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enhance: [2.5]Add example for milvus client about expression template (…
…#2444) master pr: #2443 Signed-off-by: Cai Zhang <[email protected]>
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import numpy as np | ||
from pymilvus import ( | ||
MilvusClient, | ||
DataType, | ||
) | ||
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fmt = "\n=== {:30} ===\n" | ||
search_latency_fmt = "search latency = {:.4f}s" | ||
num_entities, dim = 3000, 8 | ||
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collection_name = "hello_milvus" | ||
milvus_client = MilvusClient("http://localhost:19530") | ||
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has_collection = milvus_client.has_collection(collection_name, timeout=5) | ||
if has_collection: | ||
milvus_client.drop_collection(collection_name) | ||
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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) | ||
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index_params = milvus_client.prepare_index_params() | ||
index_params.add_index(field_name = "embeddings", index_type = "IVF_FLAT", metric_type="L2", nlist=128) | ||
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print(fmt.format("Create collection `hello_milvus`")) | ||
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milvus_client.create_collection(collection_name, schema=schema, index_params=index_params, consistency_level="Strong") | ||
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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 | ||
] | ||
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rows = [ {"pk": entities[0][i], "random": entities[1][i], "embeddings": entities[2][i]} for i in range (num_entities)] | ||
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insert_result = milvus_client.insert(collection_name, rows) | ||
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print(fmt.format("Start loading")) | ||
milvus_client.load_collection(collection_name) | ||
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field_name = "embeddings" | ||
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req_list = [] | ||
nq = 1 | ||
default_limit = 5 | ||
vectors_to_search = [] | ||
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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}, | ||
} | ||
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search_param = { | ||
"data": vectors_to_search, | ||
"anns_field": field_name, | ||
"param": {"metric_type": "L2"}, | ||
"limit": default_limit} | ||
vectors_to_search = rng.random((nq, dim)) | ||
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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) | ||
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for hits in result: | ||
for hit in hits: | ||
print(f"hit: {hit}") | ||
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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) | ||
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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}`") | ||
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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") | ||
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milvus_client.delete(collection_name=collection_name, filter=filter, filter_params=filter_params) | ||
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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") | ||
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print(fmt.format("Release collection")) | ||
milvus_client.release_collection(collection_name) | ||
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print(fmt.format("Drop collection")) | ||
milvus_client.drop_collection(collection_name) |