict = {
@@ -150,17 +151,17 @@ class LocalDocQA:
         failed_files = []
         if isinstance(filepath, str):
             if not os.path.exists(filepath):
-                print("路径不存在")
+                logger.error("路径不存在")
                 return None
             elif os.path.isfile(filepath):
                 file = os.path.split(filepath)[-1]
                 try:
                     docs = load_file(filepath, SENTENCE_SIZE)
-                    print(f"{file} 已成功加载")
+                    logger.info(f"{file} 已成功加载")
                     loaded_files.append(filepath)
                 except Exception as e:
-                    print(e)
-                    print(f"{file} 未能成功加载")
+                    logger.error(e)
+                    logger.error(f"{file} 未能成功加载")
                     return None
             elif os.path.isdir(filepath):
                 docs = []
@@ -170,23 +171,23 @@ class LocalDocQA:
                         docs += load_file(fullfilepath, SENTENCE_SIZE)
                         loaded_files.append(fullfilepath)
                     except Exception as e:
-                        print(e)
+                        logger.error(e)
                         failed_files.append(file)
 
                 if len(failed_files) > 0:
-                    print("以下文件未能成功加载：")
+                    logger.error("以下文件未能成功加载：")
                     for file in failed_files:
-                        print(f"{file}\n")
+                        logger.error(f"{file}\n")
 
         else:
             docs = []
             for file in filepath:
                 docs += load_file(file, SENTENCE_SIZE)
-                print(f"{file} 已成功加载")
+                logger.info(f"{file} 已成功加载")
                 loaded_files.append(file)
 
         if len(docs) > 0:
-            print("文件加载完毕，正在生成向量库")
+            logger.info("文件加载完毕，正在生成向量库")
             if vs_path and os.path.isdir(vs_path):
                 try:
                     self.vector_store = FAISS.load_local(vs_path, text2vec)
@@ -233,7 +234,7 @@ class LocalDocQA:
         prompt += "\n\n".join([f"({k}): " + doc.page_content for k, doc in enumerate(related_docs_with_score)])
         prompt += "\n\n---\n\n"
         prompt = prompt.encode('utf-8', 'ignore').decode()   # avoid reading non-utf8 chars
-        # print(prompt)
+        # logger.info(prompt)
         response = {"query": query, "source_documents": related_docs_with_score}
         return response, prompt
 
@@ -262,7 +263,7 @@ def construct_vector_store(vs_id, vs_path, files, sentence_size, history, one_co
     else:
         pass
         # file_status = "文件未成功加载，请重新上传文件"
-    # print(file_status)
+    # logger.info(file_status)
     return local_doc_qa, vs_path
 
 @Singleton
@@ -278,7 +279,7 @@ class knowledge_archive_interface():
         if self.text2vec_large_chinese is None:
             # < -------------------预热文本向量化模组--------------- >
             from toolbox import ProxyNetworkActivate
-            print('Checking Text2vec ...')
+            logger.info('Checking Text2vec ...')
             from langchain.embeddings.huggingface import HuggingFaceEmbeddings
             with ProxyNetworkActivate('Download_LLM'):    # 临时地激活代理网络
                 self.text2vec_large_chinese = HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")

crazy_functions/下载arxiv论文翻译摘要.py

@@ -1,17 +1,19 @@
+import re, requests, unicodedata, os
 from toolbox import update_ui, get_log_folder
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 from toolbox import CatchException, report_exception, get_conf
-import re, requests, unicodedata, os
-from .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
+from loguru import logger
+
 def download_arxiv_(url_pdf):
     if 'arxiv.org' not in url_pdf:
         if ('.' in url_pdf) and ('/' not in url_pdf):
             new_url = 'https://arxiv.org/abs/'+url_pdf
-            print('下载编号：', url_pdf, '自动定位：', new_url)
+            logger.info('下载编号：', url_pdf, '自动定位：', new_url)
             # download_arxiv_(new_url)
             return download_arxiv_(new_url)
         else:
-            print('不能识别的URL！')
+            logger.info('不能识别的URL！')
             return None
     if 'abs' in url_pdf:
         url_pdf = url_pdf.replace('abs', 'pdf')
@@ -42,15 +44,12 @@ def download_arxiv_(url_pdf):
     requests_pdf_url = url_pdf
     file_path = download_dir+title_str
 
-    print('下载中')
+    logger.info('下载中')
     proxies = get_conf('proxies')
     r = requests.get(requests_pdf_url, proxies=proxies)
     with open(file_path, 'wb+') as f:
         f.write(r.content)
-    print('下载完成')
-
-    # print('输出下载命令：','aria2c -o \"%s\" %s'%(title_str,url_pdf))
-    # subprocess.call('aria2c --all-proxy=\"172.18.116.150:11084\" -o \"%s\" %s'%(download_dir+title_str,url_pdf), shell=True)
+    logger.info('下载完成')
 
     x = "%s  %s %s.bib" % (paper_id, other_info['year'], other_info['authors'])
     x = x.replace('?', '？')\
@@ -63,19 +62,9 @@ def download_arxiv_(url_pdf):
 
 
 def get_name(_url_):
-    import os
     from bs4 import BeautifulSoup
-    print('正在获取文献名！')
-    print(_url_)
-
-    # arxiv_recall = {}
-    # if os.path.exists('./arxiv_recall.pkl'):
-    #     with open('./arxiv_recall.pkl', 'rb') as f:
-    #         arxiv_recall = pickle.load(f)
-
-    # if _url_ in arxiv_recall:
-    #     print('在缓存中')
-    #     return arxiv_recall[_url_]
+    logger.info('正在获取文献名！')
+    logger.info(_url_)
 
     proxies = get_conf('proxies')
     res = requests.get(_url_, proxies=proxies)
@@ -92,7 +81,7 @@ def get_name(_url_):
         other_details['abstract'] = abstract
     except:
         other_details['year'] = ''
-        print('年份获取失败')
+        logger.info('年份获取失败')
 
     # get author
     try:
@@ -101,7 +90,7 @@ def get_name(_url_):
         other_details['authors'] = authors
     except:
         other_details['authors'] = ''
-        print('authors获取失败')
+        logger.info('authors获取失败')
 
     # get comment
     try:
@@ -116,11 +105,11 @@ def get_name(_url_):
             other_details['comment'] = ''
     except:
         other_details['comment'] = ''
-        print('年份获取失败')
+        logger.info('年份获取失败')
 
     title_str = BeautifulSoup(
         res.text, 'html.parser').find('title').contents[0]
-    print('获取成功：', title_str)
+    logger.info('获取成功：', title_str)
     # arxiv_recall[_url_] = (title_str+'.pdf', other_details)
     # with open('./arxiv_recall.pkl', 'wb') as f:
     #     pickle.dump(arxiv_recall, f)

crazy_functions/交互功能函数模板.py

@@ -1,6 +1,5 @@
 from toolbox import CatchException, update_ui
-from .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
 
 @CatchException
 def 交互功能模板函数(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):

crazy_functions/函数动态生成.py

@@ -16,8 +16,8 @@ Testing:
 
 from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, is_the_upload_folder
 from toolbox import promote_file_to_downloadzone, get_log_folder, update_ui_lastest_msg
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
-from .crazy_utils import input_clipping, try_install_deps
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
+from crazy_functions.crazy_utils import input_clipping, try_install_deps
 from crazy_functions.gen_fns.gen_fns_shared import is_function_successfully_generated
 from crazy_functions.gen_fns.gen_fns_shared import get_class_name
 from crazy_functions.gen_fns.gen_fns_shared import subprocess_worker

crazy_functions/命令行助手.py

@@ -1,6 +1,6 @@
 from toolbox import CatchException, update_ui, gen_time_str
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .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 input_clipping
 import copy, json
 
 @CatchException

crazy_functions/多智能体.py

@@ -6,13 +6,14 @@
 """
 
 
+import time
 from toolbox import CatchException, update_ui, gen_time_str, trimmed_format_exc, ProxyNetworkActivate
 from toolbox import get_conf, select_api_key, update_ui_lastest_msg, Singleton
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_plugin_arg
 from crazy_functions.crazy_utils import input_clipping, try_install_deps
 from crazy_functions.agent_fns.persistent import GradioMultiuserManagerForPersistentClasses
 from crazy_functions.agent_fns.auto_agent import AutoGenMath
-import time
+from loguru import logger
 
 def remove_model_prefix(llm):
     if llm.startswith('api2d-'): llm = llm.replace('api2d-', '')
@@ -80,12 +81,12 @@ def 多智能体终端(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_
     persistent_key = f"{user_uuid}->多智能体终端"
     if persistent_class_multi_user_manager.already_alive(persistent_key):
         # 当已经存在一个正在运行的多智能体终端时，直接将用户输入传递给它，而不是再次启动一个新的多智能体终端
-        print('[debug] feed new user input')
+        logger.info('[debug] feed new user input')
         executor = persistent_class_multi_user_manager.get(persistent_key)
         exit_reason = yield from executor.main_process_ui_control(txt, create_or_resume="resume")
     else:
         # 运行多智能体终端 (首次)
-        print('[debug] create new executor instance')
+        logger.info('[debug] create new executor instance')
         history = []
         chatbot.append(["正在启动: 多智能体终端", "插件动态生成, 执行开始, 作者 Microsoft & Binary-Husky."])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面

crazy_functions/总结word文档.py

@@ -1,7 +1,7 @@
 from toolbox import update_ui
 from toolbox import CatchException, report_exception
 from toolbox import write_history_to_file, promote_file_to_downloadzone
-from .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
 fast_debug = False
 
 

crazy_functions/总结音视频.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, report_exception, select_api_key, update_ui, get_conf
-from .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
 from toolbox import write_history_to_file, promote_file_to_downloadzone, get_log_folder
 
 def split_audio_file(filename, split_duration=1000):

crazy_functions/批量总结PDF文档.py

@@ -1,16 +1,18 @@
+from loguru import logger
+
 from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str
 from toolbox import CatchException, report_exception
 from toolbox import write_history_to_file, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-from .crazy_utils import read_and_clean_pdf_text
-from .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 read_and_clean_pdf_text
+from crazy_functions.crazy_utils import input_clipping
 
 
 
 def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     file_write_buffer = []
     for file_name in file_manifest:
-        print('begin analysis on:', file_name)
+        logger.info('begin analysis on:', file_name)
         ############################## <第 0 步，切割PDF> ##################################
         # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
         # 的长度必须小于 2500 个 Token
@@ -38,7 +40,7 @@ def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot,
         last_iteration_result = paper_meta  # 初始值是摘要
         MAX_WORD_TOTAL = 4096 * 0.7
         n_fragment = len(paper_fragments)
-        if n_fragment >= 20: print('文章极长，不能达到预期效果')
+        if n_fragment >= 20: logger.warning('文章极长，不能达到预期效果')
         for i in range(n_fragment):
             NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
             i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}"

crazy_functions/批量总结PDF文档pdfminer.py

@@ -1,6 +1,7 @@
+from loguru import logger
 from toolbox import update_ui
 from toolbox import CatchException, report_exception
-from .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
 from toolbox import write_history_to_file, promote_file_to_downloadzone
 
 fast_debug = False
@@ -57,7 +58,6 @@ def readPdf(pdfPath):
         layout = device.get_result()
         for obj in layout._objs:
             if isinstance(obj, pdfminer.layout.LTTextBoxHorizontal):
-                # print(obj.get_text())
                 outTextList.append(obj.get_text())
 
     return outTextList
@@ -66,7 +66,7 @@ def readPdf(pdfPath):
 def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     import time, glob, os
     from bs4 import BeautifulSoup
-    print('begin analysis on:', file_manifest)
+    logger.info('begin analysis on:', file_manifest)
     for index, fp in enumerate(file_manifest):
         if ".tex" in fp:
             with open(fp, 'r', encoding='utf-8', errors='replace') as f:

crazy_functions/批量翻译PDF文档_NOUGAT.py

@@ -1,9 +1,9 @@
 from toolbox import CatchException, report_exception, get_log_folder, gen_time_str
 from toolbox import update_ui, promote_file_to_downloadzone, update_ui_lastest_msg, disable_auto_promotion
 from toolbox import write_history_to_file, promote_file_to_downloadzone
-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 crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
+from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+from crazy_functions.crazy_utils import read_and_clean_pdf_text
 from .pdf_fns.parse_pdf import parse_pdf, get_avail_grobid_url, translate_pdf
 from shared_utils.colorful import *
 import copy
@@ -60,7 +60,7 @@ def 批量翻译PDF文档(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     # 清空历史，以免输入溢出
     history = []
 
-    from .crazy_utils import get_files_from_everything
+    from crazy_functions.crazy_utils import get_files_from_everything
     success, file_manifest, project_folder = get_files_from_everything(txt, type='.pdf')
     if len(file_manifest) > 0:
         # 尝试导入依赖，如果缺少依赖，则给出安装建议

crazy_functions/数学动画生成manim.py

@@ -1,4 +1,5 @@
 import os
+from loguru import logger
 from toolbox import CatchException, update_ui, gen_time_str, promote_file_to_downloadzone
 from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 from crazy_functions.crazy_utils import input_clipping
@@ -34,10 +35,10 @@ def eval_manim(code):
         return f'gpt_log/{time_str}.mp4'
     except subprocess.CalledProcessError as e:
         output = e.output.decode()
-        print(f"Command returned non-zero exit status {e.returncode}: {output}.")
+        logger.error(f"Command returned non-zero exit status {e.returncode}: {output}.")
         return f"Evaluating python script failed: {e.output}."
     except:
-        print('generating mp4 failed')
+        logger.error('generating mp4 failed')
         return "Generating mp4 failed."
 
 

crazy_functions/理解PDF文档内容.py

@@ -1,13 +1,12 @@
+from loguru import logger
 from toolbox import update_ui
 from toolbox import CatchException, report_exception
-from .crazy_utils import read_and_clean_pdf_text
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
+from crazy_functions.crazy_utils import read_and_clean_pdf_text
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 
 
 def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    import tiktoken
-    print('begin analysis on:', file_name)
+    logger.info('begin analysis on:', file_name)
 
     ############################## <第 0 步，切割PDF> ##################################
     # 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
@@ -36,7 +35,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     last_iteration_result = paper_meta  # 初始值是摘要
     MAX_WORD_TOTAL = 4096
     n_fragment = len(paper_fragments)
-    if n_fragment >= 20: print('文章极长，不能达到预期效果')
+    if n_fragment >= 20: logger.warning('文章极长，不能达到预期效果')
     for i in range(n_fragment):
         NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
         i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {paper_fragments[i]}"
@@ -57,7 +56,7 @@ def 解析PDF(file_name, llm_kwargs, plugin_kwargs, chatbot, history, system_pro
     chatbot.append([i_say_show_user, gpt_say])
 
     ############################## <第 4 步，设置一个token上限，防止回答时Token溢出> ##################################
-    from .crazy_utils import input_clipping
+    from crazy_functions.crazy_utils import input_clipping
     _, final_results = input_clipping("", final_results, max_token_limit=3200)
     yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
 

crazy_functions/生成函数注释.py

@@ -1,12 +1,12 @@
+from loguru import logger
 from toolbox import update_ui
 from toolbox import CatchException, report_exception
 from toolbox import write_history_to_file, promote_file_to_downloadzone
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
-fast_debug = False
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
 
 def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     import time, os
-    print('begin analysis on:', file_manifest)
+    logger.info('begin analysis on:', file_manifest)
     for index, fp in enumerate(file_manifest):
         with open(fp, 'r', encoding='utf-8', errors='replace') as f:
             file_content = f.read()
@@ -16,22 +16,20 @@ def 生成函数注释(file_manifest, project_folder, llm_kwargs, plugin_kwargs,
         chatbot.append((i_say_show_user, "[Local Message] waiting gpt response."))
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
 
-        if not fast_debug:
-            msg = '正常'
-            # ** gpt request **
-            gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
-                i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
+        msg = '正常'
+        # ** gpt request **
+        gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
+            i_say, i_say_show_user, llm_kwargs, chatbot, history=[], sys_prompt=system_prompt)   # 带超时倒计时
 
