mantzaris/LMDiskANN.jl
Julia Implementation of Low Memory Disk ANN (LM-DiskANN)
This project helps developers working with large datasets of numerical feature vectors, such as image embeddings or text embeddings. It takes these vectors and builds an efficient index on disk, allowing for quick searches to find the most similar vectors without consuming too much RAM. Data scientists and machine learning engineers can use this to power tasks like recommendation engines or semantic search.
No commits in the last 6 months.
Use this if you need to quickly find similar data points within a very large collection of high-dimensional vectors, and your system has limited memory.
Not ideal if your dataset of vectors is small enough to fit comfortably in memory, as the disk-based operations might introduce unnecessary overhead.
Stars
7
Forks
1
Language
Julia
License
MIT
Category
Last pushed
Jun 14, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/mantzaris/LMDiskANN.jl"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
MariaDB/server
MariaDB server is a community developed fork of MySQL server. Started by core members of the...
AlayaDB-AI/AlayaLite
AlayaLite – A Fast, Flexible Vector Database for Everyone.
infiniflow/infinity
The AI-native database built for LLM applications, providing incredibly fast hybrid search of...
nnethercott/hannoy
Production-ready KV-backed HNSW implementation in Rust using LMDB
dingodb/dingo
A multi-modal vector database that supports upserts and vector queries using unified SQL...