Text-to-SQL for PostgreSQL
Turn plain English into expert-level PostgreSQL queries with AI
For people who know what data they want but need help writing the PostgreSQL SQL — whether they are learning SQL, working with unfamiliar schemas, or need help with advanced PostgreSQL features like CTEs, window functions, or JSONB operators.
About this tool
Writing SQL by hand is a core skill, but even experienced developers hit walls when working with unfamiliar schemas, advanced PostgreSQL features, or nuanced query logic. A query that seems straightforward in your head — "show me which customers are churning" — can require multiple JOINs, date arithmetic, window functions, and careful NULL handling to express correctly in SQL. This tool bridges the gap between what you want to ask your database and the precise PostgreSQL syntax required to get the answer.
Describe your data question in plain English — "show me the top 10 customers by revenue last quarter, broken down by product category" — and the AI generates production-ready PostgreSQL SQL. It handles the full spectrum of query complexity: simple SELECT statements, multi-table JOINs, recursive CTEs for hierarchical data like org charts or category trees, window functions like ROW_NUMBER(), LAG(), LEAD(), and NTILE(), aggregations with GROUP BY and HAVING, correlated and uncorrelated subqueries, and PostgreSQL-specific features like DISTINCT ON for first-per-group queries, LATERAL joins for row-by-row correlated lookups, array operations (ANY, ALL, unnest, array_agg, array_remove), JSONB queries (->, ->>, @>, jsonb_path_query, jsonb_each), and date/time arithmetic with interval types and date_trunc.
Unlike generic SQL converters that produce ANSI-standard SQL, this tool generates PostgreSQL-native syntax that takes full advantage of your database engine. For example, instead of the verbose ROW_NUMBER() OVER (...) WHERE rn = 1 pattern for first-per-group, it uses DISTINCT ON — a PostgreSQL feature that is simpler, often faster, and directly expresses the intent. Instead of nested subqueries for dependent lookups, it uses LATERAL joins that the PostgreSQL planner can optimize more effectively.
Safety is built into every response. All generated SQL uses parameterized queries ($1, $2) for user-supplied values rather than string interpolation, protecting against SQL injection from the start. For data-modifying statements (INSERT, UPDATE, DELETE), the tool wraps operations in explicit BEGIN/COMMIT transactions, includes RETURNING clauses so you can verify the affected rows before committing, and adds guard clauses to prevent accidental mass updates. It never generates destructive DDL (DROP TABLE, TRUNCATE, DROP DATABASE) unless you explicitly ask for it, and even then flags the risk with a prominent warning.
When your description is ambiguous — "get me the latest orders" could mean latest by creation date, by updated date, or by order number — the AI asks clarifying questions rather than guessing. It asks about your schema structure, table names, column types, relationships between tables, and business logic before generating SQL. This interactive approach produces more accurate results than tools that silently assume and generate plausible-looking but incorrect queries. If you paste your schema definition (from \d+ in psql or a migration file), the AI uses the actual column names and types to generate precise SQL on the first try.
Every generated query comes with a line-by-line explanation of the logic — what each JOIN does, why a specific filter was chosen, how the window function partitioning works — so you understand the SQL rather than blindly copying it. Each response also includes a recommendation to verify the execution plan with EXPLAIN (ANALYZE, BUFFERS) before running against production data, because a query that returns correct results can still be unacceptably slow without the right indexes or with poor statistics.
The tool also flags PostgreSQL version requirements when using features like MERGE (15+), JSON_TABLE (17+), GENERATED ALWAYS AS stored columns (12+), or INSERT...ON CONFLICT (9.5+). This prevents you from generating SQL that works on your local dev instance running PostgreSQL 17 but fails in production on PostgreSQL 13.
Whether you are learning PostgreSQL, prototyping analytics queries, writing data migration scripts, or building application queries against a complex schema, this tool gives you a starting point that follows PostgreSQL best practices — not generic ANSI SQL that ignores the powerful features your database actually supports.
- All generated SQL uses parameterized placeholders ($1, $2) for user-supplied values — never string interpolation. This prevents SQL injection when the query is used in application code.
- Data-modifying statements (INSERT, UPDATE, DELETE) are wrapped in explicit BEGIN/COMMIT transaction blocks with RETURNING clauses so results can be reviewed before committing.
