Merge branch '59-minimize-required-model-interaction' into 'dev'

Resolve "Minimize required model interaction" #59

Closes #59

See merge request objectbox/objectbox-python!44

Author: dan-obx

example/README.md

@@ -13,17 +13,18 @@ pip install --pre objectbox
 
 The following examples are available from this directory:
 
-- `tasks`: CRUD Application Example (see below for details)
-- `vectorsearch-cities`: VectorSearch Application Example (see below for details)
+- `tasks`: CRUD Example (see below for details)
+- `vectorsearch-cities`: VectorSearch Example (see below for details)
 - `ollama`: LLM + VectorSearch Embeddings Script Example (See [ollama/README.md](./ollama/README.md) for details)
 
 
-## Application Example: Tasks
+## Example: Tasks
 
 This is our classic Tasks application using a CLI.
 
 ```
-$ python -m tasks
+cd tasks
+$ python main.py
 
 Welcome to the ObjectBox tasks-list app example. Type help or ? for a list of commands.
 > new buy oat
@@ -45,13 +46,14 @@ Welcome to the ObjectBox tasks-list app example. Type help or ? for a list of co
 > exit
 ```
 
-## Vector-Search Example: Cities
+## Example: Vector-Search 
 
 This example application starts with a pre-defined set of capital cities and their geo coordinates. 
 It allows to search for nearest neighbors of a city (`city_neighbors`) or by coordinates (`neighbors`) as well as adding more locations (`add`).
 
 ```
-$ python -m vectorsearch-cities
+cd example/vector-search-cities
+$ python main.py
 
 Welcome to the ObjectBox vectorsearch-cities example. Type help or ? for a list of commands.
 > ls

example/__init__.py

@@ -33,7 +33,7 @@ based on https://ollama.com/blog/embedding-models
 ## Run Example
         
 ```
-$ python llamas.py 
+$ python main.py 
 
 Llamas are members of the camel family, which includes other large, even-toed ungulates such as camels, dromedaries, and Bactrian camels. Llamas are most closely related to alpacas, which are also native to South America and share many similarities in terms of their physical characteristics and behavior. Both llamas and alpacas belong to the family Camelidae, and are classified as ruminants due to their unique digestive system that allows them to break down cellulose in plant material.
 ```

example/ollama/README.md

@@ -3,6 +3,8 @@
 
 import ollama
 import objectbox
+from objectbox.model import *
+from objectbox.model.properties import *
 
 documents = [
   "Llamas are members of the camelid family meaning they're pretty closely related to vicuñas and camels",
@@ -13,12 +15,6 @@
   "Llamas live to be about 20 years old, though some only live for 15 years and others live to be 30 years old",
 ]
 
-
-from objectbox.model import *
-from objectbox.model.idsync import sync_model
-from objectbox.model.properties import *
-import numpy as np
-
 # Have fresh data for each start
 objectbox.Store.remove_db_files("objectbox")
 
@@ -31,11 +27,7 @@ class DocumentEmbedding:
         distance_type=VectorDistanceType.COSINE
     ))
 
-model = Model()
-model.entity(DocumentEmbedding)
-sync_model(model, os.path.join(os.path.dirname(__file__),"objectbox-model.json") )
-
-store = objectbox.Store(model=model)
+store = objectbox.Store()
 box = store.box(DocumentEmbedding)
 
 print("Documents to embed: ", len(documents))

example/ollama/llamas.py example/ollama/main.pyexample/ollama/llamas.py renamed to example/ollama/main.py

@@ -1,7 +1,16 @@
 from cmd import Cmd
 import objectbox
+from objectbox.model import *
 import time
-from .model import *
+
+@Entity()
+class Task:
+    id = Id()
+    text = String()
+
+    date_created = Date(py_type=int)
+    date_finished = Date(py_type=int)
+
 
 
 # objectbox expects date timestamp in milliseconds since UNIX epoch
@@ -15,7 +24,7 @@ def format_date(timestamp_ms: int) -> str:
 
 class TasklistCmd(Cmd):
     prompt = "> "
-    _store = objectbox.Store(model=get_objectbox_model(), directory="tasklist-db")
+    _store = objectbox.Store(directory="tasklist-db")
     _box = _store.box(Task)
 
     def do_ls(self, _):

example/tasks/__init__.py

@@ -1,10 +1,19 @@
 from cmd import Cmd
 import objectbox
+from objectbox.model import *
 import time
-from .model import *
 import csv
 import os
 
+@Entity()
+class City:
+    id = Id()
+    name = String()
+    location = Float32Vector(index=HnswIndex(
+        dimensions=2,
+        distance_type=VectorDistanceType.EUCLIDEAN
+    ))
+
 def list_cities(cities):
     print("{:3s}  {:25s}  {:>9s}  {:>9s}".format("ID", "Name", "Latitude", "Longitude"))
     for city in cities:
@@ -23,7 +32,7 @@ def __init__(self, *args):
         Cmd.__init__(self, *args)
         dbdir = "cities-db"
         new_db = not os.path.exists(dbdir)
-        self._store = objectbox.Store(model=get_objectbox_model(),directory=dbdir)
+        self._store = objectbox.Store(directory=dbdir)
         self._box = self._store.box(City)
         if new_db: 
             with open(os.path.join(os.path.dirname(__file__), 'cities.csv')) as f: