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MetaPrompt.py
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MetaPrompt.py
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from dotenv import load_dotenv
from langchain import LLMChain, PromptTemplate
from FreeLLM import ChatGPTAPI # FREE CHATGPT API
from FreeLLM import HuggingChatAPI # FREE HUGGINGCHAT API
from FreeLLM import BingChatAPI # FREE BINGCHAT API
from FreeLLM import BardChatAPI # FREE GOOGLE BARD API
from langchain.memory import ConversationBufferWindowMemory
import os
load_dotenv()
#### LOG IN FOR CHATGPT FREE LLM
select_model = input("Select the model you want to use (1 or 2) \n \
1) ChatGPT \n \
2) HuggingChat \n \
3) BingChat \n \
>>> ")
if select_model == "1":
CG_TOKEN = os.getenv("CHATGPT_TOKEN", "your-chatgpt-token")
if (CG_TOKEN != "your-chatgpt-token"):
os.environ["CHATGPT_TOKEN"] = CG_TOKEN
else:
raise ValueError("ChatGPT Token EMPTY. Edit the .env file and put your ChatGPT token")
start_chat = os.getenv("USE_EXISTING_CHAT", False)
if start_chat:
chat_id = os.getenv("CHAT_ID")
if chat_id == None:
raise ValueError("You have to set up your chat-id in the .env file")
llm= ChatGPTAPI.ChatGPT(token=os.environ["CHATGPT_TOKEN"], conversation=chat_id)
else:
llm= ChatGPTAPI.ChatGPT(token=os.environ["CHATGPT_TOKEN"])
elif select_model == "2":
llm=HuggingChatAPI.HuggingChat()
elif select_model == "3":
if os.environ["BINGCHAT_COOKIEPATH"] == "your-bingchat-cookiepath":
raise ValueError("BingChat CookiePath EMPTY. Edit the .env file and put your BingChat cookiepath")
cookie_path = os.environ["BINGCHAT_COOKIEPATH"]
llm=BingChatAPI.BingChat(cookiepath=cookie_path, conversation_style="creative")
elif select_model == "4":
if os.environ["BARDCHAT_TOKEN"] == "your-googlebard-cookiepath":
raise ValueError("GoogleBard CookiePath EMPTY. Edit the .env file and put your GoogleBard cookiepath")
cookie_path = os.environ["BARDCHAT_TOKEN"]
llm=BardChatAPI.BardChat(cookie=cookie_path)
####
def initialize_chain(instructions, memory=None):
if memory is None:
memory = ConversationBufferWindowMemory()
memory.ai_prefix = "Assistant"
template = f"""
Instructions: {instructions}
{{{memory.memory_key}}}
Human: {{human_input}}
Assistant:"""
prompt = PromptTemplate(
input_variables=["history", "human_input"],
template=template
)
chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=True,
memory=ConversationBufferWindowMemory(),
)
return chain
def initialize_meta_chain():
meta_template="""
Assistant has just had the below interactions with a User. Assistant followed their "Instructions" closely. Your job is to critique the Assistant's performance and then revise the Instructions so that Assistant would quickly and correctly respond in the future.
####
{chat_history}
####
Please reflect on these interactions.
You should first critique Assistant's performance. What could Assistant have done better? What should the Assistant remember about this user? Are there things this user always wants? Indicate this with "Critique: ...".
You should next revise the Instructions so that Assistant would quickly and correctly respond in the future. Assistant's goal is to satisfy the user in as few interactions as possible. Assistant will only see the new Instructions, not the interaction history, so anything important must be summarized in the Instructions. Don't forget any important details in the current Instructions! Indicate the new Instructions by "Instructions: ...".
"""
meta_prompt = PromptTemplate(
input_variables=["chat_history"],
template=meta_template
)
meta_chain = LLMChain(
llm=llm,
prompt=meta_prompt,
verbose=True,
)
return meta_chain
def get_chat_history(chain_memory):
memory_key = chain_memory.memory_key
chat_history = chain_memory.load_memory_variables(memory_key)[memory_key]
return chat_history
def get_new_instructions(meta_output):
delimiter = 'Instructions: '
new_instructions = meta_output[meta_output.find(delimiter)+len(delimiter):]
return new_instructions
def main(task, max_iters=3, max_meta_iters=5):
failed_phrase = 'task failed'
success_phrase = 'task succeeded'
key_phrases = [success_phrase, failed_phrase]
instructions = 'None'
for i in range(max_meta_iters):
print(f'[Episode {i+1}/{max_meta_iters}]')
chain = initialize_chain(instructions, memory=None)
output = chain.predict(human_input=task)
for j in range(max_iters):
print(f'(Step {j+1}/{max_iters})')
print(f'Assistant: {output}')
print(f'Human: ')
human_input = input()
if any(phrase in human_input.lower() for phrase in key_phrases):
break
output = chain.predict(human_input=human_input)
if success_phrase in human_input.lower():
print(f'You succeeded! Thanks for playing!')
return
meta_chain = initialize_meta_chain()
meta_output = meta_chain.predict(chat_history=get_chat_history(chain.memory))
print(f'Feedback: {meta_output}')
instructions = get_new_instructions(meta_output)
print(f'New Instructions: {instructions}')
print('\n'+'#'*80+'\n')
print(f'You failed! Thanks for playing!')
task = input("Enter the objective of the AI system: (Be realistic!) ")
max_iters = int(input("Enter the maximum number of interactions per episode: "))
max_meta_iters = int(input("Enter the maximum number of episodes: "))
main(task, max_iters, max_meta_iters)