RRepoGEO

REPOGEO REPORT · LITE

infiniflow/infinity

Default branch main · commit ce179311 · scanned 5/14/2026, 3:52:07 PM

GitHub: 4,512 stars · 420 forks

AI VISIBILITY SCORE
33 /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
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 infiniflow/infinity, 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
    Clarify the unique differentiator in the README's opening statement

    Why:

    CURRENT
    Infinity is a cutting-edge AI-native database that provides a wide range of search capabilities for rich data types such as dense vector, sparse vector, tensor, full-text, and structured data.
    COPY-PASTE FIX
    Infinity is the AI-native database built for LLM applications, uniquely unifying incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text with structured data management, all within a single system.
  • mediumreadme#2
    Add a dedicated 'Why Infinity?' section to the README

    Why:

    COPY-PASTE FIX
    Add a new section to the README, perhaps after 'Key Features':
    
    ## ✨ Why Infinity?
    Infinity stands out as a unified AI-native database, not just a vector store. It uniquely combines:
    - **Comprehensive Hybrid Search:** Incredibly fast search across dense vectors, sparse vectors, tensor (multi-vector), and full-text.
    - **Structured Data Integration:** Seamlessly manage and query structured data alongside your embeddings and text.
    - **LLM-Optimized:** Built from the ground up for Retrieval-Augmented Generation (RAG) and other demanding LLM applications, ensuring high performance and scalability.
  • lowtopics#3
    Add 'llm' and 'structured-data' to the repository topics

    Why:

    CURRENT
    ai-native, approximate-nearest-neighbor-search, bm25, cpp20, cpp20-modules, embedding, full-text-search, hnsw, hybrid-search, information-retrival, multi-vector, nearest-neighbor-search, rag, search-engine, tensor-database, vector, vector-database, vector-search, vectordatabase
    COPY-PASTE FIX
    ai-native, approximate-nearest-neighbor-search, bm25, cpp20, cpp20-modules, embedding, full-text-search, hnsw, hybrid-search, information-retrival, llm, multi-vector, nearest-neighbor-search, rag, search-engine, structured-data, tensor-database, vector, vector-database, vector-search, vectordatabase

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 infiniflow/infinity
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. PostgreSQL · recommended 2×
  3. Weaviate · recommended 1×
  4. Elasticsearch · recommended 1×
  5. Qdrant · recommended 1×
  • CATEGORY QUERY
    What database offers fast hybrid search for dense vectors, sparse vectors, and full-text in LLM apps?
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate
    3. Elasticsearch
    4. Qdrant
    5. Milvus
    6. Zilliz
    7. PostgreSQL
    8. pgvector

    AI recommended 8 alternatives but never named infiniflow/infinity. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a high-performance AI-native database for RAG applications needing multi-vector and full-text search.
    you: not recommended
    AI recommended (in order):
    1. Pinecone
    2. Weaviate (weaviate/weaviate)
    3. Qdrant (qdrant/qdrant)
    4. Milvus (milvus-io/milvus)
    5. Elasticsearch (elastic/elasticsearch)
    6. PostgreSQL
    7. pgvector (pgvector/pgvector)
    8. rum (postgrespro/rum)

    AI recommended 8 alternatives but never named infiniflow/infinity. 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 infiniflow/infinity?
    pass
    AI did not name infiniflow/infinity — 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 infiniflow/infinity in production, what risks or prerequisites should they evaluate first?
    pass
    AI named infiniflow/infinity 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 infiniflow/infinity solve, and who is the primary audience?
    pass
    AI named infiniflow/infinity 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
  • Prioritized action items8 vs 3 in Lite