File size: 3,936 Bytes
778d47d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 | import argparse
import os
import json
from datasets import Dataset, DatasetDict
from planner import get_answer_llamacpp, get_answer_vllm, get_answer_openai
from openai import OpenAI
from dotenv import load_dotenv
from planner import _make_str_response, _execute_sql, is_execution_correct
import re
from utils import norm_sql_query
from tqdm import tqdm
from multiprocessing import Pool
# Set up argument parser
parser = argparse.ArgumentParser()
parser.add_argument('--input_file', type=str, default='./data/multi-agents/fixed/gpt-4o-mini-validator-fixer-bird_with_evidence_train.jsonl')
parser.add_argument('--output_dir', type=str, default='./data/multi-agents/fixed/sft-gpt-4o-mini-validator-fixer-bird_with_evidence_train')
parser.add_argument('--num_processes', type=int, default=16)
args = parser.parse_args()
# Define the prompt template
PROMPT = """{schema}
Question: {question}
External knowledge: {evidence}
Generated SQL query: {sql_query}
Execution response:
{execution_response}
Feedback for the SQL query:
"""
COMPLETION = """<select>
{feedback_select}
</select>
<condition>
{feedback_condition}
</condition>
FIXED SQL: {fixed_sql}"""
def norm_feedback(feedback, token):
feedback = token + feedback.split(token)[-1]
return feedback
def extract_sql_in_code_block(pred_sql_text):
sql_block_match = re.search(r"```(.+?)```", pred_sql_text, re.DOTALL)
if sql_block_match:
sql_query = sql_block_match.group(1).strip()
if sql_query.startswith("sql"):
sql_query = sql_query.replace("sql", "").strip()
return sql_query
else:
return pred_sql_text
def process_sample(index_sample):
index, sample = index_sample
feedback_select = sample['validator_select'] or 'SELECT.\nNone'
feedback_condition = sample['validator_condition'] or "CONDITION.\nNone"
feedback_join = sample['validator_join'] or "JOIN.\nNone"
feedback_join = "JOIN." + feedback_join.split("JOIN.")[-1]
feedback_select = norm_feedback(feedback_select, "SELECT.")
feedback_condition = norm_feedback(feedback_condition, "CONDITION.")
feedback_join = norm_feedback(feedback_join, "JOIN.")
prompt = PROMPT.format(
schema=sample['schema_sequence'],
question=sample['question'],
evidence=sample['evidence'],
sql_query=sample['predict_sql'],
execution_response=sample['pred_result']
)
fixed_sql = sample['fixed_sql']
if type(fixed_sql) == list:
fixed_sql = fixed_sql[0]
fixed_sql = extract_sql_in_code_block(fixed_sql)
if fixed_sql != "None":
true_result, has_error = _execute_sql("./" + sample["db_path"], sample["sql"])
pred_result, has_error = _execute_sql("./" + sample["db_path"], fixed_sql)
if not is_execution_correct(true_result, pred_result):
print("-"*20)
print('True:', true_result)
print('Pred:', pred_result)
# completion = norm_sql_query(sample['sql'], sample['schema'])
fixed_sql = sample['sql']
completion = COMPLETION.format(
feedback_select=feedback_select,
feedback_condition=feedback_condition,
# feedback_join=feedback_join,
fixed_sql=fixed_sql
)
return {
'prompt_id': str(index),
'messages': {
'prompt': prompt,
'completion': completion
}
}
def main():
with open(args.input_file) as fp:
data = [json.loads(line) for line in fp]
with Pool(processes=args.num_processes) as pool:
results = list(tqdm(pool.imap(process_sample, enumerate(data)), total=len(data)))
sft_data = [result for result in results if result is not None]
dataset = DatasetDict({
'train': Dataset.from_list(sft_data),
'test': Dataset.from_list(sft_data[:100]),
})
dataset.save_to_disk(args.output_dir)
print(dataset)
if __name__ == "__main__":
main()
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