-            chatbot[-1] = (i_say_show_user, gpt_say)
-            history.append(i_say_show_user); history.append(gpt_say)
-            yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
-            if not fast_debug: time.sleep(2)
-
-    if not fast_debug:
-        res = write_history_to_file(history)
-        promote_file_to_downloadzone(res, chatbot=chatbot)
-        chatbot.append(("完成了吗？", res))
+        chatbot[-1] = (i_say_show_user, gpt_say)
+        history.append(i_say_show_user); history.append(gpt_say)
         yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
+        time.sleep(2)
+
+    res = write_history_to_file(history)
+    promote_file_to_downloadzone(res, chatbot=chatbot)
+    chatbot.append(("完成了吗？", res))
+    yield from update_ui(chatbot=chatbot, history=history, msg=msg) # 刷新界面
 
 
 

crazy_functions/生成多种Mermaid图表.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, update_ui, report_exception
-from .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
 from crazy_functions.plugin_template.plugin_class_template import (
     GptAcademicPluginTemplate,
 )
@@ -201,8 +201,7 @@ def 解析历史输入(history, llm_kwargs, file_manifest, chatbot, plugin_kwarg
     MAX_WORD_TOTAL = 4096
     n_txt = len(txt)
     last_iteration_result = "从以下文本中提取摘要。"
-    if n_txt >= 20:
-        print("文章极长，不能达到预期效果")
+
     for i in range(n_txt):
         NUM_OF_WORD = MAX_WORD_TOTAL // n_txt
         i_say = f"Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words in Chinese: {txt[i]}"

crazy_functions/知识库问答.py

@@ -1,6 +1,6 @@
 from toolbox import CatchException, update_ui, ProxyNetworkActivate, update_ui_lastest_msg, get_log_folder, get_user
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
-
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, get_files_from_everything
+from loguru import logger
 install_msg ="""
 
 1. python -m pip install torch --index-url https://download.pytorch.org/whl/cpu
@@ -40,7 +40,7 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     except Exception as e:
         chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        # from .crazy_utils import try_install_deps
+        # from crazy_functions.crazy_utils import try_install_deps
         # try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
         # yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
         return
@@ -60,7 +60,7 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     # < -------------------预热文本向量化模组--------------- >
     chatbot.append(['<br/>'.join(file_manifest), "正在预热文本向量化模组, 如果是第一次运行, 将消耗较长时间下载中文向量化模型..."])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    print('Checking Text2vec ...')
+    logger.info('Checking Text2vec ...')
     from langchain.embeddings.huggingface import HuggingFaceEmbeddings
     with ProxyNetworkActivate('Download_LLM'):    # 临时地激活代理网络
         HuggingFaceEmbeddings(model_name="GanymedeNil/text2vec-large-chinese")
@@ -68,7 +68,7 @@ def 知识库文件注入(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     # < -------------------构建知识库--------------- >
     chatbot.append(['<br/>'.join(file_manifest), "正在构建知识库..."])
     yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-    print('Establishing knowledge archive ...')
+    logger.info('Establishing knowledge archive ...')
     with ProxyNetworkActivate('Download_LLM'):    # 临时地激活代理网络
         kai = knowledge_archive_interface()
         vs_path = get_log_folder(user=get_user(chatbot), plugin_name='vec_store')
@@ -93,7 +93,7 @@ def 读取知识库作答(txt, llm_kwargs, plugin_kwargs, chatbot, history, syst
     except Exception as e:
         chatbot.append(["依赖不足", f"{str(e)}\n\n导入依赖失败。请用以下命令安装" + install_msg])
         yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
-        # from .crazy_utils import try_install_deps
+        # from crazy_functions.crazy_utils import try_install_deps
         # try_install_deps(['zh_langchain==0.2.1', 'pypinyin'], reload_m=['pypinyin', 'zh_langchain'])
         # yield from update_ui_lastest_msg("安装完成，您可以再次重试。", chatbot, history)
         return

crazy_functions/联网的ChatGPT.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
 import requests
 from bs4 import BeautifulSoup
 from request_llms.bridge_all import model_info
@@ -23,8 +23,8 @@ def google(query, proxies):
             item = {'title': title, 'link': link}
             results.append(item)
 
-    for r in results:
-        print(r['link'])
+    # for r in results:
+    #     print(r['link'])
     return results
 
 def scrape_text(url, proxies) -> str:

crazy_functions/联网的ChatGPT_bing版.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, update_ui
-from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
+from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive, input_clipping
 import requests
 from bs4 import BeautifulSoup
 from request_llms.bridge_all import model_info
@@ -22,8 +22,8 @@ def bing_search(query, proxies=None):
             item = {'title': title, 'link': link}
             results.append(item)
 
-    for r in results:
-        print(r['link'])
+    # for r in results:
+    #     print(r['link'])
     return results
 
 

crazy_functions/解析JupyterNotebook.py

@@ -64,7 +64,7 @@ def parseNotebook(filename, enable_markdown=1):
 
 
 def ipynb解释(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
-    from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
+    from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
 
     if ("advanced_arg" in plugin_kwargs) and (plugin_kwargs["advanced_arg"] == ""): plugin_kwargs.pop("advanced_arg")
     enable_markdown = plugin_kwargs.get("advanced_arg", "1")

crazy_functions/询问多个大语言模型.py

@@ -1,5 +1,5 @@
 from toolbox import CatchException, update_ui, get_conf
-from .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
 import datetime
 @CatchException
 def 同时问询(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_request):

crazy_functions/语音助手.py

@@ -1,11 +1,13 @@
 from toolbox import update_ui
 from toolbox import CatchException, get_conf, markdown_convertion
+from request_llms.bridge_all import predict_no_ui_long_connection
 from crazy_functions.crazy_utils import input_clipping
 from crazy_functions.agent_fns.watchdog import WatchDog
-from request_llms.bridge_all import predict_no_ui_long_connection
+from crazy_functions.live_audio.aliyunASR import AliyunASR
+from loguru import logger
+
 import threading, time
 import numpy as np
-from .live_audio.aliyunASR import AliyunASR
 import json
 import re
 
@@ -42,9 +44,9 @@ class AsyncGptTask():
             gpt_say_partial = predict_no_ui_long_connection(inputs=i_say, llm_kwargs=llm_kwargs, history=history, sys_prompt=sys_prompt,
                                                             observe_window=observe_window[index], console_slience=True)
         except ConnectionAbortedError as token_exceed_err:
-            print('至少一个线程任务Token溢出而失败', e)
+            logger.error('至少一个线程任务Token溢出而失败', e)
         except Exception as e:
-            print('至少一个线程任务意外失败', e)
+            logger.error('至少一个线程任务意外失败', e)
 
     def add_async_gpt_task(self, i_say, chatbot_index, llm_kwargs, history, system_prompt):
         self.observe_future.append([""])

crazy_functions/读文章写摘要.py

@@ -1,12 +1,11 @@
 from toolbox import update_ui
 from toolbox import CatchException, report_exception
 from toolbox import write_history_to_file, promote_file_to_downloadzone
-from .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
 
 
 def 解析Paper(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
     import time, glob, os
-    print('begin analysis on:', file_manifest)
     for index, fp in enumerate(file_manifest):
         with open(fp, 'r', encoding='utf-8', errors='replace') as f:
             file_content = f.read()

crazy_functions/谷歌检索小助手.py

@@ -1,4 +1,4 @@
-from .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
 from toolbox import CatchException, report_exception, promote_file_to_downloadzone
 from toolbox import update_ui, update_ui_lastest_msg, disable_auto_promotion, write_history_to_file
 import logging

docs/Dockerfile+JittorLLM

@@ -1 +0,0 @@
-# 此Dockerfile不再维护，请前往docs/GithubAction+JittorLLMs

docs/GithubAction+AllCapacityBeta

@@ -1,57 +0,0 @@
-# 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"]

docs/GithubAction+NoLocal+Latex+Arm

@@ -0,0 +1,25 @@
+# 此Dockerfile适用于“无本地模型”的环境构建，如果需要使用chatglm等本地模型，请参考 docs/Dockerfile+ChatGLM
+# - 1 修改 `config.py`
+# - 2 构建 docker build -t gpt-academic-nolocal-latex -f docs/GithubAction+NoLocal+Latex .
+# - 3 运行 docker run -v /home/fuqingxu/arxiv_cache:/root/arxiv_cache --rm -it --net=host gpt-academic-nolocal-latex
+
+FROM menghuan1918/ubuntu_uv_ctex:latest
+ENV DEBIAN_FRONTEND=noninteractive
+SHELL ["/bin/bash", "-c"]
+WORKDIR /gpt
+COPY . .
+RUN /root/.cargo/bin/uv venv --seed \
+    && source .venv/bin/activate \
+    && /root/.cargo/bin/uv pip install openai numpy arxiv rich colorama Markdown pygments pymupdf python-docx pdfminer \
+    && /root/.cargo/bin/uv pip install -r requirements.txt \
+    && /root/.cargo/bin/uv clean
+
+# 对齐python3
+RUN rm -f /usr/bin/python3 && ln -s /gpt/.venv/bin/python /usr/bin/python3
+RUN rm -f /usr/bin/python && ln -s /gpt/.venv/bin/python /usr/bin/python
+
+# 可选步骤，用于预热模块
+RUN python3 -c 'from check_proxy import warm_up_modules; warm_up_modules()'
+
+# 启动
+CMD ["python3", "-u", "main.py"]

docs/WithFastapi.md

@@ -4,7 +4,7 @@ We currently support fastapi in order to solve sub-path deploy issue.
 
 1. change CUSTOM_PATH setting in `config.py`
 
-``` sh
+```sh
 nano config.py
 ```
 
@@ -35,9 +35,8 @@ if __name__ == "__main__":
     main()
 ```
 
-
 3. Go!
 
