RRepoGEO

REPOGEO REPORT · LITE

wxywb/history_rag

Default branch master · commit 8fe03e2b · scanned 5/20/2026, 8:08:54 AM

GitHub: 1,038 stars · 138 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
23 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
1 pass · 0 warn · 1 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 wxywb/history_rag, 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
  • highabout#1
    Add a concise repository description

    Why:

    COPY-PASTE FIX
    A RAG-based Chinese history Q&A application using vector databases (Milvus/Zilliz Cloud) and LLMs (GPT-4, Qwen, Gemini) to provide accurate answers and reduce hallucinations.
  • hightopics#2
    Add relevant topics to the repository

    Why:

    COPY-PASTE FIX
    rag, llm, chinese-history, milvus, zilliz-cloud, llama-index, question-answering, vector-database, gpt4
  • highlicense#3
    Add a LICENSE file to the repository

    Why:

    CURRENT
    (no LICENSE file detected — the repo has no recognizable license)
    COPY-PASTE FIX
    Create a LICENSE file in the repository root, choosing an appropriate open-source license (e.g., MIT, Apache-2.0, GPL-3.0) and adding its content.

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 wxywb/history_rag
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Neo4j
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Neo4j · recommended 2×
  2. Amazon Neptune · recommended 2×
  3. LangChain · recommended 2×
  4. LlamaIndex · recommended 2×
  5. Google Cloud Vision AI · recommended 1×
  • CATEGORY QUERY
    How to build an accurate question answering system from historical documents using AI?
    you: not recommended
    AI recommended (in order):
    1. Google Cloud Vision AI
    2. Amazon Textract
    3. Abbyy FineReader Engine
    4. NLTK
    5. SpaCy
    6. OpenIE
    7. Neo4j
    8. Amazon Neptune
    9. Elasticsearch
    10. Pinecone
    11. Weaviate
    12. Hugging Face Transformers Library
    13. BERT
    14. RoBERTa
    15. ELECTRA
    16. DeBERTa
    17. T5
    18. GPT-3/4
    19. LangChain
    20. LlamaIndex
    21. Prodigy
    22. Doccano
    23. all-MiniLM-L6-v2
    24. multi-qa-mpnet-base-dot-v1
    25. google/flan-t5-large
    26. meta-llama/Llama-2-7b-chat-hf
    27. distilbert-base-uncased-distilled-squad

    AI recommended 27 alternatives but never named wxywb/history_rag. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are effective methods to reduce large language model hallucinations in factual Q&A?
    you: not recommended
    AI recommended (in order):
    1. LangChain
    2. LlamaIndex
    3. Haystack
    4. OpenAI API
    5. Hugging Face Transformers
    6. Google Cloud Vertex AI
    7. OpenAI GPT-4
    8. Anthropic Claude
    9. Google Gemini
    10. Neo4j
    11. Amazon Neptune

    AI recommended 11 alternatives but never named wxywb/history_rag. This is the gap to close.

    Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

  • Metadata completeness
    fail

    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 wxywb/history_rag?
    pass
    AI did not name wxywb/history_rag — 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 wxywb/history_rag in production, what risks or prerequisites should they evaluate first?
    pass
    AI named wxywb/history_rag 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 wxywb/history_rag solve, and who is the primary audience?
    pass
    AI named wxywb/history_rag 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 wxywb/history_rag. 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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wxywb/history_rag — 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