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import json
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
# Define the schema for the database
db_schema = {
"products": ["product_id", "name", "price", "description", "type"],
"orders": ["order_id", "product_id", "quantity", "order_date"],
"customers": ["customer_id", "name", "email", "phone_number"]
}
# Load the model and tokenizer
model_name = "EleutherAI/gpt-neox-20b" # You can also use "Llama-2-7b" or another model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
def generate_sql_query(context, question):
"""
Generate an SQL query based on the question and context.
Args:
context (str): Description of the database schema or table relationships.
question (str): User's natural language query.
Returns:
str: Generated SQL query.
"""
# Prepare the prompt
prompt = f"""
Context: {context}
Question: {question}
Write an SQL query to address the question based on the context.
Query:
"""
# Tokenize input
inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=1024).to("cuda" if torch.cuda.is_available() else "cpu")
# Generate SQL query
output = model.generate(inputs.input_ids, max_length=512, num_beams=5, early_stopping=True)
query = tokenizer.decode(output[0], skip_special_tokens=True)
# Extract query from the output
sql_query = query.split("Query:")[-1].strip()
return sql_query
# Schema as a context for the model
schema_description = json.dumps(db_schema, indent=4)
# # Example interactive questions
# print("Ask a question about the database schema.")
# while True:
# user_question = input("Question: ")
# if user_question.lower() in ["exit", "quit"]:
# print("Exiting...")
# break
user_question = 'Show all products that cost more than $50'
# Generate SQL query
sql_query = generate_sql_query(schema_description, user_question)
print(f"Generated SQL Query:\n{sql_query}\n")
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