Redis vector sets on Docs

Filter expressions

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Filtered search lets you combine vector similarity search with scalar filtering. You can associate JSON attributes with elements in a vector set, and then filter results using those attributes during VSIM queries.

Performance

Mon, 01 Jan 0001 00:00:00 +0000

Query performance

Vector similarity queries using the VSIM are threaded by default. Redis uses up to 32 threads to process these queries in parallel.

Scalability

Mon, 01 Jan 0001 00:00:00 +0000

Multi-instance scalability

Vector sets can scale horizontally by sharding your data across multiple Redis instances. This is done by partitioning the dataset manually across keys and nodes.

Memory optimization

Mon, 01 Jan 0001 00:00:00 +0000

Overview

Redis vector sets are efficient, but vector similarity indexing and graph traversal require memory tradeoffs. This guide helps you manage memory use through quantization, graph tuning, and attribute choices.

Troubleshooting

Mon, 01 Jan 0001 00:00:00 +0000

Common challenges

Vector sets are approximate by design. That makes debugging trickier than with exact match queries. This section helps you understand issues with recall, filtering, and graph structure.