RRepoGEO

REPOGEO REPORT · LITE

darrencxl0301/StageRAG

Default branch main · commit 77bfc6a9 · scanned 6/25/2026, 9:12:54 PM

GitHub: 1,040 stars · 90 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)

3 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 darrencxl0301/StageRAG, 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
  • hightopics#1
    Add relevant topics to improve categorization

    Why:

    COPY-PASTE FIX
    rag, llm, hallucination, framework, python, nlp, generative-ai, production-ready, speed-precision, llama
  • mediumreadme#2
    Add a 'Why StageRAG?' section to highlight differentiators

    Why:

    COPY-PASTE FIX
    ## ✨ Why StageRAG Stands Out

StageRAG differentiates itself by offering an **explicit, structured, and configurable orchestration of multiple distinct stages** within the RAG process. Unlike single-pass RAG systems, it provides dynamic dual-mode pipelines (Speed/Precision) and built-in multi-component confidence scoring, giving you precise control over the speed-versus-accuracy trade-off and robust hallucination resistance.
  • lowhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    https://github.com/darrencxl0301/StageRAG

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 darrencxl0301/StageRAG
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Pinecone
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Pinecone · recommended 2×
  2. LlamaIndex · recommended 1×
  3. LangChain · recommended 1×
  4. Haystack · recommended 1×
  5. Weaviate · recommended 1×
  • CATEGORY QUERY
    How to build a RAG system that minimizes LLM hallucinations and improves factual accuracy?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Weaviate
    5. Pinecone
    6. Qdrant
    7. Milvus
    8. Chroma
    9. Sentence-Transformers
    10. Hugging Face Transformers
    11. Cohere Rerank
    12. BGE Reranker
    13. OpenAI API
    14. Anthropic Claude
    15. Google Gemini

    AI recommended 15 alternatives but never named darrencxl0301/StageRAG. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What framework allows balancing speed and precision in a RAG application pipeline?
    you: not recommended
    AI recommended (in order):
    1. LangChain (langchain-ai/langchain)
    2. LlamaIndex (run-llama/llama_index)
    3. Haystack (deepset-ai/haystack)
    4. Hugging Face Transformers (huggingface/transformers)
    5. Hugging Face Accelerate (huggingface/accelerate)
    6. Hugging Face Optimum (huggingface/optimum)
    7. Pinecone
    8. Weaviate (weaviate/weaviate)
    9. ChromaDB (chroma-core/chroma)
    10. RAGatouille (matsch/RAGatouille)
    11. Gradio (gradio-app/gradio)
    12. Streamlit (streamlit/streamlit)

    AI recommended 12 alternatives but never named darrencxl0301/StageRAG. 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 darrencxl0301/StageRAG?
    pass
    AI named darrencxl0301/StageRAG explicitly

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

  • If a team adopts darrencxl0301/StageRAG in production, what risks or prerequisites should they evaluate first?
    pass
    AI named darrencxl0301/StageRAG 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 darrencxl0301/StageRAG solve, and who is the primary audience?
    pass
    AI named darrencxl0301/StageRAG 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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darrencxl0301/StageRAG — 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