RRepoGEO

REPOGEO REPORT · LITE

huangjia2019/rag-in-action

Default branch master · commit d54bdb19 · scanned 6/6/2026, 6:37:59 AM

GitHub: 741 stars · 299 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 huangjia2019/rag-in-action, 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
  • highlicense#1
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a `LICENSE` file in the repository root. If the associated course or book specifies a license, use that. Otherwise, consider a standard open-source license (e.g., MIT, Apache-2.0) or clearly state the usage terms for the course materials directly in the README.
  • highabout#2
    Reposition the About description to emphasize its course/tutorial nature

    Why:

    CURRENT
    End-to-end RAG system design, evaluation, and optimization. 极客时间RAG训练营，RAG 10大组件全面拆解，4个实操项目吃透 RAG 全流程。RAG的落地，往往是面向业务做RAG，而不是反过来面向RAG做业务。这就是为什么我们需要针对不同场景、不同问题做针对性的调整、优化和定制化。魔鬼全在细节中，我们深入进去探究。
    COPY-PASTE FIX
    Official code repository for the "RAG System Development Practice" course and "RAG Practical Course" book. Provides end-to-end RAG system design, evaluation, and optimization with 10 component breakdowns and 4 practical projects. Focuses on business-oriented RAG implementation.
  • mediumtopics#3
    Add more specific topics to improve categorization

    Why:

    CURRENT
    agent, ai, llm, rag
    COPY-PASTE FIX
    rag, llm, ai, course, tutorial, end-to-end, evaluation, optimization, langchain, llamaindex, deepseek

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 huangjia2019/rag-in-action
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. Sentence Transformers · recommended 2×
  4. Weaviate · recommended 2×
  5. Pinecone · recommended 2×
  • CATEGORY QUERY
    How to build an end-to-end retrieval-augmented generation system for custom data?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Faiss
    5. Sentence Transformers
    6. OpenAI API
    7. Hugging Face Transformers
    8. Weaviate
    9. Pinecone
    10. Qdrant
    11. Elasticsearch

    AI recommended 11 alternatives but never named huangjia2019/rag-in-action. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective strategies for evaluating and optimizing RAG system performance?
    you: not recommended
    AI recommended (in order):
    1. RAGAS
    2. LlamaIndex
    3. LangChain
    4. LangSmith
    5. DeepEval
    6. Arize AI
    7. Phoenix
    8. TruLens
    9. Scale AI
    10. Appen
    11. text-embedding-ada-002
    12. BGE-large
    13. E5-large
    14. BM25
    15. FAISS
    16. Pinecone
    17. Weaviate
    18. Cohere Rerank
    19. Sentence Transformers
    20. GPT-4
    21. Claude 3
    22. Llama 3

    AI recommended 22 alternatives but never named huangjia2019/rag-in-action. 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 huangjia2019/rag-in-action?
    pass
    AI named huangjia2019/rag-in-action explicitly

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

  • If a team adopts huangjia2019/rag-in-action in production, what risks or prerequisites should they evaluate first?
    pass
    AI named huangjia2019/rag-in-action 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 huangjia2019/rag-in-action solve, and who is the primary audience?
    pass
    AI named huangjia2019/rag-in-action explicitly

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

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huangjia2019/rag-in-action — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite