> ## Documentation Index
> Fetch the complete documentation index at: https://docs.embedchain.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# 🐬 MySQL

1. Setup the MySQL loader by configuring the SQL db.

```Python theme={null}
from embedchain.loaders.mysql import MySQLLoader

config = {
    "host": "host",
    "port": "port",
    "database": "database",
    "user": "username",
    "password": "password",
}

mysql_loader = MySQLLoader(config=config)
```

For more details on how to setup with valid config, check MySQL [documentation](https://dev.mysql.com/doc/connector-python/en/connector-python-connectargs.html).

2. Once you setup the loader, you can create an app and load data using the above MySQL loader

```Python theme={null}
from embedchain.pipeline import Pipeline as App

app = App()

app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader)
# Adds `(1, 'What is your net worth, Elon Musk?', "As of October 2023, Elon Musk's net worth is $255.2 billion.")`

response = app.query(question)
# Answer: As of October 2023, Elon Musk's net worth is $255.2 billion.
```

NOTE: The `add` function of the app will accept any executable query to load data. DO NOT pass the `CREATE`, `INSERT` queries in `add` function.

3. We automatically create a chunker to chunk your SQL data, however if you wish to provide your own chunker class. Here is how you can do that:

```Python theme={null}

from embedchain.chunkers.mysql import MySQLChunker
from embedchain.config.add_config import ChunkerConfig

mysql_chunker_config = ChunkerConfig(chunk_size=1000, chunk_overlap=0, length_function=len)
mysql_chunker = MySQLChunker(config=mysql_chunker_config)

app.add("SELECT * FROM table_name;", data_type='mysql', loader=mysql_loader, chunker=mysql_chunker)
```