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fix: [2.5] Change CMake variable for switch to knowhere-cuvs (#40289) 

issue: #39883
pr: #40105

Signed-off-by: Mickael Ide <mide@nvidia.com>
Signed-off-by: Li Liu <li.liu@zilliz.com>
Co-authored-by: Micka <ide.mickael@gmail.com>
pull/40293/head
liliu-z 2025-03-03 14:48:00 +08:00 committed by GitHub
parent 2f7cce11a3
commit 6fd5af3605
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2 changed files with 2 additions and 1 deletions
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.gitignore vendored
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@ -29,6 +29,7 @@ docker-compose-devcontainer.yml.tmp
# Docker generated cache file
.docker/
.docker-gpu/
**/_artifacts/**

2
README.md
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@ -75,7 +75,7 @@ Milvus is designed to handle vector search at scale. Users can store vectors, wh
**Support for Various Vector Index Types and Hardware Acceleration**
* Milvus separates the system and core vector search engine, allowing it to support all major vector index types that are optimized for different scenarios, including HNSW, IVF, FLAT (brute-force), SCANN, and DiskANN, with [quantization-based](https://milvus.io/docs/index.md?tab=floating#IVFPQ) variations and [mmap](https://milvus.io/docs/mmap.md). Milvus optimizes vector search for advanced features such as [metadata filtering](https://milvus.io/docs/scalar_index.md#Scalar-Index) and [range search](https://milvus.io/docs/single-vector-search.md#Range-search). Additionally, Milvus implements hardware acceleration to enhance vector search performance and supports GPU indexing, such as NVIDIA's [CAGRA](https://github.com/rapidsai/raft).
* Milvus separates the system and core vector search engine, allowing it to support all major vector index types that are optimized for different scenarios, including HNSW, IVF, FLAT (brute-force), SCANN, and DiskANN, with [quantization-based](https://milvus.io/docs/index.md?tab=floating#IVFPQ) variations and [mmap](https://milvus.io/docs/mmap.md). Milvus optimizes vector search for advanced features such as [metadata filtering](https://milvus.io/docs/scalar_index.md#Scalar-Index) and [range search](https://milvus.io/docs/single-vector-search.md#Range-search). Additionally, Milvus implements hardware acceleration to enhance vector search performance and supports GPU indexing, such as NVIDIA's [CAGRA](https://github.com/rapidsai/cuvs).
**Flexible Multi-tenancy and Hot/Cold Storage**