Understanding Consistency Level in the Milvus Vector Database (Part II)

An anatomy of the mechanism behind tunable consistency levels in the Milvus vector database.

Cover_image

The underlying time tick mechanism

timetick

Guarantee Timestamp

Scenario 1: Execute search request immediately

execute_immediately

Scenario 2: Wait till “ServiceTime > GuaranteeTs”

wait_search

Consistency Levels

guarantee_ts

How to tune consistency level in Milvus?

Conduct a vector similarity search

results = collection.search(
data=[[0.1, 0.2]],
anns_field="book_intro",
param=search_params,
limit=10,
expr=None,
consistency_level="Strong"
)

Conduct a vector query

res = collection.query(
expr = "book_id in [2,4,6,8]",
output_fields = ["book_id", "book_intro"],
consistency_level="Strong"
)

What’s next

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Open-source Vector Database Powering AI Applications. #SimilaritySearch #Embeddings #MachineLearning