enhance: add bitmap index example (milvus-io#2376)

milvus-io#2375

Signed-off-by: luzhang <[email protected]>
Co-authored-by: luzhang <[email protected]>

examples/orm/bitmap_index_example.py

@@ -0,0 +1,52 @@
+from pymilvus import (
+    connections,
+    FieldSchema, CollectionSchema, DataType,
+    Collection,
+)
+
+DIMENSION = 8
+COLLECTION_NAME = "books"
+connections.connect("default", host="localhost", port="19530")
+
+fields = [
+    FieldSchema(name='id', dtype=DataType.INT64, is_primary=True),
+    FieldSchema(name='title', dtype=DataType.VARCHAR, max_length=200),
+    FieldSchema(name='type', dtype=DataType.VARCHAR, max_length=200),
+    FieldSchema(name='embeddings', dtype=DataType.FLOAT_VECTOR, dim=DIMENSION),
+]
+schema = CollectionSchema(fields=fields, enable_dynamic_field=True)
+collection = Collection(name=COLLECTION_NAME, schema=schema)
+
+data_rows = [
+    {
+        "id": 1,
+        "title": "Lord of the Flies",
+        "type": "novel",
+        "embeddings": [0.64, 0.44, 0.13, 0.47, 0.74, 0.03, 0.32, 0.6],
+    },
+    {
+        "id": 2,
+        "title": "Chinese-English dictionary",
+        "type": "reference",
+        "embeddings": [0.9, 0.45, 0.18, 0.43, 0.4, 0.4, 0.7, 0.24],
+    },
+    {
+        "id": 3,
+        "title": "My Childhood",
+        "type": "autobiographical", 
+        "embeddings": [0.43, 0.57, 0.43, 0.88, 0.84, 0.69, 0.27, 0.98],
+    },
+]
+
+collection.insert(data_rows)
+collection.create_index(
+    "embeddings", {"index_type": "FLAT", "metric_type": "L2"})
+
+# create bitmap index for scalar fields.
+collection.create_index(
+    "type", {"index_type": "BITMAP"})
+
+collection.load()
+
+res = collection.query(expr='type in ["reference"]', output_fields=["id", "type"])
+print(res)

examples/test_bitmap_index.py

@@ -0,0 +1,73 @@
+# test_bitmap_index.py demonstrates how to create bitmap index and perform query
+# 1. connect to Milvus
+# 2. create collection
+# 3. insert data
+# 4. create bitmap index
+# 5. search
+# 6. drop collection
+import time
+
+from pymilvus import (
+    MilvusClient,
+    utility,
+    FieldSchema, CollectionSchema, Function, DataType, FunctionType,
+    Collection,
+)
+
+collection_name = "text_bitmap_book"
+
+milvus_client = MilvusClient("http://localhost:19530")
+
+has_collection = milvus_client.has_collection(collection_name, timeout=5)
+if has_collection:
+    milvus_client.drop_collection(collection_name)
+
+schema = milvus_client.create_schema()
+schema.add_field("id", DataType.INT64, is_primary=True, auto_id=False)
+schema.add_field("title", DataType.VARCHAR, max_length=200)
+schema.add_field("type", DataType.VARCHAR, max_length=200)
+schema.add_field("embeddings", DataType.FLOAT_VECTOR, dim=8)
+
+index_params = milvus_client.prepare_index_params()
+index_params.add_index(
+    field_name="embeddings",
+    index_name="vec_index",
+    index_type="FLAT",
+    metric_type="L2",
+)
+index_params.add_index(
+    field_name="type",
+    index_name="type_index",
+    index_type="BITMAP",
+)
+
+ret = milvus_client.create_collection(collection_name, schema=schema, index_params=index_params, consistency_level="Strong")
+
+rows = [
+    {
+        "id": 1,
+        "title": "Lord of the Flies",
+        "type": "novel",
+        "embeddings": [0.64, 0.44, 0.13, 0.47, 0.74, 0.03, 0.32, 0.6],
+    },
+    {
+        "id": 2,
+        "title": "Chinese-English dictionary",
+        "type": "reference",
+        "embeddings": [0.9, 0.45, 0.18, 0.43, 0.4, 0.4, 0.7, 0.24],
+    },
+    {
+        "id": 3,
+        "title": "My Childhood",
+        "type": "autobiographical", 
+        "embeddings": [0.43, 0.57, 0.43, 0.88, 0.84, 0.69, 0.27, 0.98],
+    },
+]
+
+insert_result = milvus_client.insert(collection_name, rows, progress_bar=True)
+
+result = milvus_client.query(collection_name, filter='type in ["reference"]', output_fields=["type"], consistency_level="Strong")           
+print(result)
+
+# Finally, drop the collection
+milvus_client.drop_collection(collection_name)