From 063f0df6b716829534e9662574f6d04dd856de83 Mon Sep 17 00:00:00 2001
From: "cai.zhang" <cai.zhang@zilliz.com>
Date: Fri, 13 Dec 2024 17:02:39 +0800
Subject: [PATCH] enhance: Add example for milvus client about expression
 template (#2443)

Signed-off-by: Cai Zhang <cai.zhang@zilliz.com>
---
 examples/search_with_template_expression.py | 104 ++++++++++++++++++++
 1 file changed, 104 insertions(+)
 create mode 100644 examples/search_with_template_expression.py

diff --git a/examples/search_with_template_expression.py b/examples/search_with_template_expression.py
new file mode 100644
index 000000000..ac3f865c2
--- /dev/null
+++ b/examples/search_with_template_expression.py
@@ -0,0 +1,104 @@
+import numpy as np
+from pymilvus import (
+    MilvusClient,
+    DataType,
+)
+
+fmt = "\n=== {:30} ===\n"
+search_latency_fmt = "search latency = {:.4f}s"
+num_entities, dim = 3000, 8
+
+collection_name = "hello_milvus"
+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(auto_id=False, description="hello_milvus is the simplest demo to introduce the APIs")
+schema.add_field("pk", DataType.VARCHAR, is_primary=True, max_length=100)
+schema.add_field("random", DataType.DOUBLE)
+schema.add_field("embeddings", DataType.FLOAT_VECTOR, dim=dim)
+
+index_params = milvus_client.prepare_index_params()
+index_params.add_index(field_name = "embeddings", index_type = "IVF_FLAT", metric_type="L2", nlist=128)
+
+print(fmt.format("Create collection `hello_milvus`"))
+
+milvus_client.create_collection(collection_name, schema=schema, index_params=index_params, consistency_level="Strong")
+
+
+print(fmt.format("Start inserting entities"))
+rng = np.random.default_rng(seed=19530)
+entities = [
+    # provide the pk field because `auto_id` is set to False
+    [str(i) for i in range(num_entities)],
+    rng.random(num_entities).tolist(),  # field random, only supports list
+    rng.random((num_entities, dim)),    # field embeddings, supports numpy.ndarray and list
+]
+
+rows = [ {"pk": entities[0][i], "random": entities[1][i], "embeddings": entities[2][i]} for i in range (num_entities)]
+
+insert_result = milvus_client.insert(collection_name, rows)
+
+
+print(fmt.format("Start loading"))
+milvus_client.load_collection(collection_name)
+
+field_name = "embeddings"
+
+req_list = []
+nq = 1
+default_limit = 5
+vectors_to_search = []
+
+filters = {
+    "pk == {str}": {"str": "10"},
+    "pk in {list}": {"list": ["1", "10", "100"]},
+    "random > {target}": {"target": 5},
+    "random <= {target}": {"target": 111.5},
+    "{min} <= random < {max}": {"min": 0, "max": 9999},
+}
+
+search_param = {
+    "data": vectors_to_search,
+    "anns_field": field_name,
+    "param": {"metric_type": "L2"},
+    "limit": default_limit}
+vectors_to_search = rng.random((nq, dim))
+
+for filter, filter_params in filters.items():
+    print(f"search with filter: {filter}")
+    result = milvus_client.search(collection_name=collection_name, data=vectors_to_search, filter=filter, limit=3,
+                                  output_fields=["random"], search_params=search_param, filter_params=filter_params)
+
+    for hits in result:
+        for hit in hits:
+            print(f"hit: {hit}")
+
+    query_results = milvus_client.query(collection_name, filter=filter, output_fields=["random"],
+                                        filter_params=filter_params, limit=3)
+    for ret in query_results:
+        print("query result: ", ret)
+
+print(fmt.format("Search after delete"))
+ids = insert_result["ids"]
+filter = "pk in {list}"
+filter_params = {"list": [ids[0], ids[1]]}
+print(f"Start deleting with filter `{filter}`")
+
+result = milvus_client.query(collection_name, filter=filter, output_fields=["random"],
+                             filter_params=filter_params, limit=3)
+print(f"query before delete by filter=`{filter}` -> result: \n-{result[0]}\n-{result[1]}\n")
+
+milvus_client.delete(collection_name=collection_name, filter=filter, filter_params=filter_params)
+
+result = milvus_client.query(collection_name, filter=filter, output_fields=["random"],
+                             filter_params=filter_params, limit=3)
+print(f"query after delete by filter=`{filter}` -> result: {result}\n")
+
+print(fmt.format("Release collection"))
+milvus_client.release_collection(collection_name)
+
+print(fmt.format("Drop collection"))
+milvus_client.drop_collection(collection_name)