| Name | llama-index-vector-stores-mariadb JSON |
| Version |
0.3.1
JSON |
| download |
| home_page | None |
| Summary | llama-index vector_stores mariadb integration |
| upload_time | 2025-02-13 23:21:25 |
| maintainer | None |
| docs_url | None |
| author | Kalin Arsov |
| requires_python | <4.0,>=3.9 |
| license | MIT |
| keywords |
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| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# 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](https://mariadb.com/kb/en/vector-overview/).
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
```shell
pip install llama-index-vector-stores-mariadb
```
## Usage
```python
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:
```shell
docker compose -f tests/docker-compose.yaml up
pytest -v
# Clean up when you finish testing
docker compose -f tests/docker-compose.yaml down
```
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"description": "# LlamaIndex Vector_Stores Integration: MariaDB\n\nStarting with version `11.7.1`, the MariaDB relational database has vector search functionality integrated.\nThus now it can be used as a fully-functional vector store in LlamaIndex.\n\nTo learn more about the feature in MariaDB, check its [Vector Overview documentation](https://mariadb.com/kb/en/vector-overview/).\n\nPlease note that versions before `0.3.0` of this package are not compatible with MariaDB 11.7 and later.\nThey are compatible only with the one-off `MariaDB 11.6 Vector` preview release which used a slightly different syntax.\n\n## Installation\n\n```shell\npip install llama-index-vector-stores-mariadb\n```\n\n## Usage\n\n```python\nfrom llama_index.vector_stores.mariadb import MariaDBVectorStore\n\nvector_store = MariaDBVectorStore.from_params(\n host=\"localhost\",\n port=3306,\n user=\"llamaindex\",\n password=\"password\",\n database=\"vectordb\",\n table_name=\"llama_index_vectorstore\",\n embed_dim=1536, # OpenAI embedding dimension\n default_m=6, # MariaDB Vector system parameter\n ef_search=20, # MariaDB Vector system parameter\n)\n```\n\n## Development\n\n### Running Integration Tests\n\nA suite of integration tests is available to verify the MariaDB vector store integration.\nThe test suite needs a MariaDB database with vector search support up and running. If not found, the tests are skipped.\nTo facilitate that, a sample `docker-compose.yaml` file is provided, so you can simply do:\n\n```shell\ndocker compose -f tests/docker-compose.yaml up\n\npytest -v\n\n# Clean up when you finish testing\ndocker compose -f tests/docker-compose.yaml down\n```\n",
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