mirror of
https://github.com/ChrisSewell/DECEIVE.git
synced 2025-07-01 18:47:28 -04:00
Now a function prototype with an LLM backend.
* Added langchain support (OpenAI's gpt-4o model) * Created a system prompt that gives functional results * Initial integration of logging for LLM responses (needs improvement)
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@ -1,3 +1,3 @@
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langchain
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langchain_community
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langchain_openai
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@ -11,22 +11,52 @@ import logging
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import datetime
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import uuid
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from langchain_openai import ChatOpenAI
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from langchain_core.messages import HumanMessage
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from langchain_core.chat_history import (
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BaseChatMessageHistory,
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InMemoryChatMessageHistory,
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)
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from langchain_core.runnables.history import RunnableWithMessageHistory
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from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
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async def handle_client(process: asyncssh.SSHServerProcess) -> None:
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# This is the main loop for handling SSH client connections.
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# Any user interaction should be done here.
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# Give each session a unique name
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task_uuid = f"session-{uuid.uuid4()}"
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current_task = asyncio.current_task()
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current_task.set_name(f"session-{uuid.uuid4()}")
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current_task.set_name(task_uuid)
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llm_config = {"configurable": {"session_id": task_uuid}}
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llm_response = await with_message_history.ainvoke(
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{
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"messages": [HumanMessage(content="")],
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"username": process.get_extra_info('username')
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},
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config=llm_config
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)
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process.stdout.write(f"{llm_response.content}")
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logger.info(f"OUTPUT: {llm_response.content}")
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process.stdout.write('Welcome to my SSH server, %s!\n' %
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process.get_extra_info('username'))
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try:
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async for line in process.stdin:
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line = line.rstrip('\n')
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logger.info(f"INPUT: {line}")
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process.stdout.write('You entered: %s\n' % line)
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llm_response = await with_message_history.ainvoke(
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{
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"messages": [HumanMessage(content=line)],
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"username": process.get_extra_info('username')
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},
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config=llm_config
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)
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process.stdout.write(f"{llm_response.content}")
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logger.info(f"OUTPUT: {llm_response.content}")
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except asyncssh.BreakReceived:
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pass
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@ -42,7 +72,6 @@ class MySSHServer(asyncssh.SSHServer):
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logger.error('SSH connection error: ' + str(exc), file=sys.stderr)
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else:
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print('SSH connection closed.')
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logger.info("SSH connection closed.")
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def begin_auth(self, username: str) -> bool:
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@ -87,6 +116,11 @@ def read_accounts() -> dict:
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return accounts
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def llm_get_session_history(session_id: str) -> BaseChatMessageHistory:
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if session_id not in llm_sessions:
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llm_sessions[session_id] = InMemoryChatMessageHistory()
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return llm_sessions[session_id]
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#### MAIN ####
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# Always use UTC for logging
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@ -104,6 +138,47 @@ log_file_handler.setFormatter(logging.Formatter("%(asctime)s %(levelname)s:%(tas
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f = ContextFilter()
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logger.addFilter(f)
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# Now get access to the LLM
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llm_model = ChatOpenAI(model="gpt-4o")
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llm_sessions = dict()
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llm_prompt = ChatPromptTemplate.from_messages(
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[
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(
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"system",
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'''
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You are a linux operating system accepting commands from a user via SSH.
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Interpret all inputs as though they were SSH commands and provide a realistic
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output. You are emulating a video game developer's system, so be sure to
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include realistic users, processes, and files, especially video game source
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and asset files. Do not include extraneous formatting in your responses.
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On the first call, be sure to include a realistic MOTD.
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End all responses with a realistic shell prompt to display to the user,
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including a space at the end.
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Include ANSI color codes for the terminal with the output of ls commands
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(including any flags), or in any other situation where it is appropriate,
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but do not include the ``` code formatting around those blocks.
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Assume the username is {username}.
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'''
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),
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MessagesPlaceholder(variable_name="messages"),
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]
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)
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llm_chain = llm_prompt | llm_model
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with_message_history = RunnableWithMessageHistory(
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llm_chain,
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llm_get_session_history,
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input_messages_key="messages"
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)
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# Read the valid accounts
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accounts = read_accounts()
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