45 lines
1.2 KiB
Markdown
45 lines
1.2 KiB
Markdown
# Embedded Models
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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.
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In the next example, we'll define a new `Address` model and embed it within the `Customer` model.
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```python
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import datetime
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from typing import Optional
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from redis_om.model import (
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EmbeddedJsonModel,
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JsonModel,
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Field,
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)
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class Address(EmbeddedJsonModel):
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address_line_1: str
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address_line_2: Optional[str]
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city: str = Field(index=True)
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state: str = Field(index=True)
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country: str
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postal_code: str = Field(index=True)
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class Customer(JsonModel):
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first_name: str = Field(index=True)
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last_name: str = Field(index=True)
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email: str = Field(index=True)
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join_date: datetime.date
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age: int = Field(index=True)
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bio: Optional[str] = Field(index=True, full_text_search=True,
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default="")
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# Creates an embedded model.
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address: Address
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```
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With these two models and a Redis deployment with the RedisJSON module installed, we can run queries like the following:
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```python
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# Find all customers who live in San Antonio, TX
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Customer.find(Customer.address.city == "San Antonio",
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Customer.address.state == "TX")
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``` |