Have you ever found yourself needing to extract specific data from a database using simple, user-friendly language? In this article, we will walk you through a powerful method of converting natural language questions into SQL queries using Python. This method utilizes an upgraded version of a tool designed for converting textual queries into SQL commands, offering enhanced support for handling multiple tables.

What You Will Need

• Python installed on your computer
• The Transformers library from Hugging Face
• Basic understanding of SQL and Python programming

Setup Your Environment

Before diving into the code, you need to ensure you have the necessary packages installed. Using pip, you can install the required libraries with the following command:

pip install transformers

Understanding the Code

Let’s break down the Python code needed to turn your questions into SQL queries using an analogy. Imagine you’re teaching a machine to bake a cake (which represents SQL). You need to provide it with ingredients (data), instructions (questions), and a recipe (the code). Here’s a glimpse into the essential components that we will implement:

• Tokenizer: Think of this as the tool that breaks down our ingredients into manageable parts.
• Model: This is essentially our skilled baker who knows how to follow recipes based on the given instructions.
• Input Preparation: This step combines ingredients and recipes before handing them to the baker.
• Inference: Here’s where the baking happens, and you get your delicious SQL output at the end!

The Code Walkthrough

Below is the complete code that we will be using to convert questions into SQL queries:

from typing import List, Dict
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained('juierrorflan-t5-text2sql-with-schema-v2')
model = AutoModelForSeq2SeqLM.from_pretrained('juierrorflan-t5-text2sql-with-schema-v2')

def get_prompt(tables, question):
    prompt = f'convert question and table into SQL query. tables: {tables}. question: {question}'
    return prompt

def prepare_input(question: str, tables: Dict[str, List[str]]):
    tables = [f'{table_name}({",".join(tables[table_name])})' for table_name in tables]
    tables = ', '.join(tables)
    prompt = get_prompt(tables, question)
    input_ids = tokenizer(prompt, max_length=512, return_tensors='pt').input_ids
    return input_ids

def inference(question: str, tables: Dict[str, List[str]]) -> str:
    input_data = prepare_input(question=question, tables=tables)
    input_data = input_data.to(model.device)
    outputs = model.generate(inputs=input_data, num_beams=10, top_k=10, max_length=512)
    result = tokenizer.decode(token_ids=outputs[0], skip_special_tokens=True)
    return result

print(inference('how many people with name jui and age less than 25', {'people_name': ['id', 'name'], 'people_age': ['people_id', 'age']}))
print(inference('what is id with name jui and age less than 25', {'people_name': ['id', 'name'], 'people_age': ['people_id', 'age']}))

Executing Your Code

Run the above code after setting up your environment properly. It will generate SQL queries based on the questions provided. For example, asking how many people with the name “Jui” and age less than 25 will yield an SQL query that succinctly retrieves that data from the database.

Troubleshooting Tips

If you encounter issues while executing the code, consider these troubleshooting ideas:

• Ensure all necessary libraries are correctly installed.
• Check if your Python environment is set up with the right versions of packages.
• Make sure to use the correct names and attributes for your database tables.
• If the model fails to generate SQL, revisit the loaded model name or connection settings.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

Conclusion

Using the outlined method, you can seamlessly convert questions into SQL queries, making your data retrieval process efficient and intuitive. At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.