Embedded Models

NOTE: This documentation is a stub, using the same embedded JSON model example as the README.

Redis OM can store and query nested models like any document database, with the speed and power you get from Redis. Let's see how this works.

In the next example, we'll define a new Address model and embed it within the Customer model.

import datetime
from typing import Optional

from redis_om.model import (
    EmbeddedJsonModel,
    JsonModel,
    Field,
    Migrator
)

class Address(EmbeddedJsonModel):
    address_line_1: str
    address_line_2: Optional[str]
    city: str = Field(index=True)
    state: str = Field(index=True)
    country: str
    postal_code: str = Field(index=True)


class Customer(JsonModel):
    first_name: str = Field(index=True)
    last_name: str = Field(index=True)
    email: str = Field(index=True)
    join_date: datetime.date
    age: int = Field(index=True)
    bio: Optional[str] = Field(index=True, full_text_search=True,
                               default="")

    # Creates an embedded model.
    address: Address

# With these two models and a Redis deployment with the RedisJSON 
# module installed, we can run queries like the following.

# Before running queries, we need to run migrations to set up the
# indexes that Redis OM will use. You can also use the `migrate`
# CLI tool for this!
Migrator().run()

# Find all customers who live in San Antonio, TX
Customer.find(Customer.address.city == "San Antonio",
              Customer.address.state == "TX")

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Embedded Models

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