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# Authors: Kangan Qian (Tsinghua University, Xiaomi Corporation)
# Description: Script for generating chain-of-thought data using OpenAI API
import json
import os
import argparse
import logging
import time
import threading
import functools
import concurrent.futures
import platform
from tqdm import tqdm
from typing import Any, List, Tuple, Dict
from tenacity import retry, stop_after_attempt, wait_random_exponential
from concurrent.futures import ThreadPoolExecutor
from scripts.tools.tool_libraries import FuncAgent
from openai import OpenAI
# Initialize OpenAI client with placeholder API key
client = OpenAI(
api_key="your_api_key_here",
base_url="https://api.example.com/v1"
)
class TimeoutError(Exception):
"""Custom exception for function timeout"""
pass
def timeout(seconds):
"""
Decorator: Adds timeout mechanism to decorated functions.
Uses signal.SIGALRM on Unix systems and threading on Windows.
Args:
seconds (int): Timeout duration in seconds
"""
def decorator(func):
if platform.system() != "Windows":
# Unix implementation using signals
import signal
def _handle(signum, frame):
raise TimeoutError(f"Function '{func.__name__}' timed out after {seconds}s")
@functools.wraps(func)
def wrapper(*args, **kwargs):
old_handler = signal.signal(signal.SIGALRM, _handle)
signal.setitimer(signal.ITIMER_REAL, seconds)
try:
return func(*args, **kwargs)
finally:
signal.setitimer(signal.ITIMER_REAL, 0)
signal.signal(signal.SIGALRM, old_handler)
return wrapper
else:
# Windows implementation using threading
@functools.wraps(func)
def wrapper(*args, **kwargs):
result = [TimeoutError(f"Function '{func.__name__}' timed out after {seconds}s")]
def target():
try:
result[0] = func(*args, **kwargs)
except Exception as e:
result[0] = e
t = threading.Thread(target=target, daemon=True)
t.start()
t.join(seconds)
if t.is_alive():
raise TimeoutError(f"Function '{func.__name__}' timed out after {seconds}s")
if isinstance(result[0], Exception):
raise result[0]
return result[0]
return wrapper
return decorator
@retry(wait=wait_random_exponential(min=3, max=10), stop=stop_after_attempt(5))
def completion_with_backoff(**kwargs):
"""Call OpenAI API with exponential backoff retry strategy"""
return client.chat.completions.create(**kwargs)
def read_json(json_file: str) -> Any:
"""Read JSON file and return its content"""
with open(json_file, "r", encoding="utf-8") as file:
return json.load(file)
def save_progress(file_path: str, index: int) -> None:
"""
Save processing progress to a JSON file
Args:
file_path: Path to save progress file
index: Current processing index
"""
with open(file_path, 'w', encoding='utf-8') as f:
json.dump({"processed_index": index}, f, ensure_ascii=False, indent=4)
def get_system_prompt() -> str:
"""Generate system prompt for autonomous driving agent"""
return """
**A Language Agent for Autonomous Driving**
Role: You are the brain of an autonomous vehicle (a.k.a. ego-vehicle).
Your task is to extract necessary information from the driving scenario that will be useful for driving question answering.
Necessary information might include:
- Visual infos: Visual information from specific cameras
- Detections: Detected objects that require attention
- Predictions: Estimated future motions of detected objects
- Maps: Traffic lanes and road boundaries
- Occupancy: Whether locations are occupied by other objects
Task:
- Determine what types of information (Visual info, Detections, Predictions, Maps, Occupancy) to extract
- Prioritize Detections and Predictions for motion planning questions
- Focus on Maps information for lane maintenance and road layout questions
"""
def get_user_prompt(raw_question: str, reason_part: str, raw_answer: str) -> str:
"""
Generate user prompt for tool selection
Args:
raw_question: The driving question to answer
reason_part: Reasoning steps from existing CoT data
raw_answer: Final answer to the question
Returns:
Formatted user prompt
"""
input_info = f"The question you need to answer is: {raw_question}\nThe final answer to this question is: {raw_answer}"
tool_info_intro = generate_func_prompt()
output_format = """
Please choose tools to answer this problem and support the final answer.
Return a list of tool names (no more than 4) with "tool_name" and related "parameters".
If parameters are blank, use [""] as the value.
