RRepoGEO

REPOGEO REPORT · LITE

pguso/rag-from-scratch

Default branch main · commit 38e1a7a3 · scanned 5/8/2026, 5:42:41 PM

GitHub: 1,396 stars · 168 forks

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 pguso/rag-from-scratch, 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
  • highreadme#1
    Reposition the README's opening to clearly state its educational purpose

    Why:

    CURRENT
    # RAG from Scratch

**Demystify Retrieval-Augmented Generation (RAG) by building it yourself - step by step.**  
No black boxes. No cloud APIs. Just clear explanations, simple examples, and local code you fully understand.
    COPY-PASTE FIX
    # RAG from Scratch: A Step-by-Step Tutorial

**This project is a comprehensive, step-by-step tutorial to demystify Retrieval-Augmented Generation (RAG) by building it yourself from scratch.**  
No black boxes. No cloud APIs. Just clear explanations, simple examples, and local code you fully understand.
  • mediumhomepage#2
    Add a homepage URL to the repository settings

    Why:

    COPY-PASTE FIX
    https://github.com/pguso/rag-from-scratch#readme
  • lowtopics#3
    Expand topics with more specific keywords

    Why:

    CURRENT
    agents, ai-agents, educational, llm, node-llama-cpp, nodejs, rag, rag-chatbot, rag-pipeline, tutorial
    COPY-PASTE FIX
    agents, ai-agents, educational, llm, node-llama-cpp, nodejs, rag, rag-chatbot, rag-pipeline, tutorial, local-llm, vector-database, embeddings, retrieval-augmented-generation

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 pguso/rag-from-scratch
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
run-llama/llama_index
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. run-llama/llama_index · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. huggingface/transformers · recommended 1×
  4. UKPLab/sentence-transformers · recommended 1×
  5. facebookresearch/faiss · recommended 1×
  • CATEGORY QUERY
    How to build a RAG pipeline locally for educational purposes without cloud dependencies?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (run-llama/llama_index)
    2. LangChain (langchain-ai/langchain)
    3. Hugging Face Transformers (huggingface/transformers)
    4. Sentence-Transformers (UKPLab/sentence-transformers)
    5. FAISS (facebookresearch/faiss)
    6. Ollama (ollama/ollama)
    7. ChromaDB (chroma-core/chroma)

    AI recommended 7 alternatives but never named pguso/rag-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a comprehensive guide to implement RAG with local LLMs using Node.js for deep understanding.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex.TS
    2. LangChain.js
    3. Ollama
    4. axios
    5. fetch
    6. Hugging Face Transformers.js
    7. ChromaDB
    8. Pinecone
    9. llama-cpp-js
    10. hnswlib-node

    AI recommended 10 alternatives but never named pguso/rag-from-scratch. 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 pguso/rag-from-scratch?
    pass
    AI did not name pguso/rag-from-scratch — 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 pguso/rag-from-scratch in production, what risks or prerequisites should they evaluate first?
    pass
    AI named pguso/rag-from-scratch 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 pguso/rag-from-scratch solve, and who is the primary audience?
    pass
    AI did not name pguso/rag-from-scratch — 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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pguso/rag-from-scratch — 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