citrus
awesome-vector-database
citrus | awesome-vector-database | |
---|---|---|
1 | 1 | |
93 | 135 | |
- | - | |
7.6 | 9.0 | |
about 1 month ago | 5 days ago | |
Python | ||
Apache License 2.0 | Creative Commons Zero v1.0 Universal |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
citrus
-
Created a smol vector database in my free time. Looking to provide a LangChain integration soon!
It supports all the basic features like creating an index, inserting vectors and searching through them. Here's the GitHub link if anyone's interested in going over it: https://github.com/0xDebabrata/citrus
awesome-vector-database
What are some alternatives?
pgANN - Fast Approximate Nearest Neighbor (ANN) searches with a PostgreSQL database.
weaviate-examples - Weaviate vector database – examples
vector-db-benchmark - Framework for benchmarking vector search engines
numpy-feedstock - A conda-smithy repository for numpy.
vector-search-compilation - A compilation of Vector Search Databases
SimSIMD - Up to 200x Faster Inner Products and Vector Similarity — for Python, JavaScript, Rust, and C, supporting f64, f32, f16 real & complex, i8, and binary vectors using SIMD for both x86 AVX2 & AVX-512 and Arm NEON & SVE 📐
vald - Vald. A Highly Scalable Distributed Vector Search Engine
simdjson-feedstock - A conda-smithy repository for simdjson.
qdrant - Qdrant - High-performance, massive-scale Vector Database for the next generation of AI. Also available in the cloud https://cloud.qdrant.io/
mkl_random-feedstock - A conda-smithy repository for mkl_random.
pynndescent - A Python nearest neighbor descent for approximate nearest neighbors
paranoid_crypto - Paranoid's library contains implementations of checks for well known weaknesses on cryptographic artifacts.