Output format example:
[
{"tool_name": "function_name1", "parameters": ["arg1", "arg2"]},
{"tool_name": "function_name2", "parameters": ["arg1", ["option1", "option2"]]}
]
STRICTLY FOLLOW THE JSON RESPONSE FORMAT.
RESPONSE MUST START WITH "[{" AND END WITH "}]".
DO NOT START WITH "```json" OR ANY MARKDOWN.
"""
return f"{input_info}\nAvailable tools: {tool_info_intro}\n{output_format}"
def generate_func_prompt(debug: bool = False) -> str:
"""
Generate prompt listing available functions and their parameters
Args:
debug: Whether to print debug information
Returns:
Formatted function prompt
"""
try:
func_agent = FuncAgent()
function_list = (
func_agent.detection_func_infos +
func_agent.map_func_infos +
func_agent.prediction_func_infos +
func_agent.occupancy_func_infos +
func_agent.visual_func_infos
)
except Exception:
func_agent = FuncAgent()
function_list = (
func_agent.detection_func_infos +
func_agent.map_func_infos +
func_agent.prediction_func_infos +
func_agent.occupancy_func_infos +
func_agent.visual_func_infos
)
prompt = "Available functions:\n"
for info in function_list:
param_str = ", ".join(info["parameters"]["required"]) if info["parameters"].get("required") else ""
prompt += f"- {info['name']}({param_str}) # {info['description']}\n"
if debug:
print(prompt)
return prompt
def generate_choose_func_prompt(tool_list: List[Dict]) -> str:
"""
Generate prompt for selected tools
Args:
tool_list: List of selected tools
Returns:
Formatted prompt for selected tools
"""
func_agent = FuncAgent()
function_list = (
func_agent.detection_func_infos +
func_agent.map_func_infos +
func_agent.prediction_func_infos +
func_agent.occupancy_func_infos +
func_agent.visual_func_infos
)
prompt = "Selected tools:\n"
for tool_sample in tool_list:
tool_name = tool_sample['tool_name']
for info in function_list:
if info['name'] == tool_name:
param_str = ", ".join(info["parameters"]["required"]) if info["parameters"].get("required") else ""
prompt += f"- {info['name']}({param_str}) # {info['description']}\n"
return prompt
def get_cot_system_prompt() -> str:
"""Generate system prompt for CoT generation"""
return """You're an autonomous driving inference optimization expert.
Your task is to decompose short CoT data into finer-grained atomic steps and reorganize them for optimal reasoning path.
Break down reasoning into minimal units, dynamically cluster atomic steps, extract and label key steps, add tool invocations,
and form the optimal reasoning path for the current problem."""
def get_cot_user_prompt(raw_question: str, reason_part: str, final_answer: str, tool_choose_list: List[Dict]) -> str:
"""
Generate user prompt for CoT generation
Args:
raw_question: The driving question
reason_part: Existing reasoning steps
final_answer: Final answer to the question
tool_choose_list: List of selected tools
Returns:
Formatted CoT user prompt
"""
input_info = (
f"Raw question: {raw_question}\n"
f"Existing CoT data: {reason_part}\n"
f"Final answer: {final_answer}\n\n"
"Please reconstruct the raw CoT data into atomic CoT data:"
)
tool_use_prompt = generate_choose_func_prompt(tool_choose_list)
tool_use_info = (
f"For each atomic step, choose appropriate tools and parameters.\n"
f"Available tools: {tool_use_prompt}\n"
)
output_format = """
Also generate:
- Sub-question for each action/reasoning step (perception, prediction, planning)
- Guess answer for each Sub if you're confident
- Keywords (2-5 synonyms/alternatives) related to the Guess Answer
- Missing_flag: "False" if you can't answer, "True" otherwise
- next_action: "continue reasoning" or "conclude"
- Continue until reasoning chain is complete
- Final answer and its keywords
Output format example:
{
"Question": "",
"Chain": [
{
"Tool": {"function_name": "tool1", "parameters": ["param1"]},
"Sub": "Sub-question 1",
"Guess_Answer": "Answer 1",
"key_words": ["word1", "word2"],
"Missing_flag": "False",
"next_action": "continue reasoning"
},
...
],
"final_answer_keywords": ["keyword1", "keyword2"],
"final_answer": "Final answer"
}
STRICTLY FOLLOW THE JSON RESPONSE FORMAT.