-``` sh
+```sh
 python main.py
 ```

docs/translate_std.json

@@ -106,5 +106,24 @@
     "解析PDF_DOC2X_转Latex": "ParsePDF_DOC2X_toLatex",
     "解析PDF_基于DOC2X": "ParsePDF_basedDOC2X",
     "解析PDF_简单拆解": "ParsePDF_simpleDecomposition",
-    "解析PDF_DOC2X_单文件": "ParsePDF_DOC2X_singleFile"
+    "解析PDF_DOC2X_单文件": "ParsePDF_DOC2X_singleFile",
+    "注释Python项目": "CommentPythonProject",
+    "注释源代码": "CommentSourceCode",
+    "log亮黄": "log_yellow",
+    "log亮绿": "log_green",
+    "log亮红": "log_red",
+    "log亮紫": "log_purple",
+    "log亮蓝": "log_blue",
+    "Rag问答": "RagQA",
+    "sprint红": "sprint_red",
+    "sprint绿": "sprint_green",
+    "sprint黄": "sprint_yellow",
+    "sprint蓝": "sprint_blue",
+    "sprint紫": "sprint_purple",
+    "sprint靛": "sprint_indigo",
+    "sprint亮红": "sprint_bright_red",
+    "sprint亮绿": "sprint_bright_green",
+    "sprint亮黄": "sprint_bright_yellow",
+    "sprint亮蓝": "sprint_bright_blue",
+    "sprint亮紫": "sprint_bright_purple"
 }

main.py

@@ -13,16 +13,10 @@ help_menu_description = \
 </br></br>如何语音对话: 请阅读Wiki
 </br></br>如何临时更换API_KEY: 在输入区输入临时API_KEY后提交（网页刷新后失效）"""
 
+from loguru import logger
 def enable_log(PATH_LOGGING):
-    import logging
-    admin_log_path = os.path.join(PATH_LOGGING, "admin")
-    os.makedirs(admin_log_path, exist_ok=True)
-    log_dir = os.path.join(admin_log_path, "chat_secrets.log")
-    try:logging.basicConfig(filename=log_dir, level=logging.INFO, encoding="utf-8", format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    except:logging.basicConfig(filename=log_dir, level=logging.INFO,  format="%(asctime)s %(levelname)-8s %(message)s", datefmt="%Y-%m-%d %H:%M:%S")
-    # Disable logging output from the 'httpx' logger
-    logging.getLogger("httpx").setLevel(logging.WARNING)
-    print(f"所有对话记录将自动保存在本地目录{log_dir}, 请注意自我隐私保护哦！")
+    from shared_utils.logging import setup_logging
+    setup_logging(PATH_LOGGING)
 
 def encode_plugin_info(k, plugin)->str:
     import copy
@@ -42,9 +36,16 @@ def main():
     import gradio as gr
     if gr.__version__ not in ['3.32.9', '3.32.10', '3.32.11']:
         raise ModuleNotFoundError("使用项目内置Gradio获取最优体验! 请运行 `pip install -r requirements.txt` 指令安装内置Gradio及其他依赖, 详情信息见requirements.txt.")
-    from request_llms.bridge_all import predict
+    
+    # 一些基础工具
     from toolbox import format_io, find_free_port, on_file_uploaded, on_report_generated, get_conf, ArgsGeneralWrapper, DummyWith
 
+    # 对话、日志记录
+    enable_log(get_conf("PATH_LOGGING"))
+
+    # 对话句柄
+    from request_llms.bridge_all import predict
+
     # 读取配置
     proxies, WEB_PORT, LLM_MODEL, CONCURRENT_COUNT, AUTHENTICATION = get_conf('proxies', 'WEB_PORT', 'LLM_MODEL', 'CONCURRENT_COUNT', 'AUTHENTICATION')
     CHATBOT_HEIGHT, LAYOUT, AVAIL_LLM_MODELS, AUTO_CLEAR_TXT = get_conf('CHATBOT_HEIGHT', 'LAYOUT', 'AVAIL_LLM_MODELS', 'AUTO_CLEAR_TXT')
@@ -61,8 +62,6 @@ def main():
     from themes.theme import load_dynamic_theme, to_cookie_str, from_cookie_str, assign_user_uuid
     title_html = f"<h1 align=\"center\">GPT 学术优化 {get_current_version()}</h1>{theme_declaration}"
 
-    # 对话、日志记录
-    enable_log(PATH_LOGGING)
 
     # 一些普通功能模块
     from core_functional import get_core_functions
@@ -118,8 +117,8 @@ def main():
                             choices=[
                                 "常规对话", 
                                 "多模型对话", 
+                                "智能召回 RAG",
                                 # "智能上下文", 
-                                # "智能召回 RAG",
                             ], value="常规对话",
                             interactive=True, label='', show_label=False,
                             elem_classes='normal_mut_select', elem_id="gpt-submit-dropdown").style(container=False)
@@ -339,9 +338,9 @@ def main():
     # Gradio的inbrowser触发不太稳定，回滚代码到原始的浏览器打开函数
     def run_delayed_tasks():
         import threading, webbrowser, time
-        print(f"如果浏览器没有自动打开，请复制并转到以下URL：")
-        if DARK_MODE:   print(f"\t「暗色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
-        else:           print(f"\t「亮色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
+        logger.info(f"如果浏览器没有自动打开，请复制并转到以下URL：")
+        if DARK_MODE:   logger.info(f"\t「暗色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
+        else:           logger.info(f"\t「亮色主题已启用（支持动态切换主题）」: http://localhost:{PORT}")
 
         def auto_updates(): time.sleep(0); auto_update()
         def open_browser(): time.sleep(2); webbrowser.open_new_tab(f"http://localhost:{PORT}")

request_llms/bridge_all.py

@@ -9,6 +9,7 @@
     2. predict_no_ui_long_connection(...)
 """
 import tiktoken, copy, re
+from loguru import logger
 from functools import lru_cache
 from concurrent.futures import ThreadPoolExecutor
 from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask, read_one_api_model_name
@@ -51,9 +52,9 @@ class LazyloadTiktoken(object):
     @staticmethod
     @lru_cache(maxsize=128)
     def get_encoder(model):
-        print('正在加载tokenizer，如果是第一次运行，可能需要一点时间下载参数')
+        logger.info('正在加载tokenizer，如果是第一次运行，可能需要一点时间下载参数')
         tmp = tiktoken.encoding_for_model(model)
-        print('加载tokenizer完毕')
+        logger.info('加载tokenizer完毕')
         return tmp
 
     def encode(self, *args, **kwargs):
@@ -83,7 +84,7 @@ try:
     API_URL = get_conf("API_URL")
     if API_URL != "https://api.openai.com/v1/chat/completions":
         openai_endpoint = API_URL
-        print("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
+        logger.warning("警告！API_URL配置选项将被弃用，请更换为API_URL_REDIRECT配置")
 except:
     pass
 # 新版配置
@@ -248,6 +249,27 @@ model_info = {
         "token_cnt": get_token_num_gpt4,
     },
 
+    "o1-preview": {
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 128000,
+        "tokenizer": tokenizer_gpt4,
+        "token_cnt": get_token_num_gpt4,
+        "openai_disable_system_prompt": True,
+        "openai_disable_stream": True,
+    },
+    "o1-mini": {
+        "fn_with_ui": chatgpt_ui,
+        "fn_without_ui": chatgpt_noui,
+        "endpoint": openai_endpoint,
+        "max_token": 128000,
+        "tokenizer": tokenizer_gpt4,
+        "token_cnt": get_token_num_gpt4,
+        "openai_disable_system_prompt": True,
+        "openai_disable_stream": True,
+    },
+
     "gpt-4-turbo": {
         "fn_with_ui": chatgpt_ui,
         "fn_without_ui": chatgpt_noui,
@@ -407,22 +429,46 @@ model_info = {
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    # Gemini
+    # Note: now gemini-pro is an alias of gemini-1.0-pro.
+    # Warning: gemini-pro-vision has been deprecated.
+    # Support for gemini-pro-vision has been removed.
     "gemini-pro": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
         "endpoint": gemini_endpoint,
+        "has_multimodal_capacity": False,
         "max_token": 1024 * 32,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
-    "gemini-pro-vision": {
+    "gemini-1.0-pro": {
         "fn_with_ui": genai_ui,
         "fn_without_ui": genai_noui,
         "endpoint": gemini_endpoint,
+        "has_multimodal_capacity": False,
         "max_token": 1024 * 32,
         "tokenizer": tokenizer_gpt35,
         "token_cnt": get_token_num_gpt35,
     },
+    "gemini-1.5-pro": {
+        "fn_with_ui": genai_ui,
+        "fn_without_ui": genai_noui,
+        "endpoint": gemini_endpoint,
+        "has_multimodal_capacity": True,
+        "max_token": 1024 * 204800,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
+    "gemini-1.5-flash": {
+        "fn_with_ui": genai_ui,
+        "fn_without_ui": genai_noui,
+        "endpoint": gemini_endpoint,
+        "has_multimodal_capacity": True,
+        "max_token": 1024 * 204800,
+        "tokenizer": tokenizer_gpt35,
+        "token_cnt": get_token_num_gpt35,
+    },
 
     # cohere
     "cohere-command-r-plus": {
@@ -638,7 +684,7 @@ if "newbing" in AVAIL_LLM_MODELS:   # same with newbing-free
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 if "chatglmft" in AVAIL_LLM_MODELS:   # same with newbing-free
     try:
         from .bridge_chatglmft import predict_no_ui_long_connection as chatglmft_noui
@@ -654,7 +700,7 @@ if "chatglmft" in AVAIL_LLM_MODELS:   # same with newbing-free
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 上海AI-LAB书生大模型 -=-=-=-=-=-=-
 if "internlm" in AVAIL_LLM_MODELS:
     try:
@@ -671,7 +717,7 @@ if "internlm" in AVAIL_LLM_MODELS:
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 if "chatglm_onnx" in AVAIL_LLM_MODELS:
     try:
         from .bridge_chatglmonnx import predict_no_ui_long_connection as chatglm_onnx_noui
@@ -687,7 +733,7 @@ if "chatglm_onnx" in AVAIL_LLM_MODELS:
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 通义-本地模型 -=-=-=-=-=-=-
 if "qwen-local" in AVAIL_LLM_MODELS:
     try:
@@ -705,7 +751,7 @@ if "qwen-local" in AVAIL_LLM_MODELS:
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 通义-在线模型 -=-=-=-=-=-=-
 if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-max" in AVAIL_LLM_MODELS:   # zhipuai
     try:
@@ -741,7 +787,7 @@ if "qwen-turbo" in AVAIL_LLM_MODELS or "qwen-plus" in AVAIL_LLM_MODELS or "qwen-
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 零一万物模型 -=-=-=-=-=-=-
 yi_models = ["yi-34b-chat-0205","yi-34b-chat-200k","yi-large","yi-medium","yi-spark","yi-large-turbo","yi-large-preview"]
 if any(item in yi_models for item in AVAIL_LLM_MODELS):
@@ -821,7 +867,7 @@ if any(item in yi_models for item in AVAIL_LLM_MODELS):
             },
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 讯飞星火认知大模型 -=-=-=-=-=-=-
 if "spark" in AVAIL_LLM_MODELS:
     try:
@@ -839,7 +885,7 @@ if "spark" in AVAIL_LLM_MODELS:
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
     try:
         from .bridge_spark import predict_no_ui_long_connection as spark_noui
@@ -856,8 +902,8 @@ if "sparkv2" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
             }
         })
     except:
-        print(trimmed_format_exc())
-if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS:   # 讯飞星火认知大模型
+        logger.error(trimmed_format_exc())
+if any(x in AVAIL_LLM_MODELS for x in ("sparkv3", "sparkv3.5", "sparkv4")):   # 讯飞星火认知大模型
     try:
         from .bridge_spark import predict_no_ui_long_connection as spark_noui
         from .bridge_spark import predict as spark_ui
@@ -891,7 +937,7 @@ if "sparkv3" in AVAIL_LLM_MODELS or "sparkv3.5" in AVAIL_LLM_MODELS:   # 讯飞
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 if "llama2" in AVAIL_LLM_MODELS:   # llama2
     try:
         from .bridge_llama2 import predict_no_ui_long_connection as llama2_noui
@@ -907,7 +953,7 @@ if "llama2" in AVAIL_LLM_MODELS:   # llama2
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 智谱 -=-=-=-=-=-=-
 if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容配置
     try:
@@ -922,7 +968,7 @@ if "zhipuai" in AVAIL_LLM_MODELS:   # zhipuai 是glm-4的别名，向后兼容
             },
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 幻方-深度求索大模型 -=-=-=-=-=-=-
 if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
     try:
@@ -939,7 +985,7 @@ if "deepseekcoder" in AVAIL_LLM_MODELS:   # deepseekcoder
             }
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- 幻方-深度求索大模型在线API -=-=-=-=-=-=-
 if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS:
     try:
@@ -967,7 +1013,7 @@ if "deepseek-chat" in AVAIL_LLM_MODELS or "deepseek-coder" in AVAIL_LLM_MODELS:
             },
         })
     except:
-        print(trimmed_format_exc())
+        logger.error(trimmed_format_exc())
 # -=-=-=-=-=-=- one-api 对齐支持 -=-=-=-=-=-=-
 for model in [m for m in AVAIL_LLM_MODELS if m.startswith("one-api-")]:
     # 为了更灵活地接入one-api多模型管理界面，设计了此接口，例子：AVAIL_LLM_MODELS = ["one-api-mixtral-8x7b(max_token=6666)"]
@@ -980,7 +1026,7 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("one-api-")]:
         # 如果是已知模型，则尝试获取其信息
         original_model_info = model_info.get(origin_model_name.replace("one-api-", "", 1), None)
     except:
-        print(f"one-api模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        logger.error(f"one-api模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
         continue
     this_model_info = {
         "fn_with_ui": chatgpt_ui,
@@ -1011,7 +1057,7 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("vllm-")]:
     try:
         _, max_token_tmp = read_one_api_model_name(model)
     except:
-        print(f"vllm模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        logger.error(f"vllm模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
         continue
     model_info.update({
         model: {
@@ -1038,7 +1084,7 @@ for model in [m for m in AVAIL_LLM_MODELS if m.startswith("ollama-")]:
     try:
         _, max_token_tmp = read_one_api_model_name(model)
     except:
-        print(f"ollama模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
+        logger.error(f"ollama模型 {model} 的 max_token 配置不是整数，请检查配置文件。")
         continue
     model_info.update({
         model: {
@@ -1074,6 +1120,24 @@ if len(AZURE_CFG_ARRAY) > 0:
         if azure_model_name not in AVAIL_LLM_MODELS:
             AVAIL_LLM_MODELS += [azure_model_name]
 
+# -=-=-=-=-=-=- Openrouter模型对齐支持 -=-=-=-=-=-=-
+# 为了更灵活地接入Openrouter路由，设计了此接口
+for model in [m for m in AVAIL_LLM_MODELS if m.startswith("openrouter-")]:
+    from request_llms.bridge_openrouter import predict_no_ui_long_connection as openrouter_noui
+    from request_llms.bridge_openrouter import predict as openrouter_ui
+    model_info.update({
+        model: {
+            "fn_with_ui": openrouter_ui,
+            "fn_without_ui": openrouter_noui,
+            # 以下参数参考gpt-4o-mini的配置, 请根据实际情况修改
+            "endpoint": openai_endpoint,
+            "has_multimodal_capacity": True,
+            "max_token": 128000,
+            "tokenizer": tokenizer_gpt4,
+            "token_cnt": get_token_num_gpt4,
+        },
+    })
+
 
 # -=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=--=-=-=-=-=-=-=-=
 # -=-=-=-=-=-=-=-=-=- ☝️ 以上是模型路由 -=-=-=-=-=-=-=-=-=
@@ -1219,5 +1283,5 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot,
     if additional_fn: # 根据基础功能区 ModelOverride 参数调整模型类型
         llm_kwargs, additional_fn, method = execute_model_override(llm_kwargs, additional_fn, method)
 
+    # 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)
-

request_llms/bridge_chatglm3.py

@@ -18,7 +18,7 @@ class GetGLM3Handle(LocalLLMHandle):
 
     def load_model_and_tokenizer(self):
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
-        from transformers import AutoModel, AutoTokenizer
+        from transformers import AutoModel, AutoTokenizer, BitsAndBytesConfig
         import os, glob
         import os
         import platform
@@ -45,15 +45,13 @@ class GetGLM3Handle(LocalLLMHandle):
                 chatglm_model = AutoModel.from_pretrained(
                     pretrained_model_name_or_path=_model_name_,
                     trust_remote_code=True,
-                    device="cuda",
-                    load_in_4bit=True,
+                    quantization_config=BitsAndBytesConfig(load_in_4bit=True),
                 )
             elif LOCAL_MODEL_QUANT == "INT8":  # INT8
                 chatglm_model = AutoModel.from_pretrained(
                     pretrained_model_name_or_path=_model_name_,
                     trust_remote_code=True,
-                    device="cuda",
-                    load_in_8bit=True,
+                    quantization_config=BitsAndBytesConfig(load_in_8bit=True),
                 )
             else:
                 chatglm_model = AutoModel.from_pretrained(

request_llms/bridge_chatglmft.py

@@ -1,12 +1,13 @@
 
 from transformers import AutoModel, AutoTokenizer
+from loguru import logger
+from toolbox import update_ui, get_conf
+from multiprocessing import Process, Pipe
 import time
 import os
 import json
 import threading
 import importlib
-from toolbox import update_ui, get_conf
-from multiprocessing import Process, Pipe
 
 load_message = "ChatGLMFT尚未加载，加载需要一段时间。注意，取决于`config.py`的配置，ChatGLMFT消耗大量的内存（CPU）或显存（GPU），也许会导致低配计算机卡死 ……"
 
@@ -78,7 +79,7 @@ class GetGLMFTHandle(Process):
                     config.pre_seq_len = model_args['pre_seq_len']
                     config.prefix_projection = model_args['prefix_projection']
 
-                    print(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}")
+                    logger.info(f"Loading prefix_encoder weight from {CHATGLM_PTUNING_CHECKPOINT}")
                     model = AutoModel.from_pretrained(model_args['model_name_or_path'], config=config, trust_remote_code=True)
                     prefix_state_dict = torch.load(os.path.join(CHATGLM_PTUNING_CHECKPOINT, "pytorch_model.bin"))
                     new_prefix_state_dict = {}
@@ -88,7 +89,7 @@ class GetGLMFTHandle(Process):
                     model.transformer.prefix_encoder.load_state_dict(new_prefix_state_dict)
 
                     if model_args['quantization_bit'] is not None and model_args['quantization_bit'] != 0:
-                        print(f"Quantized to {model_args['quantization_bit']} bit")
+                        logger.info(f"Quantized to {model_args['quantization_bit']} bit")
                         model = model.quantize(model_args['quantization_bit'])
                     model = model.cuda()
                     if model_args['pre_seq_len'] is not None:

request_llms/bridge_chatgpt.py

@@ -12,11 +12,12 @@ import json
 import os
 import re
 import time
-import logging
 import traceback
 import requests
 import random
 
+from loguru import logger
+
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
 from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
@@ -133,21 +134,32 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
     observe_window = None：
         用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
