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Update gpt_request so it can run in the newest version of openai package #199

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Update gpt_request so it can run in the newest version of openai package #199

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570d39f
Update run_gpt.sh
robinji0 Apr 4, 2025
7dce0f7
Update gpt_request.py
robinji0 Apr 4, 2025
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5 changes: 3 additions & 2 deletions bird/llm/run/run_gpt.sh
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Original file line number Diff line number Diff line change
Expand Up @@ -8,6 +8,7 @@ cot='True'
no_cot='Fales'

YOUR_API_KEY=''
BASE_URL='https://api.openai.com/v1'

engine1='code-davinci-002'
engine2='text-davinci-003'
Expand All @@ -21,11 +22,11 @@ data_kg_output_path='./exp_result/turbo_output_kg/'


echo 'generate GPT3.5 batch with knowledge'
python3 -u ./src/gpt_request.py --db_root_path ${db_root_path} --api_key ${YOUR_API_KEY} --mode ${mode} \
python3 -u ./src/gpt_request.py --db_root_path ${db_root_path} --api_key ${YOUR_API_KEY} --base_url ${BASE_URL} --mode ${mode} \
--engine ${engine3} --eval_path ${eval_path} --data_output_path ${data_kg_output_path} --use_knowledge ${use_knowledge} \
--chain_of_thought ${no_cot}

echo 'generate GPT3.5 batch without knowledge'
python3 -u ./src/gpt_request.py --db_root_path ${db_root_path} --api_key ${YOUR_API_KEY} --mode ${mode} \
python3 -u ./src/gpt_request.py --db_root_path ${db_root_path} --api_key ${YOUR_API_KEY} --base_url ${BASE_URL} --mode ${mode} \
--engine ${engine3} --eval_path ${eval_path} --data_output_path ${data_output_path} --use_knowledge ${not_use_knowledge} \
--chain_of_thought ${no_cot}
80 changes: 57 additions & 23 deletions bird/llm/src/gpt_request.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,14 +10,13 @@
from typing import Dict, List, Tuple

import backoff
import openai
from openai import OpenAI
import pandas as pd
import sqlparse
from tqdm import tqdm
'''openai configure'''

openai.debug=True

# Initialize OpenAI client instead of using the global openai module
client = None # We'll initialize this with the API key later

def new_directory(path):
if not os.path.exists(path):
Expand Down Expand Up @@ -147,51 +146,85 @@ def generate_combined_prompts_one(db_path, question, knowledge=None):
return combined_prompts

def quota_giveup(e):
return isinstance(e, openai.error.RateLimitError) and "quota" in str(e)
return "quota" in str(e)

# Updated backoff decorator to use more generic exception handling
@backoff.on_exception(
backoff.constant,
openai.error.OpenAIError,
Exception, # Using generic Exception instead of OpenAI specific error
giveup=quota_giveup,
raise_on_giveup=True,
interval=20
)
def connect_gpt(engine, prompt, max_tokens, temperature, stop):
# print(prompt)
global client
try:
result = openai.Completion.create(engine=engine, prompt=prompt, max_tokens=max_tokens, temperature=temperature, stop=stop)
# Use the new client-based API
if engine.startswith("code-"):
# For codex models, use completions endpoint
response = client.completions.create(
model=engine,
prompt=prompt,
max_tokens=max_tokens,
temperature=temperature,
stop=stop
)
return response
else:
# For newer models, use chat completions endpoint
response = client.chat.completions.create(
model=engine,
messages=[{"role": "user", "content": prompt}],
max_tokens=max_tokens,
temperature=temperature,
stop=stop
)
return response
except Exception as e:
result = 'error:{}'.format(e)
return result
def collect_response_from_gpt(db_path_list, question_list, api_key, engine, knowledge_list=None):
return f'error:{e}'

def collect_response_from_gpt(db_path_list, question_list, api_key, base_url, engine, knowledge_list=None):
'''
:param db_path: str
:param question_list: []
:return: dict of responses collected from openai
'''
global client
client = OpenAI(api_key=api_key, base_url=base_url)

responses_dict = {}
response_list = []
openai.api_key = api_key

for i, question in tqdm(enumerate(question_list)):
print('--------------------- processing {}th question ---------------------'.format(i))
print('the question is: {}'.format(question))

if knowledge_list:
cur_prompt = generate_combined_prompts_one(db_path=db_path_list[i], question=question, knowledge=knowledge_list[i])
else:
cur_prompt = generate_combined_prompts_one(db_path=db_path_list[i], question=question)

plain_result = connect_gpt(engine=engine, prompt=cur_prompt, max_tokens=256, temperature=0, stop=['--', '\n\n', ';', '#'])
# pdb.set_trace()
# plain_result = connect_gpt(engine=engine, prompt=cur_prompt, max_tokens=256, temperature=0, stop=['</s>'])
# determine wheter the sql is wrong

if type(plain_result) == str:

# Parse the response based on its type
if isinstance(plain_result, str):
sql = plain_result
else:
sql = 'SELECT' + plain_result['choices'][0]['text']

# responses_dict[i] = sql
# Handle different response formats based on the API version
if hasattr(plain_result, 'choices') and hasattr(plain_result.choices[0], 'text'):
# Old completions API format
sql = 'SELECT' + plain_result.choices[0].text
elif hasattr(plain_result, 'choices') and hasattr(plain_result.choices[0], 'message'):
# New chat completions API format
message_content = plain_result.choices[0].message.content
if message_content.startswith('SELECT'):
sql = message_content
else:
sql = 'SELECT' + message_content
else:
# Fallback
sql = f"Error: Unexpected response format: {plain_result}"

db_id = db_path_list[i].split('/')[-1].split('.sqlite')[0]
sql = sql + '\t----- bird -----\t' + db_id # to avoid unpredicted \t appearing in codex results
response_list.append(sql)
Expand Down Expand Up @@ -245,6 +278,7 @@ def generate_sql_file(sql_lst, output_path=None):
args_parser.add_argument('--db_root_path', type=str, default='')
# args_parser.add_argument('--db_name', type=str, required=True)
args_parser.add_argument('--api_key', type=str, required=True)
args_parser.add_argument('--base_url', type=str, default='https://api.openai.com/v1')
args_parser.add_argument('--engine', type=str, required=True, default='code-davinci-002')
args_parser.add_argument('--data_output_path', type=str)
args_parser.add_argument('--chain_of_thought', type=str)
Expand All @@ -259,9 +293,9 @@ def generate_sql_file(sql_lst, output_path=None):
assert len(question_list) == len(db_path_list) == len(knowledge_list)

if args.use_knowledge == 'True':
responses = collect_response_from_gpt(db_path_list=db_path_list, question_list=question_list, api_key=args.api_key, engine=args.engine, knowledge_list=knowledge_list)
responses = collect_response_from_gpt(db_path_list=db_path_list, question_list=question_list, api_key=args.api_key, base_url=args.base_url, engine=args.engine, knowledge_list=knowledge_list)
else:
responses = collect_response_from_gpt(db_path_list=db_path_list, question_list=question_list, api_key=args.api_key, engine=args.engine, knowledge_list=None)
responses = collect_response_from_gpt(db_path_list=db_path_list, question_list=question_list, api_key=args.api_key, base_url=args.base_url, engine=args.engine, knowledge_list=None)

if args.chain_of_thought == 'True':
output_name = args.data_output_path + 'predict_' + args.mode + '_cot.json'
Expand Down