核心哲学：连接、统一、回应

概览

什么是 MindsDB？

MindsDB 是一个创新的平台，旨在使用户能够构建能够对大规模联邦数据进行复杂查询的人工智能系统。它作为一个强大的查询引擎，简化了将 AI 集成到各种应用程序中的过程，使开发者和企业都能轻松使用。通过 MindsDB，用户可以利用机器学习模型来增强数据分析能力，而无需具备广泛的 AI 专业知识。

MindsDB 的特点

用户友好的界面：MindsDB 提供直观的界面，使用户能够轻松创建和管理 AI 模型，而无需深厚的技术知识。
联邦学习：该平台支持联邦学习，使模型能够从分散的数据源中学习，同时保持数据隐私。
集成能力：MindsDB 与流行的数据库和数据源无缝集成，使 AI 易于融入现有工作流程。
实时预测：用户可以从数据中获得实时预测和洞察，增强决策过程。
开源：作为一个开源平台，MindsDB 鼓励社区贡献和合作，促进创新和改进。

如何使用 MindsDB

安装：首先在本地机器或服务器上安装 MindsDB。您可以在官方 MindsDB 网站上找到安装说明。
连接数据源：将 MindsDB 与您首选的数据库或数据源集成。这可以通过用户界面或使用 API 调用完成。
创建模型：利用平台的工具创建针对您特定数据和用例的机器学习模型。
训练模型：使用历史数据训练您的模型，以提高其准确性和性能。
进行预测：一旦训练完成，您可以使用模型对新数据进行预测，提供有价值的洞察和答案。

常见问题解答

问：MindsDB 适合初学者吗？

答：是的，MindsDB 设计为用户友好，使初学者和没有广泛 AI 知识的人都能轻松使用。

问：我可以与任何数据库一起使用 MindsDB 吗？

答：MindsDB 支持与多种数据库的集成，包括 MySQL、PostgreSQL 等，允许灵活的数据管理。

问：MindsDB 是免费使用的吗？

答：MindsDB 是一个开源平台，这意味着它是免费的。但是，可能会有可用的高级功能或支持选项。

问：我可以使用 MindsDB 进行哪些类型的预测？

答：MindsDB 可用于多种预测，包括分类、回归和时间序列预测，具体取决于您的数据和模型配置。

问：MindsDB 如何确保数据隐私？

答：MindsDB 采用联邦学习技术，使模型能够在不传输敏感信息的情况下从数据中学习，从而维护隐私和安全。

详情

<div align="center"> <a href="https://pypi.org/project/MindsDB/" target="_blank"><img src="https://badge.fury.io/py/MindsDB.svg" alt="MindsDB Release"></a> <a href="https://www.python.org/downloads/" target="_blank"><img src="https://img.shields.io/badge/python-3.10.x%7C%203.11.x-brightgreen.svg" alt="Python supported"></a> <a href="https://ossrank.com/p/630"><img src="https://shields.io/endpoint?url=https://ossrank.com/shield/630"></a> <img alt="PyPI - Downloads" src="https://img.shields.io/pypi/dm/Mindsdb"> <a href="https://hub.docker.com/u/mindsdb" target="_blank"><img src="https://img.shields.io/docker/pulls/mindsdb/mindsdb" alt="Docker pulls"></a> <br /> <br /> <a href="https://github.com/mindsdb/mindsdb"> <img src="/docs/assets/mindsdb_logo.png" alt="MindsDB" width="300"> </a> <p align="center"> <br /> <a href="https://www.mindsdb.com?utm_medium=community&utm_source=github&utm_campaign=mindsdb%20repo">Website</a> · <a href="https://docs.mindsdb.com?utm_medium=community&utm_source=github&utm_campaign=mindsdb%20repo">Docs</a> · <a href="https://mdb.ai/register">Demo</a> · <a href="https://mindsdb.com/joincommunity?utm_medium=community&utm_source=github&utm_campaign=mindsdb%20repo">Community Slack</a> </p> </div>

MindsDB enables humans, AI, agents, and applications to get highly accurate answers across sprawled and large scale data sources.

MindsDB has an MCP server built in that enables your MCP applications to connect, unify and respond to questions over large-scale federated data—spanning databases, data warehouses, and SaaS applications.

Minds Demo

Play with Minds demo, and see the power of MindsDB at answering questions from structured to unstructured data, whether it's scattered across SaaS applications, databases, or... hibernating in data warehouses like that $100 bill in your tuxedo pocket from prom night, lost, waiting to be discovered.

Install MindsDB Server

MindsDB is an open-source server that can be deployed anywhere - from your laptop to the cloud, and everywhere in between. And yes, you can customize it to your heart's content.

Using Docker Desktop. This is the fastest and recommended way to get started and have it all running.
Using Docker. This is also simple, but gives you more flexibility on how to further customize your server.
Using PyPI. This option enables you to contribute to MindsDB.

Core Philosophy: Connect, Unify, Respond

MindsDB's architecture is built around three fundamental capabilities:

Connect Your Data

You can connect to hundreds of enterprise data sources (learn more). These integrations allow MindsDB to access data wherever it resides, forming the foundation for all other capabilities.

Unify Your Data

Once connected, these data sources can be queried using a full SQL dialect, as if they were all part of a single database. MindsDB’s federated query engine translates your SQL queries and executes them on the appropriate connected data sources.

When working with many data sources, it’s important to prepare and unify your data before generating responses from it. MindsDB SQL offers virtual tables (views, knowledge bases, ml-models) to allow working with heterogeneous data as if it were unified in a single organized system.

VIEWS – Simplify data access by creating unified views across different sources (no-ETL).
KNOWLEDGE BASES – Index and organize unstructured data for efficient retrieval.
ML MODELS – Apply AI/ML transformations to gain insights from your data.

Unification of data can be automated using JOBs

JOBS – Schedule synchronization and transformation tasks for real-time processing.

Respond From Your Data

Chat with Your Data

AGENTS – Configure built-in agents specialized in answering questions over your connected and unified data.
MCP – Connect to MindsDB through the MCP (Model Context Protocol) for seamless interaction.

🤝 Contribute

Interested in contributing to MindsDB? Follow our installation guide for development.

You can find our contribution guide here.

We welcome suggestions! Feel free to open new issues with your ideas, and we’ll guide you.

This project adheres to a Contributor Code of Conduct. By participating, you agree to follow its terms.

Also, check out our community rewards and programs.

🤍 Support

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

Here’s how you can get community support:

Ask a question in our Slack Community.
Join our GitHub Discussions.
Post on Stack Overflow with the MindsDB tag.

For commercial support, please contact the MindsDB team.

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