     """
+    from request_llms.bridge_all import model_info
+
     watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
-    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
+
+    if model_info[llm_kwargs['llm_model']].get('openai_disable_stream', False): stream = False
+    else: stream = True
+
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=stream)
     retry = 0
     while True:
         try:
             # make a POST request to the API endpoint, stream=False
-            from .bridge_all import model_info
             endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
             response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
+                                    json=payload, stream=stream, timeout=TIMEOUT_SECONDS); break
         except requests.exceptions.ReadTimeout as e:
             retry += 1
             traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+
+    if not stream:
+        # 该分支仅适用于不支持stream的o1模型，其他情形一律不适用
+        chunkjson = json.loads(response.content.decode())
+        gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
+        return gpt_replying_buffer
 
     stream_response = response.iter_lines()
     result = ''
@@ -190,10 +202,13 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
                     if (time.time()-observe_window[1]) > watch_dog_patience:
                         raise RuntimeError("用户取消了程序。")
         else: raise RuntimeError("意外Json结构："+delta)
-    if json_data and json_data['finish_reason'] == 'content_filter':
-        raise RuntimeError("由于提问含不合规内容被Azure过滤。")
-    if json_data and json_data['finish_reason'] == 'length':
+
+    finish_reason = json_data.get('finish_reason', None) if json_data else None
+    if finish_reason == 'content_filter':
+        raise RuntimeError("由于提问含不合规内容被过滤。")
+    if finish_reason == 'length':
         raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
+
     return result
 
 
@@ -208,7 +223,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     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
     """
-    from .bridge_all import model_info
+    from request_llms.bridge_all import model_info
     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)))
@@ -237,6 +252,10 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     chatbot.append((_inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
+    # 禁用stream的特殊模型处理
+    if model_info[llm_kwargs['llm_model']].get('openai_disable_stream', False): stream = False
+    else: stream = True
+
     # check mis-behavior
     if is_the_upload_folder(user_input):
         chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
@@ -270,7 +289,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         try:
             # make a POST request to the API endpoint, stream=True
             response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
+                                    json=payload, stream=stream, timeout=TIMEOUT_SECONDS);break
         except:
             retry += 1
             chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
@@ -278,10 +297,15 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
             yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
             if retry > MAX_RETRY: raise TimeoutError
 
-    gpt_replying_buffer = ""
 
-    is_head_of_the_stream = True
+    if not stream:
+        # 该分支仅适用于不支持stream的o1模型，其他情形一律不适用
+        yield from handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history)
+        return
+
     if stream:
+        gpt_replying_buffer = ""
+        is_head_of_the_stream = True
         stream_response =  response.iter_lines()
         while True:
             try:
@@ -317,7 +341,6 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                     # 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(chunkjson['choices'][0]["delta"]) == 0):
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        # logging.info(f'[response] {gpt_replying_buffer}')
                         log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
                         break
                     # 处理数据流的主体
@@ -343,12 +366,24 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                     chunk_decoded = chunk.decode()
                     error_msg = chunk_decoded
                     chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析异常" + error_msg) # 刷新界面
+                    logger.error(error_msg)
                     return
+        return  # return from stream-branch
+
+def handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history):
+    try:
+        chunkjson = json.loads(response.content.decode())
+        gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
+        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+        history[-1] = gpt_replying_buffer
+        chatbot[-1] = (history[-2], history[-1])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    except Exception as e:
+        yield from update_ui(chatbot=chatbot, history=history, msg="Json解析异常" + response.text) # 刷新界面
 
 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
-    from .bridge_all import model_info
+    from request_llms.bridge_all import model_info
     openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
     if "reduce the length" in error_msg:
         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
@@ -381,6 +416,8 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
     """
     整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
     """
+    from request_llms.bridge_all import model_info
+
     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。")
 
@@ -409,10 +446,16 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
     else:
         enable_multimodal_capacity = False
 
+    conversation_cnt = len(history) // 2
+    openai_disable_system_prompt = model_info[llm_kwargs['llm_model']].get('openai_disable_system_prompt', False)
+
+    if openai_disable_system_prompt:
+        messages = [{"role": "user", "content": system_prompt}]
+    else:
+        messages = [{"role": "system", "content": system_prompt}]
+
     if not enable_multimodal_capacity:
         # 不使用多模态能力
-        conversation_cnt = len(history) // 2
-        messages = [{"role": "system", "content": system_prompt}]
         if conversation_cnt:
             for index in range(0, 2*conversation_cnt, 2):
                 what_i_have_asked = {}
@@ -434,8 +477,6 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
         messages.append(what_i_ask_now)
     else:
         # 多模态能力
-        conversation_cnt = len(history) // 2
-        messages = [{"role": "system", "content": system_prompt}]
         if conversation_cnt:
             for index in range(0, 2*conversation_cnt, 2):
                 what_i_have_asked = {}
@@ -486,7 +527,6 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
             "gpt-3.5-turbo-16k-0613",
             "gpt-3.5-turbo-0301",
         ])
-        logging.info("Random select model:" + model)
 
     payload = {
         "model": model,
@@ -496,10 +536,6 @@ def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:st
         "n": 1,
         "stream": stream,
     }
-    # try:
-    #     print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    # except:
-    #     print('输入中可能存在乱码。')
+
     return headers,payload
 
-

request_llms/bridge_chatgpt_vision.py

@@ -8,15 +8,15 @@
     2. predict_no_ui_long_connection：支持多线程
 """
 
+import os
 import json
 import time
-import logging
 import requests
 import base64
-import os
 import glob
+from loguru import logger
 from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc, is_the_upload_folder, \
-    update_ui_lastest_msg, get_max_token, encode_image, have_any_recent_upload_image_files
+    update_ui_lastest_msg, get_max_token, encode_image, have_any_recent_upload_image_files, log_chat
 
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
@@ -100,7 +100,6 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
     raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
     def make_media_input(inputs, image_paths):
         for image_path in image_paths:
             inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
@@ -185,7 +184,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
                         lastmsg = chatbot[-1][-1] + f"\n\n\n\n「{llm_kwargs['llm_model']}调用结束，该模型不具备上下文对话能力，如需追问，请及时切换模型。」"
                         yield from update_ui_lastest_msg(lastmsg, chatbot, history, delay=1)
-                        logging.info(f'[response] {gpt_replying_buffer}')
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
                         break
                     # 处理数据流的主体
                     status_text = f"finish_reason: {chunkjson['choices'][0].get('finish_reason', 'null')}"
@@ -210,7 +209,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     error_msg = chunk_decoded
                     chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key)
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    logger.error(error_msg)
                     return
 
 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg, api_key=""):
@@ -301,10 +300,7 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, image_paths):
         "presence_penalty": 0,
         "frequency_penalty": 0,
     }
-    try:
-        print(f" {llm_kwargs['llm_model']} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
+
     return headers, payload, api_key
 
 

request_llms/bridge_chatgpt_website.py

@@ -1,281 +0,0 @@
-# 借鉴了 https://github.com/GaiZhenbiao/ChuanhuChatGPT 项目
-
-"""
-    该文件中主要包含三个函数
-
-    不具备多线程能力的函数：
-    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
-
-    具备多线程调用能力的函数
-    2. predict_no_ui_long_connection：支持多线程
-"""
-
-import json
-import time
-import gradio as gr
-import logging
-import traceback
-import requests
-import importlib
-
-# config_private.py放自己的秘密如API和代理网址
-# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
-from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history, trimmed_format_exc
-proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG = \
-    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG')
-
-timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
-                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
-
-def get_full_error(chunk, stream_response):
-    """
-        获取完整的从Openai返回的报错
-    """
-    while True:
-        try:
-            chunk += next(stream_response)
-        except:
-            break
-    return chunk
-
-
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None, console_slience=False):
-    """
-    发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
-    inputs：
-        是本次问询的输入
-    sys_prompt:
-        系统静默prompt
-    llm_kwargs：
-        chatGPT的内部调优参数
-    history：
-        是之前的对话列表
-    observe_window = None：
-        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
-    """
-    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
-    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=True)
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=False
-            from .bridge_all import model_info
-            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS); break
-        except requests.exceptions.ReadTimeout as e:
-            retry += 1
-            traceback.print_exc()
-            if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
-
-    stream_response =  response.iter_lines()
-    result = ''
-    while True:
-        try: chunk = next(stream_response).decode()
-        except StopIteration:
-            break
-        except requests.exceptions.ConnectionError:
-            chunk = next(stream_response).decode() # 失败了，重试一次？再失败就没办法了。
-        if len(chunk)==0: continue
-        if not chunk.startswith('data:'):
-            error_msg = get_full_error(chunk.encode('utf8'), stream_response).decode()
-            if "reduce the length" in error_msg:
-                raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
-            else:
-                raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
-        if ('data: [DONE]' in chunk): break # api2d 正常完成
-        json_data = json.loads(chunk.lstrip('data:'))['choices'][0]
-        delta = json_data["delta"]
-        if len(delta) == 0: break
-        if "role" in delta: continue
-        if "content" in delta:
-            result += delta["content"]
-            if not console_slience: print(delta["content"], end='')
-            if observe_window is not None:
-                # 观测窗，把已经获取的数据显示出去
-                if len(observe_window) >= 1: observe_window[0] += delta["content"]
-                # 看门狗，如果超过期限没有喂狗，则终止
-                if len(observe_window) >= 2:
-                    if (time.time()-observe_window[1]) > watch_dog_patience:
-                        raise RuntimeError("用户取消了程序。")
-        else: raise RuntimeError("意外Json结构："+delta)
-    if json_data['finish_reason'] == 'content_filter':
-        raise RuntimeError("由于提问含不合规内容被Azure过滤。")
-    if json_data['finish_reason'] == 'length':
-        raise ConnectionAbortedError("正常结束，但显示Token不足，导致输出不完整，请削减单次输入的文本量。")
-    return result
-
-
-def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_prompt='', stream = True, additional_fn=None):
-    """
-    发送至chatGPT，流式获取输出。
-    用于基础的对话功能。
-    inputs 是本次问询的输入
-    top_p, temperature是chatGPT的内部调优参数
-    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
-    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 additional_fn is not None:
-        from core_functional import handle_core_functionality
-        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
-
-    raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
-    chatbot.append((inputs, ""))
-    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
-
-    try:
-        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, stream)
-    except RuntimeError as e:
-        chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
-        yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
-        return
-
-    history.append(inputs); history.append("")
-
-    retry = 0
-    while True:
-        try:
-            # make a POST request to the API endpoint, stream=True
-            from .bridge_all import model_info
-            endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
-            response = requests.post(endpoint, headers=headers, proxies=proxies,
-                                    json=payload, stream=True, timeout=TIMEOUT_SECONDS);break
-        except:
-            retry += 1
-            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
-            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
-            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
-            if retry > MAX_RETRY: raise TimeoutError
-
-    gpt_replying_buffer = ""
-
-    is_head_of_the_stream = True
-    if stream:
-        stream_response =  response.iter_lines()
-        while True:
-            try:
-                chunk = next(stream_response)
-            except StopIteration:
-                # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
-                chunk_decoded = chunk.decode()
-                error_msg = chunk_decoded
-                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                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()):
-                # 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:
-                        # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
-                        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']}"
-                    delta = chunkjson['choices'][0]["delta"]
-                    if "content" in delta:
-                        gpt_replying_buffer = gpt_replying_buffer + delta["content"]
-                    history[-1] = gpt_replying_buffer
-                    chatbot[-1] = (history[-2], history[-1])
-                    yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
-                except Exception as e:
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
-                    chunk = get_full_error(chunk, stream_response)
-                    chunk_decoded = chunk.decode()
-                    error_msg = chunk_decoded
-                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
-                    yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
-                    return
-
-def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
-    from .bridge_all import model_info
-    openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
-    if "reduce the length" in error_msg:
-        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.)")
-                        # 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:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
-    elif "exceeded your current quota" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
-    elif "account is not active" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "associated with a deactivated account" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
-    elif "bad forward key" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
-    elif "Not enough point" in error_msg:
-        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
-    else:
-        from toolbox import regular_txt_to_markdown
-        tb_str = '```\n' + trimmed_format_exc() + '```'
-        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
-    return chatbot, history
-
-def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
-    """
-    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
-    """
-    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。")
