import re from concurrent.futures import ThreadPoolExecutor, as_completed from swarms.models import OpenAIChat class AutoTempAgent: """ AutoTemp is a tool for automatically selecting the best temperature setting for a given task. Flow: 1. Generate outputs at a range of temperature settings. 2. Evaluate each output using the default temperature setting. 3. Select the best output based on the evaluation score. 4. Return the best output. Args: temperature (float, optional): The default temperature setting to use. Defaults to 0.5. api_key (str, optional): Your OpenAI API key. Defaults to None. alt_temps ([type], optional): A list of alternative temperature settings to try. Defaults to None. auto_select (bool, optional): If True, the best temperature setting will be automatically selected. Defaults to True. max_workers (int, optional): The maximum number of workers to use when generating outputs. Defaults to 6. Returns: [type]: [description] Examples: >>> from swarms.demos.autotemp import AutoTemp >>> autotemp = AutoTemp() >>> autotemp.run("Generate a 10,000 word blog on mental clarity and the benefits of meditation.", "0.4,0.6,0.8,1.0,1.2,1.4") Best AutoTemp Output (Temp 0.4 | Score: 100.0): Generate a 10,000 word blog on mental clarity and the benefits of meditation. """ def __init__( self, temperature: float = 0.5, api_key: str = None, alt_temps=None, auto_select=True, max_workers=6, ): self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4] self.auto_select = auto_select self.max_workers = max_workers self.temperature = temperature self.alt_temps = alt_temps self.llm = OpenAIChat( openai_api_key=api_key, temperature=temperature, ) def evaluate_output(self, output: str): """Evaluate the output using the default temperature setting.""" eval_prompt = f""" Evaluate the following output which was generated at a temperature setting of {self.temperature}. Provide a precise score from 0.0 to 100.0, considering the criteria of relevance, clarity, utility, pride, and delight. Output to evaluate: --- {output} --- """ score_text = self.llm(prompt=eval_prompt) score_match = re.search(r"\b\d+(\.\d)?\b", score_text) return round(float(score_match.group()), 1) if score_match else 0.0 def run(self, task: str, temperature_string): """Run the AutoTemp agent.""" temperature_list = [ float(temp.strip()) for temp in temperature_string.split(",") ] outputs = {} scores = {} with ThreadPoolExecutor(max_workers=self.max_workers) as executor: future_to_temp = { executor.submit(self.llm.generate, task, temp): temp for temp in temperature_list } for future in as_completed(future_to_temp): temp = future_to_temp[future] output_text = future.result() outputs[temp] = output_text scores[temp] = self.evaluate_output(output_text, temp) if not scores: return "No valid outputs generated.", None sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True) best_temp, best_score = sorted_scores[0] best_output = outputs[best_temp] return ( f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}" if self.auto_select else "\n".join( f"Temp {temp} | Score: {score}:\n{outputs[temp]}" for temp, score in sorted_scores ) )