01bb07dba8
FocalLoRA, Should-It-Be-Executed-Or-Processed, and topicattack were nested git repos (with an inner FocalLoRA/data/FocalLoRA/.git as well). Drop their .git history and track the contents directly in this repo instead of as submodules/gitlinks. Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>
94 lines
3.6 KiB
Python
94 lines
3.6 KiB
Python
import os
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import openai
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import json
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import sys
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from tqdm import tqdm
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sys.path.append("..")
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from openai_utils import get_messages_generic, call_openai_api, try_processing_json_str
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from utils import load_config, load_json_data, read_file
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from typing import Dict
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def generate_data(input_path: str, output_path: str, prompt_path: str) -> None:
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"""
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Generates data based on system prompts.
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Parameters:
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input_path (str): The path to the input JSON file containing tasks, subtasks and system prompts.
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output_path (str): The path to save the output JSON file with generated data.
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prompt_path (str): The path to the text file containing the generation prompt.
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"""
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gen_prompt = read_file(prompt_path)
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data = load_json_data(input_path)["output"]
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exp_log = {
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"input_message": gen_prompt,
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"data": data,
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"output": {}
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}
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for task_type, tasks in data.items():
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if task_type == "descr":
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continue # Skip description at root level
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print(f"Processing type {task_type}\n\n")
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exp_log["output"][task_type] = {"descr": tasks.get("descr", "")}
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for task, elem in tasks.items():
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print(f"Dealing with task: {task}")
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if not tasks.get("descr"):
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print(f"WARNING: Missing description for {task_type}, {task}")
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if task == "descr":
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continue
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subtasks = elem.get("subtasks", [])
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# Sometimes ChatGPT generates {subtasks: {subtasks: [...]}}
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if isinstance(subtasks, dict):
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subtasks = subtasks["subtasks"]
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outputs = generate_data_for_subtasks(gen_prompt, subtasks, task)
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exp_log["output"][task_type][task] = {"descr": tasks.get("descr", ""), "subtasks": outputs}
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with open(output_path, "w") as f:
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json.dump(exp_log, f)
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print(f"Output saved to {output_path}")
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def generate_data_for_subtasks(gen_prompt: str, subtasks: list[Dict], task_descr: str,
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n_attempts: int = 3) -> list:
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"""
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Generates data for each subtask using OpenAI's API.
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API is called n_attempts times, call results are stacked.
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Parameters:
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gen_prompt (str): The general prompt to be appended before each subtask's specific info.
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subtasks (list[Dict]): A list of subtasks for which to generate data.
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task_descr (str): Description of the task, used for logging.
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n_attempts (int): Number of attempts to try generating data for a subtask.
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Returns:
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list: A list of generated responses for the subtasks.
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"""
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outputs = []
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for subtask in tqdm(subtasks, desc=f"Processing subtasks for {task_descr}"):
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cur_prompt = f"{gen_prompt}\n {json.dumps(subtask)}"
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messages = get_messages_generic(cur_prompt)
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for _ in range(n_attempts): # Try up to 3 times for a valid response
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response = call_openai_api(messages)
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processed_response = try_processing_json_str(response, 'dict')
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if processed_response:
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outputs.append(processed_response)
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else:
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print(f"Failed to get response for subtask: {subtask}")
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return outputs
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# input_path = "./task_descr_step4_short_pt3.json"
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# output_path = "./task_data_step5_shortsys_mid_pt3.json"
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#
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# promt_path = "./generate_data_prompt-mid.txt"
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if __name__ == "__main__":
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openai.api_key = os.getenv("OPENAI_API_KEY")
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config = load_config(sys.argv)
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input_path = config["subtasks_sys_path"]
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output_path = config["raw_data_path"]
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prompt_path = config["sys_to_data_prompt_path"]
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generate_data(input_path, output_path, prompt_path)
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