# DeepInsight-AI/DeepBI

LLM based data scientist, AI native data application.  AI-driven infinite thinking redefines BI.

Repository: https://github.com/DeepInsight-AI/DeepBI
Canonical: https://ross.abutalabs.com/products/deepbi
Homepage: https://www.deepbi.com/
Language: Python
License: MIT
License Family: permissive
Topics: csv, gpt, gpt-4, mysql, redis, ai, bi, data, llm, analysis
Last push: 2025-04-14T07:26:14+00:00

## Health v2 (maintenance only)
Score: 24/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 16, release rhythm 8, longevity 72
- inputs: {"age_days": 1017, "days_push": 506, "days_rel": 694, "gap_med": null, "n_releases_24m": 1}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2370, forks 371 (observed 2026-08-28T04:06:41.737108+00:00)

## What it is
DeepBI is an AI-native business intelligence platform that uses large language models to let users explore, query, visualize, and share data through natural-language conversations. It supports multiple data sources (MySQL, PostgreSQL, Doris, StarRocks, MongoDB, CSV/Excel) and assembles results into dashboards.

## Use cases
- analyze csv data with natural language questions
- generate sql queries from plain english prompts
- build dashboards from conversational data queries
- connect mysql and postgres databases for ai-driven analysis
- self-host an llm-powered business intelligence tool
- visualize excel spreadsheet data via chat

## When to choose
- you want conversational, LLM-driven data exploration instead of writing SQL by hand
- you need a self-hosted BI tool supporting multiple databases and CSV/Excel imports
- you want to assemble AI-generated visualizations into shareable dashboards

## When to avoid
- you need a fully mature, enterprise-supported BI suite with strict governance and audit features
- you cannot send data to external LLM APIs due to privacy constraints
- you only need static reporting without conversational querying

## Facets
- artifact type: application
- maturity: active
- function: data-visualization, nlp, llm-inference, chatbot, analytics, database
- domain: data-science, analytics, data-visualization, large-language-models, databases, self-hosted
- platform: windows, self-hosted, python
- tags: business-intelligence, conversational-analytics, text-to-sql, dashboards, llm-powered, data-analysis, docker, linux, macos, web-server

## Member repositories
- DeepInsight-AI/DeepBI (main) score 24

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:06:41.737108+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T02:35:24.218493+00:00, confidence not recorded.
  - readme: https://github.com/DeepInsight-AI/DeepBI (fetched 2026-08-28T04:06:41.737108+00:00, sha c3e0dd1fdf66)
  - homepage: https://www.deepbi.com/ (fetched 2026-08-29T10:16:27.802401+00:00, sha 56532062ef7b)
  - site_page: https://www.deepbi.com/about (fetched 2026-08-29T10:16:27.810332+00:00, sha c7b777488202)
  - site_page: https://www.deepbi.com/pricing (fetched 2026-08-29T10:16:27.805656+00:00, sha 26b1186bf58e)
  - site_page: https://www.deepbi.com/faq1.html (fetched 2026-08-29T10:16:27.807547+00:00, sha 7a40e6a98a60)
- Data as of 2026-08-30T08:39:29.467469+00:00.