OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Data & Analytics

Deepbi

DeepBI is a self-hostable, AI-native data analysis application that uses large language models for conversational data exploration and query generation, supports persistent visualizations and dashboards, and connects to databases and imported CSV or Excel files.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Deepbi — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use DeepBI when conversational exploration of a supported database or imported CSV/Excel file is useful, especially when results should be retained as visualizations and arranged into dashboards.

Who it is for

  • People who want to explore supported databases or imported spreadsheets through conversational analysis.
  • Teams that want to save generated visualizations and assemble them into dashboards.
  • Operators seeking a self-hosted analytics application with documented Windows, Linux, macOS and Docker installation options.

Documented uses

  • Ask questions about data and receive data or analysis results through dialogue.
  • Generate persistent queries and visualizations conversationally.
  • Combine saved visualizations into dashboards.
  • Analyze data from MySQL, PostgreSQL, Doris, StarRocks, MongoDB, CSV and Excel sources.
Before you choose

Requirements & limits

The project is public and MIT-licensed. The repository documents Windows, Linux, macOS and Docker installation options.

  • Supported sources include MySQL, PostgreSQL, Doris, StarRocks, MongoDB and CSV/Excel imports.
  • Docker installation requires Docker and Docker Compose; Ubuntu installation specifies Redis, PostgreSQL 16 and Python 3.8.x.
  • The README lists minimum server memory of 1 core and 2 GB, and recommends 2 cores and 4 GB.
  • Automated data analysis reports are identified as a feature to be developed, not a current documented capability.
  • The README lists English and Chinese language support.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install DeepBI with Docker

Prepare a Docker-based DeepBI installation and access its documented web interface.

Before you start

  • Docker and Docker Compose installed
  • DeepBI project files, obtained by cloning the repository or downloading its ZIP

Steps

  1. Obtain the DeepBI project files.

  2. Change into the DeepBI project directory.

  3. Run ./Install.sh.

  4. Access the web interface at http://ip:8338.

Expected outcome

The documented installation path serves DeepBI at the host address on port 8338; the README also lists port 8339 as a default port.

Guide limitations

  • The repository documents these setup steps but this guide does not verify a successful installation.
Documentation 1 ↗
Source-based practical guide

Install DeepBI directly on Ubuntu

Prepare the documented Ubuntu installation and its web access address.

Before you start

  • Redis
  • PostgreSQL 16
  • Python 3.8.x
  • The README recommends using a virtual environment.

Steps

  1. Clone the DeepBI repository.

  2. Run . ubuntu_install.sh from the project directory, as specified by the README.

  3. Access the web interface at http://ip:8338.

Expected outcome

The documented Ubuntu installation path uses port 8338 for web access; port 8339 is also listed as a default port.

Guide limitations

  • The README specifies Redis access at 127.0.0.1 without a password for this setup.
  • The steps describe the repository's documented installation path and do not verify a successful installation.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
11 October 2026
Original website title
DeepInsight-AI/DeepBI · Official homepage title · retained in its source language
How we review sourcesSuggest a correction