RRepoGEO

REPOGEO REPORT · LITE

mrdbourke/simple-local-rag

Default branch main · commit 4670ebfc · scanned 6/15/2026, 10:52:44 AM

GitHub: 985 stars · 294 forks

AI VISIBILITY SCORE
17 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 mrdbourke/simple-local-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 specific topics to improve categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    rag, retrieval-augmented-generation, llm, local-llm, ollama, pytorch, python, tutorial, example, pdf-chat, gpu, machine-learning, deep-learning
  • highlicense#2
    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 with the text of the MIT License.
  • mediumreadme#3
    Reposition README's opening to highlight 'tutorial' and 'from scratch'

    Why:

    CURRENT
    # Simple Local RAG Tutorial

Local RAG pipeline we're going to build:
    COPY-PASTE FIX
    # Simple Local RAG Tutorial: Build a Retrieval Augmented Generation (RAG) Pipeline From Scratch

This repository provides a comprehensive, step-by-step tutorial for building a complete RAG pipeline that runs entirely locally on an NVIDIA GPU.

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 mrdbourke/simple-local-rag
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. Haystack · recommended 1×
  4. Faiss · recommended 1×
  5. Sentence Transformers · recommended 1×
  • CATEGORY QUERY
    How to build a retrieval augmented generation system for local document querying?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Faiss
    5. Sentence Transformers
    6. Hugging Face Transformers
    7. ChromaDB
    8. Qdrant
    9. Weaviate

    AI recommended 9 alternatives but never named mrdbourke/simple-local-rag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best open-source tools for building a local PDF chat assistant?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. FAISS
    4. Chroma
    5. Ollama
    6. PDFMiner.six
    7. Sentence-Transformers

    AI recommended 7 alternatives but never named mrdbourke/simple-local-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
    fail

    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 mrdbourke/simple-local-rag?
    pass
    AI did not name mrdbourke/simple-local-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 mrdbourke/simple-local-rag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named mrdbourke/simple-local-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 mrdbourke/simple-local-rag solve, and who is the primary audience?
    pass
    AI did not name mrdbourke/simple-local-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?

Embed your GEO score

Drop this badge into the README of mrdbourke/simple-local-rag. 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/mrdbourke/simple-local-rag.svg)](https://repogeo.com/en/r/mrdbourke/simple-local-rag)
HTML
<a href="https://repogeo.com/en/r/mrdbourke/simple-local-rag"><img src="https://repogeo.com/badge/mrdbourke/simple-local-rag.svg" alt="RepoGEO" /></a>
Pro

Subscribe to Pro for deep diagnoses

mrdbourke/simple-local-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