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MyVector

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MyVector brings the power of vector similarity search directly to your MySQL database. Built as a native plugin, it integrates seamlessly with your existing infrastructure, allowing you to build powerful semantic search, recommendation engines, and AI-powered applications without the need for external services.

It's fast, scalable, and designed for real-world use cases, from simple word embeddings to complex image and audio analysis.

Get Started View on GitHub

Supported platforms

Linux (including the official Docker images on GHCR) and macOS are supported for building and running MyVector. Microsoft Windows is not a supported build target at this time — use Linux containers or a Unix-like host for production builds and deployments. See Building on macOS for macOS notes.

Why MyVector?

Feature MyVector Other Solutions
Deployment Native MySQL Plugin: No extra services to manage. Often requires a separate, dedicated vector database.
Data Sync Real-time: Automatic index updates via MySQL binlogs. Manual data synchronization or complex ETL pipelines.
Performance Highly Optimized: Built on the high-performance HNSWlib. Performance varies; may require significant tuning.
Ease of Use Simple SQL Interface: Use familiar SQL UDFs and procedures. Custom APIs and query languages.
Cost Open Source: Free to use and modify. Can be expensive, especially at scale.
Ecosystem Leverage MySQL: Use your existing tools, connectors, and expertise. Requires a new ecosystem of tools and connectors.

Architecture

flowchart LR
  App["Application / SQL Client"] --> MySQL["MySQL Server"];
  MySQL --> UDFs["MyVector UDFs"];
  MySQL --> Proc["MyVector Stored Procedures"];
  UDFs --> Index["Vector Index (HNSW/KNN)"];
  Proc --> Index;
  Binlog["MySQL Binlog"] --> Sync["Binlog Listener"];
  Sync --> Index;
  Index --> Data["MyVector Data Files"];

Features

  • Approximate Nearest Neighbor (ANN) Search: Blazing-fast similarity search using the HNSW algorithm.
  • Exact K-Nearest Neighbor (KNN) Search: Brute-force search for 100% recall.
  • Multiple Distance Metrics: L2 (Euclidean), Cosine, and Inner Product.
  • Real-time Index Updates: Automatically keep your vector indexes in sync with your data using MySQL binlogs.
  • Persistent Indexes: Save and load indexes to and from disk for fast restarts.
  • Native MySQL Integration: Implemented as a standard MySQL plugin with User-Defined Functions (UDFs).

Acknowledgments

  • hnswlib: For the high-performance HNSW implementation.
  • The MySQL Community: For creating a powerful and extensible database.

License

MyVector is licensed under the GNU General Public License v2.0.

For information about third-party dependencies and their licenses, see the NOTICE file and the licenses/ directory in the repository.