Text-to-SQL with LLMs – Embracing the Future of Data Interaction

In an age where data is the cornerstone of decision-making, the ability to interact seamlessly with databases is invaluable. This is where Text-to-SQL, powered by Large Language Models (LLMs), is revolutionizing the way we handle data. But what exactly is Text-to-SQL, and how are LLMs like GPT-3 and Google’s PaLM making a difference?

Text-to-SQL technology is a bridge between natural language and database queries. Traditionally, querying databases required proficiency in SQL, a barrier for many. Text-to-SQL changes this, enabling queries in plain language that are then translated into SQL.

Instead of writing a sql query such as:

SELECT name FROM employees WHERE department = 'Sales';

to find out the names of all employees in the Sales department, imagine simply asking:

“Who are the employees in the Sales department?”

Text-to-SQL applications are increasingly gaining traction, offering a user-friendly bridge between the intricate world of SQL queries and the straightforwardness of business language.

In this article, we’ll delve into three key strategies for implementing Text-to-SQL applications: The Context-Window Approach, the Retrieval Augmentation Generation (RAG) SQL-to-TEXT Approach, and End-to-End Fine-Tuning. Each of these methods offers unique advantages and challenges in the quest to make data more accessible and interactive.

Let’s get things started!

Also: 9 Business Use Cases for OpenAI GPT

LLM-Basics for Text-to-SQL

LLMs like GPT-4 for LLMA2 are pretrained AI models that carry out tasks when presented with a prompt (the input). Among other things they are capable of converting natural language into SQL statements.

Also: ChatGPT Prompt Engineering Guide: Practical Advice for Business Use Cases

If you just give them the input query along with the command to convert to SQL, they will try to infer the schema in the most streight forward way. But what works usually better is when you give them additional information about the database, along with relationships, keys, attributes, etc.

A recet paper evaluates the capabilities of ChatGPT for convert natural language to SQL:
A comprehensive evaluation of ChatGPT’s zero-shot Text-to-SQL capability

Database Schema vs Token Limit

The current state is that LLMs can only process a certain amount of data at the same time. This amount is defined by the token limit, or context window. For instance, the standard model GPT-3.5-Turbo has a token window of 16,000 tokens, equating to approximately 8,000 words. While this is sufficient for simpler databases with a few tables, for more intricate schemas, you will quickly hit that limit.

From Zero-Shot to Few-Shot Learning

LLMs can fullfil many tasks out of the box, just by receiving the task. However, typically you can improve their performance and tailor answers to your expectations by providing them with examples. The same is true for text-to-sql, in particular when you want the LLM to understand your data structure.

We differentiate between giving not examples (zero-shot), a single example (one-shot), and more than one examples (few-shot). The table below provides a few examples for these three types. The number of examples we can provide, is limited by the context window. In general, adding more (high quality) examples will lead to better and more consistent results, but it will also increase costs and latency, because the LLM will need to process the additional data. So at the end its about finding a sound balance.

LLM-Based Approaches for Implementing Text-to-SQL

The integration of Text-to-SQL with Large Language Models (LLMs) offers a groundbreaking way to bridge natural language and database queries. However, there is no one size fits it all approach. Based on the complexity of the database structure, a different approach may be suitable.

In this article, I will discuss three common approaches on how you can implement a text-to-sql solution.

1. Everything in Context Window
2. Augmentation Retrieval Generation
3. LLM Fine-Tuning

Understanding these different ways is crucial for effectively leveraging Text-to-SQL technology.

The table below gives an overview of the three approaches:

Let’s look into these approach a bit more in detail.

Approach 1: Everything in Context Window

The most streightforward method for integrating Text-to-SQ, is the “Everything in Context Window” approach. The idea is to just input a simplified version of your database schema directly into the context window of the LLM. This method is particularly useful for enabling the LLM to understand and generate SQL queries based on natural language input that corresponds to your specific database structure.

Here’s a more detailed description of this approach:

Key Aspects of the Context Window Approach

1. Schema Simplification:
   • The goal is to distill the database schema down to its core elements. This means including table names, column names, primary keys, foreign keys, and data types.
   • The simplified schema should be concise yet comprehensive enough for the LLM to understand the relationships and constraints within the database.
2. Formatted Text Input:
   • The schema is typically formatted as plain text. This could be in the form of a list or a table-like structure that is easy for the model to parse.
   • Consistency in formatting across different tables and relationships is crucial for clarity.
3. Inclusion of Relationships:
   • Clearly indicate how different tables are related. Specify which columns serve as primary keys and which are foreign keys that link to other tables.
   • Describing relationships is vital for the LLM to accurately generate SQL queries involving joins and complex queries.

