RRepoGEO

REPOGEO REPORT · LITE

postgresml/korvus

Default branch main · commit 7c060357 · scanned 5/16/2026, 3:18:45 PM

GitHub: 1,461 stars · 50 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 postgresml/korvus, 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 paragraph to emphasize RAG SDK identity

    Why:

    CURRENT
    Korvus is a search SDK that unifies the entire RAG pipeline in a single database query. Built on top of Postgres with bindings for Python, JavaScript and Rust, Korvus delivers high-performance, customizable search capabilities with minimal infrastructure concerns.
    COPY-PASTE FIX
    Korvus is the **unified RAG pipeline search SDK** that lets you build and deploy advanced Retrieval Augmented Generation (RAG) applications entirely within PostgreSQL, using a single database query. It provides high-performance, customizable search capabilities with bindings for Python, JavaScript, and Rust, eliminating the need for external vector databases or complex orchestration.
  • mediumtopics#2
    Add more specific topics to clarify RAG and in-database capabilities

    Why:

    CURRENT
    ai, embeddings, javascript, llm, ml, python, rag, search, sql
    COPY-PASTE FIX
    ai, embeddings, javascript, llm, ml, python, rag, search, sql, vector-search, in-database-rag
  • lowreadme#3
    Enhance the 'Why Korvus?' section to highlight its unique differentiator

    Why:

    COPY-PASTE FIX
    In the '🏆 Why Korvus?' section, add a sentence like: 'Unlike solutions requiring separate vector databases or complex orchestration layers, Korvus unifies the entire RAG pipeline—from embedding generation to retrieval and re-ranking—directly within your Postgres database, accessible via a single SQL query.'

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 postgresml/korvus
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
pg_embedding
Recommended in 1 of 2 queries
COMPETITOR LEADERBOARD
  1. pg_embedding · recommended 1×
  2. OpenAI · recommended 1×
  3. Hugging Face · recommended 1×
  4. pgvector · recommended 1×
  5. PostgreSQL's Built-in Full-Text Search · recommended 1×
  • CATEGORY QUERY
    How to unify an entire RAG pipeline within a single Postgres database query?
    you: not recommended
    AI recommended (in order):
    1. pg_embedding
    2. OpenAI
    3. Hugging Face
    4. pgvector
    5. PostgreSQL's Built-in Full-Text Search
    6. PL/pgSQL
    7. PL/Python
    8. TimescaleDB
    9. Supabase
    10. Cohere

    AI recommended 10 alternatives but never named postgresml/korvus. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Looking for a high-performance RAG search SDK with Python and JavaScript bindings.
    you: not recommended
    AI recommended (in order):
    1. Weaviate (weaviate/weaviate)
    2. Pinecone
    3. Qdrant (qdrant/qdrant)
    4. Milvus (milvus-io/milvus)
    5. Zilliz
    6. Elasticsearch (elastic/elasticsearch)
    7. Faiss (facebookresearch/faiss)
    8. Chroma (chroma-core/chroma)

    AI recommended 8 alternatives but never named postgresml/korvus. 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 postgresml/korvus?
    pass
    AI named postgresml/korvus explicitly

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

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

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

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