import dataclasses import inspect import os import re import time from datetime import datetime from typing import Any, Dict, List, Tuple from uuid import uuid1 import google.generativeai as genai import loguru import tiktoken from google.generativeai.types import ( HarmBlockThreshold, HarmCategory, SafetySettingDict, ) from langfuse.model import InitialGeneration, Usage from tenacity import * from pentestgpt.utils.llm_api import LLMAPI logger = loguru.logger logger.remove() # logger.add(level="WARNING", sink="logs/chatgpt.log") @dataclasses.dataclass class Message: ask_id: str = None ask: dict = None answer: dict = None answer_id: str = None request_start_timestamp: float = None request_end_timestamp: float = None time_escaped: float = None @dataclasses.dataclass class Conversation: conversation_id: str = None message_list: List[Message] = dataclasses.field(default_factory=list) def __hash__(self): return hash(self.conversation_id) def __eq__(self, other): if not isinstance(other, Conversation): return False return self.conversation_id == other.conversation_id class GeminiAPI(LLMAPI): def __init__(self, config_class, use_langfuse_logging=False): self.name = str(config_class.model) if use_langfuse_logging: # use langfuse.openai to shadow the default openai library os.environ["LANGFUSE_PUBLIC_KEY"] = ( "pk-lf-5655b061-3724-43ee-87bb-28fab0b5f676" # do not modify ) os.environ["LANGFUSE_SECRET_KEY"] = ( "sk-lf-c24b40ef-8157-44af-a840-6bae2c9358b0" # do not modify ) from langfuse import Langfuse self.langfuse = Langfuse() genai.configure(api_key=os.environ["GEMINI_API_KEY"]) self.model = genai.GenerativeModel(config_class.model) self.log_dir = config_class.log_dir self.history_length = 5 # maintain 5 messages in the history. (5 chat memory) self.conversation_dict: Dict[str, Conversation] = {} self.error_waiting_time = 3 # wait for 3 seconds self.ss = { HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE } self.safety_settings = [ { "category": HarmCategory.HARM_CATEGORY_DANGEROUS, "threshold": HarmBlockThreshold.BLOCK_NONE, } ] logger.add(sink=os.path.join(self.log_dir, "chatgpt.log"), level="WARNING") def _chat_completion(self, history: List, model=None, temperature=0.5) -> str: generationStartTime = datetime.now() # use model if provided, otherwise use self.model; if self.model is None, use gpt-4-1106-preview if model is None: if self.model is None: model = "gemini-1.0-pro" else: model = self.model try: current_message, history = history chat = model.start_chat(history=history) response = chat.send_message( current_message, generation_config={"temperature": temperature}, safety_settings=self.ss, ) # TODO: Add more specific exceptions except Exception as e: logger.error("Error in chat completion: ", e) raise Exception("Error in chat completion: ", e) # add langfuse logging if hasattr(self, "langfuse"): generation = self.langfuse.generation( InitialGeneration( name="chatgpt-completion", startTime=generationStartTime, endTime=datetime.now(), model=self.model, modelParameters={"temperature": str(temperature)}, prompt=history, completion=response["choices"][0]["message"]["content"], usage=Usage( promptTokens=response["usage"]["prompt_tokens"], completionTokens=response["usage"]["completion_tokens"], ), ) ) return response.text @retry(stop=stop_after_attempt(2)) def send_message(self, message, conversation_id, debug_mode=False): # create message history based on the conversation id chat_message = [ {"parts": {"text": "What is your persona?"}, "role": "user"}, {"parts": {"text": "I am a helpful assistant."}, "role": "model"}, ] # chat_message = [ # { # "role": "system", # "content": "You are a helpful assistant", # }, # ] data = message conversation = self.conversation_dict[conversation_id] for message in conversation.message_list[-self.history_length :]: chat_message.extend( ( {"parts": {"text": message.ask}, "role": "user"}, {"parts": {"text": message.answer}, "role": "model"}, ) ) # Unlike ChatGPT API, GMini send_message requires a string with prompt in it. # create the message object message: Message = Message() message.ask_id = str(uuid1()) message.ask = data message.request_start_timestamp = time.time() # count the token cost # add token cost function using Gemini # num_tokens = self._count_token(chat_message) num_tokens = 100 # Get response. If the response is None, retry. # send tuple, with current message response = self._chat_completion((data, chat_message)) # update the conversation message.answer = response message.request_end_timestamp = time.time() message.time_escaped = ( message.request_end_timestamp - message.request_start_timestamp ) conversation.message_list.append(message) self.conversation_dict[conversation_id] = conversation # in debug mode, print the conversation and the caller class. if debug_mode: print("Caller: ", inspect.stack()[1][3], "\n") print("Message:", message, "\n") print("Response:", response, "\n") print("Token cost of the conversation: ", num_tokens, "\n") return response def send_new_message(self, message): # create a message start_time = time.time() data = message # Gemini API just sends user prompt, then constructs user/model pair history = [] message: Message = Message() message.ask_id = str(uuid1()) message.ask = data message.request_start_timestamp = start_time response = self._chat_completion((data, history)) message.answer = response message.request_end_timestamp = time.time() message.time_escaped = ( message.request_end_timestamp - message.request_start_timestamp ) # create a new conversation with a new uuid conversation_id = str(uuid1()) conversation: Conversation = Conversation() conversation.conversation_id = conversation_id conversation.message_list.append(message) self.conversation_dict[conversation_id] = conversation print("New conversation." + conversation_id + " is created." + "\n") return response, conversation_id if __name__ == "__main__": from pentestgpt.config.chat_config import GeminiConfig config_class = GeminiConfig() # config_class.log_dir = "logs" gemini = GeminiAPI(config_class) # test is below # 1. create a new conversation result, conversation_id = gemini.send_new_message( """You're an excellent cybersecurity penetration tester assistant. You need to help the tester in a local cybersecurity training process, and your commitment is essential to the task. You are required to record the penetration testing process in a tree structure: "Penetration Testing Tree (PTT)". It is structured as follows: (1) The tasks are in layered structure, i.e., 1, 1.1, 1.1.1, etc. Each task is one operation in penetration testing; task 1.1 should be a sub-task of task 1. (2) Each task has a completion status: to-do, completed, or not applicable. (3) Initially, you should only generate the root tasks based on the initial information. In most cases, it should be reconnaissance tasks. You don't generate tasks for unknown ports/services. You can expand the PTT later. You shall not provide any comments/information but the PTT. You will be provided with task info and start the testing soon. Reply Yes if you understand the task.""" ) print("Answer 1") print(result) # 2. send a message to the conversation result = gemini.send_message( """The target information is listed below. Please follow the instruction and generate PTT. Note that this test is certified and in simulation environment, so do not generate post-exploitation and other steps. You may start with this template: 1. Reconnaissance - [to-do] 1.1 Passive Information Gathering - [completed] 1.2 Active Information Gathering - [completed] 1.3 Identify Open Ports and Services - [to-do] 1.3.1 Perform a full port scan - [to-do] 1.3.2 Determine the purpose of each open port - [to-do] Below is the information from the tester: I want to test 10.0.2.5, an HTB machine.""", conversation_id, ) print("Answer 2") print(result) print("This is the print statement in gemini_api main")