-
-    headers = {
-        "Content-Type": "application/json",
-    }
-
-    conversation_cnt = len(history) // 2
-
-    messages = [{"role": "system", "content": system_prompt}]
-    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)
-
-    payload = {
-        "model": llm_kwargs['llm_model'].strip('api2d-'),
-        "messages": messages,
-        "temperature": llm_kwargs['temperature'],  # 1.0,
-        "top_p": llm_kwargs['top_p'],  # 1.0,
-        "n": 1,
-        "stream": stream,
-        "presence_penalty": 0,
-        "frequency_penalty": 0,
-    }
-    try:
-        print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
-    return headers,payload
-
-

request_llms/bridge_claude.py

@@ -9,13 +9,14 @@
     具备多线程调用能力的函数
     2. predict_no_ui_long_connection：支持多线程
 """
-import logging
 import os
 import time
 import traceback
 import json
 import requests
+from loguru import logger
 from toolbox import get_conf, update_ui, trimmed_format_exc, encode_image, every_image_file_in_path, log_chat
+
 picture_system_prompt = "\n当回复图像时,必须说明正在回复哪张图像。所有图像仅在最后一个问题中提供,即使它们在历史记录中被提及。请使用'这是第X张图像:'的格式来指明您正在描述的是哪张图像。"
 Claude_3_Models = ["claude-3-haiku-20240307", "claude-3-sonnet-20240229", "claude-3-opus-20240229", "claude-3-5-sonnet-20240620"]
 
@@ -101,7 +102,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             retry += 1
             traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
     stream_response = response.iter_lines()
     result = ''
     while True:
@@ -116,12 +117,11 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                 if need_to_pass:
                     pass
                 elif is_last_chunk:
-                    # logging.info(f'[response] {result}')
+                    # logger.info(f'[response] {result}')
                     break
                 else:
                     if chunkjson and chunkjson['type'] == 'content_block_delta':
                         result += chunkjson['delta']['text']
-                        print(chunkjson['delta']['text'], end='')
                         if observe_window is not None:
                             # 观测窗，把已经获取的数据显示出去
                             if len(observe_window) >= 1:
@@ -134,7 +134,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                 chunk = get_full_error(chunk, stream_response)
                 chunk_decoded = chunk.decode()
                 error_msg = chunk_decoded
-                print(error_msg)
+                logger.error(error_msg)
                 raise RuntimeError("Json解析不合常规")
 
     return result
@@ -200,7 +200,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
             retry += 1
             traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
     stream_response = response.iter_lines()
     gpt_replying_buffer = ""
 
@@ -217,7 +217,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     pass
                 elif is_last_chunk:
                     log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
-                    # logging.info(f'[response] {gpt_replying_buffer}')
+                    # logger.info(f'[response] {gpt_replying_buffer}')
                     break
                 else:
                     if chunkjson and chunkjson['type'] == 'content_block_delta':
@@ -230,7 +230,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 chunk = get_full_error(chunk, stream_response)
                 chunk_decoded = chunk.decode()
                 error_msg = chunk_decoded
-                print(error_msg)
+                logger.error(error_msg)
                 raise RuntimeError("Json解析不合常规")
 
 def multiple_picture_types(image_paths):

request_llms/bridge_cohere.py

@@ -13,11 +13,9 @@
 import json
 import time
 import gradio as gr
-import logging
 import traceback
 import requests
-import importlib
-import random
+from loguru import logger
 
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@@ -98,7 +96,7 @@ def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[],
             retry += 1
             traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
 
     stream_response = response.iter_lines()
     result = ''
@@ -153,7 +151,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
     raw_input = inputs
-    # logging.info(f'[raw_input] {raw_input}')
+    # logger.info(f'[raw_input] {raw_input}')
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
@@ -237,7 +235,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
                     error_msg = chunk_decoded
                     chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    logger.error(error_msg)
                     return
 
 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):

request_llms/bridge_deepseekcoder.py

@@ -1,12 +1,13 @@
 model_name = "deepseek-coder-6.7b-instruct"
 cmd_to_install = "未知" # "`pip install -r request_llms/requirements_qwen.txt`"
 
-import os
 from toolbox import ProxyNetworkActivate
 from toolbox import get_conf
-from .local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
+from request_llms.local_llm_class import LocalLLMHandle, get_local_llm_predict_fns
 from threading import Thread
+from loguru import logger
 import torch
+import os
 
 def download_huggingface_model(model_name, max_retry, local_dir):
     from huggingface_hub import snapshot_download
@@ -15,7 +16,7 @@ def download_huggingface_model(model_name, max_retry, local_dir):
             snapshot_download(repo_id=model_name, local_dir=local_dir, resume_download=True)
             break
         except Exception as e:
-            print(f'\n\n下载失败，重试第{i}次中...\n\n')
+            logger.error(f'\n\n下载失败，重试第{i}次中...\n\n')
     return local_dir
 # ------------------------------------------------------------------------------------------------------------------------
 # 🔌💻 Local Model
@@ -112,7 +113,6 @@ class GetCoderLMHandle(LocalLLMHandle):
         generated_text = ""
         for new_text in self._streamer:
             generated_text += new_text
-            # print(generated_text)
             yield generated_text
 
 

request_llms/bridge_google_gemini.py

@@ -8,15 +8,15 @@ import os
 import time
 from request_llms.com_google import GoogleChatInit
 from toolbox import ChatBotWithCookies
-from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat
+from toolbox import get_conf, update_ui, update_ui_lastest_msg, have_any_recent_upload_image_files, trimmed_format_exc, log_chat, encode_image
 
 proxies, TIMEOUT_SECONDS, MAX_RETRY = get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY')
 timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
                   '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
 
 
-def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="", observe_window=None,
-                                  console_slience=False):
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=[],
+                                  console_slience:bool=False):
     # 检查API_KEY
     if get_conf("GEMINI_API_KEY") == "":
         raise ValueError(f"请配置 GEMINI_API_KEY。")
@@ -44,9 +44,20 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             raise RuntimeError(f'{gpt_replying_buffer} 对话错误')
     return gpt_replying_buffer
 
+def make_media_input(inputs, image_paths):
+    image_base64_array = []
+    for image_path in image_paths:
+        path = os.path.abspath(image_path)
+        inputs = inputs + f'<br/><br/><div align="center"><img src="file={path}"></div>'
+        base64 = encode_image(path)
+        image_base64_array.append(base64)
+    return inputs, image_base64_array
 
 def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
             history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+    
+    from .bridge_all import model_info
+
     # 检查API_KEY
     if get_conf("GEMINI_API_KEY") == "":
         yield from update_ui_lastest_msg(f"请配置 GEMINI_API_KEY。", chatbot=chatbot, history=history, delay=0)
@@ -57,18 +68,17 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         from core_functional import handle_core_functionality
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
-    if "vision" in llm_kwargs["llm_model"]:
-        have_recent_file, image_paths = have_any_recent_upload_image_files(chatbot)
-        if not have_recent_file:
-            chatbot.append((inputs, "没有检测到任何近期上传的图像文件，请上传jpg格式的图片，此外，请注意拓展名需要小写"))
-            yield from update_ui(chatbot=chatbot, history=history, msg="等待图片") # 刷新界面
-            return
-        def make_media_input(inputs, image_paths):
-            for image_path in image_paths:
-                inputs = inputs + f'<br/><br/><div align="center"><img src="file={os.path.abspath(image_path)}"></div>'
-            return inputs
-        if have_recent_file:
-            inputs = make_media_input(inputs, image_paths)
+    # multimodal capacity
+    # inspired by codes in bridge_chatgpt
+    has_multimodal_capacity = model_info[llm_kwargs['llm_model']].get('has_multimodal_capacity', False)
+    if has_multimodal_capacity:
+        has_recent_image_upload, image_paths = have_any_recent_upload_image_files(chatbot, pop=True)
+    else:
+        has_recent_image_upload, image_paths = False, []
+    if has_recent_image_upload:
+        inputs, image_base64_array = make_media_input(inputs, image_paths)
+    else:
+        inputs, image_base64_array = inputs, []
 
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history)
@@ -76,7 +86,7 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
     retry = 0
     while True:
         try:
-            stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt)
+            stream_response = genai.generate_chat(inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity)
             break
         except Exception as e:
             retry += 1
@@ -112,7 +122,6 @@ def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWith
         yield from update_ui(chatbot=chatbot, history=history)
 
 
-
 if __name__ == '__main__':
     import sys
     llm_kwargs = {'llm_model': 'gemini-pro'}

request_llms/bridge_internlm.py

@@ -65,10 +65,10 @@ class GetInternlmHandle(LocalLLMHandle):
 
     def llm_stream_generator(self, **kwargs):
         import torch
-        import logging
         import copy
         import warnings
         import torch.nn as nn
+        from loguru import logger as logging 
         from transformers.generation.utils import LogitsProcessorList, StoppingCriteriaList, GenerationConfig
 
         # 🏃‍♂️🏃‍♂️🏃‍♂️ 子进程执行
@@ -119,7 +119,7 @@ class GetInternlmHandle(LocalLLMHandle):
         elif generation_config.max_new_tokens is not None:
             generation_config.max_length = generation_config.max_new_tokens + input_ids_seq_length
             if not has_default_max_length:
-                logging.warn(
+                logging.warning(
                     f"Both `max_new_tokens` (={generation_config.max_new_tokens}) and `max_length`(="
                     f"{generation_config.max_length}) seem to have been set. `max_new_tokens` will take precedence. "
                     "Please refer to the documentation for more information. "

request_llms/bridge_moonshot.py

@@ -5,7 +5,6 @@
 import json
 import os
 import time
-import logging
 
 from toolbox import get_conf, update_ui, log_chat
 from toolbox import ChatBotWithCookies

request_llms/bridge_ollama.py

@@ -13,11 +13,11 @@
 import json
 import time
 import gradio as gr
-import logging
 import traceback
 import requests
 import importlib
 import random
+from loguru import logger
 
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@@ -81,7 +81,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             retry += 1
             traceback.print_exc()
             if retry > MAX_RETRY: raise TimeoutError
-            if MAX_RETRY!=0: print(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
 
     stream_response = response.iter_lines()
     result = ''
@@ -96,7 +96,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
             try:
                 if is_last_chunk:
                     # 判定为数据流的结束，gpt_replying_buffer也写完了
-                    logging.info(f'[response] {result}')
+                    logger.info(f'[response] {result}')
                     break
                 result += chunkjson['message']["content"]
                 if not console_slience: print(chunkjson['message']["content"], end='')
@@ -112,7 +112,7 @@ def predict_no_ui_long_connection(inputs, llm_kwargs, history=[], sys_prompt="",
                 chunk = get_full_error(chunk, stream_response)
                 chunk_decoded = chunk.decode()
                 error_msg = chunk_decoded
-                print(error_msg)
+                logger.error(error_msg)
                 raise RuntimeError("Json解析不合常规")
     return result
 
@@ -134,7 +134,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
         inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
 
     raw_input = inputs
-    logging.info(f'[raw_input] {raw_input}')
+    logger.info(f'[raw_input] {raw_input}')
     chatbot.append((inputs, ""))
     yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
 
@@ -183,7 +183,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                 try:
                     if is_last_chunk:
                         # 判定为数据流的结束，gpt_replying_buffer也写完了
-                        logging.info(f'[response] {gpt_replying_buffer}')
+                        logger.info(f'[response] {gpt_replying_buffer}')
                         break
                     # 处理数据流的主体
                     try:
@@ -202,7 +202,7 @@ def predict(inputs, llm_kwargs, plugin_kwargs, chatbot, history=[], system_promp
                     error_msg = chunk_decoded
                     chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
                     yield from update_ui(chatbot=chatbot, history=history, msg="Json异常" + error_msg) # 刷新界面
-                    print(error_msg)
+                    logger.error(error_msg)
                     return
 
 def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
@@ -265,8 +265,5 @@ def generate_payload(inputs, llm_kwargs, history, system_prompt, stream):
         "messages": messages,
         "options": options,
     }
-    try:
-        print(f" {llm_kwargs['llm_model']} : {conversation_cnt} : {inputs[:100]} ..........")
-    except:
-        print('输入中可能存在乱码。')
+
     return headers,payload

request_llms/bridge_openrouter.py

@@ -0,0 +1,541 @@
+"""
+    该文件中主要包含三个函数
+
+    不具备多线程能力的函数：
+    1. predict: 正常对话时使用，具备完备的交互功能，不可多线程
+
+    具备多线程调用能力的函数
+    2. predict_no_ui_long_connection：支持多线程
+"""
+
+import json
+import os
+import re
+import time
+import traceback
+import requests
+import random
+from loguru import logger
+
+# config_private.py放自己的秘密如API和代理网址
+# 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
+from toolbox import get_conf, update_ui, is_any_api_key, select_api_key, what_keys, clip_history
+from toolbox import trimmed_format_exc, is_the_upload_folder, read_one_api_model_name, log_chat
+from toolbox import ChatBotWithCookies, have_any_recent_upload_image_files, encode_image
+proxies, TIMEOUT_SECONDS, MAX_RETRY, API_ORG, AZURE_CFG_ARRAY = \
+    get_conf('proxies', 'TIMEOUT_SECONDS', 'MAX_RETRY', 'API_ORG', 'AZURE_CFG_ARRAY')
+
+timeout_bot_msg = '[Local Message] Request timeout. Network error. Please check proxy settings in config.py.' + \
+                  '网络错误，检查代理服务器是否可用，以及代理设置的格式是否正确，格式须是[协议]://[地址]:[端口]，缺一不可。'
+
+def get_full_error(chunk, stream_response):
+    """
+        获取完整的从Openai返回的报错
+    """
+    while True:
+        try:
+            chunk += next(stream_response)
+        except:
+            break
+    return chunk
+
+def make_multimodal_input(inputs, image_paths):
+    image_base64_array = []
+    for image_path in image_paths:
+        path = os.path.abspath(image_path)
+        base64 = encode_image(path)
+        inputs = inputs + f'<br/><br/><div align="center"><img src="file={path}" base64="{base64}"></div>'
+        image_base64_array.append(base64)
+    return inputs, image_base64_array
+
+def reverse_base64_from_input(inputs):
+    # 定义一个正则表达式来匹配 Base64 字符串（假设格式为 base64="<Base64编码>"）
+    # pattern = re.compile(r'base64="([^"]+)"></div>')
+    pattern = re.compile(r'<br/><br/><div align="center"><img[^<>]+base64="([^"]+)"></div>')
+    # 使用 findall 方法查找所有匹配的 Base64 字符串
+    base64_strings = pattern.findall(inputs)
+    # 返回反转后的 Base64 字符串列表
+    return base64_strings
+
+def contain_base64(inputs):
+    base64_strings = reverse_base64_from_input(inputs)
+    return len(base64_strings) > 0
+
+def append_image_if_contain_base64(inputs):
+    if not contain_base64(inputs):
+        return inputs
+    else:
+        image_base64_array = reverse_base64_from_input(inputs)
+        pattern = re.compile(r'<br/><br/><div align="center"><img[^><]+></div>')
+        inputs = re.sub(pattern, '', inputs)
+        res = []
+        res.append({
+            "type": "text",
+            "text": inputs
+        })
+        for image_base64 in image_base64_array:
+            res.append({
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/jpeg;base64,{image_base64}"
+                }
+            })
+        return res
+
+def remove_image_if_contain_base64(inputs):
+    if not contain_base64(inputs):
+        return inputs
+    else:
+        pattern = re.compile(r'<br/><br/><div align="center"><img[^><]+></div>')
+        inputs = re.sub(pattern, '', inputs)
+        return inputs
+
+def decode_chunk(chunk):
+    # 提前读取一些信息 （用于判断异常）
+    chunk_decoded = chunk.decode()
+    chunkjson = None
+    has_choices = False
+    choice_valid = False
+    has_content = False
+    has_role = False
+    try:
