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suncloudsmoon/HyperVectorDB-APIFixes: HyperVectorDB – simple. powerful. A local vector database built in C#, engineered for effortless precision. Explore your data using Cosine, Jaccard, Euclidean, Manhattan, Chebyshev, and Canberra distances.

HyperVectorDB-APIFixes is a fork of HyperVectorDB that changes the OpenAI library dependency (the newer OpenAI-dotnet library is used). HyperVectorDB is a local vector database built in C# that supports various distance/similarity measures. It is designed to store vectors and associated documents and perform high-performance vector queries. This project supports Cosine Similarity, Jaccard Dissimilarity, as well as Euclidean, Manhattan, Chebyshev, and Canberra distances. If you are looking for a python library to do the same thing check out John Dagdelen https://github.com/jdagdelen/hyperDB

dotnet add package HyperVectorDB-APIFixes

• Query/Response Caching: Currently only supported for cosine similarity queries. This feature allows the database to cache the results of a query for a given vector, so that the next time the same vector is queried, the results are returned immediately. This feature is useful for applications that require frequent queries on the same vector.
• Cache invalidation: Cache invalidation is supported for cosine similarity queries. The cache is invalidated when a new vector is added to the database, or when a vector is removed from the database.
• Query Functions: The database supports several types of queries for similarity and distance measures:
  • Cosine Similarity: This function performs a Cosine Similarity query on the database.
  • Jaccard Dissimilarity: This function performs a Jaccard Dissimilarity query on the database.
  • Euclidean Distance: This function performs a Euclidean Distance query on the database.
  • Manhattan Distance: This function performs a Manhattan Distance query on the database.
  • Chebyshev Distance: This function performs a Chebyshev Distance query on the database.
  • Canberra Distance: This function performs a Canberra Distance query on the database.
• Automatic Parallelization: When configured, the database will automatically split across multiple files and memory regions to take full advantage of async IO on store and multithreading on query.
• Data Compression: When saved to disk, the database uses LZ4 compression

Each query function returns the top k documents and their corresponding similarity or distance values. The value of k is configurable and defaults to 5.

Please note that this project is currently in its development phase. Some functions still need to be tested, and caching for some query types is yet to be implemented.

Example usage comming soon

Contributions are welcome. Please feel free to fork the project, make changes, and open a pull request. Please make sure to test all changes thoroughly.

See LICENSE.

It started out with me getting back into artificial intellegence and wanting to do so using C#. I was unable to find anything that would suite my needs for a vector database. Then John Dagdelen put together this vector store in python https://github.com/jdagdelen/hyperDB, it was faily basic at the time posted without that many lines of code so I decided to try and use gpt to port it to C#. This was somewhat successful but it did not quite work as needed so this project was born.

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