LlamaIndex Vector_Stores Integration: MariaDB

Starting with version 11.7.1, the MariaDB relational database has vector search functionality integrated. Thus now it can be used as a fully-functional vector store in LlamaIndex.

To learn more about the feature in MariaDB, check its Vector Overview documentation.

Please note that versions before 0.3.0 of this package are not compatible with MariaDB 11.7 and later. They are compatible only with the one-off MariaDB 11.6 Vector preview release which used a slightly different syntax.

Installation

pip install llama-index-vector-stores-mariadb

Usage

from llama_index.vector_stores.mariadb import MariaDBVectorStore

vector_store = MariaDBVectorStore.from_params(
    host="localhost",
    port=3306,
    user="llamaindex",
    password="password",
    database="vectordb",
    table_name="llama_index_vectorstore",
    embed_dim=1536,  # OpenAI embedding dimension
    default_m=6,  # MariaDB Vector system parameter
    ef_search=20,  # MariaDB Vector system parameter
)

Development

Running Integration Tests

A suite of integration tests is available to verify the MariaDB vector store integration. The test suite needs a MariaDB database with vector search support up and running. If not found, the tests are skipped. To facilitate that, a sample docker-compose.yaml file is provided, so you can simply do:

docker compose -f tests/docker-compose.yaml up

pytest -v

# Clean up when you finish testing
docker compose -f tests/docker-compose.yaml down