+        chunkjson = json.loads(chunk_decoded[6:])
+        has_choices = 'choices' in chunkjson
+        if has_choices: choice_valid = (len(chunkjson['choices']) > 0)
+        if has_choices and choice_valid: has_content = ("content" in chunkjson['choices'][0]["delta"])
+        if has_content: has_content = (chunkjson['choices'][0]["delta"]["content"] is not None)
+        if has_choices and choice_valid: has_role = "role" in chunkjson['choices'][0]["delta"]
+    except:
+        pass
+    return chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role
+
+from functools import lru_cache
+@lru_cache(maxsize=32)
+def verify_endpoint(endpoint):
+    """
+        检查endpoint是否可用
+    """
+    if "你亲手写的api名称" in endpoint:
+        raise ValueError("Endpoint不正确, 请检查AZURE_ENDPOINT的配置! 当前的Endpoint为:" + endpoint)
+    return endpoint
+
+def predict_no_ui_long_connection(inputs:str, llm_kwargs:dict, history:list=[], sys_prompt:str="", observe_window:list=None, console_slience:bool=False):
+    """
+    发送至chatGPT，等待回复，一次性完成，不显示中间过程。但内部用stream的方法避免中途网线被掐。
+    inputs：
+        是本次问询的输入
+    sys_prompt:
+        系统静默prompt
+    llm_kwargs：
+        chatGPT的内部调优参数
+    history：
+        是之前的对话列表
+    observe_window = None：
+        用于负责跨越线程传递已经输出的部分，大部分时候仅仅为了fancy的视觉效果，留空即可。observe_window[0]：观测窗。observe_window[1]：看门狗
+    """
+    from request_llms.bridge_all import model_info
+
+    watch_dog_patience = 5 # 看门狗的耐心, 设置5秒即可
+
+    if model_info[llm_kwargs['llm_model']].get('openai_disable_stream', False): stream = False
+    else: stream = True
+
+    headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt=sys_prompt, stream=stream)
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=False
+            endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=stream, timeout=TIMEOUT_SECONDS); break
+        except requests.exceptions.ReadTimeout as e:
+            retry += 1
+            traceback.print_exc()
+            if retry > MAX_RETRY: raise TimeoutError
+            if MAX_RETRY!=0: logger.error(f'请求超时，正在重试 ({retry}/{MAX_RETRY}) ……')
+
+    if not stream:
+        # 该分支仅适用于不支持stream的o1模型，其他情形一律不适用
+        chunkjson = json.loads(response.content.decode())
+        gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
+        return gpt_replying_buffer
+
+    stream_response = response.iter_lines()
+    result = ''
+    json_data = None
+    while True:
+        try: chunk = next(stream_response)
+        except StopIteration:
+            break
+        except requests.exceptions.ConnectionError:
+            chunk = next(stream_response) # 失败了，重试一次？再失败就没办法了。
+        chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
+        if len(chunk_decoded)==0: continue
+        if not chunk_decoded.startswith('data:'):
+            error_msg = get_full_error(chunk, stream_response).decode()
+            if "reduce the length" in error_msg:
+                raise ConnectionAbortedError("OpenAI拒绝了请求:" + error_msg)
+            elif """type":"upstream_error","param":"307""" in error_msg:
+                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。")
+            else:
+                raise RuntimeError("OpenAI拒绝了请求：" + error_msg)
+        if ('data: [DONE]' in chunk_decoded): break # api2d 正常完成
+        # 提前读取一些信息 （用于判断异常）
+        if (has_choices and not choice_valid) or ('OPENROUTER PROCESSING' in chunk_decoded):
+            # 一些垃圾第三方接口的出现这样的错误，openrouter的特殊处理
+            continue
+        json_data = chunkjson['choices'][0]
+        delta = json_data["delta"]
+        if len(delta) == 0: break
+        if (not has_content) and has_role: continue
+        if (not has_content) and (not has_role): continue # raise RuntimeError("发现不标准的第三方接口："+delta)
+        if has_content: # has_role = True/False
+            result += delta["content"]
+            if not console_slience: print(delta["content"], end='')
+            if observe_window is not None:
+                # 观测窗，把已经获取的数据显示出去
+                if len(observe_window) >= 1:
+                    observe_window[0] += delta["content"]
+                # 看门狗，如果超过期限没有喂狗，则终止
+                if len(observe_window) >= 2:
+                    if (time.time()-observe_window[1]) > watch_dog_patience:
+                        raise RuntimeError("用户取消了程序。")
+        else: raise RuntimeError("意外Json结构："+delta)
+    if json_data and json_data['finish_reason'] == 'content_filter':
+        raise RuntimeError("由于提问含不合规内容被Azure过滤。")
+    if json_data and json_data['finish_reason'] == 'length':
+        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
+
+
+def predict(inputs:str, llm_kwargs:dict, plugin_kwargs:dict, chatbot:ChatBotWithCookies,
+            history:list=[], system_prompt:str='', stream:bool=True, additional_fn:str=None):
+    """
+    发送至chatGPT，流式获取输出。
+    用于基础的对话功能。
+    inputs 是本次问询的输入
+    top_p, temperature是chatGPT的内部调优参数
+    history 是之前的对话列表（注意无论是inputs还是history，内容太长了都会触发token数量溢出的错误）
+    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
+    """
+    from request_llms.bridge_all import model_info
+    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
+
+    user_input = inputs
+    if additional_fn is not None:
+        from core_functional import handle_core_functionality
+        inputs, history = handle_core_functionality(additional_fn, inputs, history, chatbot)
+
+    # 多模态模型
+    has_multimodal_capacity = model_info[llm_kwargs['llm_model']].get('has_multimodal_capacity', False)
+    if has_multimodal_capacity:
+        has_recent_image_upload, image_paths = have_any_recent_upload_image_files(chatbot, pop=True)
+    else:
+        has_recent_image_upload, image_paths = False, []
+    if has_recent_image_upload:
+        _inputs, image_base64_array = make_multimodal_input(inputs, image_paths)
+    else:
+        _inputs, image_base64_array = inputs, []
+    chatbot.append((_inputs, ""))
+    yield from update_ui(chatbot=chatbot, history=history, msg="等待响应") # 刷新界面
+
+    # 禁用stream的特殊模型处理
+    if model_info[llm_kwargs['llm_model']].get('openai_disable_stream', False): stream = False
+    else: stream = True
+
+    # check mis-behavior
+    if is_the_upload_folder(user_input):
+        chatbot[-1] = (inputs, f"[Local Message] 检测到操作错误！当您上传文档之后，需点击“**函数插件区**”按钮进行处理，请勿点击“提交”按钮或者“基础功能区”按钮。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="正常") # 刷新界面
+        time.sleep(2)
+
+    try:
+        headers, payload = generate_payload(inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity, stream)
+    except RuntimeError as e:
+        chatbot[-1] = (inputs, f"您提供的api-key不满足要求，不包含任何可用于{llm_kwargs['llm_model']}的api-key。您可能选择了错误的模型或请求源。")
+        yield from update_ui(chatbot=chatbot, history=history, msg="api-key不满足要求") # 刷新界面
+        return
+
+    # 检查endpoint是否合法
+    try:
+        endpoint = verify_endpoint(model_info[llm_kwargs['llm_model']]['endpoint'])
+    except:
+        tb_str = '```\n' + trimmed_format_exc() + '```'
+        chatbot[-1] = (inputs, tb_str)
+        yield from update_ui(chatbot=chatbot, history=history, msg="Endpoint不满足要求") # 刷新界面
+        return
+
+    # 加入历史
+    if has_recent_image_upload:
+        history.extend([_inputs, ""])
+    else:
+        history.extend([inputs, ""])
+
+    retry = 0
+    while True:
+        try:
+            # make a POST request to the API endpoint, stream=True
+            response = requests.post(endpoint, headers=headers, proxies=proxies,
+                                    json=payload, stream=stream, timeout=TIMEOUT_SECONDS);break
+        except:
+            retry += 1
+            chatbot[-1] = ((chatbot[-1][0], timeout_bot_msg))
+            retry_msg = f"，正在重试 ({retry}/{MAX_RETRY}) ……" if MAX_RETRY > 0 else ""
+            yield from update_ui(chatbot=chatbot, history=history, msg="请求超时"+retry_msg) # 刷新界面
+            if retry > MAX_RETRY: raise TimeoutError
+
+
+    if not stream:
+        # 该分支仅适用于不支持stream的o1模型，其他情形一律不适用
+        yield from handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history)
+        return
+
+    if stream:
+        gpt_replying_buffer = ""
+        is_head_of_the_stream = True
+        stream_response =  response.iter_lines()
+        while True:
+            try:
+                chunk = next(stream_response)
+            except StopIteration:
+                # 非OpenAI官方接口的出现这样的报错，OpenAI和API2D不会走这里
+                chunk_decoded = chunk.decode()
+                error_msg = chunk_decoded
+                # 首先排除一个one-api没有done数据包的第三方Bug情形
+                if len(gpt_replying_buffer.strip()) > 0 and len(error_msg) == 0:
+                    yield from update_ui(chatbot=chatbot, history=history, msg="检测到有缺陷的非OpenAI官方接口，建议选择更稳定的接口。")
+                    break
+                # 其他情况，直接返回报错
+                chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                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
+
+            # 提前读取一些信息 （用于判断异常）
+            chunk_decoded, chunkjson, has_choices, choice_valid, has_content, has_role = decode_chunk(chunk)
+
+            if is_head_of_the_stream and (r'"object":"error"' not in chunk_decoded) and (r"content" 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:
+                    if (has_choices and not choice_valid) or ('OPENROUTER PROCESSING' in chunk_decoded):
+                        # 一些垃圾第三方接口的出现这样的错误, 或者OPENROUTER的特殊处理,因为OPENROUTER的数据流未连接到模型时会出现OPENROUTER PROCESSING
+                        continue
+                    if ('data: [DONE]' not in chunk_decoded) and len(chunk_decoded) > 0 and (chunkjson is None):
+                        # 传递进来一些奇怪的东西
+                        raise ValueError(f'无法读取以下数据，请检查配置。\n\n{chunk_decoded}')
+                    # 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(chunkjson['choices'][0]["delta"]) == 0):
+                        # 判定为数据流的结束，gpt_replying_buffer也写完了
+                        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+                        break
+                    # 处理数据流的主体
+                    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
+                    if has_content:
+                        # 正常情况
+                        gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
+                    elif has_role:
+                        # 一些第三方接口的出现这样的错误，兼容一下吧
+                        continue
+                    else:
+                        # 至此已经超出了正常接口应该进入的范围，一些垃圾第三方接口会出现这样的错误
+                        if chunkjson['choices'][0]["delta"]["content"] is None: continue # 一些垃圾第三方接口出现这样的错误，兼容一下吧
+                        gpt_replying_buffer = gpt_replying_buffer + chunkjson['choices'][0]["delta"]["content"]
+
+                    history[-1] = gpt_replying_buffer
+                    chatbot[-1] = (history[-2], history[-1])
+                    yield from update_ui(chatbot=chatbot, history=history, msg=status_text) # 刷新界面
+                except Exception as e:
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析不合常规") # 刷新界面
+                    chunk = get_full_error(chunk, stream_response)
+                    chunk_decoded = chunk.decode()
+                    error_msg = chunk_decoded
+                    chatbot, history = handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg)
+                    yield from update_ui(chatbot=chatbot, history=history, msg="Json解析异常" + error_msg) # 刷新界面
+                    logger.error(error_msg)
+                    return
+        return  # return from stream-branch
+
+def handle_o1_model_special(response, inputs, llm_kwargs, chatbot, history):
+    try:
+        chunkjson = json.loads(response.content.decode())
+        gpt_replying_buffer = chunkjson['choices'][0]["message"]["content"]
+        log_chat(llm_model=llm_kwargs["llm_model"], input_str=inputs, output_str=gpt_replying_buffer)
+        history[-1] = gpt_replying_buffer
+        chatbot[-1] = (history[-2], history[-1])
+        yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
+    except Exception as e:
+        yield from update_ui(chatbot=chatbot, history=history, msg="Json解析异常" + response.text) # 刷新界面
+
+def handle_error(inputs, llm_kwargs, chatbot, history, chunk_decoded, error_msg):
+    from request_llms.bridge_all import model_info
+    openai_website = ' 请登录OpenAI查看详情 https://platform.openai.com/signup'
+    if "reduce the length" in error_msg:
+        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.)")
+    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:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Incorrect API key. OpenAI以提供了不正确的API_KEY为由, 拒绝服务. " + openai_website)
+    elif "exceeded your current quota" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You exceeded your current quota. OpenAI以账户额度不足为由, 拒绝服务." + openai_website)
+    elif "account is not active" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Your account is not active. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+    elif "associated with a deactivated account" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] You are associated with a deactivated account. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+    elif "API key has been deactivated" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] API key has been deactivated. OpenAI以账户失效为由, 拒绝服务." + openai_website)
+    elif "bad forward key" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Bad forward key. API2D账户额度不足.")
+    elif "Not enough point" in error_msg:
+        chatbot[-1] = (chatbot[-1][0], "[Local Message] Not enough point. API2D账户点数不足.")
+    else:
+        from toolbox import regular_txt_to_markdown
+        tb_str = '```\n' + trimmed_format_exc() + '```'
+        chatbot[-1] = (chatbot[-1][0], f"[Local Message] 异常 \n\n{tb_str} \n\n{regular_txt_to_markdown(chunk_decoded)}")
+    return chatbot, history
+
+def generate_payload(inputs:str, llm_kwargs:dict, history:list, system_prompt:str, image_base64_array:list=[], has_multimodal_capacity:bool=False, stream:bool=True):
+    """
+    整合所有信息，选择LLM模型，生成http请求，为发送请求做准备
+    """
+    from request_llms.bridge_all import model_info
+
+    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。")
+
+    if llm_kwargs['llm_model'].startswith('vllm-'):
+        api_key = 'no-api-key'
+    else:
+        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})
+        if llm_kwargs['llm_model'] in AZURE_CFG_ARRAY.keys():
+            azure_api_key_unshared = AZURE_CFG_ARRAY[llm_kwargs['llm_model']]["AZURE_API_KEY"]
+            headers.update({"api-key": azure_api_key_unshared})
+
+    if has_multimodal_capacity:
+        # 当以下条件满足时，启用多模态能力：
+        # 1. 模型本身是多模态模型（has_multimodal_capacity）
+        # 2. 输入包含图像（len(image_base64_array) > 0）
+        # 3. 历史输入包含图像（ any([contain_base64(h) for h in history]) ）
+        enable_multimodal_capacity = (len(image_base64_array) > 0) or any([contain_base64(h) for h in history])
+    else:
+        enable_multimodal_capacity = False
+
+    conversation_cnt = len(history) // 2
+    openai_disable_system_prompt = model_info[llm_kwargs['llm_model']].get('openai_disable_system_prompt', False)
+
+    if openai_disable_system_prompt:
+        messages = [{"role": "user", "content": system_prompt}]
+    else:
+        messages = [{"role": "system", "content": system_prompt}]
+
+    if not enable_multimodal_capacity:
+        # 不使用多模态能力
+        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"] = remove_image_if_contain_base64(history[index])
+                what_gpt_answer = {}
+                what_gpt_answer["role"] = "assistant"
+                what_gpt_answer["content"] = remove_image_if_contain_base64(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)
+    else:
+        # 多模态能力
+        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"] = append_image_if_contain_base64(history[index])
+                what_gpt_answer = {}
+                what_gpt_answer["role"] = "assistant"
+                what_gpt_answer["content"] = append_image_if_contain_base64(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"] = []
+        what_i_ask_now["content"].append({
+            "type": "text",
+            "text": inputs
+        })
+        for image_base64 in image_base64_array:
+            what_i_ask_now["content"].append({
+                "type": "image_url",
+                "image_url": {
+                    "url": f"data:image/jpeg;base64,{image_base64}"
+                }
+            })
+        messages.append(what_i_ask_now)
+
+
+    model = llm_kwargs['llm_model']
+    if llm_kwargs['llm_model'].startswith('api2d-'):
+        model = llm_kwargs['llm_model'][len('api2d-'):]
+    if llm_kwargs['llm_model'].startswith('one-api-'):
+        model = llm_kwargs['llm_model'][len('one-api-'):]
+        model, _ = read_one_api_model_name(model)
+    if llm_kwargs['llm_model'].startswith('vllm-'):
+        model = llm_kwargs['llm_model'][len('vllm-'):]
+        model, _ = read_one_api_model_name(model)
+    if llm_kwargs['llm_model'].startswith('openrouter-'):
+        model = llm_kwargs['llm_model'][len('openrouter-'):]
+        model= read_one_api_model_name(model)
+    if model == "gpt-3.5-random": # 随机选择, 绕过openai访问频率限制
+        model = random.choice([
+            "gpt-3.5-turbo",
+            "gpt-3.5-turbo-16k",
+            "gpt-3.5-turbo-1106",
+            "gpt-3.5-turbo-0613",
+            "gpt-3.5-turbo-16k-0613",
+            "gpt-3.5-turbo-0301",
+        ])
+
+    payload = {
+        "model": model,
+        "messages": messages,
+        "temperature": llm_kwargs['temperature'],  # 1.0,
+        "top_p": llm_kwargs['top_p'],  # 1.0,
+        "n": 1,
+        "stream": stream,
+    }
+
+    return headers,payload
+
+

request_llms/bridge_stackclaude.py

@@ -1,12 +1,13 @@
