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DeepInsight-AI

DeepInsight-AI/DeepBI

AI-native data analysis platform where users query, visualize and share data from SQL databases and CSV/Excel files through conversation with an LLM.

Open-source alternative to:TableauPower BI

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What is it?

DeepBI is a business intelligence platform built around large language models. Instead of writing SQL or building charts by hand, users ask questions in natural language and get query results, analysis and visualizations back. Generated queries and charts can be saved and assembled into dashboards.

The platform connects to MySQL, PostgreSQL, Doris, StarRocks and MongoDB, and can import CSV or Excel files directly. The interface is available in Chinese and English and runs on Windows (including WSL), Linux and macOS.

Deployment options include a Windows executable, a Docker setup started with an install script, or a manual install on Ubuntu that requires Redis, PostgreSQL 16 and Python 3.8.

Key features

  • 01Conversational data analysis that returns query results and insights from natural-language questions.
  • 02Generates persistent queries and visualizations through dialogue.
  • 03Dashboards that combine saved visualizations.
  • 04Connects to MySQL, PostgreSQL, Doris, StarRocks and MongoDB, plus CSV/Excel import.
  • 05Runs on Windows, WSL, Linux and macOS, with Chinese and English interfaces.

Use cases

  • Letting non-SQL users explore business data by asking questions in plain language.
  • Analyzing CSV or Excel exports without setting up a database.
  • Building shared dashboards on top of an existing MySQL or PostgreSQL warehouse.

Quick start

  1. 1

    Clone the repository with git

    git clone https://github.com/DeepInsight-AI/DeepBI.git
  2. 2

    Enter the project directory

    cd DeepBI
  3. 3

    Run the Docker install script from the README

    ./Install.sh
  4. 4

    Open the web interface at http://ip:8338

Things to know

  • Docker install requires docker and docker-compose on the local machine.
  • The Ubuntu install needs Python 3.8.x, Redis and PostgreSQL 16 set up manually.
  • Automated data analysis reports are listed in the README as still to be developed.

In collections

Dashboards & business intelligence toolsData science courses & tools

Topics

#business-intelligence#llm#data-analysis#text-to-sql#data-visualization#dashboard#docker#python#ai#analysis

FAQ

What is DeepBI?
AI-native data analysis platform where users query, visualize and share data from SQL databases and CSV/Excel files through conversation with an LLM. DeepBI is a business intelligence platform built around large language models. Instead of writing SQL or building charts by hand, users ask questions in natural language and get query results, analysis and visualizations back. Generated queries and charts can be saved and assembled into dashboards.
Is DeepBI free to use? What license does it have?
DeepBI is open source under the MIT license. Check the license terms before using it in commercial products.
Is DeepBI still maintained?
Updated within 6 months. The last commit to DeepBI was on Aug 28, 2026.
What can I use DeepBI for?
Letting non-SQL users explore business data by asking questions in plain language. Analyzing CSV or Excel exports without setting up a database. Building shared dashboards on top of an existing MySQL or PostgreSQL warehouse.
How do I install DeepBI?
Clone the repository with git: git clone https://github.com/DeepInsight-AI/DeepBI.git Enter the project directory: cd DeepBI Run the Docker install script from the README: ./Install.sh Open the web interface at http://ip:8338
Where can I get DeepBI?
The source code and installation instructions are on GitHub: https://github.com/DeepInsight-AI/DeepBI

Summary generated by AI from the project's README. GitHub stats refresh daily.

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