@@ -13,7 +13,7 @@ As an offline-first solution, ObjectBox makes sure your app reliably works offli
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_ Table of Contents_
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- [ Feature Highlights] ( #feature-highlights )
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- - [ Code Example (CRUD - Create, Read, Update, Delete)] ( #code-example-crud--- create-read-update-delete )
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+ - [ Code Example (CRUD - Create, Read, Update, Delete)] ( #code-example-crud-create-read-update-delete )
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- [ Getting Started] ( #getting-started )
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- [ Alpha Notes] ( #alpha-notes )
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- [ Help wanted] ( #help-wanted )
@@ -37,7 +37,8 @@ Feature Highlights
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* MacOS x86-64 and arm64 (Intel 64-bit and Apple Silicon)
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* Windows x86-64 (64-bit)
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- #### Code Example (CRUD - Create, Read, Update, Delete)
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+ Code Example: CRUD (Create, Read, Update, Delete)
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+ -------------------------------------------------
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What does using ObjectBox in Python look like?
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@@ -55,15 +56,16 @@ store = Store()
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# Get a box for the "Person" entity; a Box is the main interaction point with objects and the database.
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box = store.box(Person)
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- person = Person()
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- person.name = " Joe Green"
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+ person = Person(name = " Joe Green" )
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id = box.put(person) # Create
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person = box.get(id ) # Read
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person.name = " Joe Black"
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box.put(person) # Update
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box.remove(person) # Delete
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```
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+ Ready for more? Check the [ example folder] ( https://github.com/objectbox/objectbox-python/tree/main/example ) .
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Getting started
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---------------
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Latest version: 4.0.0 (2024-05-28)
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Do you prefer to dive right into working examples?
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We have you covered in the [ example] ( https://github.com/objectbox/objectbox-python/tree/main/example ) folder.
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- It comes with a task list application and a vector search example using cities.
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- Additionally, for AI enthusiasts, we provide an "ollama" example,
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- which integrates a local LLM (via [ ollama] ( https://ollama.com ) )
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+ It comes with a task list application and a vector search example using cities (CLI app and Jupyter notebook).
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+ For AI developers , we provide an "ollama" example, which integrates a local LLM (via [ ollama] ( https://ollama.com ) )
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with ObjectBox to manage and search embeddings effectively.
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Help wanted
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