RRepoGEO

REPOGEO REPORT · LITE

run-llama/rags

Default branch main · commit 4bec2702 · scanned 5/24/2026, 11:07:57 AM

GitHub: 6,535 stars · 659 forks

AI VISIBILITY SCORE
35 /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
3 / 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 run-llama/rags, 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 README H1 and opening sentence to clarify its role as a template

    Why:

    CURRENT
    # RAGs

RAGs is a Streamlit app that lets you create a RAG pipeline from a data source using natural language.
    COPY-PASTE FIX
    # RAGs: A Streamlit App Template for Natural Language RAG Pipelines

RAGs is a deployable Streamlit app template that lets you quickly create a RAG pipeline from a data source using natural language, inspired by OpenAI's GPTs.
  • mediumhomepage#2
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a URL to a live demo of the Streamlit app (e.g., on Streamlit Cloud) or a dedicated project page.
  • lowtopics#3
    Add more specific topics to clarify the repo's nature as a template/example

    Why:

    CURRENT
    agent, chatbot, chatgpt, gpts, llamaindex, llm, openai, rag, streamlit
    COPY-PASTE FIX
    agent, chatbot, chatgpt, gpts, llamaindex, llm, openai, rag, streamlit, template, starter-kit, example-app, demo

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 run-llama/rags
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangChain
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangChain · recommended 2×
  2. LlamaIndex · recommended 2×
  3. Haystack (deepset AI) · recommended 1×
  4. OpenAI API · recommended 1×
  5. Anthropic's Claude · recommended 1×
  • CATEGORY QUERY
    How to build a custom RAG system using natural language prompts?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack (deepset AI)
    4. OpenAI API
    5. Anthropic's Claude
    6. Google's Gemini
    7. sentence-transformers
    8. HuggingFace Transformers
    9. Hugging Face Datasets
    10. Pinecone
    11. Weaviate
    12. Chroma
    13. FAISS (Facebook AI Similarity Search)
    14. Qdrant

    AI recommended 14 alternatives but never named run-llama/rags. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an easy way to create a personalized AI assistant from my own data.
    you: not recommended
    AI recommended (in order):
    1. ChatGPT Plus
    2. GPTs (Custom GPTs)
    3. Poe
    4. Chatbase
    5. CustomGPT
    6. Voiceflow
    7. LangChain
    8. OpenAI APIs
    9. Anthropic APIs
    10. LlamaIndex

    AI recommended 10 alternatives but never named run-llama/rags. 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 run-llama/rags?
    pass
    AI named run-llama/rags explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

  • If a team adopts run-llama/rags in production, what risks or prerequisites should they evaluate first?
    pass
    AI named run-llama/rags 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 run-llama/rags solve, and who is the primary audience?
    pass
    AI named run-llama/rags explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

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run-llama/rags — 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