RRepoGEO

REPOGEO REPORT · LITE

datawhalechina/self-dify

Default branch main · commit 4ff93a92 · scanned 6/24/2026, 10:23:05 AM

GitHub: 502 stars · 52 forks

AI VISIBILITY SCORE
35 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 1 warn · 0 fail
Objective metadata checks
AI knows your name
3 / 3
Direct prompts that named your repo
HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface datawhalechina/self-dify, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highhomepage#1
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/datawhalechina/self-dify
  • mediumtopics#2
    Refine topics to emphasize 'guide' and 'Dify application development'

    Why:

    CURRENT
    agents, dify, llms, rag
    COPY-PASTE FIX
    agents, dify, llms, rag, tutorial, guide, llm-application-development, dify-guide
  • lowreadme#3
    Add a concise introductory sentence about Dify to the README's '教程介绍' section

    Why:

    CURRENT
    本教程将全面指导你如何快速搭建自己的 AI 应用环境，从 Docker 的安装与配置开始，到本地部署 Dify 并自定义 AI 助手功能，让你轻松实现“猜病例”、“甜蜜哄人”、“新生入学入学指南”、“小红书读书卡片”与“面试宝典”等多种特色 AI 应用。并教会你从基础智能体到使用工作流，再到知识库、DeepResearch、数据库、MCP、复杂任务编排等高阶任务，由浅入深地学习掌握基于 Dify 的大模型应用开发。
    COPY-PASTE FIX
    Dify 是一个一体化的 LLM 应用开发平台，它简化了提示词工程、RAG、Agent 和工作流的构建，让开发者能够快速创建和部署 AI 原生应用。本教程将全面指导你如何快速搭建自己的 AI 应用环境，从 Docker 的安装与配置开始，到本地部署 Dify 并自定义 AI 助手功能，让你轻松实现“猜病例”、“甜蜜哄人”、“新生入学入学指南”、“小红书读书卡片”与“面试宝典”等多种特色 AI 应用。并教会你从基础智能体到使用工作流，再到知识库、DeepResearch、数据库、MCP、复杂任务编排等高阶任务，由浅入深地学习掌握基于 Dify 的大模型应用开发。

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall
0 / 2
0% of queries surface datawhalechina/self-dify
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. FAISS · recommended 2×
  4. ChromaDB · recommended 2×
  5. Haystack · recommended 1×
  • CATEGORY QUERY
    How can I build and deploy my own AI assistant applications locally with RAG capabilities?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Ollama
    5. FAISS
    6. ChromaDB
    7. Rasa
    8. Hugging Face Transformers
    9. Hugging Face `sentence-transformers`
    10. FAISS
    11. ChromaDB

    AI recommended 11 alternatives but never named datawhalechina/self-dify. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What's a good guide for learning large language model application development, including agents and workflows?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. DeepLearning.AI
    4. OpenAI Cookbook
    5. Hugging Face Transformers Library
    6. Coursera
    7. AWS

    AI recommended 7 alternatives but never named datawhalechina/self-dify. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    warn

    Suggestion:

  • README presence
    pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

  • Compared to common alternatives in this category, what is the core differentiator of datawhalechina/self-dify?
    pass
    AI named datawhalechina/self-dify explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts datawhalechina/self-dify in production, what risks or prerequisites should they evaluate first?
    pass
    AI named datawhalechina/self-dify explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • In one sentence, what problem does the repo datawhalechina/self-dify solve, and who is the primary audience?
    pass
    AI named datawhalechina/self-dify explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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  • Brand-free category queries5 vs 2 in Lite
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