- Destructive DDL (DROP TABLE, TRUNCATE, DROP DATABASE) is never generated unless explicitly requested, and always includes a prominent warning about irreversibility.
- For UPDATE and DELETE, the tool recommends running the equivalent SELECT with the same WHERE clause first to verify the scope of affected rows.
Examples
-- Request: "Find customers who placed more than 5 orders last month
-- but have not ordered anything in the past week"
select
c.customer_id,
c.customer_name,
c.email,
count(o.order_id) as orders_last_month
from customers c
join orders o on o.customer_id = c.customer_id
where o.order_date >= date_trunc('month', current_date - interval '1 month')
and o.order_date < date_trunc('month', current_date)
and not exists (
select 1
from orders o2
where o2.customer_id = c.customer_id
and o2.order_date >= current_date - interval '7 days'
)
group by c.customer_id, c.customer_name, c.email
having count(o.order_id) > 5
order by orders_last_month desc;
A natural language request translated into a query combining JOIN, date arithmetic with interval, NOT EXISTS subquery, GROUP BY with HAVING, and proper date_trunc boundaries. The AI would also explain each clause and suggest verifying the plan with EXPLAIN ANALYZE.
-- Request: "Show each department's top 3 highest-paid employees
-- with their rank and how much more they earn than the next person"
select
department_name,
employee_name,
salary,
rank,
salary - next_salary as salary_gap
from (
select
d.department_name,
e.employee_name,
e.salary,
row_number() over (
partition by e.department_id order by e.salary desc
) as rank,
lead(e.salary) over (
partition by e.department_id order by e.salary desc
) as next_salary
from employees e
join departments d on d.department_id = e.department_id
) ranked
where rank <= 3
order by department_name, rank;
Demonstrates window functions (ROW_NUMBER, LEAD) with PARTITION BY to solve a top-N-per-group problem. The AI generates this from a plain English description and explains why a window function approach is more efficient than correlated subqueries.
-- Request: "Update product prices by 10% for products in the
-- 'electronics' category that have not been updated in 6 months,
-- but only if the new price stays under $1000"
begin;
update products p
set
price = p.price * 1.10,
updated_at = now()
from categories c
where c.category_id = p.category_id
and c.category_name = $1 -- 'electronics'
and p.updated_at < now() - interval '6 months'
and p.price * 1.10 < $2 -- 1000.00
returning
p.product_id,
p.product_name,
p.price as new_price,
p.price / 1.10 as old_price;
-- Review the RETURNING output, then:
-- commit;
-- Or if something looks wrong:
-- rollback;
A data-modifying statement generated with parameterized values ($1, $2), wrapped in an explicit transaction, using UPDATE...FROM for the join, with a RETURNING clause to verify results before committing. The AI flags the safety implications and recommends reviewing the output.
Inputs and outputs
What you provide
- Natural language description of the desired query
- Table names, column names, and schema details (or \d+ output)
- PostgreSQL version (if using version-specific features)
What you get
- Production-ready PostgreSQL SQL with parameterized queries
- Line-by-line explanation of the generated SQL logic
- EXPLAIN ANALYZE recommendation for plan verification
Use cases
- Converting business requirements into PostgreSQL queries for reports and dashboards without memorizing syntax
- Learning PostgreSQL-specific features by seeing how natural language maps to CTEs, window functions, LATERAL joins, and JSONB operators
- Prototyping complex analytical queries with aggregations, grouping sets, and date/time arithmetic before refining them in production
- Generating parameterized application queries for backend code with proper placeholder syntax ($1, $2) ready for pg or psycopg2
- Writing data migration scripts with INSERT...ON CONFLICT, UPDATE...FROM, or MERGE statements that handle edge cases correctly
Features
- Generates PostgreSQL-native SQL using DISTINCT ON, LATERAL joins, array operations, JSONB operators, and other features not available in generic SQL
- Uses parameterized queries ($1, $2) by default for all user-supplied values, preventing SQL injection from the start
- Asks clarifying questions about schema, column types, and business logic when the request is ambiguous rather than guessing
- Wraps data-modifying statements (INSERT, UPDATE, DELETE) in explicit transactions with safety guards
- Explains the generated SQL line by line, including why specific PostgreSQL features were chosen over alternatives
- Recommends EXPLAIN (ANALYZE, BUFFERS) verification for every generated query to catch performance issues early
Frequently asked questions
How does the text-to-SQL tool handle ambiguous requests where the schema is not clear?
