Pydantic v2 can validate the Python data your application sends to SQLite, but it does not create or manage SQLite tables. Define Pydantic models for input and output, write the database schema and SQL explicitly, and bind values with sqlite3 placeholders instead of interpolating them into query strings.
What Pydantic and SQLite each do
A Pydantic model is a typed Python data contract: it parses input into a model instance and checks the result against declared types and constraints. SQLite stores and queries records. Your application connects the two by mapping model fields to SQL columns.
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As the Pydantic model documentation puts it, “Pydantic guarantees the types and constraints of the output, not the input data.” Inputs may be converted to the declared types; use strict validation when conversion is not acceptable. By default, extra input fields are ignored. Configure the model to allow or forbid extras if that behavior does not fit your application.
Define and validate a Pydantic v2 model
This example accepts convenient type conversion and rejects unknown fields. The model validates an incoming Python dictionary before any database operation:
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import sqlite3
from pydantic import BaseModel, ConfigDict
class Task(BaseModel):
model_config = ConfigDict(extra="forbid")
title: str
done: bool = False
task = Task.model_validate({"title": "Check backups", "done": False})Use strict=True with validation when values that could be coerced—such as a string where a Boolean is expected—must instead be rejected. Choose strictness to match the input boundary; validation does not mean every original input value is accepted unchanged.
Create the SQLite schema explicitly
Write table definitions as SQL. A Pydantic model does not define SQLite column types, nullability, unique constraints, indexes, relationships, or a migration history. Those are database design decisions, and database constraints remain important because rows can be changed by code that does not pass through this model.
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CREATE TABLE IF NOT EXISTS tasks (
id INTEGER PRIMARY KEY,
title TEXT NOT NULL,
done INTEGER NOT NULL DEFAULT 0
);For a small relational record, one column per field makes filtering and database constraints direct, but you must map fields and update the schema as the application evolves. A JSON text column can simplify storage of nested or infrequently queried payloads, but makes field-level SQL queries and constraints less direct and adds JSON encoding and decoding to the storage contract. Choose based on how the data will be queried and constrained; neither approach is universal.
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model_dump() returns a recursively converted Python dictionary. Map only the fields intended for the table, then pass their values separately to SQL. Python’s sqlite3 documentation recommends placeholders instead of string formatting for values.
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payload = task.model_dump()
with sqlite3.connect("tasks.db") as con:
con.execute(
"""CREATE TABLE IF NOT EXISTS tasks (
id INTEGER PRIMARY KEY,
title TEXT NOT NULL,
done INTEGER NOT NULL DEFAULT 0
)"""
)
con.execute(
"INSERT INTO tasks (title, done) VALUES (:title, :done)",
{"title": payload["title"], "done": int(payload["done"])},
)The named placeholders are bound to values by sqlite3; they are not SQL string interpolation. Do not put user-controlled values into an f-string or concatenate them into a query. Column names and table names are SQL structure, not bound values: keep them explicit or select them from a controlled allowlist.
The example makes the Boolean-to-integer mapping explicit because the table stores done as an integer. Make similarly deliberate choices for dates, decimals, enums, nested models, and other Python values. model_dump(mode="json") produces JSON-compatible representations, but that is different from a storage policy: decide whether to bind such values into individual columns or encode an entire payload as JSON text.
Read rows and validate them back into models
By default, sqlite3 query results are tuples. Map each row to the shape expected by Pydantic, and convert database-specific representations before validation where needed.
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row = con.execute(
"SELECT title, done FROM tasks WHERE id = ?",
(1,),
).fetchone()
if row is not None:
title, done = row
saved_task = Task.model_validate({"title": title, "done": bool(done)})The example selects named columns in a known order and maps them explicitly. If you select additional columns or change the query, update that mapping deliberately; do not assume a SQLite row is already a Pydantic input dictionary.
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Keep transactions and schema changes deliberate
SQLite writes need a transaction boundary: commit successful changes and close connections responsibly. A with sqlite3.connect(...) block commits on successful exit and rolls back if an exception escapes, while the connection should still be closed. For longer-lived connections, manage commit, rollback, and closure explicitly.
Use explicit SQL migrations when columns or constraints change. Pydantic’s generated JSON Schema describes model structure for JSON Schema and OpenAPI contexts; it is not SQLite DDL or a migration plan. The Pydantic JSON Schema documentation covers JSON Schema Draft 2020-12 and OpenAPI Specification v3.1.0, not an automatic mapping to SQLite tables. Examples here use Pydantic v2 methods such as model_validate() and model_dump(); v2 has breaking API changes from v1, described in the migration guide.
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