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test: Expand MilvusOnlineStore integration test coverage #6299

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ntkathole merged 1 commit into from
Apr 22, 2026
Merged

test: Expand MilvusOnlineStore integration test coverage #6299

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68 changes: 63 additions & 5 deletions sdk/python/tests/integration/online_store/test_universal_online.py
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Original file line number Diff line number Diff line change
Expand Up @@ -921,13 +921,27 @@ def test_retrieve_online_milvus_documents(environment, fake_document_data):
df, data_source = fake_document_data
item_embeddings_feature_view = create_item_embeddings_feature_view(data_source)
fs.apply([item_embeddings_feature_view, item()])

features = [
"item_embeddings:embedding_float",
"item_embeddings:item_id",
"item_embeddings:string_feature",
]

# Empty-store query: collection exists but has no rows yet.
empty = fs.retrieve_online_documents_v2(
features=features,
query=[1.0, 2.0],
top_k=2,
distance_metric="L2",
).to_dict()
assert len(empty["embedding_float"]) == 0
assert len(empty["item_id"]) == 0

fs.write_to_online_store("item_embeddings", df)

documents = fs.retrieve_online_documents_v2(
features=[
"item_embeddings:embedding_float",
"item_embeddings:item_id",
"item_embeddings:string_feature",
],
features=features,
query=[1.0, 2.0],
top_k=2,
distance_metric="L2",
Expand All @@ -948,6 +962,50 @@ def test_retrieve_online_milvus_documents(environment, fake_document_data):
f"Integration test: embedding {i} has {len(embedding)} dimensions, expected {query_dim}"
)

# Oversized top_k: dataset has 3 rows, request 5 -> expect 3 back.
all_docs = fs.retrieve_online_documents_v2(
features=features,
query=[1.0, 2.0],
top_k=5,
distance_metric="L2",
).to_dict()
assert len(all_docs["embedding_float"]) == 3
assert sorted(all_docs["item_id"]) == [1, 2, 3]

# Cosine-metric variant: separate FV so the Milvus collection is created
# with COSINE as its index metric.
cosine_fv = FeatureView(
name="item_embeddings_cosine",
entities=[item()],
schema=[
Field(
name="embedding_float",
dtype=Array(Float32),
vector_index=True,
vector_search_metric="COSINE",
),
Field(name="string_feature", dtype=String),
Field(name="float_feature", dtype=Float32),
],
source=data_source,
ttl=timedelta(hours=2),
)
fs.apply([cosine_fv])
fs.write_to_online_store("item_embeddings_cosine", df)

cosine_docs = fs.retrieve_online_documents_v2(
features=[
"item_embeddings_cosine:embedding_float",
"item_embeddings_cosine:item_id",
"item_embeddings_cosine:string_feature",
],
query=[1.0, 2.0],
top_k=2,
distance_metric="COSINE",
).to_dict()
assert len(cosine_docs["embedding_float"]) == 2
assert len(cosine_docs["item_id"]) == 2


@pytest.mark.integration
@pytest.mark.universal_online_stores(only=["milvus"])
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