Skip to main content

Documentation Index

Fetch the complete documentation index at: https://docs.paradedb.com/llms.txt

Use this file to discover all available pages before exploring further.

This guide will walk you through setting up your environment to run queries against ParadeDB. Choose your preferred tool below:
ParadeDB comes with a helpful procedure that creates a table populated with mock data to help you get started. Run the following command to create this table.
CALL paradedb.create_bm25_test_table(
  schema_name => 'public',
  table_name => 'mock_items'
);
Then, inspect the first 3 rows:
SELECT description, rating, category
FROM mock_items
LIMIT 3;
Expected Response
       description        | rating |  category
--------------------------+--------+-------------
 Ergonomic metal keyboard |      4 | Electronics
 Plastic Keyboard         |      4 | Electronics
 Sleek running shoes      |      5 | Footwear
(3 rows)
Next, let’s create a BM25 index called search_idx on this table. A BM25 index is a covering index, which means that multiple columns can be included in the same index.
CREATE INDEX search_idx ON mock_items
USING bm25 (id, description, category, rating, in_stock, created_at, metadata, weight_range)
WITH (key_field='id');
As a general rule of thumb, any columns that you want to filter, GROUP BY, ORDER BY, or aggregate as part of a full text query should be added to the index for faster performance.
Note the mandatory key_field option. See choosing a key field for more details.
You’re all set! Try running some queries.
To start you’ll need a Django project with Psycopg and django-paradedb installed. Run the following to create one:
python3 -m venv .venv
source .venv/bin/activate
pip install django psycopg django-paradedb==0.6.0
python3 -m django startproject myproject .
python3 manage.py startapp myapp
In myproject/settings.py, add 'django.contrib.postgres' and 'myapp' to INSTALLED_APPS. Then, configure DATABASES["default"] to point to Postgres:
myproject/settings.py
INSTALLED_APPS = [
    ...,
    'django.contrib.postgres',
    'myapp',
]

DATABASES = {
    "default": {
        "ENGINE": "django.db.backends.postgresql",
        "NAME": "mydatabase",
        "USER": "myuser",
        "PASSWORD": "mypassword",
        "HOST": "localhost",
        "PORT": "5432",
    }
}
We can now add a model for ParadeDB’s built-in test table and BM25 index:
models.py
from django.db import models

from django.contrib.postgres.fields import IntegerRangeField
from paradedb.indexes import BM25Index
from paradedb.queryset import ParadeDBManager
from paradedb.search import Tokenizer


class MockItem(models.Model):
    description = models.TextField(null=True, blank=True)
    rating = models.IntegerField(null=True, blank=True)
    category = models.CharField(max_length=255, null=True, blank=True)
    in_stock = models.BooleanField(null=True, blank=True)
    metadata = models.JSONField(null=True, blank=True)
    created_at = models.DateTimeField(null=True, blank=True)
    last_updated_date = models.DateField(null=True, blank=True)
    latest_available_time = models.TimeField(null=True, blank=True)
    weight_range = IntegerRangeField(null=True, blank=True)

    objects = ParadeDBManager()

    class Meta:
        db_table = "mock_items"
        indexes = [
            BM25Index(
                fields={
                    "id": {},
                    "description": {"tokenizer": Tokenizer.unicode_words()},
                    "category": {"tokenizer": Tokenizer.literal()},
                    "rating": {},
                    "in_stock": {},
                    "metadata": {"json_fields": {"fast": True}},
                    "created_at": {},
                    "last_updated_date": {},
                    "latest_available_time": {},
                    "weight_range": {},
                },
                key_field="id",
                name="search_idx",
            ),
        ]
As a general rule of thumb, any columns that you want to filter, GROUP BY, ORDER BY, or aggregate as part of a full text query should be added to the index for faster performance.
Note the mandatory key_field option. See choosing a key field for more details.
Run the migrations to create the table and index:
python3 manage.py makemigrations
python3 manage.py migrate
Now, open a Python shell with python3 manage.py shell and run the following command to populate mock_items.
from django.db import connection

