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Which distance metric should you use for text embeddings?
COSINE
L2 (Euclidean)
IP (Inner Product)
When should you choose HNSW indexing over FLAT indexing for vector search?
When you have large datasets (over 10,000 vectors) and need fast queries with acceptable 95-99% accuracy
When you need perfect 100% accuracy for all queries
When you have fewer than 1,000 vectors to index
Which data type should you use for vector storage in most AI applications?
FLOAT32
FLOAT64
INT32
When should you use Redis Hash instead of JSON for storing vectors?
When you have flat data models and need maximum memory efficiency and query performance
When your data has nested structures or multiple vectors per document
When you need JSON query capabilities
What does the EF_RUNTIME parameter control in HNSW queries?
The tradeoff between query speed and accuracy by controlling how many graph nodes are examined
The maximum number of results returned by the query
The distance metric used for similarity calculations
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