Why MyVector? • Supported platforms • Quick Start • Features • Usage • Docker • Contributing • Docs

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.

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 docs/BUILDING_MACOS.md for macOS notes.

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"];

Get up and running with MyVector in minutes using our official Docker images.

docker run -d \
  --name myvector-db \
  -p 3306:3306 \
  -e MYSQL_ROOT_PASSWORD=myvector \
  -e MYSQL_DATABASE=vectordb \
  ghcr.io/askdba/myvector:mysql-8.4

mysql -h 127.0.0.1 -u root -pmyvector vectordb

Let's use a simple example with 50-dimensional word vectors.

-- Create a table for our word vectors
CREATE TABLE words50d (
  wordid INT PRIMARY KEY,
  word VARCHAR(50),
  wordvec VARBINARY(200) COMMENT 'MYVECTOR(type=HNSW,dim=50,size=100000,dist=L2)'
);

-- Download and insert the data (from the examples/stanford50d directory)
-- In a real-world scenario, you would generate your own vectors.
-- wget https://raw.githubusercontent.com/askdba/myvector/main/examples/stanford50d/insert50d.sql
-- mysql -h 127.0.0.1 -u root -pmyvector vectordb < insert50d.sql

CALL mysql.myvector_index_build('vectordb.words50d.wordvec', 'wordid');

Find words similar to "school":

SET @school_vec = (SELECT wordvec FROM words50d WHERE word = 'school');

SELECT word, myvector_row_distance() as distance
FROM words50d
WHERE MYVECTOR_IS_ANN('vectordb.words50d.wordvec', 'wordid', @school_vec, 10);

You should see results like "university," "student," "teacher," etc. It's that easy!

• 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).

• Build an Index:

  CALL mysql.myvector_index_build('database.table.column', 'primary_key_column');

• Check Index Status:

  CALL mysql.myvector_index_status('database.table.column');

• Drop an Index:

  CALL mysql.myvector_index_drop('database.table.column');

• Save and Load an Index:

  -- Indexes are automatically saved. To load an index on startup:
  CALL mysql.myvector_index_load('database.table.column');

• Create a Vector:

  SET @my_vector = myvector_construct('[1.2, 3.4, 5.6]');

• Calculate Distance:

  SELECT myvector_distance(@vec1, @vec2, 'L2');

• Display a Vector:

  SELECT myvector_display(wordvec) FROM words50d LIMIT 1;

We provide official Docker images for various MySQL versions.

version: '3.8'

services:
  myvector:
    image: ghcr.io/askdba/myvector:mysql-8.4
    ports:
      - "3306:3306"
    environment:
      MYSQL_ROOT_PASSWORD: myvector
      MYSQL_DATABASE: vectordb
    volumes:
      - myvector-data:/var/lib/mysql

volumes:
  myvector-data:

We welcome contributions! Please see our Contributing Guide for details on how to get started.

For more detailed information, please see the docs directory:

• DEMO.md: A full demo with the Amazon Product Catalog dataset.
• ONLINE_INDEX_UPDATES.md: How to create and configure real-time index updates via MySQL binlogs.
• CONFIGURATION.md: Configuration file reference (options, defaults, validation, security).
• ANN_BENCHMARKS.md: Performance benchmarks.
• DOCKER_IMAGES.md: More on using our Docker images.
• BUILDING_MACOS.md: Building MySQL on macOS (Apple Silicon).
• CONTRIBUTING.md: Our contribution guidelines.

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

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

For information about third-party dependencies and their licenses, see:

• NOTICE - Third-party attribution notices
• licenses/ - Full license texts for all dependencies