Extend the Default MindsDB Configuration
To follow this guide, install MindsDB locally via Docker or PyPI.
Starting MindsDB with Default Configuration
Start MindsDB locally with the default configuration.
- Activate the virtual environment:
- Start MindsDB:
- Access MindsDB locally at
127.0.0.1:47334
.
By default, MindsDB starts the http
and mysql
APIs. You can define which APIs to start using the api
flag as below.
If you want to start MindsDB without the graphical user interface (GUI), use the --no_studio
flag as below.
Starting MindsDB with Extended Configuration
Start MindsDB locally with your custom configuration by providing a path to the config.json
file that stores custom config parameters listed in this section.
Below are all of the custom configuration parameters that should be set according to your requirements and saved into the config.json
file.
The permanent_storage
parameter defines where MindsDB stores copies of user files, such as uploaded files, models, and tab content. MindsDB checks the permanent_storage
location to access the latest version of a file and updates it as needed.
The location
specifies the storage type.
absent
(default): Disables permanent storage and is recommended to use when MindsDB is running locally.local
: Stores files in a local directory defined withconfig['paths']['storage']
.s3
: Stores files in an Amazon S3 bucket. This option requires thebucket
parameter that specifies the name of the S3 bucket where files will be stored.
The storage_dir
parameter specifies the directory where MindsDB stores all its files, including models, uploaded files, tab content, and the internal SQLite database (if running locally).
If this parameter is not set, the path is determined by the MINDSDB_STORAGE_DIR
environment variable. MindsDB defaults to creating a mindsdb
folder in the operating system user’s home directory.
The paths
parameter allows users to redefine the file paths for various groups of MindsDB files. By default, all paths are located within the storage_dir
. If only the root
path is defined, all other folders will be created within that directory. If this parameter is absent, the value is determined by the MINDSDB_STORAGE_DIR
environment variable.
The root
parameter defines the base directory for storing all MindsDB files. It serves the same purpose as the storage_dir
parameter.
The content
parameter specifies the directory where user-related files are stored, such as uploaded files, created models, and tab content. The internal SQLite database (if running locally) is stored in the root
directory instead.
If the ['permanent_storage']['location']
is set to 'local'
, then the storage
parameter is used to store copies of user files.
The static
parameter is used to store files for the graphical user interface (GUI) when MindsDB is run locally.
The tmp
parameter designates a directory for temporary files. Note that the operating system’s default temporary directory may also be used for some temporary files.
If the ['cache']['type']
is set to 'local'
, then the cache
parameter defines the location for storing cached files for the most recent predictions. For example, if a model is queried with identical input, the result will be stored in the cache and returned directly on subsequent queries, instead of recalculating the prediction.
The locks
parameter is used to store lock files to prevent race conditions when the content
folder is shared among multiple applications. This directory helps ensure that file access is managed properly using fcntl
locks. Note that this is not applicable for Windows OS.
The auth
parameter controls the authentication settings for APIs in MindsDB.
The http_auth_enabled
enables (True
) or disables (False
) authentication for the HTTP API, that is, the MindsDB’s GUI. This setting is essential only for local usage.
To enable authentication for the HTTP API, you must set http_auth_enabled
to True
in addition to specifying the username
and password
parameters.
To enable authentication for the MongoDB and MySQL APIs, you must define the username
and password
parameters.
The gui
parameter controls the behavior of the MindsDB graphical user interface (GUI) updates.
The autoupdate
parameter defines whether MindsDB automatically checks for and updates the GUI to the latest version when the application starts. If set to True
, MindsDB will attempt to fetch the latest available version of the GUI. If set to False
, MindsDB will not try to update the GUI on startup.
The api
parameter contains the configuration settings for running MindsDB APIs.
Currently, the supported APIs are:
http
: Configures the HTTP API. It requires thehost
andport
parameters. Alternatively, configure HTTP authentication for your MindsDB instance by setting the environment variablesMINDSDB_USERNAME
andMINDSDB_PASSWORD
before starting MindsDB, which is a recommended way for the production systems.mysql
: Configures the MySQL API. It requires thehost
andport
parameters and additionally thedatabase
andssl
parameters.mongodb
: Configures the MongoDB API. It requires thehost
andport
parameters and additionally thedatabase
parameter.
Connection parameters within each block include:
host
: Specifies the IP address or hostname where the API should run. For example,"127.0.0.1"
indicates the API will run locally.port
: Defines the port number on which the API will listen for incoming requests. The default ports are47334
for HTTP,47335
for MySQL, and47336
for MongoDB.database
(for MySQL and MongoDB): Specifies the name of the database that MindsDB uses. Users must connect to this database to interact with MindsDB through the respective API.ssl
(for MySQL API): Indicates whether SSL support is enabled for the MySQL API.
The cache
parameter controls how MindsDB stores the results of recent predictions to avoid recalculating them if the same query is run again. Note that recent predictions are cached for ML models, like Lightwood, but not in the case of large language models (LLMs), like OpenAI.
The type
parameter specifies the type of caching mechanism to use for storing prediction results.
none
: Disables caching. No prediction results are stored.local
(default): Stores prediction results in thecache
folder (as defined in thepaths
configuration). This is useful for repeated queries where the result doesn’t change.redis
: Stores prediction results in a Redis instance. This option requires theconnection
parameter, which specifies the Redis connection string.
The ml_task_queue
parameter manages the queueing system for machine learning tasks in MindsDB. ML tasks include operations such as creating, training, predicting, fine-tuning, and retraining models. These tasks can be resource-intensive, and running multiple ML tasks simultaneously may lead to Out of Memory (OOM) errors or performance degradation. To address this, MindsDB uses a task queue to control task execution and optimize resource utilization.
The type
parameter defines the type of task queue to use.
local
: Tasks are processed immediately as they appear, without a queue. This is suitable for environments where resource constraints are not a concern.redis
: Tasks are added to a Redis-based queue, and consumer process (which is run with--ml_task_consumer
) ensures that tasks are executed only when sufficient resources are available.- Using a Redis queue requires additional configuration such as the
host
,port
,db
,username
, andpassword
parameters. - To use the Redis queue, start MindsDB with the following command to initiate a queue consumer process:
python3 -m mindsdb --ml_task_queue_consumer
. This process will monitor the queue and fetch tasks for execution only when sufficient resources are available.
- Using a Redis queue requires additional configuration such as the
The file_upload_domains
parameter restricts file uploads to trusted sources by specifying a list of allowed domains. This ensures that users can only upload files from the defined sources, such as S3 or Google Drive ("file_upload_domains": ["https://s3.amazonaws.com", "https://drive.google.com"]
).
If this parameter is left empty ([]
), users can upload files from any URL without restriction.
The web_crawling_allowed_sites
parameter restricts web crawling operations to a specified list of allowed IPs or web addresses. This ensures that the application only accesses pre-approved and safe URLs ("web_crawling_allowed_sites": ["https://example.com", "https://api.mysite.com"]
).
If left empty ([]
), the application allows access to all URLs by default (marked with a wildcard in the open-source version).
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