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from pydantic import BaseModel, Field
from typing import List
from toolbox import update_ui_lastest_msg, disable_auto_promotion
from request_llms.bridge_all import predict_no_ui_long_connection
from crazy_functions.json_fns.pydantic_io import GptJsonIO, JsonStringError
import copy, json, pickle, os, sys, time
def read_avail_plugin_enum():
from crazy_functional import get_crazy_functions
plugin_arr = get_crazy_functions()
# remove plugins with out explaination
plugin_arr = {k:v for k, v in plugin_arr.items() if 'Info' in v}
plugin_arr_info = {"F_{:04d}".format(i):v["Info"] for i, v in enumerate(plugin_arr.values(), start=1)}
plugin_arr_dict = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
plugin_arr_dict_parse = {"F_{:04d}".format(i):v for i, v in enumerate(plugin_arr.values(), start=1)}
plugin_arr_dict_parse.update({f"F_{i}":v for i, v in enumerate(plugin_arr.values(), start=1)})
prompt = json.dumps(plugin_arr_info, ensure_ascii=False, indent=2)
prompt = "\n\nThe defination of PluginEnum:\nPluginEnum=" + prompt
return prompt, plugin_arr_dict, plugin_arr_dict_parse
def wrap_code(txt):
txt = txt.replace('```','')
return f"\n```\n{txt}\n```\n"
def have_any_recent_upload_files(chatbot):
_5min = 5 * 60
if not chatbot: return False # chatbot is None
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
if not most_recent_uploaded: return False # most_recent_uploaded is None
if time.time() - most_recent_uploaded["time"] < _5min: return True # most_recent_uploaded is new
else: return False # most_recent_uploaded is too old
def get_recent_file_prompt_support(chatbot):
most_recent_uploaded = chatbot._cookies.get("most_recent_uploaded", None)
path = most_recent_uploaded['path']
prompt = "\nAdditional Information:\n"
prompt = "In case that this plugin requires a path or a file as argument,"
prompt += f"it is important for you to know that the user has recently uploaded a file, located at: `{path}`"
prompt += f"Only use it when necessary, otherwise, you can ignore this file."
return prompt
def get_inputs_show_user(inputs, plugin_arr_enum_prompt):
# remove plugin_arr_enum_prompt from inputs string
inputs_show_user = inputs.replace(plugin_arr_enum_prompt, "")
inputs_show_user += plugin_arr_enum_prompt[:200] + '...'
inputs_show_user += '\n...\n'
inputs_show_user += '...\n'
inputs_show_user += '...}'
return inputs_show_user
def execute_plugin(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, user_intention):
plugin_arr_enum_prompt, plugin_arr_dict, plugin_arr_dict_parse = read_avail_plugin_enum()
class Plugin(BaseModel):
plugin_selection: str = Field(description="The most related plugin from one of the PluginEnum.", default="F_0000")
reason_of_selection: str = Field(description="The reason why you should select this plugin.", default="This plugin satisfy user requirement most")
# ⭐ ⭐ ⭐ 选择插件
yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n查找可用插件中...", chatbot=chatbot, history=history, delay=0)
gpt_json_io = GptJsonIO(Plugin)
gpt_json_io.format_instructions = "The format of your output should be a json that can be parsed by json.loads.\n"
gpt_json_io.format_instructions += """Output example: {"plugin_selection":"F_1234", "reason_of_selection":"F_1234 plugin satisfy user requirement most"}\n"""
gpt_json_io.format_instructions += "The plugins you are authorized to use are listed below:\n"
gpt_json_io.format_instructions += plugin_arr_enum_prompt
inputs = "Choose the correct plugin according to user requirements, the user requirement is: \n\n" + \
">> " + txt.rstrip('\n').replace('\n','\n>> ') + '\n\n' + gpt_json_io.format_instructions
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=[])
try:
gpt_reply = run_gpt_fn(inputs, "")
plugin_sel = gpt_json_io.generate_output_auto_repair(gpt_reply, run_gpt_fn)
except JsonStringError:
msg = f"抱歉, {llm_kwargs['llm_model']}无法理解您的需求。"
msg += "请求的Prompt为：\n" + wrap_code(get_inputs_show_user(inputs, plugin_arr_enum_prompt))
msg += "语言模型回复为：\n" + wrap_code(gpt_reply)
msg += "\n但您可以尝试再试一次\n"
yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
return
if plugin_sel.plugin_selection not in plugin_arr_dict_parse:
msg = f"抱歉, 找不到合适插件执行该任务, 或者{llm_kwargs['llm_model']}无法理解您的需求。"
msg += f"语言模型{llm_kwargs['llm_model']}选择了不存在的插件：\n" + wrap_code(gpt_reply)
msg += "\n但您可以尝试再试一次\n"
yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
return
# ⭐ ⭐ ⭐ 确认插件参数
if not have_any_recent_upload_files(chatbot):
appendix_info = ""
else:
appendix_info = get_recent_file_prompt_support(chatbot)
plugin = plugin_arr_dict_parse[plugin_sel.plugin_selection]
yield from update_ui_lastest_msg(lastmsg=f"正在执行任务: {txt}\n\n提取插件参数...", chatbot=chatbot, history=history, delay=0)
class PluginExplicit(BaseModel):
plugin_selection: str = plugin_sel.plugin_selection
plugin_arg: str = Field(description="The argument of the plugin.", default="")
gpt_json_io = GptJsonIO(PluginExplicit)
gpt_json_io.format_instructions += "The information about this plugin is:" + plugin["Info"]
inputs = f"A plugin named {plugin_sel.plugin_selection} is selected, " + \
"you should extract plugin_arg from the user requirement, the user requirement is: \n\n" + \
">> " + (txt + appendix_info).rstrip('\n').replace('\n','\n>> ') + '\n\n' + \
gpt_json_io.format_instructions
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=[])
plugin_sel = gpt_json_io.generate_output_auto_repair(run_gpt_fn(inputs, ""), run_gpt_fn)
# ⭐ ⭐ ⭐ 执行插件
fn = plugin['Function']
fn_name = fn.__name__
msg = f'{llm_kwargs["llm_model"]}为您选择了插件: `{fn_name}`\n\n插件说明：{plugin["Info"]}\n\n插件参数：{plugin_sel.plugin_arg}\n\n假如偏离了您的要求，按停止键终止。'
yield from update_ui_lastest_msg(lastmsg=msg, chatbot=chatbot, history=history, delay=2)
yield from fn(plugin_sel.plugin_arg, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, -1)
return |