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https://github.com/IntelligenzaArtificiale/Free-Auto-GPT.git
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IntelligenzaArtificiale
132 lines
4.9 KiB
Python
132 lines
4.9 KiB
Python
from langchain import LLMChain, PromptTemplate
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from FreeLLM import ChatGPTAPI # FREE CHATGPT API
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from FreeLLM import HuggingChatAPI # FREE HUGGINGCHAT API
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from langchain.memory import ConversationBufferWindowMemory
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import os
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#### LOG IN FOR CHATGPT FREE LLM
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select_model = input("Select the model you want to use (1 or 2) \n \
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1) ChatGPT \n \
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2) HuggingChat \n \
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>>> ")
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if select_model == "1":
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print("Chatgpt token : \n \
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Go to https://chat.openai.com/chat and open the developer tools by F12. \n \
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Find the __Secure-next-auth.session-token cookie in Application > Storage > Cookies > https://chat.openai.com \n \
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Copy the value in the Cooki2e Value field.")
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CG_TOKEN = input("Insert chatgpt token >>> ")
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os.environ["CHATGPT_TOKEN"] = CG_TOKEN
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start_chat = input("Do you want start a chat from existing chat? (y/n): ") # ask if you want start a chat from existing chat
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if start_chat == "y":
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chat_id = input("Insert chat-id (chat.openai.com/c/(IS THIS ->)58XXXX0f-XXXX-XXXX-XXXX-faXXXXd2b50f) ->") # ask the chat id
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llm= ChatGPTAPI.ChatGPT(token=os.environ["CHATGPT_TOKEN"], conversation=chat_id)
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else:
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llm= ChatGPTAPI.ChatGPT(token=os.environ["CHATGPT_TOKEN"])
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elif select_model == "2":
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llm=HuggingChatAPI.HuggingChat()
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####
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def initialize_chain(instructions, memory=None):
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if memory is None:
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memory = ConversationBufferWindowMemory()
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memory.ai_prefix = "Assistant"
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template = f"""
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Instructions: {instructions}
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{{{memory.memory_key}}}
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Human: {{human_input}}
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Assistant:"""
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prompt = PromptTemplate(
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input_variables=["history", "human_input"],
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template=template
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)
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chain = LLMChain(
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llm=llm,
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prompt=prompt,
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verbose=True,
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memory=ConversationBufferWindowMemory(),
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)
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return chain
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def initialize_meta_chain():
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meta_template="""
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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.
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####
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{chat_history}
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####
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Please reflect on these interactions.
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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: ...".
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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: ...".
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"""
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meta_prompt = PromptTemplate(
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input_variables=["chat_history"],
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template=meta_template
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)
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meta_chain = LLMChain(
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llm=llm,
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prompt=meta_prompt,
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verbose=True,
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)
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return meta_chain
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def get_chat_history(chain_memory):
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memory_key = chain_memory.memory_key
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chat_history = chain_memory.load_memory_variables(memory_key)[memory_key]
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return chat_history
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def get_new_instructions(meta_output):
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delimiter = 'Instructions: '
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new_instructions = meta_output[meta_output.find(delimiter)+len(delimiter):]
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return new_instructions
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def main(task, max_iters=3, max_meta_iters=5):
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failed_phrase = 'task failed'
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success_phrase = 'task succeeded'
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key_phrases = [success_phrase, failed_phrase]
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instructions = 'None'
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for i in range(max_meta_iters):
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print(f'[Episode {i+1}/{max_meta_iters}]')
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chain = initialize_chain(instructions, memory=None)
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output = chain.predict(human_input=task)
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for j in range(max_iters):
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print(f'(Step {j+1}/{max_iters})')
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print(f'Assistant: {output}')
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print(f'Human: ')
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human_input = input()
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if any(phrase in human_input.lower() for phrase in key_phrases):
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break
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output = chain.predict(human_input=human_input)
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if success_phrase in human_input.lower():
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print(f'You succeeded! Thanks for playing!')
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return
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meta_chain = initialize_meta_chain()
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meta_output = meta_chain.predict(chat_history=get_chat_history(chain.memory))
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print(f'Feedback: {meta_output}')
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instructions = get_new_instructions(meta_output)
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print(f'New Instructions: {instructions}')
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print('\n'+'#'*80+'\n')
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print(f'You failed! Thanks for playing!')
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task = input("Enter the objective of the AI system: (Be realistic!) ")
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max_iters = int(input("Enter the maximum number of interactions per episode: "))
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max_meta_iters = int(input("Enter the maximum number of episodes: "))
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main(task, max_iters, max_meta_iters) |