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Vamana vs. HNSW - Exploring ANN algorithms Part 1 - Weaviate In this article, we will cover the need for disk-based solutions, explore Vamana (how it works and contrast it against HNSW), and present the result of Vamana implementation If you are new to vector databases and ANN, I recommend you to read "Why is Vector Search so Fast?" The article explains what vector databases are and how they work
DiskANN and the Vamana Algorithm - Zilliz Learn We'll first cover Vamana, the core data structure behind DiskANN, before discussing how the on-disk portion of DiskANN utilizes a Vamana graph to perform queries efficiently Like previous tutorials, we'll also develop our implementation of the Vamana algorithm in Python
Vector databases (3): Not all indexes are created equal On-disk indexes seem to be proving a huge implementation challenge for vector database vendors, so this is a key feature of Vamana that differentiates it from other algorithms
Vamana — cuvs - docs. rapids. ai The current implementation of DiskANN in cuVS only includes the ‘in-memory’ graph construction and a serialization step that writes the index to a file This index file can be then used by the open-source DiskANN library to perform efficient search
DiskANN workflows filtered_in_memory. md at main - GitHub DiskANN provides two algorithms for building an index with filters support: filtered-vamana and stitched-vamana Here, we describe the parameters for building both apps build_memory_index cpp and apps build_stitched_index cpp are respectively used to build each kind of index
Alternative Graph-Based ANN Algorithms - apxml. com Vamana is another influential graph-based ANN algorithm, notably used as the core indexing technique in the DiskANN system, which is optimized for handling massive datasets that may not fit entirely in RAM Vamana focuses explicitly on building a graph that is provably efficient for greedy search routing