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Milvus — cloud-native vector database for billion-scale similarity search. GPU-accelerated indexing, hybrid search, multi-vector, streaming, and time travel. Distributed deployment with Kubernetes.
Passed 19 of 22 test cases with the skill, and 13 of 22 without it.
and 3 more in the full report
6 fixed13 passed either way3 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
When defining a text field named 'text' in PyMilvus CollectionSchema for standard documents, what data type and max length parameter are configured?
| Checked | Without skill | With skill |
|---|---|---|
| Uses DataType.VARCHAR as the data type | Passed | Passed |
| Sets max_length to 1000 | Missed | Passed |
The instructions your model reads when the skill loads.
Milvus is a cloud-native vector database for billion-scale similarity search. Supports GPU-accelerated indexing (IVF, HNSW, DiskANN), hybrid search (dense + sparse), multi-vector, streaming ingestion, time travel, and distributed deployment with Kubernetes.
docker compose -f https://github.com/milvus-io/milvus/releases/latest/download/milvus-standalone-docker-compose.yml up -d
from pymilvus import connections, Collection, FieldSchema, CollectionSchema, DataType
connections.connect(host="localhost", port=19530)
schema = CollectionSchema([
FieldSchema("id", DataType.INT64, is_primary=True),
FieldSchema("embedding", DataType.FLOAT_VECTOR, dim=384),
FieldSchema("text", DataType.VARCHAR, max_length=1000),
])
collection = Collection("documents", schema)
collection.create_index("embedding", {"index_type": "IVF_FLAT", "metric_type": "L2", "params": {"nlist": 128}})
collection.load()
test cases passed, vs 13 / 22 without
More skills from the same source and nearby in the registry, with their headline benchmark lift.