Convert Natural Language to SQL Queries with OpenAI API
Science & Technology
Introduction
In this article, we will explore how to leverage the OpenAI API to transform natural language queries into SQL statements, using a structured approach that includes prompt engineering and schema submission.
Introduction
Hi, my name is Ray Bernard, your OpenAI tutor. In this lesson, we will walk through a practical example of taking natural language input and converting it into a SQL query using the OpenAI API. We will use a simple schema, a predefined structure of our database, to facilitate the generation of a SQL query.
Setting the Framework
To begin with, it's essential to submit the entire schema of the database you are working with. By providing the schema, OpenAI can create a relevant SQL query that aligns with the structure of your database. One cautionary note is that if your schema is extensive, you will be charged for every word submitted, as everything needs to be tokenized and processed.
In this demonstration, we will be working with only three out of the four tables included in the example schema.
Configuring OpenAI Parameters
The OpenAI playground allows you to adjust various parameters such as:
- Token usage: Control how many tokens are allowed.
- Randomness: Determining the level of creativity in the generated SQL.
- Repetition likelihood: Whether or not the response can include repeated phrases.
- Topic initiation: Adjusting the chances of starting a new topic in the response.
After configuring these parameters, we can submit our natural language query into the OpenAI environment.
Submitting the Query
Unfortunately, when we first ran the prompt, OpenAI did not recognize that the schema had already been submitted. Instead, it generated a generic SQL query that did not align with the database structure we were working with. As a result, when executing this query against our actual database, it did not yield the expected results.
To rectify this, we will focus on writing code to process the results returned by OpenAI’s API. We will also need to ensure that we are filtering for SQL compliance so that the queries created align with our database.
Finalizing the Code
After refining our process, we submitted our prompt once again, this time with great anticipation. The result? A correct SQL query that met our requirements and was ready to be copied and pasted into our fictitious database.
Discussion on Prompt Engineering
What we’ve accomplished here is known as prompt engineering. This technique involves crafting your prompts in such a way that they yield useful responses from the AI. It is crucial to provide not only the natural language input, but also the database schema, to create an effective SQL query.
In an enterprise setting, working with distributed databases can complicate this process, as you may not know which tables to submit. To enhance this situation, consider fine-tuning your AI model. By training it on repetitive SQL queries using a special format called JSON NL (which consists of your SQL query and an accompanying prompt), OpenAI can learn your specific query structure. This way, when you request a SQL query in everyday language, it won’t prompt you for the schema; it will already know what it needs.
Additionally, we can employ what is referred to as an agent, which can autonomously access the database on your behalf, run the crafted query, and return the actual results.
Conclusion
This process demonstrates how powerful and practical the OpenAI API can be in converting natural language into SQL queries. Thank you for joining me on this journey, and I invite you to stay tuned for more videos and articles on similar topics. Don’t forget to click subscribe and share widely!
Keywords
- OpenAI API
- SQL Query
- Natural Language Processing
- Prompt Engineering
- Database Schema
- JSON NL
FAQ
Q1: What is prompt engineering?
A1: Prompt engineering is the technique of crafting requests to an AI model to yield useful and accurate responses, often involving specific instructions and context.
Q2: How does OpenAI generate SQL Queries from natural language?
A2: OpenAI generates SQL queries by understanding the natural language input and matching it to a provided database schema, allowing it to construct an appropriate query.
Q3: What is JSON NL?
A3: JSON NL is a special formatting used for fine-tuning OpenAI, consisting of SQL queries and prompt responses. It helps the AI learn specific query structures.
Q4: Can OpenAI execute SQL queries directly on a database?
A4: OpenAI itself cannot execute SQL queries directly, but you can employ an agent that can interact with the database on your behalf to run the queries and return results.
Q5: Are there costs associated with using OpenAI’s API?
A5: Yes, there are costs associated with using OpenAI’s API, particularly based on the number of tokens processed, including the length of the input schema and generated responses.