+import time
+import asyncio
+import threading
+import importlib
+
 from .bridge_newbingfree import preprocess_newbing_out, preprocess_newbing_out_simple
 from multiprocessing import Process, Pipe
 from toolbox import update_ui, get_conf, trimmed_format_exc
-import threading
-import importlib
-import logging
-import time
+from loguru import logger as logging
 from toolbox import get_conf
-import asyncio
 
 load_message = "正在加载Claude组件，请稍候..."
 

request_llms/bridge_tgui.py

@@ -8,7 +8,6 @@ import json
 import random
 import string
 import websockets
-import logging
 import time
 import threading
 import importlib

request_llms/com_google.py

@@ -7,7 +7,7 @@ import os
 import re
 import requests
 from typing import List, Dict, Tuple
-from toolbox import get_conf, encode_image, get_pictures_list, to_markdown_tabs
+from toolbox import get_conf, update_ui, encode_image, get_pictures_list, to_markdown_tabs
 
 proxies, TIMEOUT_SECONDS = get_conf("proxies", "TIMEOUT_SECONDS")
 
@@ -112,6 +112,14 @@ def html_local_img(__file, layout="left", max_width=None, max_height=None, md=Tr
     return a
 
 
+def reverse_base64_from_input(inputs):
+    pattern = re.compile(r'<br/><br/><div align="center"><img[^<>]+base64="([^"]+)"></div>')
+    base64_strings = pattern.findall(inputs)
+    return base64_strings
+
+def contain_base64(inputs):
+    base64_strings = reverse_base64_from_input(inputs)
+    return len(base64_strings) > 0
 
 class GoogleChatInit:
     def __init__(self, llm_kwargs):
@@ -119,9 +127,9 @@ class GoogleChatInit:
         endpoint = model_info[llm_kwargs['llm_model']]['endpoint']
         self.url_gemini = endpoint + "/%m:streamGenerateContent?key=%k"
 
-    def generate_chat(self, inputs, llm_kwargs, history, system_prompt):
+    def generate_chat(self, inputs, llm_kwargs, history, system_prompt, image_base64_array:list=[], has_multimodal_capacity:bool=False):
         headers, payload = self.generate_message_payload(
-            inputs, llm_kwargs, history, system_prompt
+            inputs, llm_kwargs, history, system_prompt, image_base64_array, has_multimodal_capacity
         )
         response = requests.post(
             url=self.url_gemini,
@@ -133,13 +141,16 @@ class GoogleChatInit:
         )
         return response.iter_lines()
 
-    def __conversation_user(self, user_input, llm_kwargs):
+    def __conversation_user(self, user_input, llm_kwargs, enable_multimodal_capacity):
         what_i_have_asked = {"role": "user", "parts": []}
-        if "vision" not in self.url_gemini:
+        from .bridge_all import model_info
+            
+        if enable_multimodal_capacity:
+            input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
+        else:
             input_ = user_input
             encode_img = []
-        else:
-            input_, encode_img = input_encode_handler(user_input, llm_kwargs=llm_kwargs)
+
         what_i_have_asked["parts"].append({"text": input_})
         if encode_img:
             for data in encode_img:
@@ -153,12 +164,12 @@ class GoogleChatInit:
                 )
         return what_i_have_asked
 
-    def __conversation_history(self, history, llm_kwargs):
+    def __conversation_history(self, history, llm_kwargs, enable_multimodal_capacity):
         messages = []
         conversation_cnt = len(history) // 2
         if conversation_cnt:
             for index in range(0, 2 * conversation_cnt, 2):
-                what_i_have_asked = self.__conversation_user(history[index], llm_kwargs)
+                what_i_have_asked = self.__conversation_user(history[index], llm_kwargs, enable_multimodal_capacity)
                 what_gpt_answer = {
                     "role": "model",
                     "parts": [{"text": history[index + 1]}],
@@ -168,7 +179,7 @@ class GoogleChatInit:
         return messages
 
     def generate_message_payload(
-        self, inputs, llm_kwargs, history, system_prompt
+        self, inputs, llm_kwargs, history, system_prompt, image_base64_array:list=[], has_multimodal_capacity:bool=False
     ) -> Tuple[Dict, Dict]:
         messages = [
             # {"role": "system", "parts": [{"text": system_prompt}]},  # gemini 不允许对话轮次为偶数，所以这个没有用，看后续支持吧。。。
@@ -179,25 +190,31 @@ class GoogleChatInit:
             "%m", llm_kwargs["llm_model"]
         ).replace("%k", get_conf("GEMINI_API_KEY"))
         header = {"Content-Type": "application/json"}
-        if "vision" not in self.url_gemini:  # 不是vision 才处理history
+
+        if has_multimodal_capacity:
+            enable_multimodal_capacity = (len(image_base64_array) > 0) or any([contain_base64(h) for h in history])
+        else:
+            enable_multimodal_capacity = False
+        
+        if not enable_multimodal_capacity:
             messages.extend(
-                self.__conversation_history(history, llm_kwargs)
+                self.__conversation_history(history, llm_kwargs, enable_multimodal_capacity)
             )  # 处理 history
-        messages.append(self.__conversation_user(inputs, llm_kwargs))  # 处理用户对话
+            
+        messages.append(self.__conversation_user(inputs, llm_kwargs, enable_multimodal_capacity))  # 处理用户对话
         payload = {
             "contents": messages,
             "generationConfig": {
-                # "maxOutputTokens": 800,
+                # "maxOutputTokens": llm_kwargs.get("max_token", 1024),
                 "stopSequences": str(llm_kwargs.get("stop", "")).split(" "),
                 "temperature": llm_kwargs.get("temperature", 1),
                 "topP": llm_kwargs.get("top_p", 0.8),
                 "topK": 10,
             },
         }
+
         return header, payload
 
 
 if __name__ == "__main__":
     google = GoogleChatInit()
-    # print(gootle.generate_message_payload('你好呀', {},  ['123123', '3123123'], ''))
-    # gootle.input_encode_handle('123123[123123](./123123), ![53425](./asfafa/fff.jpg)')

request_llms/com_qwenapi.py

@@ -1,7 +1,6 @@
 from http import HTTPStatus
 from toolbox import get_conf
 import threading
-import logging
 
 timeout_bot_msg = '[Local Message] Request timeout. Network error.'
 

request_llms/com_skylark2api.py

@@ -1,7 +1,7 @@
-from toolbox import get_conf
-import threading
-import logging
 import os
+import threading
+from toolbox import get_conf
+from loguru import logger as logging
 
 timeout_bot_msg = '[Local Message] Request timeout. Network error.'
 #os.environ['VOLC_ACCESSKEY'] = ''

request_llms/com_sparkapi.py

@@ -1,17 +1,18 @@
-from toolbox import get_conf, get_pictures_list, encode_image
 import base64
 import datetime
 import hashlib
 import hmac
 import json
-from urllib.parse import urlparse
 import ssl
+import websocket
+import threading
+from toolbox import get_conf, get_pictures_list, encode_image
+from loguru import logger
+from urllib.parse import urlparse
 from datetime import datetime
 from time import mktime
 from urllib.parse import urlencode
 from wsgiref.handlers import format_date_time
-import websocket
-import threading, time
 
 timeout_bot_msg = '[Local Message] Request timeout. Network error.'
 
@@ -104,7 +105,7 @@ class SparkRequestInstance():
             if llm_kwargs['most_recent_uploaded'].get('path'):
                 file_manifest = get_pictures_list(llm_kwargs['most_recent_uploaded']['path'])
                 if len(file_manifest) > 0:
-                    print('正在使用讯飞图片理解API')
+                    logger.info('正在使用讯飞图片理解API')
                     gpt_url = self.gpt_url_img
         wsParam = Ws_Param(self.appid, self.api_key, self.api_secret, gpt_url)
         websocket.enableTrace(False)
@@ -123,7 +124,7 @@ class SparkRequestInstance():
             data = json.loads(message)
             code = data['header']['code']
             if code != 0:
-                print(f'请求错误: {code}, {data}')
+                logger.error(f'请求错误: {code}, {data}')
                 self.result_buf += str(data)
                 ws.close()
                 self.time_to_exit_event.set()
@@ -140,7 +141,7 @@ class SparkRequestInstance():
 
         # 收到websocket错误的处理
         def on_error(ws, error):
-            print("error:", error)
+            logger.error("error:", error)
             self.time_to_exit_event.set()
 
         # 收到websocket关闭的处理

request_llms/com_taichu.py

@@ -4,7 +4,7 @@
 # @Descr   : 兼容最新的智谱Ai
 from toolbox import get_conf
 from toolbox import get_conf, encode_image, get_pictures_list
-import logging, os, requests
+import requests
 import json
 class TaichuChatInit:
     def __init__(self): ...

request_llms/com_zhipuglm.py

@@ -5,7 +5,8 @@
 from toolbox import get_conf
 from zhipuai import ZhipuAI
 from toolbox import get_conf, encode_image, get_pictures_list
-import logging, os
+from loguru import logger
+import os
 
 
 def input_encode_handler(inputs:str, llm_kwargs:dict):
@@ -24,7 +25,7 @@ class ZhipuChatInit:
     def __init__(self):
         ZHIPUAI_API_KEY, ZHIPUAI_MODEL = get_conf("ZHIPUAI_API_KEY", "ZHIPUAI_MODEL")
         if len(ZHIPUAI_MODEL) > 0:
-            logging.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
+            logger.error('ZHIPUAI_MODEL 配置项选项已经弃用，请在LLM_MODEL中配置')
         self.zhipu_bro = ZhipuAI(api_key=ZHIPUAI_API_KEY)
         self.model = ''
 
@@ -37,8 +38,7 @@ class ZhipuChatInit:
             what_i_have_asked['content'].append({"type": 'text', "text": user_input})
             if encode_img:
                 if len(encode_img) > 1:
-                    logging.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
-                    print("glm-4v只支持一张图片,将只取第一张图片进行处理")
+                    logger.warning("glm-4v只支持一张图片,将只取第一张图片进行处理")
                 img_d = {"type": "image_url",
                             "image_url": {
                                 "url": encode_img[0]['data']

request_llms/embed_models/bridge_all_embed.py

@@ -0,0 +1,40 @@
+import tiktoken, copy, re
+from functools import lru_cache
+from concurrent.futures import ThreadPoolExecutor
+from toolbox import get_conf, trimmed_format_exc, apply_gpt_academic_string_mask, read_one_api_model_name
+
+# Endpoint 重定向
+API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "AZURE_ENDPOINT", "AZURE_ENGINE")
+openai_endpoint = "https://api.openai.com/v1/chat/completions"
+if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/'
+azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15'
+
+
+if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint]
+
+openai_embed_endpoint = openai_endpoint.replace("chat/completions", "embeddings")
+
+from .openai_embed import OpenAiEmbeddingModel
+
+embed_model_info = {
+    # text-embedding-3-small    Increased performance over 2nd generation ada embedding model  |  1,536
+    "text-embedding-3-small": {
+        "embed_class": OpenAiEmbeddingModel,
+        "embed_endpoint": openai_embed_endpoint,
+        "embed_dimension": 1536,
+    },
+
+    # text-embedding-3-large    Most capable embedding model for both english and non-english tasks  |   3,072
+    "text-embedding-3-large": {
+        "embed_class": OpenAiEmbeddingModel,
+        "embed_endpoint": openai_embed_endpoint,
+        "embed_dimension": 3072,
+    },
+
+    # text-embedding-ada-002    Most capable 2nd generation embedding model, replacing 16 first generation models   |  1,536
+    "text-embedding-ada-002": {
+        "embed_class": OpenAiEmbeddingModel,
+        "embed_endpoint": openai_embed_endpoint,
+        "embed_dimension": 1536,
+    },
+}

request_llms/embed_models/openai_embed.py

@@ -0,0 +1,85 @@
+from llama_index.embeddings.openai import OpenAIEmbedding
+from openai import OpenAI
+from toolbox import get_conf
+from toolbox import CatchException, update_ui, get_conf, select_api_key, get_log_folder, ProxyNetworkActivate
+from shared_utils.key_pattern_manager import select_api_key_for_embed_models
+from typing import List, Any
+
+import numpy as np
+
+def mean_agg(embeddings):
+    """Mean aggregation for embeddings."""
+    return np.array(embeddings).mean(axis=0).tolist()
+
+class EmbeddingModel():
+
+    def get_agg_embedding_from_queries(
+        self,
+        queries: List[str],
+        agg_fn = None,
+    ):
+        """Get aggregated embedding from multiple queries."""
+        query_embeddings = [self.get_query_embedding(query) for query in queries]
+        agg_fn = agg_fn or mean_agg
+        return agg_fn(query_embeddings)
+
+    def get_text_embedding_batch(
+            self,
+            texts: List[str],
+            show_progress: bool = False,
+        ):
+            return self.compute_embedding(texts, batch_mode=True)
+
+
+class OpenAiEmbeddingModel(EmbeddingModel):
+
+    def __init__(self, llm_kwargs:dict=None):
+        self.llm_kwargs = llm_kwargs
+
+    def get_query_embedding(self, query: str):
+        return self.compute_embedding(query)
+
+    def compute_embedding(self, text="这是要计算嵌入的文本", llm_kwargs:dict=None, batch_mode=False):
+        from .bridge_all_embed import embed_model_info
+
+        # load kwargs
+        if llm_kwargs is None:
+            llm_kwargs = self.llm_kwargs
+        if llm_kwargs is None:
+            raise RuntimeError("llm_kwargs is not provided!")
+
+        # setup api and req url
+        api_key = select_api_key_for_embed_models(llm_kwargs['api_key'], llm_kwargs['embed_model'])
+        embed_model = llm_kwargs['embed_model']
+        base_url = embed_model_info[llm_kwargs['embed_model']]['embed_endpoint'].replace('embeddings', '')
+
+        # send and compute
+        with ProxyNetworkActivate("Connect_OpenAI_Embedding"):
+            self.oai_client = OpenAI(api_key=api_key, base_url=base_url)
+            if batch_mode:
+                input = text
+                assert isinstance(text, list)
+            else:
+                input = [text]
+                assert isinstance(text, str)
+            res = self.oai_client.embeddings.create(input=input, model=embed_model)
+
+        # parse result
+        if batch_mode:
+            embedding = [d.embedding for d in res.data]
+        else:
+            embedding = res.data[0].embedding
+        return embedding
+
+    def embedding_dimension(self, llm_kwargs=None):
+        # load kwargs
+        if llm_kwargs is None:
+            llm_kwargs = self.llm_kwargs
+        if llm_kwargs is None:
+            raise RuntimeError("llm_kwargs is not provided!")
+
+        from .bridge_all_embed import embed_model_info
+        return embed_model_info[llm_kwargs['embed_model']]['embed_dimension']
+
+if __name__ == "__main__":
+    pass

request_llms/local_llm_class.py

@@ -5,6 +5,7 @@ from toolbox import ChatBotWithCookies
 from multiprocessing import Process, Pipe
 from contextlib import redirect_stdout
 from request_llms.queued_pipe import create_queue_pipe
+from loguru import logger
 
 class ThreadLock(object):
     def __init__(self):
@@ -51,7 +52,7 @@ def reset_tqdm_output():
             getattr(sys.stdout, 'flush', lambda: None)()
 
         def fp_write(s):
-            print(s)
+            logger.info(s)
         last_len = [0]
 
         def print_status(s):
@@ -199,7 +200,7 @@ class LocalLLMHandle(Process):
                 if res.startswith(self.std_tag):
                     new_output = res[len(self.std_tag):]
                     std_out = std_out[:std_out_clip_len]
-                    print(new_output, end='')
+                    logger.info(new_output, end='')
                     std_out = new_output + std_out
                     yield self.std_tag + '\n```\n' + std_out + '\n```\n'
                 elif res == '[Finish]':

request_llms/oai_std_model_template.py

@@ -1,8 +1,8 @@
 import json
 import time
-import logging
 import traceback
 import requests
+from loguru import logger
 
 # config_private.py放自己的秘密如API和代理网址
 # 读取时首先看是否存在私密的config_private配置文件（不受git管控），如果有，则覆盖原config文件
@@ -106,10 +106,7 @@ def generate_message(input, model, key, history, max_output_token, system_prompt
         "stream": True,
         "max_tokens": max_output_token,
     }
-    try:
-        print(f" {model} : {conversation_cnt} : {input[:100]} ..........")
-    except:
-        print("输入中可能存在乱码。")
+
     return headers, playload
 
 
@@ -196,7 +193,7 @@ def get_predict_function(
                 if retry > MAX_RETRY:
                     raise TimeoutError
                 if MAX_RETRY != 0:
-                    print(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
+                    logger.error(f"请求超时，正在重试 ({retry}/{MAX_RETRY}) ……")
 
         stream_response = response.iter_lines()
         result = ""
@@ -219,18 +216,17 @@ def get_predict_function(
             ):
                 chunk = get_full_error(chunk, stream_response)
                 chunk_decoded = chunk.decode()
-                print(chunk_decoded)
+                logger.error(chunk_decoded)
                 raise RuntimeError(
                     f"API异常,请检测终端输出。可能的原因是:{finish_reason}"
                 )
             if chunk:
                 try:
                     if finish_reason == "stop":
-                        logging.info(f"[response] {result}")
+                        if not console_slience:
+                            print(f"[response] {result}")
                         break
                     result += response_text
-                    if not console_slience:
-                        print(response_text, end="")
                     if observe_window is not None:
                         # 观测窗，把已经获取的数据显示出去
                         if len(observe_window) >= 1:
@@ -243,7 +239,7 @@ def get_predict_function(
                     chunk = get_full_error(chunk, stream_response)
                     chunk_decoded = chunk.decode()
                     error_msg = chunk_decoded
-                    print(error_msg)
