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docs/embedded.md

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+# Embedded Models
+
+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.
+
+```python
+import datetime
+from typing import Optional
+
+from redis_om.model import (
+    EmbeddedJsonModel,
+    JsonModel,
+    Field,
+)
+
+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:
+
+```python
+# Find all customers who live in San Antonio, TX
+Customer.find(Customer.address.city == "San Antonio",
+              Customer.address.state == "TX")
+```