Comparison of IVFFlat and HNSW Algorithms
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Updated
Jun 13, 2024 - Python
Comparison of IVFFlat and HNSW Algorithms
The thesis presents the parallelisation of a state-of-the art clustering algorithm, FISHDBC. This objective has been achived by improving the main data structures and components of the algorithm: HNSW, MST and HDBSCAN. My contribution is based on a lock-free strategy, completely wrote in Python.
A specialized implementation of the Hierarchical Navigable Small World (HNSW) data structure adapted for efficient nearest neighbor lookup of approximate matching hashes
S3 vector database for LLM Agents and RAG.
(distributed) vector database
⚡ A fast embedded library for approximate nearest neighbor search
MinHash, LSH, LSH Forest, Weighted MinHash, HyperLogLog, HyperLogLog++, LSH Ensemble and HNSW
Unified embedding generation and search engine. Also available on cloud - cloud.marqo.ai
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