RRepoGEO

REPOGEO REPORT · LITE

datastax/jvector

Default branch main · commit 46bd1158 · scanned 5/20/2026, 8:31:59 AM

GitHub: 1,712 stars · 152 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 datastax/jvector, 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 to immediately state JVector's core value

    Why:

    CURRENT
    The current README starts with "## Introduction to approximate nearest neighbor search\n\nExact nearest neighbor search (k-nearest-neighbor or KNN) is prohibitively expensive at higher dimensions..."
    COPY-PASTE FIX
    Add a concise introductory paragraph *before* "## Introduction to approximate nearest neighbor search" that clearly states JVector is a pure Java, embedded, graph-based ANN index designed for high-performance, real-time vector search. For example: "JVector is a pure Java, embedded, graph-based Approximate Nearest Neighbor (ANN) index designed for high-performance, real-time vector search and similarity calculations within Java applications. It merges the hierarchical structure of HNSW with the efficient Vamana algorithm (from DiskANN) to provide a robust solution for dynamic datasets."
  • mediumtopics#2
    Refine topics to emphasize "embedded" and "real-time" and clarify scope

    Why:

    CURRENT
    ann, java, knn, machine-learning, search-engine, similarity-search, vector-search
    COPY-PASTE FIX
    ann, java, knn, machine-learning, similarity-search, vector-search, embedded, real-time
  • mediumhomepage#3
    Add a homepage URL to the repository metadata

    Why:

    COPY-PASTE FIX
    Add a relevant homepage URL (e.g., project website, documentation hub, or a dedicated landing page) to the repository's 'About' section.

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 datastax/jvector
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
Faiss
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. Faiss · recommended 2×
  2. Apache Lucene · recommended 2×
  3. Hnswlib · recommended 2×
  4. Weaviate · recommended 2×
  5. Milvus · recommended 2×
  • CATEGORY QUERY
    Looking for a fast Java library to perform similarity search on high-dimensional vectors.
    you: not recommended
    AI recommended (in order):
    1. Faiss
    2. Apache Lucene
    3. Hnswlib
    4. Weaviate
    5. Milvus

    AI recommended 5 alternatives but never named datastax/jvector. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking an embedded graph-based ANN index for real-time updates in Java applications.
    you: not recommended
    AI recommended (in order):
    1. Faiss
    2. Hnswlib
    3. Apache Lucene
    4. Milvus
    5. Elasticsearch
    6. Weaviate

    AI recommended 6 alternatives but never named datastax/jvector. 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 datastax/jvector?
    pass
    AI named datastax/jvector explicitly

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

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

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

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datastax/jvector — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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  • Brand-free category queries5 vs 2 in Lite
  • Prioritized action items8 vs 3 in Lite
datastax/jvector — RepoGEO report