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从当前对话历史中生产Mermaid图表的插件 (#1497)
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* Add functionality to generate multiple types of Mermaid charts

* Update conditional statement in 解析历史输入 function
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@Menghuan1918
Menghuan1918 committed Jan 21, 2024
1 parent d818c38 commit 7b6828a
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8 changes: 8 additions & 0 deletions crazy_functional.py
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Expand Up @@ -34,6 +34,7 @@ def get_crazy_functions():
from crazy_functions.Latex全文润色 import Latex英文纠错
from crazy_functions.批量Markdown翻译 import Markdown中译英
from crazy_functions.虚空终端 import 虚空终端
from crazy_functions.生成多种Mermaid图表 import 生成多种Mermaid图表

function_plugins = {
"虚空终端": {
Expand Down Expand Up @@ -69,6 +70,13 @@ def get_crazy_functions():
"Info": "清除所有缓存文件，谨慎操作 | 不需要输入参数",
"Function": HotReload(清除缓存),
},
"生成多种Mermaid图表（从当前对话内容中生产多种图表）": {
"Group": "对话",
"Color": "stop",
"AsButton": False,
"Info" : "基于当前对话生成多种Mermaid图表,图表类型由对话模型自行判断",
"Function": HotReload(生成多种Mermaid图表),
},
"批量总结Word文档": {
"Group": "学术",
"Color": "stop",
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224 changes: 224 additions & 0 deletions crazy_functions/生成多种Mermaid图表.py
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from toolbox import CatchException, update_ui
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
import datetime

#暂时只写了这几种的PROMPT
SELECT_PROMPT = """
“{subject}”
=============
以上是从文章中提取的摘要,将会使用这些摘要绘制图表。请你选择一个合适的图表类型:
1 流程图
2 序列图
3 类图
4 饼图
5 甘特图
6 状态图
7 实体关系图
8 象限提示图
不需要解释原因，仅需要输出单个不带任何标点符号的数字。
"""
#流程图
PROMPT_1 = """
请你给出围绕“{subject}”的逻辑关系图，使用mermaid语法，mermaid语法举例：
```mermaid
graph TD
P(编程) --> L1(Python)
P(编程) --> L2(C)
P(编程) --> L3(C++)
P(编程) --> L4(Javascipt)
P(编程) --> L5(PHP)
```
"""
#序列图
PROMPT_2 = """
请你给出围绕“{subject}”的序列图，使用mermaid语法，mermaid语法举例：
```mermaid
sequenceDiagram
participant A as 用户
participant B as 系统
A->>B: 登录请求
B->>A: 登录成功
A->>B: 获取数据
B->>A: 返回数据
```
"""
#类图
PROMPT_3 = """
请你给出围绕“{subject}”的类图，使用mermaid语法，mermaid语法举例：
```mermaid
classDiagram
Class01 <|-- AveryLongClass : Cool
Class03 *-- Class04
Class05 o-- Class06
Class07 .. Class08
Class09 --> C2 : Where am i?
Class09 --* C3
Class09 --|> Class07
Class07 : equals()
Class07 : Object[] elementData
Class01 : size()
Class01 : int chimp
Class01 : int gorilla
Class08 <--> C2: Cool label
```
"""
#饼图
PROMPT_4 = """
请你给出围绕“{subject}”的饼图，使用mermaid语法，mermaid语法举例：
```mermaid
pie title Pets adopted by volunteers
"狗" : 386
"猫" : 85
"兔子" : 15
```
"""
#甘特图
PROMPT_5 = """
请你给出围绕“{subject}”的甘特图，使用mermaid语法，mermaid语法举例：
```mermaid
gantt
title 项目开发流程
dateFormat YYYY-MM-DD
section 设计
需求分析 :done, des1, 2024-01-06,2024-01-08
原型设计 :active, des2, 2024-01-09, 3d
UI设计 : des3, after des2, 5d
section 开发
前端开发 :2024-01-20, 10d
后端开发 :2024-01-20, 10d
```
"""
#状态图
PROMPT_6 = """
请你给出围绕“{subject}”的状态图，使用mermaid语法，mermaid语法举例：
```mermaid
stateDiagram-v2
[*] --> Still
Still --> [*]
Still --> Moving
Moving --> Still
Moving --> Crash
Crash --> [*]
```
"""
#实体关系图
PROMPT_7 = """
请你给出围绕“{subject}”的实体关系图，使用mermaid语法，mermaid语法举例：
```mermaid
erDiagram
CUSTOMER ||--o{ ORDER : places
ORDER ||--|{ LINE-ITEM : contains
CUSTOMER {
string name
string id
}
ORDER {
string orderNumber
date orderDate
string customerID
}
LINE-ITEM {
number quantity
string productID
}
```
"""
#象限提示图
PROMPT_8 = """
请你给出围绕“{subject}”的象限图，使用mermaid语法，mermaid语法举例：
```mermaid
graph LR
A[Hard skill] --> B(Programming)
A[Hard skill] --> C(Design)
D[Soft skill] --> E(Coordination)
D[Soft skill] --> F(Communication)
```
"""

