RRepoGEO

REPOGEO REPORT · LITE

Ontos-AI/knowhere

Default branch main · commit 1e2d0d73 · scanned 6/9/2026, 12:21:26 PM

GitHub: 1,111 stars · 108 forks

AI VISIBILITY SCORE
40 /100
Critical
Category recall
0 / 2
Not recommended in any query
Rule findings
2 pass · 0 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 Ontos-AI/knowhere, 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
    Refine the 'About' description to emphasize document processing

    Why:

    CURRENT
    Knowhere extracts, parses, and outputs structured chunks ready for AI Agents and RAG.
    COPY-PASTE FIX
    Knowhere is a memory layer for AI agents and RAG, extracting, parsing, and structuring complex documents into navigable, context-rich chunks.
  • hightopics#2
    Update topics to remove vector database names and add RAG/document processing terms

    Why:

    CURRENT
    agent, ai-agents, chromadb, claude, claude-code, cursor, elasticsearch, gemini, gpt, langchain, milvus, qdrant, rag, rag-pipeline, skills
    COPY-PASTE FIX
    agent, ai-agents, rag, rag-pipeline, document-processing, unstructured-data, semantic-context, knowledge-graph, llm-memory, data-extraction, parsing, chunking, langchain, llama-index, haystack, unstructured-io
  • mediumreadme#3
    Add a comparison section to the README differentiating Knowhere from competitors

    Why:

    COPY-PASTE FIX
    Add a new section to the README titled 'Why Knowhere? (Compared to LlamaIndex, LangChain, Unstructured.io)' or 'How Knowhere Compares', explaining its unique integrated pipeline for memory creation from complex documents.

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 Ontos-AI/knowhere
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LlamaIndex
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LlamaIndex · recommended 2×
  2. LangChain · recommended 2×
  3. Haystack · recommended 2×
  4. Unstructured.io · recommended 2×
  5. OpenSearch · recommended 1×
  • CATEGORY QUERY
    Tool for preparing unstructured documents and creating structured memory for AI agents.
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. Unstructured.io
    5. OpenSearch
    6. Elasticsearch
    7. Weaviate
    8. Pinecone
    9. Qdrant

    AI recommended 9 alternatives but never named Ontos-AI/knowhere. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    How to extract semantic context and hierarchy from complex documents for RAG systems?
    you: not recommended
    AI recommended (in order):
    1. LlamaIndex
    2. LangChain
    3. Haystack
    4. spaCy
    5. Unstructured.io
    6. OpenNMT
    7. Fairseq

    AI recommended 7 alternatives but never named Ontos-AI/knowhere. This is the gap to close.

    Show full AI answer

Objective checks

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

  • Metadata completeness
    pass

  • 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 Ontos-AI/knowhere?
    pass
    AI named Ontos-AI/knowhere explicitly

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

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

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

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  • Brand-free category queries5 vs 2 in Lite
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