核心哲學：連結、統一、回應

什麼是 MindsDB？

MindsDB 是一個創新的平台，旨在幫助用戶構建能夠對大規模聯邦數據進行複雜查詢的人工智慧系統。它作為一個強大的查詢引擎，簡化了 AI 在各種應用中的集成，使開發者和企業都能輕鬆使用。通過 MindsDB，用戶可以利用機器學習模型來增強數據分析能力，而無需具備廣泛的 AI 專業知識。

MindsDB 的特點

• 用戶友好的界面：MindsDB 提供直觀的界面，使用戶能夠輕鬆創建和管理 AI 模型，而無需深入的技術知識。
• 聯邦學習：該平台支持聯邦學習，使模型能夠從去中心化的數據源學習，同時保持數據隱私。
• 集成能力：MindsDB 與流行的數據庫和數據源無縫集成，使 AI 輕鬆融入現有工作流程。
• 實時預測：用戶可以從數據中獲得實時預測和見解，增強決策過程。
• 開源：作為一個開源平台，MindsDB 鼓勵社區貢獻和合作，促進創新和改進。

如何使用 MindsDB

1. 安裝：首先在本地機器或伺服器上安裝 MindsDB。您可以在官方 MindsDB 網站 上找到安裝說明。
2. 連接數據源：將 MindsDB 與您首選的數據庫或數據源集成。這可以通過用戶界面或使用 API 調用來完成。
3. 創建模型：利用平台的工具創建針對您特定數據和用例的機器學習模型。
4. 訓練模型：使用歷史數據訓練您的模型，以提高其準確性和性能。
5. 進行預測：一旦訓練完成，您可以使用模型對新數據進行預測，提供有價值的見解和答案。

常見問題解答

問：MindsDB 適合初學者嗎？

答：是的，MindsDB 設計為用戶友好，使初學者和沒有廣泛 AI 知識的人都能輕鬆使用。

問：我可以將 MindsDB 與任何數據庫一起使用嗎？

答：MindsDB 支持與各種數據庫的集成，包括 MySQL、PostgreSQL 等，允許靈活的數據管理。

問：MindsDB 是免費使用的嗎？

答：MindsDB 是一個開源平台，這意味著它是免費使用的。然而，可能會有可用的高級功能或支持選項。

問：我可以使用 MindsDB 進行哪些類型的預測？

答：MindsDB 可用於各種預測，包括分類、回歸和時間序列預測，具體取決於您的數據和模型配置。

問：MindsDB 如何確保數據隱私？

答：MindsDB 採用聯邦學習技術，允許模型從數據中學習而不轉移敏感信息，從而維護隱私和安全。

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<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.

💚 Current Contributors

<a href="https://github.com/mindsdb/mindsdb/graphs/contributors"> <img src="https://contributors-img.web.app/image?repo=mindsdb/mindsdb" /> </a>

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