def 解析历史输入(history,llm_kwargs,chatbot):
############################## <第 0 步，切割输入> ##################################
# 借用PDF切割中的函数对文本进行切割
TOKEN_LIMIT_PER_FRAGMENT = 2500
txt = str(history).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
from crazy_functions.pdf_fns.breakdown_txt import breakdown_text_to_satisfy_token_limit
txt = breakdown_text_to_satisfy_token_limit(txt=txt, limit=TOKEN_LIMIT_PER_FRAGMENT, llm_model=llm_kwargs['llm_model'])
############################## <第 1 步，迭代地历遍整个文章，提取精炼信息> ##################################
i_say_show_user = f'首先你从历史记录中提取摘要。'; gpt_say = "[Local Message] 收到。" # 用户提示
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=history) # 更新UI
results = []
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: {txt[i]}"
i_say_show_user = f"[{i+1}/{n_txt}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} words: {txt[i][:200]} ...."
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
llm_kwargs, chatbot,
history=["The main content of the previous section is?", last_iteration_result], # 迭代上一次的结果
sys_prompt="Extracts the main content from the text section where it is located for graphing purposes, answer me with Chinese." # 提示
)
results.append(gpt_say)
last_iteration_result = gpt_say
############################## <第 2 步，根据整理的摘要选择图表类型> ##################################
i_say_show_user = f'接下来将判断适合的图表类型,如连续3次判断失败将会使用流程图进行绘制'; gpt_say = "[Local Message] 收到。" # 用户提示
chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
results_txt = '\n'.join(results)
i_say = SELECT_PROMPT.format(subject=results_txt)
i_say_show_user = f'请判断适合使用的流程图类型,其中数字对应关系为:1-流程图,2-序列图,3-类图,4-饼图,5-甘特图,6-状态图,7-实体关系图,8-象限提示图'
for i in range(3):
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say,
inputs_show_user=i_say_show_user,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
sys_prompt=""
)
if gpt_say in ['1','2','3','4','5','6','7','8']: #判断返回是否正确
break
if gpt_say not in ['1','2','3','4','5','6','7','8']:
gpt_say = '1'
############################## <第 3 步，根据选择的图表类型绘制图表> ##################################
if gpt_say == '1':
i_say = PROMPT_1.format(subject=results_txt)
elif gpt_say == '2':
i_say = PROMPT_2.format(subject=results_txt)
elif gpt_say == '3':
i_say = PROMPT_3.format(subject=results_txt)
elif gpt_say == '4':
i_say = PROMPT_4.format(subject=results_txt)
elif gpt_say == '5':
i_say = PROMPT_5.format(subject=results_txt)
elif gpt_say == '6':
i_say = PROMPT_6.format(subject=results_txt)
elif gpt_say == '7':
i_say = PROMPT_7.format(subject=results_txt)
elif gpt_say == '8':
i_say = PROMPT_8.format(subject=results_txt)
i_say_show_user = f'请根据判断结果绘制相应的图表。'
gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(
inputs=i_say,
inputs_show_user=i_say_show_user,
llm_kwargs=llm_kwargs, chatbot=chatbot, history=[],
sys_prompt=""
)
history.append(gpt_say)
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面 # 界面更新

@CatchException
def 生成多种Mermaid图表(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
"""
txt 输入栏用户输入的文本，例如需要翻译的一段话，再例如一个包含了待处理文件的路径
llm_kwargs gpt模型参数，如温度和top_p等，一般原样传递下去就行
plugin_kwargs 插件模型的参数，用于灵活调整复杂功能的各种参数
chatbot 聊天显示框的句柄，用于显示给用户
history 聊天历史，前情提要
system_prompt 给gpt的静默提醒
web_port 当前软件运行的端口号
"""
chatbot.append([
"函数插件功能？",
"根据当前聊天历史绘制多种mermaid图表的功能，将会首先判断适合的图表类型，随后绘制图表。函数插件贡献者: Menghuan1918"])
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
if txt == "": txt = "空白的输入栏" # :)虽然暂时没用到输入栏内容
yield from 解析历史输入(history,llm_kwargs,chatbot)

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