RRepoGEO

REPOGEO REPORT · LITE

datvodinh/rag-chatbot

Default branch main · commit e9de2afd · scanned 6/8/2026, 2:17:02 PM

GitHub: 665 stars · 103 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 datvodinh/rag-chatbot, 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 opening to clarify 'complete application'

    Why:

    COPY-PASTE FIX
    Add the following sentence immediately after the main title: 'This project provides a complete, easy-to-deploy application to chat with multiple PDF documents locally using open-source LLMs.'
  • highhomepage#2
    Add project homepage URL to About section

    Why:

    COPY-PASTE FIX
    https://[YOUR_PROJECT_HOMEPAGE_OR_LIVE_DEMO_URL]
  • mediumreadme#3
    Add a 'Why Choose This Project?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section titled 'Why Choose This Project?' or 'Compared to Libraries' that explains its value as a ready-to-run, end-to-end local RAG chatbot solution, contrasting it with building from scratch with libraries like LangChain or LlamaIndex. Example: 'While libraries like LangChain and LlamaIndex provide powerful components, this project offers a complete, pre-integrated, and easily deployable application for local PDF querying, saving you setup time and providing a ready-to-use UI.'

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 datvodinh/rag-chatbot
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 1×
  2. LangChain · recommended 1×
  3. deepset/Haystack · recommended 1×
  4. Faiss · recommended 1×
  5. pypdf · recommended 1×
  • CATEGORY QUERY
    How can I build a local AI assistant to query information from multiple PDF documents?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack (deepset/Haystack)
    4. Faiss
    5. pypdf
    6. PyMuPDF
    7. sentence-transformers
    8. Ollama
    9. Llama.cpp
    10. ChromaDB
    11. Weaviate
    12. Qdrant

    AI recommended 12 alternatives but never named datvodinh/rag-chatbot. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow me to chat with my local documents using an open-source language model?
    you: not recommended
    AI recommended (in order):
    1. LM Studio
    2. Ollama (ollama/ollama)
    3. PrivateGPT (imartinez/privateGPT)
    4. LocalGPT (PromtEngineer/localGPT)
    5. Jan (janhq/jan)
    6. Quivr (StanGirard/quivr)
    7. AnythingLLM (Mintplex-Labs/anythingllm)

    AI recommended 7 alternatives but never named datvodinh/rag-chatbot. 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 datvodinh/rag-chatbot?
    pass
    AI named datvodinh/rag-chatbot explicitly

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

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

    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 datvodinh/rag-chatbot. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)
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datvodinh/rag-chatbot — 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