+                    logger.error(error_msg)
                     raise RuntimeError("Json解析不合常规")
         return result
 
@@ -276,7 +272,7 @@ def get_predict_function(
             inputs, history = handle_core_functionality(
                 additional_fn, inputs, history, chatbot
             )
-        logging.info(f"[raw_input] {inputs}")
+        logger.info(f"[raw_input] {inputs}")
         chatbot.append((inputs, ""))
         yield from update_ui(
             chatbot=chatbot, history=history, msg="等待响应"
@@ -376,11 +372,11 @@ def get_predict_function(
                             history=history,
                             msg="API异常:" + chunk_decoded,
                         )  # 刷新界面
-                        print(chunk_decoded)
+                        logger.error(chunk_decoded)
                         return
 
                     if finish_reason == "stop":
-                        logging.info(f"[response] {gpt_replying_buffer}")
+                        logger.info(f"[response] {gpt_replying_buffer}")
                         break
                     status_text = f"finish_reason: {finish_reason}"
                     gpt_replying_buffer += response_text
@@ -403,7 +399,7 @@ def get_predict_function(
                     yield from update_ui(
                         chatbot=chatbot, history=history, msg="Json异常" + chunk_decoded
                     )  # 刷新界面
-                    print(chunk_decoded)
+                    logger.error(chunk_decoded)
                     return
 
     return predict_no_ui_long_connection, predict

requirements.txt

@@ -2,13 +2,15 @@ https://public.agent-matrix.com/publish/gradio-3.32.10-py3-none-any.whl
 fastapi==0.110
 gradio-client==0.8
 pypdf2==2.12.1
+httpx<=0.25.2
 zhipuai==2.0.1
 tiktoken>=0.3.3
 requests[socks]
-pydantic==2.5.2
-protobuf==3.18
-transformers>=4.27.1
+pydantic==2.9.2
+protobuf==3.20
+transformers>=4.27.1,<4.42
 scipdf_parser>=0.52
+spacy==3.7.4
 anthropic>=0.18.1
 python-markdown-math
 pymdown-extensions
@@ -27,6 +29,18 @@ edge-tts
 pymupdf
 openai
 rjsmin
+loguru
 arxiv
 numpy
 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

shared_utils/advanced_markdown_format.py

@@ -2,6 +2,8 @@ import markdown
 import re
 import os
 import math
+
+from loguru import logger
 from textwrap import dedent
 from functools import lru_cache
 from pymdownx.superfences import fence_code_format
@@ -227,14 +229,14 @@ def fix_dollar_sticking_bug(txt):
 
             if single_stack_height > 0:
                 if txt[:(index+1)].find('\n') > 0 or txt[:(index+1)].find('<td>') > 0 or txt[:(index+1)].find('</td>') > 0:
-                    print('公式之中出现了异常 (Unexpect element in equation)')
+                    logger.error('公式之中出现了异常 (Unexpect element in equation)')
                     single_stack_height = 0
                     txt_result += ' $'
                     continue
 
             if double_stack_height > 0:
                 if txt[:(index+1)].find('\n\n') > 0:
-                    print('公式之中出现了异常 (Unexpect element in equation)')
+                    logger.error('公式之中出现了异常 (Unexpect element in equation)')
                     double_stack_height = 0
                     txt_result += '$$'
                     continue
@@ -253,13 +255,13 @@ def fix_dollar_sticking_bug(txt):
                 txt = txt[(index+2):]
             else:
                 if double_stack_height != 0:
-                    # print(txt[:(index)])
-                    print('发现异常嵌套公式')
+                    # logger.info(txt[:(index)])
+                    logger.info('发现异常嵌套公式')
                 if single_stack_height == 0:
                     single_stack_height = 1
                 else:
                     single_stack_height = 0
-                    # print(txt[:(index)])
+                    # logger.info(txt[:(index)])
                 txt_result += txt[:(index+1)]
                 txt = txt[(index+1):]
             break

shared_utils/colorful.py

@@ -1,5 +1,6 @@
 import platform
 from sys import stdout
+from loguru import logger
 
 if platform.system()=="Linux":
     pass
@@ -59,3 +60,29 @@ def sprint亮紫(*kw):
     return "\033[1;35m"+' '.join(kw)+"\033[0m"
 def sprint亮靛(*kw):
     return "\033[1;36m"+' '.join(kw)+"\033[0m"
+
+def log红(*kw,**kargs):
+    logger.opt(depth=1).info(sprint红(*kw))
+def log绿(*kw,**kargs):
+    logger.opt(depth=1).info(sprint绿(*kw))
+def log黄(*kw,**kargs):
+    logger.opt(depth=1).info(sprint黄(*kw))
+def log蓝(*kw,**kargs):
+    logger.opt(depth=1).info(sprint蓝(*kw))
+def log紫(*kw,**kargs):
+    logger.opt(depth=1).info(sprint紫(*kw))
+def log靛(*kw,**kargs):
+    logger.opt(depth=1).info(sprint靛(*kw))
+
+def log亮红(*kw,**kargs):
+    logger.opt(depth=1).info(sprint亮红(*kw))
+def log亮绿(*kw,**kargs):
+    logger.opt(depth=1).info(sprint亮绿(*kw))
+def log亮黄(*kw,**kargs):
+    logger.opt(depth=1).info(sprint亮黄(*kw))
+def log亮蓝(*kw,**kargs):
+    logger.opt(depth=1).info(sprint亮蓝(*kw))
+def log亮紫(*kw,**kargs):
+    logger.opt(depth=1).info(sprint亮紫(*kw))
+def log亮靛(*kw,**kargs):
+    logger.opt(depth=1).info(sprint亮靛(*kw))

shared_utils/config_loader.py

@@ -2,7 +2,7 @@ import importlib
 import time
 import os
 from functools import lru_cache
-from shared_utils.colorful import print亮红, print亮绿, print亮蓝
+from shared_utils.colorful import log亮红, log亮绿, log亮蓝
 
 pj = os.path.join
 default_user_name = 'default_user'
@@ -30,13 +30,13 @@ def read_env_variable(arg, default_value):
         env_arg = os.environ[arg]
     else:
         raise KeyError
-    print(f"[ENV_VAR] 尝试加载{arg}，默认值：{default_value} --> 修正值：{env_arg}")
+    log亮绿(f"[ENV_VAR] 尝试加载{arg}，默认值：{default_value} --> 修正值：{env_arg}")
     try:
         if isinstance(default_value, bool):
             env_arg = env_arg.strip()
             if env_arg == 'True': r = True
             elif env_arg == 'False': r = False
-            else: print('Enter True or False, but have:', env_arg); r = default_value
+            else: log亮红('Expect `True` or `False`, but have:', env_arg); r = default_value
         elif isinstance(default_value, int):
             r = int(env_arg)
         elif isinstance(default_value, float):
@@ -51,13 +51,13 @@ def read_env_variable(arg, default_value):
             assert arg == "proxies"
             r = eval(env_arg)
         else:
-            print亮红(f"[ENV_VAR] 环境变量{arg}不支持通过环境变量设置! ")
+            log亮红(f"[ENV_VAR] 环境变量{arg}不支持通过环境变量设置! ")
             raise KeyError
     except:
-        print亮红(f"[ENV_VAR] 环境变量{arg}加载失败! ")
+        log亮红(f"[ENV_VAR] 环境变量{arg}加载失败! ")
         raise KeyError(f"[ENV_VAR] 环境变量{arg}加载失败! ")
 
-    print亮绿(f"[ENV_VAR] 成功读取环境变量{arg}")
+    log亮绿(f"[ENV_VAR] 成功读取环境变量{arg}")
     return r
 
 
@@ -80,21 +80,21 @@ def read_single_conf_with_lru_cache(arg):
     if arg == 'API_URL_REDIRECT':
         oai_rd = r.get("https://api.openai.com/v1/chat/completions", None) # API_URL_REDIRECT填写格式是错误的，请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`
         if oai_rd and not oai_rd.endswith('/completions'):
-            print亮红("\n\n[API_URL_REDIRECT] API_URL_REDIRECT填错了。请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`。如果您确信自己没填错，无视此消息即可。")
+            log亮红("\n\n[API_URL_REDIRECT] API_URL_REDIRECT填错了。请阅读`https://github.com/binary-husky/gpt_academic/wiki/项目配置说明`。如果您确信自己没填错，无视此消息即可。")
             time.sleep(5)
     if arg == 'API_KEY':
-        print亮蓝(f"[API_KEY] 本项目现已支持OpenAI和Azure的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,azure-key3\"")
-        print亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
+        log亮蓝(f"[API_KEY] 本项目现已支持OpenAI和Azure的api-key。也支持同时填写多个api-key，如API_KEY=\"openai-key1,openai-key2,azure-key3\"")
+        log亮蓝(f"[API_KEY] 您既可以在config.py中修改api-key(s)，也可以在问题输入区输入临时的api-key(s)，然后回车键提交后即可生效。")
         if is_any_api_key(r):
-            print亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
+            log亮绿(f"[API_KEY] 您的 API_KEY 是: {r[:15]}*** API_KEY 导入成功")
         else:
-            print亮红("[API_KEY] 您的 API_KEY 不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行。")
+            log亮红(f"[API_KEY] 您的 API_KEY（{r[:15]}***）不满足任何一种已知的密钥格式，请在config文件中修改API密钥之后再运行（详见`https://github.com/binary-husky/gpt_academic/wiki/api_key`）。")
     if arg == 'proxies':
         if not read_single_conf_with_lru_cache('USE_PROXY'): r = None # 检查USE_PROXY，防止proxies单独起作用
         if r is None:
-            print亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
+            log亮红('[PROXY] 网络代理状态：未配置。无代理状态下很可能无法访问OpenAI家族的模型。建议：检查USE_PROXY选项是否修改。')
         else:
-            print亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', r)
+            log亮绿('[PROXY] 网络代理状态：已配置。配置信息如下：', str(r))
             assert isinstance(r, dict), 'proxies格式错误，请注意proxies选项的格式，不要遗漏括号。'
     return r
 

shared_utils/cookie_manager.py

@@ -90,23 +90,6 @@ def make_history_cache():
 
 
 
-# """
-# with gr.Row():
-#     txt = gr.Textbox(show_label=False, placeholder="Input question here.", elem_id='user_input_main').style(container=False)
-#     txtx = gr.Textbox(show_label=False, placeholder="Input question here.", elem_id='user_input_main').style(container=False)
-# with gr.Row():
-#     btn_value = "Test"
-#     elem_id = "TestCase"
-#     variant = "primary"
-#     input_list = [txt, txtx]
-#     output_list = [txt, txtx]
-#     input_name_list = ["txt(input)", "txtx(input)"]
-#     output_name_list = ["txt", "txtx"]
-#     js_callback = """(txt, txtx)=>{console.log(txt); console.log(txtx);}"""
-#     def function(txt, txtx):
-#         return "booo", "goooo"
-#     create_button_with_javascript_callback(btn_value, elem_id, variant, js_callback, input_list, output_list, function, input_name_list, output_name_list)
-# """
 def create_button_with_javascript_callback(btn_value, elem_id, variant, js_callback, input_list, output_list, function, input_name_list, output_name_list):
     import gradio as gr
     middle_ware_component = gr.Textbox(visible=False, elem_id=elem_id+'_buffer')

shared_utils/handle_upload.py

@@ -8,6 +8,7 @@ import gradio
 import shutil
 import glob
 from shared_utils.config_loader import get_conf
+from loguru import logger
 
 def html_local_file(file):
     base_path = os.path.dirname(__file__)  # 项目目录
@@ -100,7 +101,7 @@ def extract_archive(file_path, dest_dir):
         with zipfile.ZipFile(file_path, "r") as zipobj:
             zipobj._extract_member = lambda a,b,c: zip_extract_member_new(zipobj, a,b,c)    # 修复中文乱码的问题
             zipobj.extractall(path=dest_dir)
-            print("Successfully extracted zip archive to {}".format(dest_dir))
+            logger.info("Successfully extracted zip archive to {}".format(dest_dir))
 
     elif file_extension in [".tar", ".gz", ".bz2"]:
         with tarfile.open(file_path, "r:*") as tarobj:
@@ -113,7 +114,7 @@ def extract_archive(file_path, dest_dir):
                     raise Exception(f"Attempted Path Traversal in {member.name}")
 
             tarobj.extractall(path=dest_dir)
-            print("Successfully extracted tar archive to {}".format(dest_dir))
+            logger.info("Successfully extracted tar archive to {}".format(dest_dir))
 
     # 第三方库，需要预先pip install rarfile
     # 此外，Windows上还需要安装winrar软件，配置其Path环境变量，如"C:\Program Files\WinRAR"才可以
@@ -123,9 +124,9 @@ def extract_archive(file_path, dest_dir):
 
             with rarfile.RarFile(file_path) as rf:
                 rf.extractall(path=dest_dir)
-                print("Successfully extracted rar archive to {}".format(dest_dir))
+                logger.info("Successfully extracted rar archive to {}".format(dest_dir))
         except:
-            print("Rar format requires additional dependencies to install")
+            logger.info("Rar format requires additional dependencies to install")
             return "\n\n解压失败! 需要安装pip install rarfile来解压rar文件。建议：使用zip压缩格式。"
 
     # 第三方库，需要预先pip install py7zr
@@ -135,9 +136,9 @@ def extract_archive(file_path, dest_dir):
 
             with py7zr.SevenZipFile(file_path, mode="r") as f:
                 f.extractall(path=dest_dir)
-                print("Successfully extracted 7z archive to {}".format(dest_dir))
+                logger.info("Successfully extracted 7z archive to {}".format(dest_dir))
         except:
-            print("7z format requires additional dependencies to install")
+            logger.info("7z format requires additional dependencies to install")
             return "\n\n解压失败! 需要安装pip install py7zr来解压7z文件"
     else:
         return ""

shared_utils/key_pattern_manager.py

@@ -8,13 +8,20 @@ from shared_utils.config_loader import get_conf as get_conf
 pj = os.path.join
 default_user_name = 'default_user'
 
-
+# match openai keys
+openai_regex = re.compile(
+    r"sk-[a-zA-Z0-9_-]{48}$|" +
+    r"sk-[a-zA-Z0-9_-]{92}$|" +
+    r"sk-proj-[a-zA-Z0-9_-]{48}$|"+
+    r"sk-proj-[a-zA-Z0-9_-]{124}$|"+
+    r"sess-[a-zA-Z0-9]{40}$"
+)
 def is_openai_api_key(key):
     CUSTOM_API_KEY_PATTERN = get_conf('CUSTOM_API_KEY_PATTERN')
     if len(CUSTOM_API_KEY_PATTERN) != 0:
         API_MATCH_ORIGINAL = re.match(CUSTOM_API_KEY_PATTERN, key)
     else:
-        API_MATCH_ORIGINAL = re.match(r"sk-[a-zA-Z0-9]{48}$|sk-proj-[a-zA-Z0-9]{48}$|sess-[a-zA-Z0-9]{40}$", key)
+        API_MATCH_ORIGINAL = openai_regex.match(key)
     return bool(API_MATCH_ORIGINAL)
 
 
@@ -27,6 +34,9 @@ def is_api2d_key(key):
     API_MATCH_API2D = re.match(r"fk[a-zA-Z0-9]{6}-[a-zA-Z0-9]{32}$", key)
     return bool(API_MATCH_API2D)
 
+def is_openroute_api_key(key):
+    API_MATCH_OPENROUTE = re.match(r"sk-or-v1-[a-zA-Z0-9]{64}$", key)
+    return bool(API_MATCH_OPENROUTE)
 
 def is_cohere_api_key(key):
     API_MATCH_AZURE = re.match(r"[a-zA-Z0-9]{40}$", key)
@@ -67,7 +77,7 @@ def select_api_key(keys, llm_model):
     avail_key_list = []
     key_list = keys.split(',')
 
-    if llm_model.startswith('gpt-') or llm_model.startswith('one-api-'):
+    if llm_model.startswith('gpt-') or llm_model.startswith('one-api-') or llm_model.startswith('o1-'):
         for k in key_list:
             if is_openai_api_key(k): avail_key_list.append(k)
 
@@ -82,9 +92,29 @@ def select_api_key(keys, llm_model):
     if llm_model.startswith('cohere-'):
         for k in key_list:
             if is_cohere_api_key(k): avail_key_list.append(k)
+    
+    if llm_model.startswith('openrouter-'):
+        for k in key_list:
+            if is_openroute_api_key(k): avail_key_list.append(k)
 
     if len(avail_key_list) == 0:
         raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源（左上角更换模型菜单中可切换openai,azure,claude,cohere等请求源）。")
 
     api_key = random.choice(avail_key_list) # 随机负载均衡
     return api_key
+
+
+def select_api_key_for_embed_models(keys, llm_model):
+    import random
+    avail_key_list = []
+    key_list = keys.split(',')
+
+    if llm_model.startswith('text-embedding-'):
+        for k in key_list:
+            if is_openai_api_key(k): avail_key_list.append(k)
+
+    if len(avail_key_list) == 0:
+        raise RuntimeError(f"您提供的api-key不满足要求，不包含任何可用于{llm_model}的api-key。您可能选择了错误的模型或请求源。")
+
+    api_key = random.choice(avail_key_list) # 随机负载均衡
+    return api_key

shared_utils/logging.py

@@ -0,0 +1,69 @@
+from loguru import logger
+import logging
+import sys
+import os
+
+def chat_log_filter(record):
+    return "chat_msg" in record["extra"]
+
+def not_chat_log_filter(record):
+    return "chat_msg" not in record["extra"]
+
+def formatter_with_clip(record):
+    # Note this function returns the string to be formatted, not the actual message to be logged
+    # record["extra"]["serialized"] = "555555"
+    max_len = 12
+    record['function_x'] = record['function'].center(max_len)
+    if len(record['function_x']) > max_len:
+        record['function_x'] = ".." + record['function_x'][-(max_len-2):]
+    record['line_x'] = str(record['line']).ljust(3)
+    return '<green>{time:HH:mm}</green> | <cyan>{function_x}</cyan>:<cyan>{line_x}</cyan> | <level>{message}</level>\n'
+
+def setup_logging(PATH_LOGGING):
+    
+    admin_log_path = os.path.join(PATH_LOGGING, "admin")
+    os.makedirs(admin_log_path, exist_ok=True)
+    sensitive_log_path = os.path.join(admin_log_path, "chat_secrets.log")
+    regular_log_path = os.path.join(admin_log_path, "console_log.log")
+    logger.remove()
+    logger.configure(
+        levels=[dict(name="WARNING", color="<g>")],
+    )
+
+    logger.add(
+        sys.stderr, 
+        format=formatter_with_clip,
+        # format='<green>{time:HH:mm}</green> | <cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>',
+        filter=(lambda record: not chat_log_filter(record)),
+        colorize=True,
+        enqueue=True
+    )
+
+    logger.add(
+        sensitive_log_path, 
+        format='<green>{time:MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>',
+        rotation="10 MB",
+        filter=chat_log_filter,
+        enqueue=True, 
+    )
+
+    logger.add(
+        regular_log_path, 
+        format='<green>{time:MM-DD HH:mm:ss}</green> | <level>{level: <8}</level> | <cyan>{name}</cyan>:<cyan>{function}</cyan>:<cyan>{line}</cyan> - <level>{message}</level>',
+        rotation="10 MB",
+        filter=not_chat_log_filter,
+        enqueue=True, 
+    )
+
+    logging.getLogger("httpx").setLevel(logging.WARNING)
+
+    logger.warning(f"所有对话记录将自动保存在本地目录{sensitive_log_path}, 请注意自我隐私保护哦！")
+
+
+# logger.bind(chat_msg=True).info("This message is logged to the file!")
+# logger.debug(f"debug message")
+# logger.info(f"info message")
+# logger.success(f"success message")
+# logger.error(f"error message")
+# logger.add("special.log", filter=lambda record: "special" in record["extra"])
+# logger.debug("This message is not logged to the file")

tests/test_anim_gen.py

@@ -0,0 +1,12 @@
+"""
+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.")