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import sys
sys.path.append("../")
import re
from LLM.base_LLM import *
from utils import extract
from muti_prompts import *
llm = OpenAILLM()
# design state
def gen_coder_task(environment_prompt):
chat_history = [{"role":"user","content":f"<target>{environment_prompt}</target>"}]
response = llm.get_response(chat_history,gen_coder_task_system_prompt)
response = extract(response,"task")
print(f"coder_task = {response}")
return response
def get_cot_result(target):
chat_history = [{"role":"user","content":f"<target>{target}</target>"}]
response = llm.get_response(chat_history,design_states_cot_system_prompt)
print(response)
return response
def get_desgin_states(target,index):
chat_history = [{"role":"user","content":f"<target>{target}</target>"}]
design_state_system_prompt = get_design_state_system_prompt(index)
response = llm.get_response(chat_history,system_prompt=design_state_system_prompt)
print(response)
# 使用正则表达式提取数据
pattern = r'<state>(.*?)<\/state>'
states = re.findall(pattern, response, re.DOTALL)
# 创建包含字典的列表
result_list = []
for state in states:
state_name = extract(state,"state_name")
roles = extract(state,"roles")
environment_prompt = extract(state,"describe")
# 创建字典并添加到结果列表
state_dict = {
"state_name": state_name,
"environment_prompt": environment_prompt,
"roles": roles.split(" ")
}
result_list.append(state_dict)
# 打印结果
print("design states")
for item in result_list:
print(item)
return result_list
def gen_agent_style(agents,design_states,index):
agents_styles = {}
scene = ""
design_agents_style_system_prompt = get_design_agents_style_system_prompt(index)
for design_state in design_states:
scene +=design_state["environment_prompt"] + "\n"
for agent in agents:
chat_history = [{"role":"user","content":f"<scene>{scene}</scene>,<target>{agent}</target>"}]
style = llm.get_response(chat_history,design_agents_style_system_prompt)
style = extract(style,"style")
agents_styles[agent] = style
print(agents_styles)
return agents_styles
def gen_agent_state(agent,environment_prompt,index):
design_agent_state_system_prompt = get_design_agent_state_system_prompt(index)
agent_state = {}
chat_history = [{"role":"user","content":f"<scene>{environment_prompt}</scene>,<target>{agent}</target>"}]
response = llm.get_response(chat_history,design_agent_state_system_prompt)
role = extract(response,"role")
task = extract(response,"task")
rule = extract(response,"rule")
demonstrations = extract(response,"demonstrations")
agent_state["style"] = {"role":role}
agent_state["task"] = {"task":task}
agent_state["rule"] = {"rule":rule}
agent_state["demonstrations"] = {"demonstrations":demonstrations}
print(agent_state)
return agent_state
def gen_begin_role_query(environment_prompt,roles,index):
roles = " ".join(roles)
design_begin_role_query_system_prompt = get_design_begin_role_query_system_prompt(index)
chat_history = [{"role":"user","content":f"<scene>{environment_prompt}</scene>\n<roles>{roles}</roles>"}]
response = llm.get_response(chat_history,design_begin_role_query_system_prompt)
begin_role = extract(response,"begin_role")
begin_query = extract(response,"begin_query")
print(f"{begin_role}:{begin_query}")
return begin_role,begin_query
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