Database Query Generator

Choose the database that owns your data

The same business question can require different query syntax and connection settings. Begin with your engine and version. The links below lead to the relevant SQL examples or warehouse setup, so you can use details that match your environment.

MongoDB uses document queries and aggregation pipelines rather than ordinary relational SQL. Check the connection guide for available connectors and setup. Connecting several systems does not automatically let a single query join their data. Arrange an approved reporting layer when an analysis spans separate sources.

If you already know your SQL dialect, go directly to the general SQL construction example. For a broader explanation of the approach, read natural-language database querying.

Turn a question into a checked database query

1. Connect an approved reporting source. Use the connection fields for your database and an account limited to the intended tables or views. Desktop connects from your computer; hosted Chatbot uses its server-side connection path.
2. Explain the business meaning. Identify the entity being counted, relationships, units, date scope and exclusions. Table names alone do not explain whether a reservation is still open or whether a stock value is current.
3. Ask for an inspectable answer. Specify columns, grouping, order and a reasonable output limit. Ask the assistant to state assumptions. Inspect the generated SQL and results in the connected workflow; do not rely on a prompt to prevent unwanted access.
4. Reconcile and refine. Compare the result with a small known case. Then ask a follow-up, such as changing the output size, and confirm that the original filters and metric definition remain intact.

The database context and follow-up guide explains how to prepare reusable context. Training data and documentation can supply approved business definitions; they do not replace database permissions or guarantee correctness.

A query brief you can adapt

Database: PostgreSQL; use my approved reporting tables.
One query_items row per SKU; on_hand is the current stock snapshot.
Reservations: one row per reservation; only status = 'open' counts.
Available units = on_hand minus the sum of open reservation quantities.
Include active = 1 only. Exclude unknown (NULL) on_hand and flag that exclusion.
Find items below their own reorder_point.
Return SKU, available units, reorder point; at most two rows.
Sort by available units ascending, then SKU ascending to break ties.
Keep negative availability visible. Do not make inventory changes.
Explain assumptions and show the SQL alongside the result.

Use approved schema descriptions, not passwords or connection secrets, in a question. Replace fictional table names with the real reporting objects. If the inventory is split by warehouse, add that key to both the grain and the join; a SKU-only join would answer a different question.

Worked example: which items need replenishment?

A request for the “two lowest-stock items” leaves important decisions open. Here, available stock means current on-hand units minus open reservations. Include only active items below their individual reorder point, exclude unknown stock, and sort ties by SKU. Negative availability remains visible as a shortage.

This fictional dataset has five items. A has ten units and two open reservations totaling seven. B has three units and a cancelled reservation that must not count. C has no stock or reservations. D is inactive. E has an unknown stock value, which is not treated as zero.

-- Synthetic reference data. Use a disposable PostgreSQL test session.
CREATE TEMP TABLE query_items (
  sku TEXT PRIMARY KEY, on_hand INTEGER,
  reorder_point INTEGER NOT NULL, active INTEGER NOT NULL
);
CREATE TEMP TABLE query_reservations (
  reservation_id INTEGER PRIMARY KEY, sku TEXT,
  quantity INTEGER NOT NULL, status TEXT NOT NULL
);
INSERT INTO query_items VALUES
  ('A', 10, 5, 1), ('B', 3, 8, 1), ('C', 0, 2, 1),
  ('D', 50, 60, 0), ('E', NULL, 5, 1);
INSERT INTO query_reservations VALUES
  (1, 'A', 4, 'open'), (2, 'A', 3, 'open'),
  (3, 'B', 9, 'cancelled'), (4, 'D', 10, 'open');

PostgreSQL reference SQL

WITH open_reservations AS (
  SELECT sku, SUM(quantity) AS reserved_units
  FROM query_reservations
  WHERE status = 'open'
  GROUP BY sku
)
SELECT i.sku,
       i.on_hand - COALESCE(r.reserved_units, 0) AS available_units,
       i.reorder_point
FROM query_items AS i
LEFT JOIN open_reservations AS r ON r.sku = i.sku
WHERE i.active = 1
  AND i.on_hand IS NOT NULL
  AND i.on_hand - COALESCE(r.reserved_units, 0) < i.reorder_point
ORDER BY available_units ASC, i.sku ASC
LIMIT 2;

C comes first at zero units. A and B both have three, so the explicit SKU tie-break selects A for the second row. Increasing the limit to three includes B. Without that tie-break, a two-row result would not specify which tied item belongs in the answer.

Reservations are summed before joining so each item contributes one row. Counting reservation records instead of summing quantities would give A eight available units instead of three. Treating missing stock as zero would bring E into the report and conceal a data-quality problem. Review those decisions before acting on replenishment advice.

The reference was executed locally in PostgreSQL 18.3 through PGlite 0.5.8 using only synthetic data. Checks cover tied rows, cancelled reservations, unknown stock, inactive items, an empty result and changed reservations. This verifies the reference SQL, not AI accuracy or your live database. Temporary tables here are for a disposable test session, not an import method for a separate application connection.

Keep the meaning when changing SQL dialects

This table is a syntax guide, not a claim that the fixture ran on every engine. Compare PostgreSQL LIMIT, MySQL SELECT, SQL Server TOP and BigQuery query syntax. A dialect change requires checking the whole query, including date arithmetic, JSON and identifiers.

Choose the workflow and validate its boundaries

A disconnected text-to-SQL tool is useful when you want a draft from an approved schema description. A connected assistant is useful when you need to inspect results and ask follow-ups. A visual query builder exposes columns, joins and filters through controls. AskYourDatabase focuses on conversational querying; this page does not promise a drag-and-drop designer or an unrestricted free service.

For correctness, check missing values, duplicate relationships, units and tie rules against an independent answer. The text-to-SQL evaluation kit provides a broader acceptance process. Record failures and clarifications as well as successful queries.

For access, enforce restrictions through approved database accounts, views and the applicable application controls. Read-only access limits writes but may still expose too many rows. Generated SQL, an instruction in the prompt and a small result limit do not establish authorization.

For cost, check the execution plan and warehouse controls with the database owner. Returning two inventory rows may still require aggregating every open reservation. For data handling, review security and model data flow: local database connectivity does not mean local inference. Questions, schema and selected results can reach external services. Evaluate the actual model endpoint and retained records for private deployments.

Once the reporting query is accepted, the SQL dashboard guide shows how to validate repeated reporting. Customer-facing access requires a separate embedded chatbot evaluation, including user and tenant boundaries.

Database query generator FAQs

Start with one question you can verify

Download AskYourDatabase Desktop, select your approved database and test a question with a known answer. Review current plans for your chosen workflow, or discuss a private deployment with the team before connecting restricted data.