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使用OpenChatBI代理进行自然语言商业智能分析

👤 ҉Breeze🌔 📦 v1.0.0 ⭐ 3.7 ⬇️ 111 下载
🤖 AI-Agent 免费 🔑 需 API Key

📖 技能介绍


name: "使用OpenChatBI代理进行自然语言商业智能分析" slug: "run-natural-language-bi-analysis-with-openchatbi-agents" version: "1.0.0" displayName: "使用OpenChatBI代理进行自然语言商业智能分析" summary: "当代理需要将业务问题转化为SQL、图表、异常分析和可解释的商业智能结果时,部署OpenChatBI。" license: "MIT" description: "当代理需要将业务问题转化为SQL、图表、异常分析和可解释的商业智能结果时,部署OpenChatBI。" github_stars: 595 verification: "security_reviewed" source: "https://github.com/1991513ccie-png/skills" author: "1991513ccie-png" publisher_type: "individual" category: "Data Extraction & Transformation" framework: "Multi-Framework" tool_ecosystem: github_repo: "1991513ccie-png/skills" github_stars: 595


使用OpenChatBI代理进行自然语言商业智能分析

Deploy OpenChatBI when an agent needs to turn business questions into SQL, charts, anomaly analysis, and explainable BI results.

Prerequisites

Python, pip, database connection, configured LLM provider, optional MCP tools, optional Gradio or Streamlit UI

Installation

Use the upstream install or setup path that matches your environment: - git clone git@github.com:zhongyu09/openchatbi - uv sync - pip install openchatbi - uv sync --group dev

Requirements and caveats from upstream: - Code Execution: Execute Python code for data analysis and visualization - multi-dimensional drill-down (Adtributor) and Python execution to cover trend forecasting, anomaly detection, - Python 3.11 or higher

Basic usage or getting-started notes: - Demo - Access to a supported LLM provider (OpenAI, Anthropic, etc.) - Data Warehouse (Database) credentials (like Presto, PostgreSQL, MySQL, etc.)

小葱技能7w4.net有更新,你可以访问看下。

  • Source: https://github.com/1991513ccie-png/skills
  • Extracted from upstream docs: https://raw.githubusercontent.com/zhongyu09/openchatbi/HEAD/README.md

Documentation

  • https://zhongyu09.github.io/openchatbi/

🤖 AI 评测

这个Skill质量中等偏上,来自一个较受欢迎的开源项目,文档完整清晰,能清楚告诉用户需要什么前提条件。优点是功能强大,能将自然语言问题转化为SQL和图表分析,支持多种数据库和大语言模型。不足是安装说明较为简略,对于不熟悉技术的用户可能存在一定门槛。建议有Python和数据库经验的用户使用。

📊 多维度评分

适应性3.9
规范性3.6
有效性3.9
可靠性3.3
可信度4.3

📁 包含文件 (3 个)

📄 SKILL.md 2 KB
📄 _meta.json 174 B
📄 _skillhub_meta.json 218 B