When you describe a query like "show me recent orders" without specifying table names, column names, or what "recent" means, the AI does not guess. It asks clarifying questions: What table stores orders? What column represents the date — created_at, order_date, or placed_at? Does "recent" mean the last 7 days, 30 days, or since a specific date? What columns do you want in the result? Are there any filters like status or customer segment? This matters because guessing wrong produces SQL that either errors out or, worse, silently returns incorrect results that look plausible. The more context you provide up front — table names, column types, relationships between tables, business definitions — the more accurate the first query will be. If you have a schema definition (from \d+ in psql or a migration file), paste it along with your question for the best results. The AI uses the actual column names, types, and constraints from your schema to generate precise SQL. It will also ask about your PostgreSQL version if the query involves features that differ across versions, such as MERGE or JSON_TABLE.
Is the generated SQL safe to run directly in production?
The generated SQL follows safety best practices but should always be reviewed before running against production data. For SELECT queries, the main risk is performance — a query that works on a dev database with 1,000 rows may time out on a production table with 100 million rows. That is why every response includes a recommendation to run EXPLAIN (ANALYZE, BUFFERS) first. For data-modifying statements (INSERT, UPDATE, DELETE), the tool wraps operations in explicit transactions so you can review the results with RETURNING before committing. All user-supplied values use parameterized placeholders ($1, $2) rather than string interpolation, which prevents SQL injection when you use the query in application code. The tool never generates destructive DDL (DROP TABLE, TRUNCATE, DROP DATABASE) unless you explicitly request it, and even then it adds clear warnings. However, AI-generated SQL can still contain logical errors — always verify that the WHERE clause matches exactly the rows you intend to affect.
What PostgreSQL-specific features does this tool support that generic SQL converters miss?
Most text-to-SQL tools generate ANSI-standard SQL that works across databases but misses PostgreSQL's strongest features. This tool generates PostgreSQL-native syntax including: DISTINCT ON for first-row-per-group queries (much simpler and often faster than the ROW_NUMBER window function workaround), LATERAL joins for correlated subqueries in the FROM clause that the planner can optimize effectively, array operations (ANY, ALL, unnest, array_agg, array_remove) for working with array columns and function results, JSONB operators (->, ->>, @>, ?, jsonb_path_query) for querying semi-structured data without needing a separate document store, INSERT...ON CONFLICT (upsert) with DO UPDATE SET clauses for atomic insert-or-update operations, GENERATE_SERIES for time-series gap filling and generating test data, recursive CTEs with WITH RECURSIVE for traversing hierarchical data like org charts or category trees, range types (tstzrange, int4range) with containment and overlap operators, full-text search with tsvector, tsquery, and ranking functions like ts_rank, and FILTER clauses on aggregates for computing multiple conditional aggregations in a single pass. When a feature requires a minimum PostgreSQL version (for example, MERGE requires 15+, JSON_TABLE requires 17+), the tool notes the requirement so you can verify compatibility with your deployment.
Can I use this tool to generate INSERT, UPDATE, and DELETE statements, not just SELECT?
Yes. The tool handles the full range of DML operations, not just read queries. For INSERT, it generates single-row inserts, multi-row INSERT INTO...VALUES with parameterized placeholders, INSERT...SELECT for data transformations, and INSERT...ON CONFLICT (upsert) with proper conflict targets and DO UPDATE SET clauses for atomic insert-or-update logic. For UPDATE, it supports UPDATE...SET with subqueries, UPDATE...FROM for join-based updates (a PostgreSQL extension to standard SQL that avoids correlated subqueries), and conditional updates with CASE expressions for different values based on row data. For DELETE, it handles DELETE...USING for join-based deletes and cascading scenarios where you need to remove rows based on conditions in related tables. Every data-modifying statement is wrapped in a BEGIN/COMMIT transaction block with a RETURNING clause so you can inspect which rows were affected before committing. The tool uses parameterized placeholders ($1, $2) for all literal values to prevent SQL injection, and flags any statement that could affect a large number of rows, recommending a SELECT with the same WHERE clause first to confirm the scope before executing the modification.
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