import logging import os import time import openai logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s') logger = logging.getLogger(__name__) class OpenAI: def __init__( self, api_key, strategy="cot", evaluation_strategy="value", api_base="", api_model="", ): if api_key == "" or api_key is None: api_key = os.environ.get("OPENAI_API_KEY", "") if api_key != "": openai.api_key = api_key else: raise Exception("Please provide OpenAI API key") if api_base == ""or api_base is None: api_base = os.environ.get("OPENAI_API_BASE", "") # if not set, use the default base path of "https://api.openai.com/v1" if api_base != "": # e.g. https://api.openai.com/v1/ or your custom url openai.api_base = api_base print(f'Using custom api_base {api_base}') if api_model == "" or api_model is None: api_model = os.environ.get("OPENAI_API_MODEL", "") if api_model != "": self.api_model = api_model else: self.api_model = "text-davinci-003" print(f'Using api_model {self.api_model}') self.use_chat_api = 'gpt' in self.api_model self.strategy = strategy self.evaluation_strategy = evaluation_strategy def run( self, prompt, max_tokens, temperature, k=1, stop=None ): while True: try: if self.use_chat_api: messages = [ { "role": "user", "content": prompt } ] response = openai.ChatCompletion.create( model=self.api_model, messages=messages, max_tokens=max_tokens, temperature=temperature, ) else: response = openai.Completion.create( engine=self.api_model, prompt=prompt, n=k, max_tokens=max_tokens, stop=stop, temperature=temperature, ) with open("openai.logs", 'a') as log_file: log_file.write("\n" + "-----------" + '\n' +"Prompt : "+ prompt+"\n") return response except openai.error.RateLimitError as e: sleep_duratoin = os.environ.get("OPENAI_RATE_TIMEOUT", 30) print(f'{str(e)}, sleep for {sleep_duratoin}s, set it by env OPENAI_RATE_TIMEOUT') time.sleep(sleep_duratoin) def openai_choice2text_handler(self, choice): if self.use_chat_api: text = choice['message']['content'] else: text = choice.text.strip() return text def generate_text(self, prompt, k): if self.use_chat_api: thoughts = [] for _ in range(k): response = self.run(prompt, 400, 0.5, k) text = self.openai_choice2text_handler(response.choices[0]) thoughts += [text] # print(f'thoughts: {thoughts}') return thoughts else: response = self.run(prompt, 300, 0.5, k) thoughts = [self.openai_choice2text_handler(choice) for choice in response.choices] return thoughts def generate_thoughts( self, state, k, initial_prompt, rejected_solutions=None ): if (type(state) == str): state_text = state else: state_text = '\n'.join(state) print("New state generating thought:", state, "\n\n") prompt = f""" Accomplish the task below by decomposing it as many very explicit subtasks as possible, be very explicit and thorough denoted by a search process, highlighted by markers ‘1’,..., ‘3’ as “first operations” guiding subtree exploration for the OBJECTIVE, focus on the third subtree exploration. Produce prospective search steps (e.g., the subtree exploration ‘5. 11 + 1’) and evaluates potential subsequent steps to either progress towards a solution or retrace to another viable subtree then be very thorough and think atomically then provide solutions for those subtasks, then return the definitive end result and then summarize it ########## OBJECTIVE {initial_prompt} ################### """ thoughts = self.generate_text(prompt, k) # print(f"Generated thoughts: {thoughts}") return thoughts def generate_solution(self, initial_prompt, state, rejected_solutions=None): try: if isinstance(state, list): state_text = '\n'.join(state) else: state_text = state prompt = f""" Generate a series of solutions to comply with the user's instructions, you must generate solutions on the basis of determining the most reliable solution in the shortest amount of time, while taking rejected solutions into account and learning from them. Considering the reasoning provided:\n\n ###'{state_text}'\n\n### Devise the best possible solution for the task: {initial_prompt}, Here are evaluated solutions that were rejected: ###{rejected_solutions}###, complete the {initial_prompt} without making the same mistakes you did with the evaluated rejected solutions. Be simple. Be direct. Provide intuitive solutions as soon as you think of them.""" answer = self.generate_text(prompt, 1) print(f'Generated Solution Summary {answer}') return answer except Exception as e: logger.error(f"Error in generate_solutions: {e}") return None def evaluate_states(self, states, initial_prompt): if not states: return {} if self.evaluation_strategy == 'value': state_values = {} for state in states: if (type(state) == str): state_text = state else: state_text = '\n'.join(state) print("We receive a state of type", type(state), "For state: ", state, "\n\n") prompt = f""" To achieve the following goal: '{initial_prompt}', pessimistically value the context of the past solutions and more importantly the latest generated solution you had AS A FLOAT BETWEEN 0 AND 1\n Past solutions:\n\n {state_text}\n If the solutions is not making fast progress in achieving the goal, give it a lower score. Evaluate all solutions AS A FLOAT BETWEEN 0 and 1:\n, DO NOT RETURN ANYTHING ELSE """ response = self.run(prompt, 10, 1) try: value_text = self.openai_choice2text_handler(response.choices[0]) # print(f'state: {value_text}') value = float(value_text) print(f"Evaluated Thought Value: {value}") except ValueError: value = 0 state_values[state] = value return state_values else: raise ValueError("Invalid evaluation strategy. Choose 'value' or 'vote'.") class AoTAgent: def __init__( self, num_thoughts: int = None, max_steps: int = None, value_threshold: float = None, pruning_threshold=0.5, backtracking_threshold=0.4, initial_prompt=None, openai_api_key: str = None, model = None, ): self.num_thoughts = num_thoughts self.max_steps = max_steps self.value_threshold = value_threshold self.backtracking_threshold = backtracking_threshold self.pruning_threshold = pruning_threshold self.initial_prompt = initial_prompt self.output = [] self.openai_api_key = openai_api_key self.model = model self.model = self.model or OpenAI(api_key=self.openai_api_key) def solve(self): try: self.dfs(self.initial_prompt, 1) if not self.output: logger.error("No valid thoughts were generated during DFS") return None best_state, _ = max(self.output, key=lambda x: x[1]) solution = self.model.generate_solution(self.initial_prompt, best_state) print(f"Solution is {solution}") return solution if solution else best_state except Exception as error: logger.error(f"Error in tot_dfs: {error}") raise error def dfs(self, state, step): if step > self.max_steps: thought, value = self.evaluate_thought(state) self.output.append((thought, value)) return thoughts = self.generate_and_filter_thoughts(state) for next_state in thoughts: state_value = self.evaluated_thoughts[next_state] if state_value > self.value_threshold: child = (state, next_state) if isinstance(state, str) else (*state, next_state) self.dfs(child, step + 1) #backtracking best_value = max([value for _, value in self.output]) if best_value < self.backtracking_threshold: self.output.pop() continue def generate_and_filter_thoughts(self, state): thoughts = self.model.generate_thoughts( state, self.num_thoughts, self.initial_prompt ) self.evaluated_thoughts = self.model.evaluate_states( thoughts, self.initial_prompt ) filtered_thoughts = [thought for thought in thoughts if self.evaluated_thoughts[thought] >= self.pruning_threshold] print(f"filtered_thoughts: {filtered_thoughts}") return filtered_thoughts def evaluate_thought(self, state): thought = self.model.generate_thoughts(state, 1, self.initial_prompt) value = self.model.evaluate_states([state], self.initial_prompt)[state] print(f"Evaluated thought: {value}") return thought, value