RRepoGEO

REPOGEO REPORT · LITE

pguso/rag-from-scratch

Default branch main · commit 38e1a7a3 · scanned 6/18/2026, 9:23:05 AM

GitHub: 1,461 stars · 173 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 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
    Add a clear 'What this isn't' statement to the README

    Why:

    COPY-PASTE FIX
    Add this sentence early in the README, perhaps after the initial description: "Important: This project is a learning resource for understanding RAG fundamentals, not a production-ready framework like LangChain or LlamaIndex."
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    https://pguso.github.io/rag-from-scratch/
  • lowtopics#3
    Add more specific educational RAG topics

    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, rag-from-scratch, learn-rag

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
LlamaIndex/LlamaIndex
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex/LlamaIndex · recommended 1×
  2. langchain-ai/langchain · recommended 1×
  3. deepset-ai/haystack · recommended 1×
  4. huggingface/transformers · recommended 1×
  5. facebookresearch/faiss · recommended 1×
  • CATEGORY QUERY
    How to implement RAG pipeline locally to deeply understand its components?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (LlamaIndex/LlamaIndex)
    2. LangChain (langchain-ai/langchain)
    3. Haystack (deepset-ai/haystack)
    4. Transformers (huggingface/transformers)
    5. FAISS (facebookresearch/faiss)
    6. Sentence-Transformers (UKPLab/sentence-transformers)
    7. ChromaDB (chroma-core/chroma)
    8. Pinecone
    9. Ollama (ollama/ollama)
    10. Llama.cpp (ggerganov/llama.cpp)
    11. llama-cpp-python (abetlen/llama-cpp-python)

    AI recommended 11 alternatives but never named pguso/rag-from-scratch. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a practical guide to build RAG with local LLMs using Node.js for learning purposes.
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LangChain.js
    3. ChromaDB
    4. Ollama Embeddings
    5. transformers.js
    6. dotenv

    AI recommended 6 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 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?

Embed your GEO score

Drop this badge into the README of pguso/rag-from-scratch. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

RepoGEO badge previewLive preview
MARKDOWN (README)
[![RepoGEO](https://repogeo.com/badge/pguso/rag-from-scratch.svg)](https://repogeo.com/en/r/pguso/rag-from-scratch)
HTML
<a href="https://repogeo.com/en/r/pguso/rag-from-scratch"><img src="https://repogeo.com/badge/pguso/rag-from-scratch.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

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