The Text-to-SQL agent allows users to interact with a database using natural language queries instead of writing SQL code directly. It leverages large language models (LLMs) trained on code, including SQL, to translate natural language questions into executable SQL queries, execute those queries against a database, and then return the results in a human-readable format.

The Text-to-SQL agent is built using several key technologies:

• LangGraph: Used to build the core ReAct agent, which handles the reasoning and action execution.
• Next.js: Provides the frontend application framework for the user interface.
• watsonx.ai: Supplies the large language models (LLMs), such as Mistral Large, for natural language processing and SQL generation.
• SQLite 3: An in-memory database used for storing and querying data in this demonstration.
• LangChain: A framework on which LangGraph is based, providing tools and components for working with LLMs, including message handling and tool definitions.
• TypeScript: Used for type-safe development.
• Tailwind CSS: Used for styling the frontend application.

The agent interacts with the database through a custom tool called "GetFromDB". This tool is defined within the LangChain framework and acts as an interface between the LLM and the database. When the LLM determines that a database query is needed to answer a user's question, it calls the "GetFromDB" tool, providing a generated SQL query as input. The "GetFromDB" tool then executes this SQL query against the SQLite database and returns the results to the LLM, which then processes them into a natural language response for the user. The database schema (customer and order tables) is also provided to the LLM as part of the tool definition, helping it generate accurate SQL queries.

A ReAct agent is an AI agent that combines "Reasoning" and "Acting" to solve problems. In the context of this Text-to-SQL agent, the ReAct agent, built with LangGraph, enables the LLM to:

• Reason: Understand the user's natural language query, identify the need for database interaction, and formulate a plan (e.g., generate a SQL query).
• Act: Utilize available tools, specifically the "GetFromDB" tool, to execute the formulated SQL query against the database.
• Observe: Receive the results from the database query.
• Reflect: Use the observed results to form a coherent natural language answer for the user.

This iterative process of reasoning and acting allows the agent to handle complex queries that require external information from the database.

The application manages LLM responses and user inputs through a "message history" state variable in the Next.js frontend. This history stores different types of messages:

• SystemMessage: A hidden prompt that guides the LLM's behavior and informs it about its role (e.g., "generate SQLite queries, use GetFromDB tool").
• HumanMessage: Represents messages or queries submitted by the user.
• AIMessage: Represents the responses generated by the large language model.

These messages are serialized to JSON before being sent from the frontend (client-side) to the backend (server-side) actions.ts file, and then deserialized for processing by LangChain and the LLM. This ensures proper communication and context retention.

The demonstration uses an in-memory SQLite database with two tables:

• customer table: Contains ID, email, and name columns.
• order table: Contains ID, customer_id (a foreign key linking to the customer table), product, and amount columns.

The database is seeded with mock data: 10 sample customer entries and several order entries linked to these customers, allowing the agent to perform queries involving joins and aggregations (e.g., "how many customers do I have?", "which customer placed most orders?").

The source briefly mentions the importance of "guardrails" for a Text-to-SQL agent. This implies that for production environments, it's crucial to implement security measures to prevent the LLM from having "unlimited control of your database." While not detailed in the source, this would typically involve:

• SQL injection prevention: Ensuring that generated SQL queries are safe and cannot be manipulated by malicious user input.
• Access control: Limiting the LLM's permissions to only what is necessary (e.g., read-only access for certain tables).
• Validation and sanitization: Thoroughly checking and cleaning user input before it's used to generate SQL.
• Monitoring and logging: Tracking the queries executed by the agent for auditing and error detection.

The Next.js frontend provides a simple yet effective user interface:

• Input Box and Send Button: Allows users to type their natural language queries and submit them.
• Message Display: Shows a chat-like history of user queries and AI responses.
• Loading State: A visual indicator (button text changing to "Loading," input box disabled) informs the user when the LLM is processing a request, preventing multiple submissions and confusion.
• Dynamic Rendering: Messages are dynamically rendered from the application's state, distinguishing between HumanMessages (user) and AIMessages (LLM) and replacing initial placeholder messages.
• Clear History: The application implies the ability to clear message history by suggesting "refresh so we have a clean message history," important for managing the LLM's context window.