Quick Start¶

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

1. Start the Docker Container¶

docker run -d \
  --name myvector-db \
  -p 3306:3306 \
  -e MYSQL_ROOT_PASSWORD=myvector \
  -e MYSQL_DATABASE=vectordb \
  ghcr.io/askdba/myvector:mysql8.4

Local trial only

This uses a fixed, publicly-known password with the port bound to all interfaces — fine for trying MyVector out on your own machine, but don't run it this way on a shared or internet-facing host. Use a real secret and bind to 127.0.0.1:3306:3306 for anything beyond local testing.

2. Connect to MySQL¶

mysql -h 127.0.0.1 -u root -pmyvector vectordb

3. Create a Table and Insert Data¶

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

Use the native MYVECTOR column type (not a COMMENT-based declaration) so the plugin can rewrite the table's DDL correctly:

-- Create a table for our word vectors
CREATE TABLE words50d (
  wordid INT AUTO_INCREMENT PRIMARY KEY,
  word VARCHAR(200),
  wordvec MYVECTOR(type=HNSW,dim=50,size=400000,dist=L2,m=64,ef=100)
);

Download and load the sample vectors (50-dimensional GloVe word vectors, from the examples/stanford50d directory). In a real-world scenario, you would generate your own vectors.

curl -L -o /tmp/insert50d.sql.gz \
  https://raw.githubusercontent.com/askdba/myvector/main/examples/stanford50d/insert50d.sql.gz
gunzip -c /tmp/insert50d.sql.gz | mysql -h 127.0.0.1 -u root -pmyvector vectordb

4. Build the Vector Index¶

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

5. Run a Similarity Search¶

Find words similar to "school":

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

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

myvector_row_distance() requires the row's id column as its argument.

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

Manual Installation¶

If you are running your own MySQL instance (not using the Docker images), install the plugin manually:

mysql -u root -p -e "INSTALL PLUGIN myvector SONAME 'myvector.so';"
mysql -u root -p < sql/myvectorplugin.sql

Plugin vs. Component

The plugin (INSTALL PLUGIN) is the current stable path and supports MySQL 8.0, 8.4, and 9.0. The component (INSTALL COMPONENT) is the forward path for MySQL 8.4 and 9.7 (LTS). MySQL 8.0 plugin support will be maintained through MySQL 8.0 EOL; no component build is planned for 8.0.

See Docker Images for the full list of available image tags.

Next Steps¶

Usage & API Reference — full SQL function and stored procedure reference
Demo — a complete walkthrough with the Amazon Product Catalog dataset
Configuration — configuration file reference