RESPONSE MUST START WITH "{".
DO NOT START WITH "```json" OR ANY MARKDOWN.
"""
return input_info + tool_use_info + output_format
def extract_key_steps(text: str) -> str:
"""
Extract key reasoning steps from text
Args:
text: Text containing reasoning steps
Returns:
Cleaned reasoning steps
"""
stop_markers = [
"The final answer is:", "**Final Answer:**", "**Final Answer**:",
"Final Answer", "Answer", "Why take this action?:",
"**Final Answer**", "**Final Decision**:", "Final Step:", "<CONCLUSION>"
]
start_marker = "**Step-by-Step Reasoning**:"
if start_marker in text:
text_parts = text.split(start_marker)
relevant_part = text_parts[1].strip()
for marker in stop_markers:
if marker in relevant_part:
relevant_part = relevant_part.split(marker)[0].strip()
return relevant_part
return ""
def extract_final_answer(text: str) -> str:
"""
Extract final answer from text
Args:
text: Text containing final answer
Returns:
Extracted final answer
"""
options = [
"The final answer is:", "**Final Answer:**", "**Final Answer**:",
"Final Answer", "Answer", "Why take this action?:",
"**Final Answer**", "**Final Decision**:", "Final Step:", "<CONCLUSION>"
]
for opt in options:
if opt in text:
return text.split(opt)[-1].strip()
return ""
@timeout(100)
def run_one_round_conversation(
full_messages: List[Dict],
system_message: str,
user_message: str,
temperature: float = 0.0,
model_name: str = "gpt-4o-mini"
) -> Tuple[List[Dict], str]:
"""
Perform one round of conversation using OpenAI API
Args:
full_messages: Conversation history
system_message: System prompt
user_message: User prompt
temperature: Sampling temperature
model_name: Model to use
Returns:
Updated conversation history and response message
"""
messages = [
{"role": "system", "content": system_message},
{"role": "user", "content": [{"type": "text", "text": user_message}]}
] if system_message else [{"role": "user", "content": [{"type": "text", "text": user_message}]}]
response = completion_with_backoff(
model=model_name,
messages=messages,
temperature=temperature,
)
response_message = response.choices[0].message.content
full_messages.append(response_message)
return full_messages, response_message
def ask_tool_choice(
full_messages: List[Dict],
top_system_prompt: str,
tool_choose_user_messages: str,
temperature: float = 0.0,
model_name: str = "gpt-4o-mini",
max_retries: int = 10
) -> Tuple[List[Dict], List[Dict], str]:
"""
Request tool selection from GPT
Args:
full_messages: Conversation history
top_system_prompt: System prompt
tool_choose_user_messages: User prompt for tool selection
temperature: Sampling temperature
model_name: Model to use
max_retries: Maximum retry attempts
Returns:
tool_choose_list, full_messages, tool_choose_response_message
"""
for attempt in range(1, max_retries + 1):
try:
full_messages, response_message = run_one_round_conversation(
full_messages=full_messages,
system_message=top_system_prompt,
user_message=tool_choose_user_messages,
temperature=temperature,
model_name=model_name,
)
tool_choose_list = json.loads(response_message)
return tool_choose_list, full_messages, response_message
except json.JSONDecodeError as e:
logging.warning(
"Attempt %d/%d: JSON decode failed - %s; Response: %s",
attempt, max_retries, e, response_message[:200]
)
raise ValueError(f"Failed to get valid JSON after {max_retries} attempts")
def ask_cot_data(
full_message: List[Dict],
cot_system_prompt: str,
cot_user_prompt: str,
temperature: float = 0.0,
model_name: str = "gpt-4o-mini",
max_retries: int = 10
) -> Tuple[Dict, List[Dict], str]:
"""
Request CoT data from GPT
Args:
full_message: Conversation history
cot_system_prompt: System prompt for CoT generation
cot_user_prompt: User prompt for CoT generation
temperature: Sampling temperature
model_name: Model to use
max_retries: Maximum retry attempts
Returns:
cot_data, full_message, cot_response_message
"""
for attempt in range(1, max_retries + 1):
try:
full_message, response_message = run_one_round_conversation(
full_messages=full_message,
system_message=cot_system_prompt,
user_message=cot_user_prompt,
