MindsDB is the platform for building AI from enterprise data. You can create, serve, and fine-tune models in real-time from your database, vector store, and application data.

About us

MindsDB is the platform for building AI from enterprise data.

With MindsDB, you can deploy, serve, and fine-tune models in real-time, utilizing data from databases, vector stores, or applications, to build AI-powered apps - using universal tools developers already know.

MindsDB integrates with numerous data sources, including databases, vector stores, and applications, and popular AI/ML frameworks, including AutoML and LLMs. MindsDB connects data sources with AI/ML frameworks and automates routine workflows between them. By doing so, we bring data and AI together, enabling the intuitive implementation of customized AI systems.

Learn more about features and use cases of MindsDB here.

Get Started

To get started, install MindsDB locally via Docker or Docker Desktop, following the instructions in linked doc pages.

MindsDB enhances SQL syntax to enable seamless development and deployment of AI-powered applications. Furthermore, users can interact with MindsDB not only via SQL API but also via REST APIs, Python SDK, JavaScript SDK, and MongoDB-QL.

Examples

MindsDB enables you to deploy AI/ML models, send predictions to your application, and automate AI workflows.

Discover more tutorials and use cases here.

AI Workflow Automation

This category of use cases involves tasks that get data from a data source, pass it through an AI/ML model, and write the output to a data destination.

Common use cases are anomaly detection, data indexing/labeling/cleaning, and data transformation.

This example showcases the data enrichment flow, where input data comes from a PostgreSQL database and is passed through an OpenAI model to generate new content which is saved into a data destination.

We take customer reviews from a PostgreSQL database. Then, we deploy an OpenAI model that analyzes all customer reviews and assigns sentiment values. Finally, to automate the workflow for incoming customer reviews, we create a job that generates and saves AI output into a data destination.

AI System Deployment

This category of use cases involves creating AI systems composed of multiple connected parts, including various AI/ML models and data sources, and exposing such AI systems via APIs.

Common use cases are agents and assistants, recommender systems, forecasting systems, and semantic search.

This example showcases AI agents, a feature developed by MindsDB. AI agents can be assigned certain skills, including text-to-SQL skills and knowledge bases. Skills provide an AI agent with input data that can be in the form of a database, a file, or a website.

We create a text-to-SQL skill based on the car sales dataset and deploy a conversational model, which are both components of an agent. Then, we create an agent and assign this skill and this model to it. This agent can be queried to ask questions about data stored in assigned skills.

Agents are accessible via API endpoints.

Contribute

If you’d like to contribute to MindsDB, install MindsDB for development following this instruction.

You’ll find the contribution guide here.

We are always open to suggestions, so feel free to open new issues with your ideas, and we can guide you!

This project is released with a Contributor Code of Conduct. By participating in this project, you agree to follow its terms.

Also, check out the rewards and community programs here.

Support

If you find a bug, please submit an issue on GitHub here.

Here is how you can get community support:

Post a question at MindsDB Slack Community.
Ask for help at our GitHub Discussions.
Ask a question at Stackoverflow with a MindsDB tag.

If you need commercial support, please contact the MindsDB team.

Current contributors

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