RRepoGEO

REPOGEO REPORT · LITE

AllAboutAI-YT/easy-local-rag

Default branch main · commit 0e64997a · scanned 6/24/2026, 4:32:04 PM

GitHub: 1,220 stars · 338 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)

3 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 AllAboutAI-YT/easy-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 relevant topics to the repository

    Why:

    COPY-PASTE FIX
    local-rag, ollama, email-rag, pdf-rag, retrieval-augmented-generation, python, ai-assistant, offline-rag, local-llm
  • highhomepage#2
    Add a homepage URL to the repository

    Why:

    COPY-PASTE FIX
    https://www.youtube.com/c/AllAboutAI
  • mediumreadme#3
    Add a concise introductory sentence to the README

    Why:

    CURRENT
    # SuperEasy 100% Local RAG with Ollama + Email RAG
    COPY-PASTE FIX
    # SuperEasy 100% Local RAG with Ollama + Email RAG

This repository provides a complete, ready-to-run Python application for building a fully local and offline Retrieval Augmented Generation (RAG) system, enabling you to chat with your own documents (PDFs, text, JSON) and emails using Ollama.

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 AllAboutAI-YT/easy-local-rag
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 1×
  2. LlamaIndex · recommended 1×
  3. ChromaDB · recommended 1×
  4. Nomic Embed · recommended 1×
  5. Hugging Face Transformers · recommended 1×
  • CATEGORY QUERY
    How can I set up a fully local RAG system for private document querying?
    you: not recommended
    AI recommended (in order):
    1. Ollama
    2. LlamaIndex
    3. ChromaDB
    4. Nomic Embed
    5. Hugging Face Transformers
    6. FAISS
    7. LangChain
    8. LanceDB
    9. LocalAI
    10. Qdrant
    11. Sentence-Transformers
    12. PrivateGPT

    AI recommended 12 alternatives but never named AllAboutAI-YT/easy-local-rag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are simple tools for building a RAG application over local emails and PDFs?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex (run-llama/llama_index)
    2. Haystack (deepset-ai/haystack)
    3. LangChain (langchain-ai/langchain)
    4. Gradio (gradio-app/gradio)
    5. FAISS (facebookresearch/faiss)
    6. PyPDF2 (pypdf/pypdf)
    7. Python's email module
    8. sentence-transformers (UKPLab/sentence-transformers)
    9. RAGatouille (RAGatouille/RAGatouille)

    AI recommended 9 alternatives but never named AllAboutAI-YT/easy-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
    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 AllAboutAI-YT/easy-local-rag?
    pass
    AI did not name AllAboutAI-YT/easy-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 AllAboutAI-YT/easy-local-rag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named AllAboutAI-YT/easy-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 AllAboutAI-YT/easy-local-rag solve, and who is the primary audience?
    pass
    AI did not name AllAboutAI-YT/easy-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?

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AllAboutAI-YT/easy-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