You can use DBeaver’s AI features to turn a natural-language question into SQL: configure a supported AI provider, select the database connection and scope, ask a focused question, then review the generated SQL before running it. The AI can help draft queries, but you remain responsible for checking that the SQL and its business logic are correct.
What DBeaver’s AI data analyst workflow does
DBeaver’s AI Assistant adds natural-language features for database work, including AI chat, SQL generation and editing, SQL explanations, and help with errors. Which features are available depends on your DBeaver edition and configuration; some are marked PRO-only in DBeaver’s current AI Assistant documentation.
The workflow is not an autonomous analyst that knows your business definitions. You provide the database context and question; the assistant proposes SQL, which you should inspect, refine, and execute deliberately.
Configure an AI provider
- Check edition and provider access. DBeaver lists OpenAI and GitHub Copilot as supported providers. Azure OpenAI, Google Gemini, Ollama, Anthropic Claude, Amazon Bedrock, and Grok are listed as PRO-only. Confirm current availability in DBeaver’s provider documentation, since supported options and edition requirements can change.
- Confirm the provider account permits third-party access. DBeaver notes that some paid provider plans may not allow access from third-party applications. Check the provider’s current plan terms before connecting.
- Create an AI profile. Open DBeaver’s AI Assistant settings, choose a provider, and enter its API token as described in AI Assistant settings. Treat the token as a secret; do not include it in prompts or share it in screenshots.
- Review context controls. DBeaver provides controls for the amount of metadata and sample data shared, and connection filters can restrict which tables appear to AI features. Check the settings and your organization’s data rules before using a connection with sensitive information.
DBeaver says AI processing follows the provider’s privacy policies. That does not mean database context stays exclusively on your machine: review the selected provider’s current terms and determine whether sending the available metadata or sample data is permitted.
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Select the database context before asking
In DBeaver AI Chat, choose the database connection and, if useful, a scope before entering a request. The selected connection tells the assistant which database to use when generating SQL; scope helps narrow the context. DBeaver documents this sequence in its AI Chat guide.
Choose the connection carefully if you have several environments, such as development, staging, and production. A well-written prompt cannot compensate for selecting the wrong database.
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Ask a question the assistant can turn into SQL
DBeaver recommends using English for best results, understanding the database structure, naming known tables or columns, and refining requests iteratively. State the measure, relevant records, and time period rather than relying on ambiguous business shorthand.
Example: customer invoices
DBeaver’s AI command documentation gives the example: “show all customers with invoices in the last month.” For a more specific business question, you could ask: “Using the customers and invoices tables, list each customer with at least one invoice dated in the previous calendar month. Return customer name and invoice count. Ask me which date field to use if the schema has more than one.”
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The added detail is a practical prompting approach, not a guarantee of correctness. “Last month” could mean the previous calendar month or the last 30 days, and the right invoice date field depends on the schema and reporting rules.
Example: a known table and field
DBeaver’s AI Assistant page demonstrates an @ai request: “show films in which Grace Mostel starred.” If you know relevant schema details, include them in your own request—for example, name the tables or columns you expect the assistant to use. Vendor examples illustrate prompt style; they do not establish that an arbitrary answer will be accurate.
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Review and run the generated SQL
After you submit a request in AI Chat, DBeaver lets you execute the generated SQL, open it in the SQL Editor, or copy it. For a safer review-first workflow, open the query in the SQL Editor, check the connection and SQL, make any necessary changes, and then execute it. See the AI Chat documentation for the available actions.
- Check that the query uses the intended tables, joins, and columns.
- Verify filters, date boundaries, grouping, and the meaning of calculated measures against your reporting requirements.
- Confirm the active database connection and whether the query is read-only or changes data.
- For unfamiliar or consequential queries, test in an appropriate non-production environment or with a suitably restricted account.
Know the difference between AI Chat and AI commands
AI Chat generates a query that you can review, open in the editor, copy, or execute. The SQL Editor’s AI command feature can generate and execute SQL directly from a natural-language request. DBeaver documents these as separate workflows in its AI Chat and AI command guides.
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In the documented AI command behavior, SELECT queries execute immediately by default, while modification and schema queries require confirmation by default. Settings can change that behavior. DBeaver warns: “If confirmations are disabled and autocommit is on, AI commands can change data immediately.” Keep confirmations enabled for modifications and schema changes, and inspect generated SQL before running it.
Choosing a provider and workflow
There is no established universal best provider in the available DBeaver documentation. Compare the options using the factors that matter for your environment:
- DBeaver access: Is the provider supported by your edition, or marked PRO-only?
- Account terms: Does your provider plan permit use through a third-party application?
- Data handling: Do the provider’s privacy terms and your organization’s rules allow the database context that may be shared?
- Cost and limits: Do the provider’s current pricing, model access, and usage limits fit your needs?
- Execution review: Do you want to review the generated SQL in the editor, or use direct AI commands with the relevant execution safeguards?
Provider features, terms, and DBeaver edition restrictions can change, so check the linked documentation and provider terms when configuring your setup.
About the Team Edition video
Denis Magda’s tutorial, published March 12, 2024, demonstrates an AI data agent or analyst in DBeaver Team Edition that interprets user questions, generates SQL, and executes it to retrieve business data: Creating AI Data Analyst With DBeaver. It shows the stated workflow, not independent evidence that generated SQL is always accurate or ready for production use.
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