with connection.cursor() as cursor:
    cursor.execute("""
        CALL paradedb.create_bm25_test_table(
          schema_name => 'public',
          table_name  => 'mock_items_tmp'
        );
        INSERT INTO public.mock_items
        SELECT * FROM public.mock_items_tmp;
        DROP TABLE public.mock_items_tmp;
    """)
You’re all set! Try running some queries in your Python shell.
To get started, install SQLAlchemy, Alembic, Psycopg, and sqlalchemy-paradedb.
python3 -m venv .venv
source .venv/bin/activate
pip install sqlalchemy psycopg alembic sqlalchemy-paradedb==0.6.0
Initialize Alembic:
alembic init migrations
Then update the Alembic configuration to point to your database:
alembic.ini
sqlalchemy.url = postgresql+psycopg://myuser:mypassword@localhost:5432/mydatabase
ParadeDB comes with a built-in test table that we’ll run our queries against. Create a models.py file with a model and search index for that table:
from __future__ import annotations

from datetime import date, datetime, time
from typing import Any

from sqlalchemy import Boolean, Date, DateTime, Index, Integer, String, Text, Time
from sqlalchemy.dialects.postgresql import INT4RANGE, JSONB, Range
from sqlalchemy.orm import DeclarativeBase, Mapped, mapped_column

from paradedb.sqlalchemy import indexing


class Base(DeclarativeBase):
    pass


class MockItem(Base):
    __tablename__ = "mock_items"

    id: Mapped[int] = mapped_column(Integer, primary_key=True)
    description: Mapped[str | None] = mapped_column(Text, nullable=True)
    rating: Mapped[int | None] = mapped_column(Integer, nullable=True)
    category: Mapped[str | None] = mapped_column(String(255), nullable=True)
    in_stock: Mapped[bool | None] = mapped_column(Boolean, nullable=True)
    metadata_: Mapped[dict[str, Any] | None] = mapped_column("metadata", JSONB, nullable=True)
    created_at: Mapped[datetime | None] = mapped_column(DateTime, nullable=True)
    last_updated_date: Mapped[date | None] = mapped_column(Date, nullable=True)
    latest_available_time: Mapped[time | None] = mapped_column(Time, nullable=True)
    weight_range: Mapped[Range[int] | None] = mapped_column(INT4RANGE, nullable=True)


Index(
    "search_idx",
    indexing.BM25Field(MockItem.id),
    indexing.BM25Field(MockItem.description),
    indexing.BM25Field(MockItem.category),
    indexing.BM25Field(MockItem.rating),
    indexing.BM25Field(MockItem.in_stock),
    indexing.BM25Field(MockItem.metadata_),
    indexing.BM25Field(MockItem.created_at),
    indexing.BM25Field(MockItem.last_updated_date),
    indexing.BM25Field(MockItem.latest_available_time),
    indexing.BM25Field(MockItem.weight_range),
    postgresql_using="bm25",
    postgresql_with={"key_field": "id"},
)
As a general rule of thumb, any columns that you want to filter, GROUP BY, ORDER BY, or aggregate as part of a full text query should be added to the index for faster performance.
Note the mandatory key_field option. See choosing a key field for more details.
Copy this configuration into your migrations/env.py:
migrations/env.py
from logging.config import fileConfig

from sqlalchemy import engine_from_config, text
from sqlalchemy import pool

from alembic import context

# This import is required for autogenerated ParadeDB migrations
# to work properly.
import paradedb.sqlalchemy.alembic  # noqa: F401
from models import Base

config = context.config

if config.config_file_name is not None:
    fileConfig(config.config_file_name)

target_metadata = Base.metadata

# The ParadeDB Docker image comes pre-bundled with some popular
# extensions like PostGIS. PostGIS automatically creates a table
# called `spatial_ref_sys`. This tells Alembic not to drop it even
# though it isn't tracked in Alembic's metadata.
IGNORED_TABLES = {"spatial_ref_sys"}


def include_object(object, name, type_, reflected, compare_to):
    if type_ == "table" and reflected and name in IGNORED_TABLES:
        return False
    return True


def run_migrations_offline() -> None:
    url = config.get_main_option("sqlalchemy.url")
    context.configure(
        url=url,
        target_metadata=target_metadata,
        literal_binds=True,
        dialect_opts={"paramstyle": "named"},
    )

    with context.begin_transaction():
        context.run_migrations()


def run_migrations_online() -> None:
    connectable = engine_from_config(
        config.get_section(config.config_ini_section, {}),
        prefix="sqlalchemy.",
        poolclass=pool.NullPool,
    )

    with connectable.connect() as connection:
        # This prevents Alembic from modifying tables outside
        # of the `public` schema.
        connection.execute(text("SET search_path TO public"))
        connection.commit()
        context.configure(
            connection=connection,
            target_metadata=target_metadata,
            include_object=include_object,
        )

        with context.begin_transaction():
            context.run_migrations()


