RRepoGEO

REPOGEO REPORT · LITE

run-llama/chat-llamaindex

Default branch main · commit 179c9071 · scanned 6/12/2026, 5:07:35 PM

GitHub: 985 stars · 277 forks

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 run-llama/chat-llamaindex, 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 repository description

    Why:

    COPY-PASTE FIX
    An example full-stack application for building and sharing LLM chatbots that answer questions using your own data (PDFs, text documents), powered by LlamaIndex and LlamaCloud.
  • mediumreadme#2
    Clarify the README's opening statement to emphasize its role as an example application

    Why:

    CURRENT
    Welcome to LlamaIndex Chat. You can create and share LLM chatbots that know your data (PDF or text documents).
    COPY-PASTE FIX
    Welcome to LlamaIndex Chat, an example full-stack application for building and sharing LLM chatbots that know your data (PDF or text documents). This repository serves as a reference implementation for creating data-aware AI chat experiences with LlamaIndex and LlamaCloud.

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/chat-llamaindex
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. Streamlit · recommended 2×
  3. Gradio · recommended 2×
  4. OpenAI API · recommended 1×
  5. Anthropic Claude · recommended 1×
  • CATEGORY QUERY
    How to build an AI chatbot that answers questions using my own document collection?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. OpenAI API
    3. Anthropic Claude
    4. Google Gemini
    5. GPT-4
    6. GPT-3.5 Turbo
    7. Claude 3 Opus
    8. Sonnet
    9. Haiku
    10. text-embedding-ada-002
    11. ChromaDB
    12. Pinecone
    13. Weaviate
    14. Streamlit
    15. Gradio
    16. Flask
    17. Next.js
    18. FastAPI

    AI recommended 18 alternatives but never named run-llama/chat-llamaindex. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework helps create and deploy custom data-aware LLM chat applications?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. Microsoft Semantic Kernel
    5. OpenAI Assistants API
    6. Gradio
    7. Streamlit

    AI recommended 7 alternatives but never named run-llama/chat-llamaindex. 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/chat-llamaindex?
    pass
    AI did not name run-llama/chat-llamaindex — 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 run-llama/chat-llamaindex in production, what risks or prerequisites should they evaluate first?
    pass
    AI named run-llama/chat-llamaindex 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/chat-llamaindex solve, and who is the primary audience?
    pass
    AI named run-llama/chat-llamaindex explicitly

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

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MARKDOWN (README)
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run-llama/chat-llamaindex — 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