Merge pull request #51 from shru0405/Python-cloud

Added JSON serialization and method calls in hello_epsilla,py

Author: eric-epsilla

hello_epsilla_cloud_embedding.md

@@ -0,0 +1,79 @@
+import sys
+import json
+
+try:
+    from pyepsilla import cloud
+except ImportError as e:
+    print("Failed to import pyepsilla. Ensure the library is installed.")
+    sys.exit(1)
+
+try:
+    # Connect to Epsilla Cloud
+    client = cloud.Client(
+        project_id="project-id",
+        api_key="epsilla-cloud-api-key",
+    )
+
+    # Connect to Vectordb
+    db = client.vectordb(db_id="6accafb1-476d-43b0-aa64-12ebfbf7442d")
+
+    # Data for creating table
+    table_fields = [
+        {"name": "ID", "dataType": "INT", "primaryKey": True},
+        {"name": "Doc", "dataType": "STRING"},
+        {"name": "Embedding", "dataType": "VECTOR_FLOAT", "dimensions": 4},
+    ]
+    print("Table fields:", table_fields)
+
+    # Create a table with schema
+    status_code, response = db.create_table(
+        table_name="MyTable",
+        table_fields=table_fields,
+    )
+    print(status_code, response)
+
+    # Data for inserting records
+    records = [
+        {"ID": 1, "Doc": "Berlin", "Embedding": [0.05, 0.61, 0.76, 0.74]},
+        {"ID": 2, "Doc": "London", "Embedding": [0.19, 0.81, 0.75, 0.11]},
+        {"ID": 3, "Doc": "Moscow", "Embedding": [0.36, 0.55, 0.47, 0.94]},
+        {"ID": 4, "Doc": "San Francisco", "Embedding": [0.18, 0.01, 0.85, 0.80]},
+        {"ID": 5, "Doc": "Shanghai", "Embedding": [0.24, 0.18, 0.22, 0.44]},
+    ]
+    print("Records:", records)
+
+    # Insert new vector records into table
+    status_code, response = db.insert(
+        table_name="MyTable",
+        records=records,
+    )
+    print(status_code, response)
+
+    # Query parameters
+    query_vector = [0.35, 0.55, 0.47, 0.94]
+    print("Query vector:", query_vector)
+
+    # Query Vectors with specific response field, otherwise it will return all fields
+    status_code, response = db.query(
+        table_name="MyTable",
+        query_field="Embedding",
+        query_vector=query_vector,
+        response_fields=["Doc"],
+        limit=2,
+    )
+    print(status_code, response)
+
+    # Delete specific records from table
+    primary_keys_to_delete = [4, 5]
+    print("Primary keys to delete:", primary_keys_to_delete)
+    status_code, response = db.delete(table_name="MyTable", primary_keys=primary_keys_to_delete)
+    print(status_code, response)
+    status_code, response = db.delete(table_name="MyTable", filter="Doc <> 'San Francisco'")
+    print(status_code, response)
+
+    # Drop table
+    status_code, response = db.drop_table(table_name="MyTable")
+    print(status_code, response)
+except Exception as e:
+    print(f"An error occurred: {e}")
+