{"repo":"valkey-io/valkey-search","free":true,"listed":false,"github":"https://github.com/valkey-io/valkey-search","clone":"git clone https://github.com/valkey-io/valkey-search.git","description":"valkey-search is a C++ module which extends valkey with vector search and secondary indexing capabilities. It enables users to index and query data stored in Valkey using complex queries with filters while maintaining high performance and scalability.","language":"C++","stars":140,"topics":["valkey","search-engine","analytics","cpp","recommender-system","search","search-engines","similarity-search","vector-search","hybrid-search"],"license":"BSD-3-Clause","category":"analytics","readme_excerpt":"valkey-search Valkey-Search (BSD-3-Clause), provided as a Valkey module, is a high-performance Search engine optimized for AI-driven / Search / Analytics / Recommendation System related workloads. It delivers single-digit millisecond latency and high QPS, capable of handling billions of vectors with over 99% recall as part of vector searches. It also provides support for hybrid / pure non vector workloads including Numeric, Tag, and Full-text searches. Valkey-Search allows users to create indexes and perform searches, incorporating complex filters. Users can index data using either Valkey Hash or Valkey-JSON data types. The vector queries support Approximate Nearest Neighbor (ANN) search with HNSW and exact matching using K-Nearest Neighbors (KNN). Supported Commands For a detailed description of the supported commands and configuration options, see the Command Reference. For comprehensive examples, refer to the Quick Start Guide. Scaling valkey-search supports both Standalone and Cluster modes. Query processing and ingestion scale linearly with CPU cores in both modes. For large storage requirements, users can leverage Cluster mode for horizontal scaling of the keyspace. If replica lag is acceptable, users can achieve horizontal query scaling by directing clients to read from replicas. Performance valkey-search achieves high performance by storing vectors in-memory and applying optimizations throughout the stack to efficiently utilize the host resources, such as: - Paralleli","default_branch":null,"files":null,"tree":[],"storefront":"/r/valkey-io","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/valkey-io/valkey-search/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}