RRepoGEO

REPOGEO REPORT · LITE

Danielskry/Awesome-RAG

Default branch main · commit b3fd3b7d · scanned 5/23/2026, 7:03:13 AM

GitHub: 1,203 stars · 128 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
22 /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
1 / 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 Danielskry/Awesome-RAG, 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 'awesome-list' to repository topics

    Why:

    CURRENT
    artificial-intelligence, generative-ai, large-language-models, rag, retrieval-augmented-generation
    COPY-PASTE FIX
    artificial-intelligence, generative-ai, large-language-models, rag, retrieval-augmented-generation, awesome-list
  • highreadme#2
    Reposition README's opening to highlight its function as a selection guide

    Why:

    CURRENT
    A curated resource map of tools, frameworks, techniques, and learning materials for building Retrieval-Augmented Generation (RAG) systems. This repository catalogs the RAG ecosystem and provides links to authoritative sources, tutorials, and implementations to help you explore and build RAG applications.
    COPY-PASTE FIX
    Navigate the complex landscape of Retrieval-Augmented Generation (RAG) with this curated resource map. This repository catalogs tools, frameworks, techniques, and learning materials, providing links to authoritative sources, tutorials, and implementations to help you explore, compare, and build RAG applications.
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    [Add a relevant URL, e.g., a blog post about the list or a dedicated project page]

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 Danielskry/Awesome-RAG
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. LlamaIndex · recommended 2×
  3. Haystack · recommended 2×
  4. Pinecone · recommended 2×
  5. Weaviate · recommended 2×
  • CATEGORY QUERY
    How can I improve LLM accuracy and reduce hallucinations using external knowledge sources?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Pinecone
    5. Weaviate
    6. Chroma
    7. FAISS

    AI recommended 7 alternatives but never named Danielskry/Awesome-RAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best frameworks and tools for building robust retrieval augmented generation systems?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. RAGatouille
    5. Faiss
    6. Weaviate
    7. Pinecone

    AI recommended 7 alternatives but never named Danielskry/Awesome-RAG. 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 Danielskry/Awesome-RAG?
    pass
    AI did not name Danielskry/Awesome-RAG — 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?

  • If a team adopts Danielskry/Awesome-RAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named Danielskry/Awesome-RAG 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 Danielskry/Awesome-RAG solve, and who is the primary audience?
    pass
    AI did not name Danielskry/Awesome-RAG — 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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Danielskry/Awesome-RAG — 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