Example of a Simplified Schema Format

Database Schema Overview:

Table: Employees
- employee_id (Primary Key)
- name
- department_id (Foreign Key: Departments.department_id)
- role

Table: Departments
- department_id (Primary Key)
- department_name

Relationships:
- Employees.department_id -> Departments.department_id (Employees are linked to Departments)

Advantages & Limitations

Immediate understanding of database structure, leading to more accurate SQL query generation. It is easy to implement an cost-efficient for simple databases with few tables and simple relationships.

A few things to consider:

• Be mindful of the LLM’s context window size limitations. Overloading the context window with too much information can lead to less effective query generation.
• While the schema should be detailed enough to provide a clear understanding, it should also be concise to prevent overwhelming the model.
• The effectiveness of this approach can vary depending on the specific LLM’s training and capabilities in parsing and utilizing the provided schema information.

Approach 2: Augmentation Retrieval Generation (RAG)

The Augmentation Retrieval Generation (RAG) approach is a more dynamic method of integrating text-to-sql with Large Language Models (LLMs). Let’s assume the LLM relies on a large amount of information on structured data to perform correct text-to-sql generation. If the amount of data becomes to complex, an approach that purely relies on the context window won’t work. The next best alternative is to structure and store the meta information about the database, its tables and relationships in a knowledgebase. We then only retrieve the information that is needed to process the user query. Let’s look at this approach in more detail.

The Two Phases of the RAG Approach

Unlike directly inputting all database metadata into the LLM, the RAG approach operates in two phases.

1. Identification Phase:
   • The LLM first processes the user’s natural language query to identify key entities and the user’s intent.
   • This phase focuses on understanding what the user is asking for without yet delving into database specifics.
2. Retrieval and Augmentation Phase:
   • The system then performs a targeted search in a separate database or knowledge base to retrieve relevant metadata. This could involve fetching information about specific tables, columns, or relationships pertinent to the user’s query.
   • This retrieved information is then augmented or combined with the original user query, creating an enriched context for the LLM.

End-to-End Example

Natural Language Query:

"Show me the latest transactions of client John Doe."

Identification Phase:

• The LLM analyzes this query to identify key entities and intents. Here, the key entities are “transactions” and “John Doe,” and the intent is to retrieve recent transaction records for this specific client.

Retrieval and Augmentation Phase:

• The system then searches an external database or knowledge base for metadata related to “transactions” and “John Doe.”
• It might retrieve information like the table where transactions are stored (e.g., Transactions table), the relevant columns (e.g., client_name, transaction_date, amount), and the specific client details (e.g., records where client_name = 'John Doe').

Enriched Query for LLM:

• The retrieved information is combined with the original query, forming an enriched context. The LLM now understands that it needs to generate a SQL query for the Transactions table, specifically targeting records related to “John Doe” and focusing on the most recent entries.

Resultant SQL Query (Hypothetical):

SELECT * FROM Transactions WHERE client_name = 'John Doe' ORDER BY transaction_date DESC LIMIT 10;

In this example, the RAG approach effectively breaks down the process, initially focusing on understanding the user’s query and then retrieving specific database details necessary for formulating an accurate SQL query. This approach allows for handling complex queries in a more structured and efficient manner.

Advantages & Limitations

Compared to a pure Context Window Approach, the RAG approach is better suited for larger and more complex databases, as it avoids overwhelming the LLM with excessive information at once. By providing only relevant information, it maintains the model’s efficiency and improves the accuracy of the generated SQL queries. It can handle dynamic queries more effectively as it retrieves and processes information based on each specific query.

While more scalable than the context window approach, RAG can still struggle with extremely complex databases, particularly those with many intricate relationships. The approach may face challenges in maintaining consistency in query responses, especially when dealing with varying or ambiguous user intents. The effectiveness of this approach is partly contingent on the robustness and accuracy of the external information retrieval system. Considering how to structure the information about tables and relationships is key. Doublicate or similar names may pose additional challenges.

Approach 3: LLM Fine-Tuning

One of the most potent strategies in integrating Text-to-SQL with LLMs is the fine-tuning approach. This method involves custom training of a pre-trained LLM on specific datasets relevant to the particular database and use case. Fine-tuning allows the model to adapt to the unique characteristics and requirements of a specific domain or dataset, thus improving its ability to generate accurate SQL queries from natural language inputs.

