Learn to Build an AI SQL Assistant with LLM
Table of Contents
- Introduction
- How an LLM-Based Application Works
- Benefits of Using an LLM-based Application
- Developing an LLM Application
- Libraries Required
- Creating the Front End
- Loading the Prompt
- Creating the LLM Chain
- Executing the SQL Query
- Displaying the Results
- Conclusion
Introduction
Welcome to this tutorial on building an LLM-based application that communicates with a database to extract information. In this tutorial, we will explore how to Create an application that can take user's questions in natural language and convert them into SQL queries using LLM. The generated SQL queries will then be executed in the database and the results will be returned to the user. This Type of application is particularly useful in real business scenarios, such as the reporting layer of data warehousing projects, where users may not have SQL expertise but still need to access information from the database quickly.
How an LLM-based Application Works
The LLM-based application We Are building follows a simple process. It takes user's questions in natural language as input and passes them to the underlying LLM model. The LLM model then converts the natural language question into an SQL query. This SQL query is then passed to the database by our application, executed, and the final output is returned to the user. The application provides a user-friendly interface where users can input their queries and receive the desired information in a structured format.
Benefits of Using an LLM-based Application
- No SQL expertise required: Users who do not have SQL expertise can use natural language to get quick information from the database.
- Quick and easy access to information: The application processes the natural language queries and generates the SQL queries, eliminating the need for manual query writing.
- Ability to handle complex queries: The LLM model can identify the tables to be used, join them, and extract the required information, even in complex queries.
- Improved efficiency: With the LLM-based application, users can retrieve information from the database faster, resulting in improved efficiency in decision-making processes.
Developing an LLM Application
To develop an LLM-based application, we will need several libraries such as LinkedIn, Streamlit, OpenAI, and Snowflake Python Connector. These libraries will provide the necessary functionality to build the front end, communicate with the LLM model, and execute SQL queries in the database.
Libraries Required
The following libraries are required to develop the LLM-based application:
- LinkedIn: A framework for developing applications on LLM.
- Streamlit: A library used to create the front end of the application.
- OpenAI: A library used to communicate with the LLM model.
- Snowflake Python Connector: A library used to connect to the Snowflake database.
Creating the Front End
To create the front end of the application, we will use Streamlit. Streamlit allows us to create a user-friendly interface where users can input their queries. We will also create tabs for different sections, such as the results, SQL query, and ER Diagram.
Loading the Prompt
The prompt is a crucial part of the LLM-based application as it provides Context and instructions to the LLM model. We will load the prompt from a separate file that contains the necessary information about the application, including the tables' DDL and example use cases.
Creating the LLM Chain
To Interact with the LLM model, we need to create an instance of the LLM chain. This chain will be responsible for generating SQL queries based on the user's input. We will set the temperature to 0 to ensure consistent query generation.
Executing the SQL Query
Once we have the SQL query generated by the LLM model, we need to execute it in the database. We will use the Snowflake Python Connector library to execute the query and retrieve the results.
Displaying the Results
The final step is to display the results to the user. We will use the Streamlit library to display the results in the appropriate tabs, such as the results tab, SQL query tab, and ER diagram tab.
Conclusion
In this tutorial, we have learned how to build an LLM-based application that communicates with a database to extract information. By following the step-by-step process, we have created a user-friendly interface where users can input their queries in natural language and receive the desired results. This type of application is highly beneficial in real business scenarios where users may not have SQL expertise but still need to access information from the database quickly. With an LLM-based application, users can improve their efficiency and decision-making processes.
Highlights
- Building an LLM-based application that communicates with a database to extract information
- Using natural language queries to generate SQL queries using LLM
- Providing a user-friendly interface for easy input and retrieval of information
- Handling complex queries and joining tables dynamically
- Improving efficiency and decision-making processes in real business scenarios
FAQ
Q: How does an LLM-based application work?
A: An LLM-based application takes user's questions in natural language as input, uses LLM to convert them into SQL queries, executes the queries in the database, and returns the results to the user.
Q: What are the benefits of using an LLM-based application?
A: Using an LLM-based application eliminates the need for SQL expertise, provides quick access to information, handles complex queries, and improves efficiency in decision-making processes.
Q: What libraries are required to develop an LLM-based application?
A: The required libraries include LinkedIn, Streamlit, OpenAI, and Snowflake Python Connector.
Q: How can I create the front end of the application?
A: You can use the Streamlit library to create a user-friendly interface where users can input their queries.
Q: How does the LLM chain work?
A: The LLM chain interacts with the LLM model to generate SQL queries based on user's input.
Q: How can I execute the generated SQL queries in the database?
A: You can use the Snowflake Python Connector library to execute the SQL queries in the database.
Q: Can an LLM-based application handle complex queries?
A: Yes, the LLM model can identify tables, join them, and extract the required information, even in complex queries.
Q: How does an LLM-based application improve efficiency?
A: By providing quick access to information, an LLM-based application improves efficiency in decision-making processes.
Q: Is the LLM-based application suitable for real business scenarios?
A: Yes, an LLM-based application is specifically helpful in real business scenarios, such as the reporting layer of data warehousing projects. Users without SQL expertise can use natural language to retrieve quick information from the database.