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Generate SQL queries from schema only - AI-powered

YuliaYulia
52198 views
2/3/2026
Leads ManagementGoogle SheetsZoho CRMAutomationData Sync
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This workflow is a modification of the previous template on how to create an SQL agent with LangChain and SQLite.

The key difference – the agent has access only to the database schema, not to the actual data. To achieve this, SQL queries are made outside the AI Agent node, and the results are never passed back to the agent.

This approach allows the agent to generate SQL queries based on the structure of tables and their relationships, without having to access the actual data.

This makes the process more secure and efficient, especially in cases where data confidentiality is crucial.

🚀 Setup

To get started with this workflow, you’ll need to set up a free MySQL server and import your database (check Step 1 and 2 in this tutorial).

Of course, you can switch MySQL to another SQL database such as PostgreSQL, the principle remains the same. The key is to download the schema once and save it locally to avoid repeated remote connections.

Run the top part of the workflow once to download and store the MySQL chinook database schema file on the server.

With this approach, we avoid the need to repeatedly connect to a remote db4free database and fetch the schema every time. As a result, we reach greater processing speed and efficiency.

🗣️ Chat with your data

  1. Start a chat: send a message in the chat window.
  2. The workflow loads the locally saved MySQL database schema, without having the ability to touch the actual data. The file contains the full structure of your MySQL database for analysis.
  3. The Langchain AI Agent receives the schema, your input and begins to work.
  4. The AI Agent generates SQL queries and brief comments based solely on the schema and the user’s message.
  5. An IF node checks whether the AI Agent has generated a query. When:
  • Yes: the AI Agent passes the SQL query to the next MySQL node for execution.
  • No: You get a direct answer from the Agent without further action.
  1. The workflow formats the results of the SQL query, ensuring they are convenient to read and easy to understand.
  2. Once formatted, you get both the Agent answer and the query result in the chat window.

🌟 Example queries

Try these sample queries to see the schema-driven AI Agent in action:

  1. Would you please list me all customers from Germany?

  2. What are the music genres in the database?

  3. What tables are available in the database?

  4. Please describe the relationships between tables. - In this example, the AI Agent does not need to create the SQL query.

And if you prefer to keep the data private, you can manually execute the generated SQL query in your own environment using any database client or tool you trust 🗄️

💭 The AI Agent memory node does not store the actual data as we run SQL-queries outside the agent. It contains the database schema, user questions and the initial Agent reply. Actual SQL query results are passed to the chat window, but the values are not stored in the Agent memory.

AI-Powered SQL Query Generator from Schema Only

This n8n workflow leverages AI to generate SQL queries based solely on a provided database schema. It simplifies the process of creating complex queries by understanding the database structure and generating appropriate SQL statements.

What it does

This workflow automates the following steps:

  1. Triggers Manually: The workflow is initiated by a manual trigger, allowing you to run it on demand.
  2. Sets Initial Data: It prepares the input data, likely including the database schema or a prompt for the AI.
  3. Conditional Logic (If Node): It includes an 'If' node, indicating a conditional branch in the workflow. This might be used to check for specific input conditions before proceeding.
  4. AI Agent Interaction: It uses an 'AI Agent' node, powered by Langchain, to process the input and generate SQL queries. This agent likely utilizes a large language model to understand the schema and formulate queries.
  5. OpenAI Chat Model: The 'AI Agent' node is configured to use an 'OpenAI Chat Model' for its language processing capabilities, indicating the use of OpenAI's powerful AI models.
  6. Simple Memory: A 'Simple Memory' node is included, suggesting that the AI agent maintains some conversational context or remembers previous interactions within a single workflow execution.
  7. File Operations (Read/Write/Convert/Extract): The workflow includes nodes for reading, writing, converting, and extracting files from disk. This suggests that the database schema might be provided as a file, or the generated SQL queries could be saved to a file in various formats (e.g., CSV, JSON, HTML, PDF).
  8. Merge Data: A 'Merge' node is present, which could be used to combine data from different branches of the 'If' node or to integrate results from file operations with the AI agent's output.
  9. No Operation (Placeholder): A 'No Operation' node acts as a placeholder or a point where the workflow might pause or end without performing any specific action, potentially indicating a successful completion or a branch that doesn't require further processing.
  10. MySQL Interaction: The workflow includes a 'MySQL' node, which implies that it can connect to and potentially execute the generated SQL queries against a MySQL database.
  11. Chat Trigger: A 'Chat Trigger' node is present, suggesting an alternative or additional way to initiate the workflow, possibly by receiving a chat message containing a request for a SQL query.

Prerequisites/Requirements

  • n8n Instance: A running n8n instance.
  • OpenAI API Key: For the 'OpenAI Chat Model' to function, you will need an OpenAI API key configured as a credential in n8n.
  • MySQL Database (Optional): If you intend to execute the generated queries, access to a MySQL database and its connection details will be required.
  • File Storage: Access to a local disk or connected file storage if using the file read/write/convert/extract nodes.

Setup/Usage

  1. Import the Workflow: Download the JSON file and import it into your n8n instance.
  2. Configure Credentials:
    • Set up your OpenAI API key as a credential for the 'OpenAI Chat Model' node.
    • If connecting to MySQL, configure your MySQL credentials (host, port, user, password, database) for the 'MySQL' node.
  3. Provide Schema Input:
    • Manual Trigger: When using the 'Manual Trigger', you will need to manually input the database schema (e.g., as a JSON string, plain text, or by reading it from a file using the 'Read/Write Files from Disk' node).
    • Chat Trigger: If using the 'Chat Trigger', configure it to listen for specific chat messages that would include or reference the database schema.
  4. Customize AI Agent: Review and adjust the 'AI Agent' node's prompt and configuration to best suit your SQL generation needs.
  5. Run the Workflow:
    • Click "Execute Workflow" for the 'Manual Trigger'.
    • Send a configured chat message if using the 'Chat Trigger'.
  6. Review Output: The generated SQL queries will be available in the output of the 'AI Agent' node, and potentially saved to a file or executed against your MySQL database depending on the workflow's configuration.

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