The Process of Fine-Tuning

1. Dataset Preparation: This step involves creating or assembling a dataset that is representative of the specific use case. For a Text-to-SQL application, this would typically include pairs of natural language queries and their corresponding SQL queries, tailored to the specific database schema.
2. Initial Model Training: The process begins with a pre-trained LLM, such as GPT-3 or BERT, which has already learned a broad array of language patterns and structures.
3. Custom Training (Fine-Tuning): The model is then further trained (fine-tuned) on the prepared dataset. This stage helps the model to align its language understanding capabilities with the specific patterns, terminology, and structures found in the target domain or database.
4. Iterative Refinement: Fine-tuning is often an iterative process. The model’s performance is continuously evaluated and refined based on feedback and performance metrics. This could involve adjusting training parameters, adding more data, or tweaking the model architecture.

Example of Data Preparation for Fine-Tuning:

The dataset should consist of pairs of natural language queries and their respective SQL queries. These pairs act as examples that the model will learn from.

1. Natural Language Query:
   • This is a user’s question or request stated in everyday language.
   • Example: “What is the total revenue from sales this month?”
2. Corresponding SQL Query:
   • This is the SQL command that represents the natural language query.
   • Example: SELECT SUM(revenue) FROM sales WHERE date BETWEEN '2021-07-01' AND '2021-07-31';

Creating a Representative Dataset:

• The dataset should cover a broad range of queries that reflect different types of SQL operations such as SELECT, UPDATE, JOIN, GROUP BY, etc.
• It should include queries of varying complexities – from simple queries involving a single table to more complex ones that require joins across multiple tables.

Annotation and Accuracy:

• Each pair in the dataset must be accurately annotated to ensure that the SQL query correctly represents the natural language query.
• It’s crucial to verify the correctness of both the SQL queries and their natural language counterparts.

Diversity and Domain-Specific Data:

• The dataset should be diverse, covering different aspects and structures within the database.
• For domain-specific applications, include terminology and query structures relevant to that domain.

Advantages & Limitations

The fine-tuning approach is the most sophisticated and best suitable for complex databases with complex relationships and many attributes. While fine-tuning offers a tailored and often more accurate approach, it is generally more resource-intensive and costly. It requires a significant investment in terms of data preparation and computational resources but can yield superior results, especially for specialized or complex applications. Also be aware that you require high-quality training data, which might be a challenge to generate. In general, the more complex your database and the possible user queries, the more training data will be required. Also consider that changes in the database will require you to repeat the fine-tuning process and add new training data, which can be challenging in fast-chaning environments.

Each of these approaches has its unique strengths and is suitable for different scenarios in Text-to-SQL applications. The choice depends on factors such as the complexity of the database, the volume of data, and the specific requirements of the application.

Additional Considerations

Finally, a few additional things to consider when building text-to-sql applications with LLMs.

• Combining Approaches: For sophisticated use cases, a hybrid approach combining fine-tuning and RAG can be employed. This combination leverages the strengths of both methods, offering a robust solution for complex scenarios.
• Self-Correction Mechanism: Incorporating a self-correction mechanism into the Text-to-SQL process can significantly enhance the accuracy and reliability of the generated SQL queries. This involves the LLM identifying potential errors or ambiguities in its initial query generation based on the database response and iteratively refining its output. Self-correction is particularly valuable in dynamic environments where database schemas evolve or user queries vary significantly.
• Balancing Complexity and Performance: While self-correction adds a layer of sophistication, it also requires careful balance to avoid excessive computational demands. This feature is particularly beneficial in scenarios where accuracy is paramount, and resources permit iterative processing.

Summary

The adoption of Text-to-SQL in business processes transcends mere convenience; it represents a pivotal stride towards making data access more democratic. This innovation empowers individuals without deep SQL expertise to retrieve and scrutinize data, significantly enhancing decision-making processes and streamlining business operations.

In this blog article, we delve into the transformative impact of integrating Text-to-SQL technology into business processes. We explore three primary approaches: Data in Context Window, Retrieval-Augmented Generation (RAG), and End-to-End Fine-Tuning. Each approach is examined for its unique challenges and benefits, from intuitive database interactions to handling complex data structures and tailoring solutions to specific use cases.

Embrace this transformative journey with Text-to-SQL, and unlock the full potential of your data.

Sources

• Enhancing Text-to-SQL Translation for Financial System Design
• A comprehensive evaluation of ChatGPT’s zero-shot Text-to-SQL capability
• StructGPT: A General Framework for Large Language Model to Reason over Structured Data
• Talk to your Database using RAG and LLMs