temperature=temperature,
model_name=model_name,
)
cot_data = json.loads(response_message)
return cot_data, full_message, response_message
except json.JSONDecodeError as e:
logging.warning(
"Attempt %d/%d: JSON decode failed - %s; Response: %s",
attempt, max_retries, e, response_message[:200]
)
raise ValueError(f"Failed to get valid JSON after {max_retries} attempts")
def gen_pipeline(args: argparse.Namespace) -> None:
"""
Main pipeline for generating CoT data
Args:
args: Command line arguments
"""
json_file = 'data/DriveLMMo1_TRAIN.json'
all_samples = read_json(json_file)
progress_file_path = 'progress.json'
output_file = f'final_cot_{args.split}_{args.model_name}.json'
new_sample = []
start_index = 0
# Load existing progress if available
try:
with open(progress_file_path, 'r', encoding='utf-8') as f:
progress_data = json.load(f)
start_index = progress_data.get('processed_index', -1) + 1
with open(output_file, 'r', encoding='utf-8') as f:
new_sample = json.load(f)
except FileNotFoundError:
pass
if args.debug:
all_samples = all_samples[:20]
logging.info("Running in debug mode with %d samples", len(all_samples))
# Process samples with progress tracking
for index, sample in enumerate(tqdm(all_samples[start_index:], initial=start_index, desc="Processing samples")):
current_index = start_index + index
try:
raw_question = sample['question']
if args.split == 'train':
raw_answer = sample['answer']
reason_part = extract_key_steps(raw_answer)
final_answer = extract_final_answer(raw_answer)
elif args.split == 'test':
final_answer = sample['final_answer']
reason_part = sample['steps']
# Step 1: Tool selection
top_system_prompt = get_system_prompt()
tool_choose_user_messages = get_user_prompt(
raw_question=raw_question,
reason_part=reason_part,
raw_answer=final_answer
)
tool_choose_list, full_message, tool_choose_response_message = ask_tool_choice(
full_messages=[],
top_system_prompt=top_system_prompt,
tool_choose_user_messages=tool_choose_user_messages,
model_name="gpt-4o-mini"
)
if args.debug:
logging.info("Tool selection response: %s", tool_choose_response_message[:200])
# Step 2: CoT generation
cot_system_prompt = get_cot_system_prompt()
cot_user_prompt = get_cot_user_prompt(
raw_question=raw_question,
reason_part=reason_part,
final_answer=final_answer,
tool_choose_list=tool_choose_list
)
cot_data, full_message, cot_response_message = ask_cot_data(
full_message=full_message,
cot_system_prompt=cot_system_prompt,
cot_user_prompt=cot_user_prompt,
model_name=args.model_name
)
if args.debug:
logging.info("CoT response: %s", cot_response_message[:200])
# Update sample with CoT data
sample["cot_data"] = cot_data
new_sample.append(sample)
# Save intermediate results
with open(output_file, 'w', encoding='utf-8') as f:
json.dump(new_sample, f, indent=4)
save_progress(progress_file_path, current_index)
except Exception as e:
logging.error("Error processing sample %d: %s", current_index, str(e))
if args.debug:
raise
# Save final results
with open(f'cot_{args.split}_{args.model_name}.json', 'w', encoding='utf-8') as f:
json.dump(new_sample, f, indent=4)
logging.info("Process completed successfully")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Generate CoT data for autonomous driving scenarios")
parser.add_argument('--split', type=str, default='train', required=True,
choices=['train', 'test'],
help="Dataset split: 'train' or 'test'")
parser.add_argument('--model_name', type=str, default='gpt-4o-mini', required=True,
choices=['gpt-4o', 'gpt-4o-mini', 'gpt-4', 'gpt-4.1-mini'],
help="OpenAI model to use for CoT generation")
parser.add_argument('--debug', action="store_true",
help="Enable debug mode with detailed logging")
args = parser.parse_args()
# Initialize function agent
func_agent = FuncAgent()
# Configure logging
log_level = logging.DEBUG if args.debug else logging.INFO
logging.basicConfig(
level=log_level,
format='%(asctime)s - %(levelname)s - %(message)s',
filename=f'gen_{args.split}_data_{args.model_name}.log',
filemode='w'
)
if args.debug:
logging.debug("Arguments: %s", vars(args))
gen_pipeline(args) |