RRepoGEO

REPOGEO REPORT · LITE

DjangoPeng/openai-quickstart

Default branch main · commit 5c2a5ab3 · scanned 6/23/2026, 11:38:29 AM

GitHub: 1,761 stars · 1,160 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE
28 /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
2 / 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 DjangoPeng/openai-quickstart, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    large-language-models, llms, genai, langchain, openai, quickstart, guide, tutorial, application-development, python, aichatbot
  • highreadme#2
    Clarify README's opening sentence to emphasize 'application guide'

    Why:

    CURRENT
    本项目旨在为所有对大型语言模型及其在生成式人工智能（AIGC）场景中应用的人们提供一站式学习资源。通过提供理论基础，开发基础，和实践示例，该项目对这些前沿主题提供了全面的指导。
    COPY-PASTE FIX
    本项目是一个实用的、动手实践的指南和快速入门项目，旨在帮助开发者使用OpenAI的GPT系列等大型语言模型和LangChain等框架构建生成式AI应用。通过提供理论基础、开发基础和实践示例，本项目对这些前沿主题提供了全面的指导。
  • mediumhomepage#3
    Add the repository URL as the homepage

    Why:

    COPY-PASTE FIX
    https://github.com/DjangoPeng/openai-quickstart

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 DjangoPeng/openai-quickstart
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. OpenAI API · recommended 2×
  3. LlamaIndex · recommended 1×
  4. Hugging Face Transformers · recommended 1×
  5. Anthropic API · recommended 1×
  • CATEGORY QUERY
    How to develop generative AI applications effectively using large language model development tools?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Hugging Face Transformers
    4. OpenAI API
    5. Anthropic API
    6. Google Gemini API
    7. Weights & Biases
    8. Guardrails AI
    9. PromptLayer

    AI recommended 9 alternatives but never named DjangoPeng/openai-quickstart. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Where can I find hands-on examples and a comprehensive guide for implementing large language models?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers Library
    2. OpenAI API
    3. LangChain
    4. Google AI for Developers
    5. DeepLearning.AI
    6. PyTorch
    7. TensorFlow
    8. Awesome-LLM

    AI recommended 8 alternatives but never named DjangoPeng/openai-quickstart. 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 DjangoPeng/openai-quickstart?
    pass
    AI named DjangoPeng/openai-quickstart explicitly

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

  • If a team adopts DjangoPeng/openai-quickstart in production, what risks or prerequisites should they evaluate first?
    pass
    AI named DjangoPeng/openai-quickstart 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 DjangoPeng/openai-quickstart solve, and who is the primary audience?
    pass
    AI did not name DjangoPeng/openai-quickstart — likely talking about a different project

    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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