RRepoGEO

REPOGEO REPORT · LITE

jamwithai/beginner-local-rag-system

Default branch main · commit 1959b181 · scanned 6/9/2026, 1:53:32 PM

GitHub: 533 stars · 213 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 jamwithai/beginner-local-rag-system, 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

2 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION
  • highabout#1
    Add a concise About description

    Why:

    COPY-PASTE FIX
    Build a private, offline RAG system for personal documents using OpenSearch, Sentence Transformers, and local LLMs. Perfect for beginners seeking a privacy-friendly, CPU-only solution.
  • mediumhomepage#2
    Add the blog guide URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://www.jamwithai.com/blog/build-a-local-llm-based-rag-system-for-your-personal-documents-part-1

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 jamwithai/beginner-local-rag-system
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Ollama
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Ollama · recommended 2×
  2. Hugging Face Transformers · recommended 1×
  3. Sentence Transformers · recommended 1×
  4. FAISS · recommended 1×
  5. Llama.cpp · recommended 1×
  • CATEGORY QUERY
    How to build a private document search system using large language models offline?
    you: not recommended
    AI recommended (in order):
    1. Hugging Face Transformers
    2. Sentence Transformers
    3. FAISS
    4. Llama.cpp
    5. Ollama
    6. ChromaDB
    7. LanceDB
    8. Elasticsearch
    9. Weaviate

    AI recommended 9 alternatives but never named jamwithai/beginner-local-rag-system. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for an open-source solution to query personal documents with local AI models.
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama
    3. LlamaIndex
    4. LangChain
    5. PrivateGPT
    6. LocalGPT
    7. Quivr

    AI recommended 7 alternatives but never named jamwithai/beginner-local-rag-system. 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 jamwithai/beginner-local-rag-system?
    pass
    AI did not name jamwithai/beginner-local-rag-system — 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 jamwithai/beginner-local-rag-system in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jamwithai/beginner-local-rag-system 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 jamwithai/beginner-local-rag-system solve, and who is the primary audience?
    pass
    AI did not name jamwithai/beginner-local-rag-system — 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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jamwithai/beginner-local-rag-system — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

  • Deep reports10 / month
  • Brand-free category queries5 vs 2 in Lite
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