KGraph
KGraph is an open-source library for fast approximate nearest neighbor search in high-dimensional vector spaces, applicable to vector database solutions.
About this tool
KGraph
KGraph is an open-source library for fast approximate nearest neighbor (ANN) search in high-dimensional vector spaces, with applications in vector database solutions and similarity search.
Features
- k-NN Graph Construction: Builds k-nearest neighbor graphs for datasets.
- Online k-NN Search: Supports fast approximate search using the constructed k-NN graph as an index.
- Heuristic Algorithms: Implements generic and fast heuristic algorithms for ANN search.
- C++ API: Main API is in C++, providing maximum flexibility and performance. Users define custom similarity functions via oracles (callback classes).
- Python Wrapper: Provides a Python API (
kgraph), supporting Euclidean and Angular distances on rows of NumPy matrices. - Parameter Tuning: Both index construction and search support various optional parameters to tune performance.
- Efficient Oracle Implementations: Includes oracle implementations for common similarity measures.
- No Assumptions on Similarity Properties: Algorithms do not assume properties like the triangle inequality.
- Installation: Can be built using CMake or a provided Makefile; Python API installable with
python setup.py install. - Open Source: Source code available on GitHub under an open-source license.
Pricing
KGraph is open-source and free to use.
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