sharp-007

sharp-007 / ChatBI_OEE

Public

ChatBI OEE 设备综合效率 传统仪表盘 + AI 对话分析

github.comsharp-007ChatBI_OEE chatbi industrial-ai nl2sql oee
42
8
100% credibility
Found Apr 13, 2026 at 42 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

A user-friendly web application for querying and visualizing manufacturing OEE data using natural language conversations and interactive dashboards.

How It Works

1
🔍 Discover ChatBI OEE

You find this handy tool on GitHub that lets factory folks analyze machine performance without any tech hassle.

2
💾 Load Sample Factory Data

You set up the included example records of production runs, machine downtimes, and equipment details to see it in action.

3
🧠 Connect Smart Helper

You link a thinking AI service so it can understand everyday questions about your factory data.

4
🚀 Start the Web App

With a simple command, your personal analysis dashboard comes alive in your web browser.

5
Pick Your Way to Explore
💬
Chat Query

Ask questions in plain words like 'Which machines ran best last month?'

📈
OEE Dashboard

View instant overviews, trends, rankings, and downtime breakdowns.

6
📊 See Magic Insights

Your questions turn into colorful charts, summaries, and rankings that reveal factory improvements.

✅ Boost Factory Efficiency

You now spot top machines, fix downtime issues, and make smarter decisions effortlessly.

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Star Growth

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AI-Generated Review

What is ChatBI_OEE?

ChatBI_OEE is a Python-based web app that lets manufacturing teams analyze OEE (Overall Equipment Effectiveness) data through a chat interface and interactive dashboards. Users ask natural language questions like "Which equipment has the highest downtime?" and get SQL-generated results, interpretations, and charts instantly, powered by an LLM. It ships with a full MySQL schema, seed data for 2026 Q1, and Streamlit frontend for quick local demos alongside FastAPI APIs for OEE trends, rankings, and loss breakdowns.

Why is it gaining traction?

It stands out by blending chatbi-style NL2SQL queries with oee-specific metrics like availability, performance, and quality rates, all in one deployable package—no need for separate BI tools or custom SQL. Multi-turn conversations maintain context for follow-ups, and auto-recommended charts (bars, lines, gauges) make insights visual without manual config. With 42 stars, devs grab it for its realistic manufacturing dataset and easy .env setup using Qwen LLM.

Who should use this?

Manufacturing engineers tracking shop floor efficiency across shifts and lines. Ops analysts ditching spreadsheets for chat-driven downtime or loss analysis. Python devs prototyping industrial analytics apps, especially those integrating LLMs with domain-specific data.

Verdict

Solid starter for OEE dashboards with chatbi smarts, but at 1.0% credibility and 42 stars, it's early—docs are README-only, no tests. Fork and extend if you need manufacturing BI fast; skip for production without hardening security and scaling.

(198 words)

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