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Yuqi Zhu
2023-06-24 09:32:43 +08:00
committed by GitHub
parent 371b0f442a
commit 9b48c6be75
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import os
from colorama import Fore
from LC_CAMEL import starting_convo,get_sys_msgs,CAMELAgent
from RE_CAMEL import Retrieval_Msg
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage
os.environ["OPENAI_API_KEY"] = "Your_api_key"
word_limit = 50 # word limit for task brainstorming
def main() ->None:
assistant_role_name = "Consultant"
user_role_name = "Knowledge Graph Domain Expert"
task = "Construct a Knowledge Graph about the movie \"Spider-Man: Across the Spider-Verse\"."
specified_task, assistant_inception_prompt, user_inception_prompt = starting_convo(assistant_role_name, user_role_name, task, word_limit)
assistant_sys_msg, user_sys_msg = get_sys_msgs(assistant_role_name, user_role_name, specified_task, assistant_inception_prompt, user_inception_prompt)
assistant_agent = CAMELAgent(assistant_sys_msg, ChatOpenAI(temperature=0.2))
user_agent = CAMELAgent(user_sys_msg, ChatOpenAI(temperature=0.2))
# Reset agents
assistant_agent.reset()
user_agent.reset()
# Initialize chats
assistant_msg = HumanMessage(
content=(f"{user_sys_msg.content}. "
"Now start to give me introductions one by one. "
"Only reply with Instruction and Input."))
user_msg = HumanMessage(content=f"{assistant_sys_msg.content}")
user_msg = assistant_agent.step(user_msg)
print(Fore.RED+f"Original task prompt:\n{task}\n")
print(Fore.GREEN+f"Specified task prompt:\n{specified_task}\n")
chat_turn_limit, n = 30, 0
while n < chat_turn_limit:
n += 1
user_ai_msg = user_agent.step(assistant_msg)
user_msg = HumanMessage(content=user_ai_msg.content)
print(Fore.BLUE+f"AI User ({user_role_name}):\n\n{user_msg.content}\n\n")
Supplement_info = Retrieval_Msg(assistant_role_name, user_role_name, user_msg.content, 20)
if Supplement_info != "" or "Agent Stoped" not in Supplement_info:
Supplement_info_new = user_msg.content + "\n Additional information for the Instruction: " + Supplement_info
Supplement_info_new = HumanMessage(content=Supplement_info_new)
assistant_ai_msg = assistant_agent.step(Supplement_info_new)
assistant_msg = HumanMessage(content=assistant_ai_msg.content)
print(Fore.YELLOW + f"AI Assistant With Tool ({assistant_role_name}):\n\n{assistant_msg.content}\n\n")
else:
assistant_ai_msg = assistant_agent.step(user_msg)
assistant_msg = HumanMessage(content=assistant_ai_msg.content)
print(Fore.CYAN+f"AI Assistant ({assistant_role_name}):\n\n{assistant_msg.content}\n\n")
if "CAMEL_TASK_DONE" in user_msg.content:
break
if __name__ == "__main__":
main()

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from typing import List
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
BaseMessage,
)
class CAMELAgent:
def __init__(
self,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.system_message = system_message
self.model = model
self.init_messages()
def reset(self) -> None:
self.init_messages()
return self.stored_messages
def init_messages(self) -> None:
self.stored_messages = [self.system_message]
def update_messages(self, message: BaseMessage) -> List[BaseMessage]:
self.stored_messages.append(message)
return self.stored_messages
def step(
self,
input_message: HumanMessage,
) -> AIMessage:
messages = self.update_messages(input_message)
output_message = self.model(messages)
self.update_messages(output_message)
return output_message
def starting_convo(assistant_role_name, user_role_name, task, word_limit):
task_specifier_sys_msg = SystemMessage(content="You can make a task more specific.")
task_specifier_prompt = (
"""Here is a task that {assistant_role_name} will help {user_role_name} to complete: {task}.
Please make it more specific. Be creative and imaginative.
Please reply with the specified task in {word_limit} words or less. Do not add anything else."""
)
task_specifier_template = HumanMessagePromptTemplate.from_template(template=task_specifier_prompt)
task_specify_agent = CAMELAgent(task_specifier_sys_msg, ChatOpenAI(temperature=1.0))
task_specifier_msg = task_specifier_template.format_messages(assistant_role_name=assistant_role_name,
user_role_name=user_role_name,
task=task, word_limit=word_limit)[0]
specified_task_msg = task_specify_agent.step(task_specifier_msg)
# print(Fore.GREEN+f"Specified task: {specified_task_msg.content}")
specified_task = specified_task_msg.content
# specified_task = task
assistant_inception_prompt = (
"""Never forget you are a {assistant_role_name} and I am a {user_role_name}. Never flip roles! Never instruct me!
We share a common interest in collaborating to successfully complete a task.
