Update example script with latest calling interface

scripts/astrapy_latest_interface.py

@@ -1,9 +1,9 @@
 import os
 import sys
 
-from dotenv import load_dotenv
+import astrapy
 
-from astrapy.db import AstraDB
+from dotenv import load_dotenv
 
 
 sys.path.append("../")
@@ -16,34 +16,31 @@
 api_endpoint = os.environ["ASTRA_DB_API_ENDPOINT"]
 
 # Initialize our vector db
-astra_db = AstraDB(token=token, api_endpoint=api_endpoint)
+my_client = astrapy.DataAPIClient(token)
+my_database = my_client.get_database_by_api_endpoint(api_endpoint)
 
 # In case we already have the collection, let's clear it out
-astra_db.delete_collection("collection_test")
+my_database.drop_collection("collection_test")
 
 # Create a new test collection for example
-astra_db_collection = astra_db.create_collection("collection_test", dimension=5)
+my_collection = my_database.create_collection("collection_test", dimension=5)
 
 # Insert a document into the test collection
-astra_db_collection.insert_one(
+my_collection.insert_one(
     {
         "_id": "1",
         "name": "Coded Cleats Copy",
         "description": "ChatGPT integrated sneakers that talk to you",
-        "$vector": [0.25, 0.25, 0.25, 0.25, 0.25],
-    }
+    },
+    vector=[0.25, 0.25, 0.25, 0.25, 0.25],
 )
 
-# Perform a few vector find operations
-astra_db_collection.vector_find([0.1, 0.1, 0.2, 0.5, 1], limit=3)
-
-astra_db_collection.vector_find(
-    [0.1, 0.1, 0.2, 0.5, 1], limit=3, filter={"name": "Coded Cleats Copy"}
+cursor = my_collection.find(
+    {},
+    vector=[0, 0.2, 0.4, 0.6, 0.8],
+    limit=2,
+    include_similarity=True,
 )
 
-astra_db_collection.vector_find(
-    [0.1, 0.1, 0.2, 0.5, 1],
-    limit=3,
-    fields=["_id", "name"],
-    include_similarity=False,
-)
+for result in cursor:
+    print(f"{result['name']}: {result['$similarity']}")