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from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from init import ACCESS_TOKEN, SYSTEM_PROMPT
from utils import extract_sql, is_sql
from database import execute
# Load the model and tokenizer
model_name = "Qwen/Qwen2.5-3B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name, use_auth_token=True)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map="auto")
def respond(message, history, system_message, max_tokens, temperature, top_p):
messages = [{"role": "system", "content": SYSTEM_PROMPT}]
# Process chat history
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
# Tokenize input
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
# Generate response
output_ids = model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True
)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
yield response
# SQL Processing and Retry Logic
if is_sql(response):
sql_query = extract_sql(response)
max_attempts = 3
attempts = 0
sql_result = None
last_error = None
while attempts < max_attempts:
try:
sql_result = execute(sql_query)
break
except Exception as e:
last_error = str(e)
attempts += 1
if attempts < max_attempts:
clarification_prompt = f"Tôi gặp lỗi khi thực hiện truy vấn SQL: {last_error}\nBạn có thể chỉnh sửa câu hỏi hoặc cung cấp thêm thông tin không?"
messages += [
{"role": "assistant", "content": response},
{"role": "user", "content": clarification_prompt},
]
# Tokenize clarification prompt
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
# Generate new response
output_ids = model.generate(
input_ids,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True
)
response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
yield response
if is_sql(response):
sql_query = extract_sql(response)
else:
retry_prompt = f"Tôi đã thử {max_attempts} lần nhưng vẫn gặp lỗi: {last_error}\nBạn có thể cung cấp thêm chi tiết về dữ liệu cần truy vấn không?"
yield retry_prompt
return
if sql_result is not None:
reformulation_prompt = f"Kết quả truy vấn SQL:\n{sql_result}\nHãy tóm tắt kết quả thành phản hồi tự nhiên."
messages += [
{"role": "assistant", "content": response},
{"role": "user", "content": reformulation_prompt},
]
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
input_ids = tokenizer.encode(input_text, return_tensors="pt").to(model.device)
output_ids = model.generate(
input_ids,
max_new_tokens=512,
temperature=temperature,
top_p=top_p,
do_sample=True
)
reformulated_response = tokenizer.decode(output_ids[0], skip_special_tokens=True)
yield reformulated_response
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