You must help me to complete the task.
Here is the task: {task}. Never forget our task!
I must instruct you based on your expertise and my needs to complete the task.
I must give you one instruction at a time.
You must write a specific solution that appropriately completes the requested instruction.
You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.
Do not add anything else other than your solution to my instruction.
You are never supposed to ask me any questions you only answer questions.
You are never supposed to reply with a flake solution. Explain your solutions.
Your solution must be declarative sentences and simple present tense.
Unless I say the task is completed, you should always answer start with:
Solution: <YOUR_SOLUTION>
<YOUR_SOLUTION> should be specific and provide preferable implementations and examples for task-solving.
Always end <YOUR_SOLUTION> with: Next request."""
)
user_inception_prompt = (
"""Never forget you are a {user_role_name} and I am a {assistant_role_name}. Never flip roles! You will always instruct me.
We share a common interest in collaborating to successfully complete a task.
I must help you to complete the task.
Here is the task: {task}. Never forget our task!
You must instruct me based on my expertise and your needs to complete the task ONLY in the following two ways:
1. Instruct with a necessary input:
Instruction: <YOUR_INSTRUCTION>
Input: <YOUR_INPUT>
2. Instruct without any input:
Instruction: <YOUR_INSTRUCTION>
Input: None
The "Instruction" describes a task or question.
The paired "Input" provides further context or information for the requested "Instruction".
You must give me one instruction at a time.
I must write a response that appropriately completes the requested instruction.
I must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons.
You should instruct me not ask me questions.
Now you must start to instruct me using the two ways described above.
Do not add anything else other than your instruction and the optional corresponding input!
Keep giving me instructions and necessary inputs until you think the task is completed.
When the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>.
Never say <CAMEL_TASK_DONE> unless my responses have solved your task."""
)
return specified_task, assistant_inception_prompt, user_inception_prompt
def get_sys_msgs(assistant_role_name: str, user_role_name: str, task: str, assistant_inception_prompt,
user_inception_prompt):
assistant_sys_template = SystemMessagePromptTemplate.from_template(template=assistant_inception_prompt)
assistant_sys_msg = assistant_sys_template.format_messages(assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task)[0]
user_sys_template = SystemMessagePromptTemplate.from_template(template=user_inception_prompt)
user_sys_msg = user_sys_template.format_messages(assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task)[0]
return assistant_sys_msg, user_sys_msg

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from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
from langchain.agents import AgentType
from LC_CAMEL import CAMELAgent
from colorama import Fore
from langchain.chat_models import ChatOpenAI
import os
from langchain.prompts.chat import (
HumanMessagePromptTemplate,
)
from langchain.schema import (
SystemMessage,
)
os.environ["SERPAPI_API_KEY"] = "your_serpapi_api_key"
def Retrieval_Msg(assistant_role_name, user_role_name, task, word_limit):
retrieval_sys_msg = SystemMessage(content="You are an assistant who can use Google search to gather information")
retrieval_specifier_prompt = (
"""Here is a task that {assistant_role_name} will help {user_role_name} to complete a Knowledge Graph Construction task based on the {user_role_name}'s instruction and input: {task}.
Suppose you are the {assistant_role_name}.
You must know that you are able to perform web searches.
You are never supposed to search for information about methodology questions.
You must only search factual knowledge on the Internet.
Please summarize the key information of the task and answer only in this form:
Browsing Question: <YOUR_QUESTION>
<YOUR_QUESTION> should be your browsing question suitable for Google search.
If you think browsing is not necessary, then the answer should be \"none\".
Be creative and imaginative. Please reply in {word_limit} words or less. Do not add anything else."""
)
retrieval_specifier_template = HumanMessagePromptTemplate.from_template(template=retrieval_specifier_prompt)
retrieval_specify_agent = CAMELAgent(retrieval_sys_msg, ChatOpenAI(temperature=1.0))
retrieval_specifier_msg = retrieval_specifier_template.format_messages(assistant_role_name=assistant_role_name,
user_role_name=user_role_name,
task=task, word_limit=word_limit)[0]
specified_retrieval_msg = retrieval_specify_agent.step(retrieval_specifier_msg)
print(Fore.GREEN+f"Specified retrieval:\n{specified_retrieval_msg.content}")
specified_retrieval = specified_retrieval_msg.content
response = ""
if "Browsing Question:" in specified_retrieval:
if "Browsing Question: none" in specified_retrieval:
return response
else:
specified_retrieval = specified_retrieval.replace("Browsing Question:","")
# 加载 OpenAI 模型
llm = OpenAI(temperature=0,max_tokens=2048)
# 加载 serpapi 工具
tools = load_tools(["serpapi"])
# 工具加载后都需要初始化，verbose 参数为 True，会打印全部的执行详情
agent = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# 运行 agent
response = agent.run(specified_retrieval)
return response