if context.is_offline_mode():
    run_migrations_offline()
else:
    run_migrations_online()
Next, add a migration to create the mock_items test table. Create a blank migration in 0001_create_mock_items_table.py by running the following command:
alembic revision --rev-id 0001 -m "Create mock_items table"
Update the generated migration to create the table:
def upgrade() -> None:
    """Upgrade schema."""
    op.execute(
        """
        CALL paradedb.create_bm25_test_table(
          schema_name => 'public',
          table_name => 'mock_items'
        )
        """
    )


def downgrade() -> None:
    """Downgrade schema."""
    op.execute("DROP TABLE IF EXISTS public.mock_items")
Then, run it with:
alembic upgrade head
Next, autogenerate a new migration to create the search index.
alembic revision --rev-id 0002 --autogenerate -m "Create search index on mock_items"
The generated migration should look like this:
0002_add_mock_items_search_index.py
"""add mock_items search index

Revision ID: 0002
Revises: 0001
Create Date: 2026-04-07 13:56:45.304941

"""
from typing import Sequence, Union

from alembic import op
import sqlalchemy as sa


# revision identifiers, used by Alembic.
revision: str = '0002'
down_revision: Union[str, Sequence[str], None] = '0001'
branch_labels: Union[str, Sequence[str], None] = None
depends_on: Union[str, Sequence[str], None] = None


def upgrade() -> None:
    """Upgrade schema."""
    # ### commands auto generated by Alembic - please adjust! ###
    op.create_bm25_index('search_idx', 'mock_items', ['id', 'description', 'category', 'rating', 'in_stock', 'metadata', 'created_at', 'last_updated_date', 'latest_available_time', 'weight_range'], key_field='id', table_schema='public')
    # ### end Alembic commands ###


def downgrade() -> None:
    """Downgrade schema."""
    # ### commands auto generated by Alembic - please adjust! ###
    op.drop_bm25_index('search_idx', if_exists=True, schema='public')
    # ### end Alembic commands ###
Then run it with:
alembic upgrade head
Finally, run python and execute the following:
from models import MockItem
from sqlalchemy import create_engine
engine = create_engine('postgresql+psycopg://myuser:mypassword@localhost:5432/mydatabase')
You’re all set! Try running some queries in your shell.
To get started, create a Rails app that uses PostgreSQL.
rails new paradedb -d postgresql
cd paradedb
Add the rails-paradedb gem to your Gemfile:
Gemfile
gem "rails-paradedb", "0.7.0", require: "parade_db"
Then install it:
bundle install
Update config/database.yml to point to your ParadeDB database:
config/database.yml
development:
  adapter: postgresql
  encoding: unicode
  database: mydatabase
  username: myuser
  password: mypassword
  host: localhost
  port: 5432
ParadeDB comes with a built-in test table that we’ll run our queries against. Generate a migration to create it:
rails generate migration CreateMockItemsTable
Update the generated migration to create mock_items:
db/migrate/*_create_mock_items_table.rb
def up
  execute <<~SQL
    CALL paradedb.create_bm25_test_table(
      schema_name => 'public',
      table_name => 'mock_items'
    );
  SQL
end

def down
  drop_table :mock_items, if_exists: true
end
Next, create a model for the mock_items table in app/models/mock_item.rb:
app/models/mock_item.rb
class MockItem < ApplicationRecord
  include ParadeDB::Model

  self.table_name = "mock_items"
  self.primary_key = "id"
end
Then, create a search index for that table in app/models/mock_item_index.rb:
app/models/mock_item_index.rb
class MockItemIndex < ParadeDB::Index
  self.table_name = :mock_items
  self.key_field = :id
  self.index_name = :search_idx
  self.fields = {
    id: nil,
    description: nil,
    category: nil,
    rating: nil,
    in_stock: nil,
    metadata: nil,
    created_at: nil,
    last_updated_date: nil,
    latest_available_time: nil,
    weight_range: nil
  }
end
As a general rule of thumb, any columns that you want to filter, GROUP BY, ORDER BY, or aggregate as part of a full text query should be added to the index for faster performance.
Note the mandatory key_field option. See choosing a key field for more details.
Generate a migration for the search index:
rails generate migration CreateMockItemsIndex
Update the generated migration to create the index:
db/migrate/*_create_mock_items_index.rb
def up
  create_paradedb_index(MockItemIndex, if_not_exists: true)
end

def down
  remove_bm25_index :mock_items, name: :search_idx, if_exists: true
end
Run the migrations:
rails db:migrate
You’re all set! Open the Rails console and run some queries.
rails console