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README.md
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README.md
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<!-- DON'T EDIT THIS SECTION, INSTEAD RE-RUN doctoc TO UPDATE -->
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- [💡 Why Redis OM?](#-why-redis-om)
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- [Starting Redis](#starting-redis)
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- [📇 Modeling your domain (and indexing it!)](#-modeling-your-domain-and-indexing-it)
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- [🔎 Querying](#-querying)
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- [Why this is important](#why-this-is-important)
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- [📇 Modeling Your Data](#-modeling-your-data)
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- [✓ Validating Data With Your Model](#-validating-data-with-your-model)
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- [🔎 Rich Queries and Embedded Models](#-rich-queries-and-embedded-models)
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- [💻 Installation](#installation)
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- [📚 Documentation](#documentation)
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- [⛏️ Troubleshooting](#troubleshooting)
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- [So how do you get RediSearch and RedisJSON?](#so-how-do-you-get-redisearch-and-redisjson)
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- [Why Redis OM?](#why)
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- [Getting started](#getting-started)
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- [Installation](#installation)
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- [Documentation](#documentation)
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- [Troubleshooting](#troubleshooting)
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- [Contributing](#contributing)
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- [License](#license)
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## 💡 Why Redis OM?
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Redis OM provides high-level abstractions for using Redis in Python, making it easy to model and query your Redis domain objects.
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Redis OM provides high-level abstractions that make it easy to model and query data in Redis with modern Python applications.
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This **preview** release contains the following features:
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* Declarative secondary-index generation
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* Fluent APIs for querying Redis
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## 🏁 Getting started
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## 📇 Modeling Your Data
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### Object Mapping
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Redis OM contains powerful declarative models that give you data validation, serialization, and persistence to Redis.
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With Redis OM, you get powerful data modeling, extensible data validation with [Pydantic](pydantic-url), and rich query expressions.
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Check out this example of how we'd model customer data with Redis OM. First, we create a `Customer` model:
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Check out this example of modeling customer data with Redis OM. First, we create a `Customer` model:
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```python
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import datetime
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bio: Optional[str]
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```
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**NOTE**: Redis OM uses Python type annotations for data validation. See the _Data Validation_ section of this README for more details.
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Now that we have a `Customer` model, let's use it to save customer data to Redis.
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First, we create a new `Customer` object:
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Or, continue reading to see how Redis OM makes data validation a snap.
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### Data Validation
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## ✓ Validating Data With Your Model
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Redis OM uses [Pydantic](pydantic-url) to validate data based on the type annotations you assign to fields in a model class.
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This validation ensures that fields like `first_name`, which the `Customer` model marked as a `str`, are always string. **But every Redis OM model is also a Pydantic model**, so you can use Pydantic validators like `EmailStr`, `Pattern`, and many more for complex validation!
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This validation ensures that fields like `first_name`, which the `Customer` model marked as a `str`, are always strings. **But every Redis OM model is also a Pydantic model**, so you can use Pydantic validators like `EmailStr`, `Pattern`, and many more for complex validations!
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As an example, because we used the `EmailStr` validator, we'll get a validation error if we try to save a `Customer` with an invalid email address:
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For example, because we used the `EmailStr` type for the `email` field, we'll get a validation error if we try to save a `Customer` with an invalid email address:
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```python
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Customer(
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)
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```
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This code generates a validation error:
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This code generates the following validation error:
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```
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Traceback:
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value is not a valid email address (type=value_error.email)
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```
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What's great about this is **any existing Pydantic validator should work** as a drop-in type annotation with a Redis OM model. You can also write arbitrarily complex custom validations!
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**Any existing Pydantic validator should work** as a drop-in type annotation with a Redis OM model. You can also write arbitrarily complex custom validations!
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To learn more, see the [documentation on data validation](docs/validation.md).
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#### Rich Queries and Embedded Models
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## 🔎 Rich Queries and Embedded Models
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Data modeling, validation, and saving models to Redis all work regardless of how you run Redis.
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Next, we'll show you the **rich query expressions** and **embedded models** Redis OM provides when the [RediSearch](redisearch-url) and [RedisJSON](redis-json-url) modules are installed in your Redis deployment.
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Next, we'll show you the **rich query expressions** and **embedded models** Redis OM provides when the [RediSearch](redisearch-url) and [RedisJSON](redis-json-url) modules are installed in your Redis deployment or you're using [Redis Enterprise](redis-enterprise-url).
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**TIP**: *Wait, what's a Redis module?* If you aren't familiar with Redis modules, review the "RediSearch and RedisJSON" section of this README.
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First, check the [FAQ](docs/faq.md). If you don't find the answer there,
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hit us up on the [Redis Discord Server](http://discord.gg/redis).
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## ✨ RediSearch and RedisJSON
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## ✨ So How Do You Get RediSearch and RedisJSON?
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Some advanced features of Redis OM rely on core features from two source available Redis modules: [RediSearch](redisearch-url) and [RedisJSON](redis-json-url).
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You can run these modules in your self-hosted Redis deployment, or you can use Redis Enterprise, which includes both modules.
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You can run these modules in your self-hosted Redis deployment, or you can use [Redis Enterprise](redis-enterprise-url), which includes both modules.
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To learn more, read [our documentation](docs/redis_modules.md).
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[redis-json-url]: https://oss.redis.com/redisjson/
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[pydantic-url]: https://github.com/samuelcolvin/pydantic
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[ulid-url]: https://github.com/ulid/spec
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[redis-enterprise-url]: https://redis.com/try-free/
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