1.0.1

Valkey Search 1.0.1 - Released Wed 11 June 2025

Bug fixes

• Module-Config: fix a possibility of of a double free (#149)
• More resilient builds on different Linux distros (#152, #154, #158, #168, #172)
• Use a managed pointer for thread safe context in coordinator (#159)
• Enable integration tests + ASAN (#162)
• Fixed drop index bug (#166)
• Fix GetRedisLocalPort function to support TLS (#144)

1.0.0

Valkey Search 1.0.0 GA - Released Wed 28 May 2025

This is the first official release of Valkey Search 1.0. This release is fully compatible with Valkey 8.1.1 and later releases.

Behavior changes

• Adding support for blocking clients during keyspace notification (#95)
• Make index backfill batch/block size configurable (#97)
• FT.SEARCH - Update neighbor sorting to always be asc (#104)
• Add OOM checking to index backfill (#135)

Bug fixes

• Avoid unsafe code in INFO during crash dump (#98)
• Support counting cores on aarch64 (#107)
• Create index with the correct DB number (#120) (#123)
• Fix dumping corrupted rdb file (#131)
• Fix empty vector query clause ft.search cmd parsing (#138)

Performance/efficiency improvements

• Allow dynamic adjustment of thread count at runtime (#126) (#129)

Build and packaging changes

• Allow building with external libs (#85)
• Don't suppress error when running python integration tests (#89)
• Add ASAN build option (#100)

Valkey Search 1.0.0 RC1 - Released Fri 28 Mar 2025

This is the first release candidate of valkey-search 1.0 that is a high-performance Vector Similarity Search engine optimized for AI-driven workloads. It delivers single-digit millisecond latency and high QPS, capable of handling billions of vectors with over 99% recall.

Valkey-Search allows users to create indexes and perform similarity searches, incorporating complex filters. It supports Approximate Nearest Neighbor (ANN) search with HNSW and exact matching using K-Nearest Neighbors (KNN). Users can index data using either Valkey Hash or Valkey-JSON data types.

Major API and Functionality

• Add the search module data type which can handle RDB load, RDB save, free, and memory usage
• Add the following search module commands:
  • FT.CREATE
  • FT.DROPINDEX
  • FT.INFO
  • FT._LIST
  • FT.SEARCH
• Supported indexes
  • Vector: HNSW and Flat
  • Non-vector: Numeric and Tag
• Index data types include Valkey Hash and Valkey JSON.
• Cluster support including cross-shard search via coordinator mode
• Linear scaling of keyspace and compute
• ACL support
• RDB serialization of metadata including the search index
• Hybrid queries combining vector and non vector indexes
• Handle key space events for data mutation
• Expose statistics and reporting memory usage to the core valkey engine

New configurations

• Add support for the following configurations: reader-threads, writer-threads, use-coordinator, log-level

1.0.0-rc1

Valkey Search 1.0.0 RC1 - Released Fri 28 Mar 2025

This is the first release candidate of valkey-search 1.0 that is a high-performance Vector Similarity Search engine optimized for AI-driven workloads. It delivers single-digit millisecond latency and high QPS, capable of handling billions of vectors with over 99% recall.

Valkey-Search allows users to create indexes and perform similarity searches, incorporating complex filters. It supports Approximate Nearest Neighbor (ANN) search with HNSW and exact matching using K-Nearest Neighbors (KNN). Users can index data using either Valkey Hash or Valkey-JSON data types.

Major API and Functionality

• Add the search module data type which can handle RDB load, RDB save, free, and memory usage
• Add the following search module commands:
  ** FT.CREATE
  ** FT.DROPINDEX
  ** FT.INFO
  ** FT._LIST
  ** FT.SEARCH
• Supported indexes
  ** Vector: HNSW and Flat
  ** Non-vector: Numeric and Tag
• Index data types include Valkey Hash and Valkey JSON.
• Cluster support
• Linear scaling of keyspace and compute
• ACL support
• RDB serialization of metadata including the search index
• Hybrid queries combining vector and non vector indexes
• Handle key space events for data mutation
• Expose statistics and reporting memory usage to the core valkey engine

New configurations

• Add support for the following configurations: reader-threads, writer-threads, use-coordinator, log-level