From b6ca0f32177fd665b75075c3f686531d679bb00e Mon Sep 17 00:00:00 2001 From: zhjwpku Date: Tue, 16 Apr 2024 08:07:25 +0000 Subject: [PATCH] deploy: dd9cfa31a80350ee0bd1ec3d7c41a1e0a18a2297 --- databases/vectordb/diskann.html | 4 ++++ databases/vectordb/index.html | 4 ++++ print.html | 8 ++++++++ searchindex.js | 2 +- searchindex.json | 2 +- 5 files changed, 18 insertions(+), 2 deletions(-) diff --git a/databases/vectordb/diskann.html b/databases/vectordb/diskann.html index f320983..6b46dd1 100644 --- a/databases/vectordb/diskann.html +++ b/databases/vectordb/diskann.html @@ -183,6 +183,10 @@

DiskANN 通过 BeamSearch(设置 beamwidth 一次读多个数据块) 和缓存最常访问的节点（eg. by caching all vertices that are C = 3 or 4 hops from the starting point s）来加速查询。 另外，DiskANN 将邻居节点的向量保存在磁盘索引文件中，来提高搜索的精度（Implicit Re-Ranking Using Full-Precision Vectors）。

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Code

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Further readings

diff --git a/print.html b/print.html index 4f909f6..7400c94 100644 --- a/print.html +++ b/print.html @@ -1167,6 +1167,10 @@

Misc

  • Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT
  • Accelerating Vector Search: Fine-Tuning GPU Index Algorithms
  • Accelerated Vector Search: Approximating with RAPIDS RAFT IVF-Flat
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  • Semantic Search: Measuring Meaning From Jaccard to Bert
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  • SPLADE for Sparse Vector Search Explain
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  • Sparse embedding or BM25?
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  • Unlock Advanced Recommendation Engines with Milvus' New Range Search

    • Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs

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    DiskANN 通过 BeamSearch(设置 beamwidth 一次读多个数据块) 和缓存最常访问的节点（eg. by caching all vertices that are C = 3 or 4 hops from the starting point s）来加速查询。 另外，DiskANN 将邻居节点的向量保存在磁盘索引文件中，来提高搜索的精度（Implicit Re-Ranking Using Full-Precision Vectors）。

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    Code

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    • microsoft/DiskANN, Graph-structured Indices for Scalable, Fast, Fresh and Filtered Approximate Nearest Neighbor Search
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    Further readings