RRepoGEO

REPOGEO REPORT · LITE

gabrielchua/RAGxplorer

Default branch main · commit 840d16ab · scanned 6/29/2026, 2:46:45 PM

GitHub: 1,221 stars · 115 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)

2 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 gabrielchua/RAGxplorer, 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 the README's opening statement to emphasize debugging and interactive visualization

    Why:

    CURRENT
    RAGxplorer is a tool to build Retrieval Augmented Generation (RAG) visualisations.
    COPY-PASTE FIX
    RAGxplorer is an open-source interactive visualization and debugging tool specifically designed to help you understand and troubleshoot the retrieval process within your Retrieval Augmented Generation (RAG) pipelines.
  • mediumhomepage#2
    Add the Streamlit demo URL as the repository homepage

    Why:

    COPY-PASTE FIX
    https://ragxplorer.streamlit.app/
  • mediumtopics#3
    Add 'debugging' to the repository topics

    Why:

    CURRENT
    interactive, llm, python, rag, streamlit, visualization
    COPY-PASTE FIX
    interactive, llm, python, rag, streamlit, visualization, debugging

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 gabrielchua/RAGxplorer
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LangSmith
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LangSmith · recommended 2×
  2. run-llama/llama_index · recommended 1×
  3. Arize AI · recommended 1×
  4. wandb/wandb · recommended 1×
  5. open-telemetry/opentelemetry-specification · recommended 1×
  • CATEGORY QUERY
    How to visualize and debug the document retrieval process in a RAG pipeline?
    you: not recommended
    AI recommended (in order):
    1. LangSmith
    2. LlamaIndex (run-llama/llama_index)
    3. Arize AI
    4. Weights & Biases (wandb/wandb)
    5. OpenTelemetry (open-telemetry/opentelemetry-specification)
    6. Haystack (deepset-ai/haystack)
    7. Ragas (explodinggradients/ragas)
    8. Streamlit (streamlit/streamlit)
    9. Gradio (gradio-app/gradio)
    10. Elasticsearch (elastic/elasticsearch)
    11. OpenSearch (opensearch-project/OpenSearch)
    12. Kibana (elastic/kibana)
    13. OpenSearch Dashboards (opensearch-project/OpenSearch-Dashboards)

    AI recommended 13 alternatives but never named gabrielchua/RAGxplorer. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What interactive tools exist for exploring and understanding RAG system components and performance?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LangSmith
    3. LangChain Playground
    4. LlamaIndex
    5. LlamaCloud
    6. LlamaIndex Playground
    7. Ragas
    8. Weights & Biases
    9. W&B Prompts
    10. DeepEval
    11. Gradio
    12. Streamlit

    AI recommended 12 alternatives but never named gabrielchua/RAGxplorer. 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 gabrielchua/RAGxplorer?
    pass
    AI named gabrielchua/RAGxplorer explicitly

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

  • If a team adopts gabrielchua/RAGxplorer in production, what risks or prerequisites should they evaluate first?
    pass
    AI named gabrielchua/RAGxplorer 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 gabrielchua/RAGxplorer solve, and who is the primary audience?
    pass
    AI named gabrielchua/RAGxplorer 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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MARKDOWN (README)
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gabrielchua/RAGxplorer — 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