deploy: 8194424

databases/vectordb/index.html

@@ -182,6 +182,12 @@ <h3 id="misc"><a class="header" href="#misc">Misc</a></h3>
 <li><a href="/assets/pdfs/The_FAISS_LIBRARY_2401.08281.pdf">THE FAISS LIBRARY</a>, 2024</li>
 <li><a href="https://www.pinecone.io/learn/series/faiss/">Faiss: The Missing Manual</a></li>
 <li><a href="https://zhang-yang.medium.com/cosine-similarity-dot-product-for-normalized-vectors-c07bdb61c9d1">Cosine similarity = dot product for normalized vectors</a></li>
+<li><a href="https://news.ycombinator.com/item?id=36942993">USearch: Smaller and faster single-file vector search engine</a></li>
+<li><a href="https://github.com/spotify/annoy">Annoy(Approximate Nearest Neighbors Oh Yeah)</a></li>
+<li><a href="https://github.com/rapidsai/raft">RAPIDS RAFT library</a></li>
+<li><a href="https://developer.nvidia.com/blog/accelerating-vector-search-using-gpu-powered-indexes-with-rapids-raft/">Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT</a></li>
+<li><a href="https://developer.nvidia.com/blog/accelerating-vector-search-fine-tuning-gpu-index-algorithms/">Accelerating Vector Search: Fine-Tuning GPU Index Algorithms</a></li>
+<li><a href="https://developer.nvidia.com/blog/accelerated-vector-search-approximating-with-rapids-raft-ivf-flat/">Accelerated Vector Search: Approximating with RAPIDS RAFT IVF-Flat</a></li>
 </ul>
 
                     </main>

databases/vectordb/pq.html

@@ -191,6 +191,8 @@ <h3 id="a-hrefassetspdfsproduct_quantization_for_nearest_neighbor_searchpdfprodu
 <h4 id="references"><a class="header" href="#references">References:</a></h4>
 <ul>
 <li><a href="https://www.pinecone.io/learn/series/faiss/product-quantization/">Product Quantization: Compressing high-dimensional vectors by 97%</a></li>
+<li><a href="https://mccormickml.com/2017/10/13/product-quantizer-tutorial-part-1/">Product Quantizers for k-NN Tutorial Part 1</a></li>
+<li><a href="https://mccormickml.com/2017/10/22/product-quantizer-tutorial-part-2/">Product Quantizers for k-NN Tutorial Part 2</a></li>
 </ul>
 
                     </main>

print.html

@@ -1161,6 +1161,12 @@ <h3 id="misc"><a class="header" href="#misc">Misc</a></h3>
 <li><a href="databases/vectordb//assets/pdfs/The_FAISS_LIBRARY_2401.08281.pdf">THE FAISS LIBRARY</a>, 2024</li>
 <li><a href="https://www.pinecone.io/learn/series/faiss/">Faiss: The Missing Manual</a></li>
 <li><a href="https://zhang-yang.medium.com/cosine-similarity-dot-product-for-normalized-vectors-c07bdb61c9d1">Cosine similarity = dot product for normalized vectors</a></li>
+<li><a href="https://news.ycombinator.com/item?id=36942993">USearch: Smaller and faster single-file vector search engine</a></li>
+<li><a href="https://github.com/spotify/annoy">Annoy(Approximate Nearest Neighbors Oh Yeah)</a></li>
+<li><a href="https://github.com/rapidsai/raft">RAPIDS RAFT library</a></li>
+<li><a href="https://developer.nvidia.com/blog/accelerating-vector-search-using-gpu-powered-indexes-with-rapids-raft/">Accelerating Vector Search: Using GPU-Powered Indexes with RAPIDS RAFT</a></li>
+<li><a href="https://developer.nvidia.com/blog/accelerating-vector-search-fine-tuning-gpu-index-algorithms/">Accelerating Vector Search: Fine-Tuning GPU Index Algorithms</a></li>
+<li><a href="https://developer.nvidia.com/blog/accelerated-vector-search-approximating-with-rapids-raft-ivf-flat/">Accelerated Vector Search: Approximating with RAPIDS RAFT IVF-Flat</a></li>
 </ul>
 <div style="break-before: page; page-break-before: always;"></div><h3 id="a-hrefdatabasesvectordbassetspdfshnsw_160309320pdfefficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable-small-world-graphsa"><a class="header" href="#a-hrefdatabasesvectordbassetspdfshnsw_160309320pdfefficient-and-robust-approximate-nearest-neighbor-search-using-hierarchical-navigable-small-world-graphsa"><a href="databases/vectordb//assets/pdfs/hnsw_1603.09320.pdf">Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs</a></a></h3>
 <blockquote>
@@ -1301,6 +1307,8 @@ <h4 id="references-4"><a class="header" href="#references-4">References:</a></h4
 <h4 id="references-5"><a class="header" href="#references-5">References:</a></h4>
 <ul>
 <li><a href="https://www.pinecone.io/learn/series/faiss/product-quantization/">Product Quantization: Compressing high-dimensional vectors by 97%</a></li>
+<li><a href="https://mccormickml.com/2017/10/13/product-quantizer-tutorial-part-1/">Product Quantizers for k-NN Tutorial Part 1</a></li>
+<li><a href="https://mccormickml.com/2017/10/22/product-quantizer-tutorial-part-2/">Product Quantizers for k-NN Tutorial Part 2</a></li>
 </ul>
 <div style="break-before: page; page-break-before: always;"></div><h3 id="a-hrefdatabasesassetspdfscituspdfcitus-distributed-postgresql-for-data-intensive-applicationsa"><a class="header" href="#a-hrefdatabasesassetspdfscituspdfcitus-distributed-postgresql-for-data-intensive-applicationsa"><a href="databases/../assets/pdfs/citus.pdf">Citus: Distributed PostgreSQL for Data-Intensive Applications</a></a></h3>
 <blockquote>