REPOGEO REPORT · LITE

postgresml/korvus

Default branch main · commit 7c060357 · scanned 6/27/2026, 12:48:15 PM

GitHub: 1,467 stars · 50 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)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

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 sentence to emphasize "end-to-end RAG pipeline SDK"

Why:

CURRENT

Korvus is a search SDK that unifies the entire RAG pipeline in a single database query.

COPY-PASTE FIX

Korvus is an **end-to-end RAG pipeline SDK** that unifies the entire Retrieval Augmented Generation workflow into a single database query, built on Postgres for Python, JavaScript, and Rust developers.

mediumtopics#2

Add more specific RAG and vector search topics

Why:

CURRENT

ai, embeddings, javascript, llm, ml, python, rag, search, sql

COPY-PASTE FIX

ai, embeddings, javascript, llm, ml, python, rag, search, sql, rag-pipeline, vector-search, ai-sdk

lowreadme#3

Enhance 'Why Korvus?' section with explicit comparisons to common alternatives

Why:

COPY-PASTE FIX

Expand the 'Why Korvus?' section to include a brief comparison or differentiation from common alternatives like standalone vector databases (e.g., `pg_vector`, Supabase Vector) or generic ML libraries (e.g., `transformers`), highlighting how Korvus provides a unified, single-query RAG pipeline.

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

Of all named tools, what % are you?

Top rival

PostgreSQL

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

PostgreSQL · recommended 1×
neondatabase/pg_embedding · recommended 1×
PL/Python · recommended 1×
PL/pgSQL · recommended 1×
huggingface/transformers · recommended 1×

CATEGORY QUERY
How to implement an entire RAG pipeline efficiently within a single database query?
you: not recommended
AI recommended (in order):
1. PostgreSQL
2. pg_embedding (neondatabase/pg_embedding)
3. PL/Python
4. PL/pgSQL
5. transformers (huggingface/transformers)
6. langchain (langchain-ai/langchain)
7. OpenAI
8. Anthropic
9. Hugging Face
10. Supabase (supabase/supabase)
11. pg_vector (pgvector/pgvector)
12. Supabase Edge Functions
13. pg_graphql (supabase/pg_graphql)
14. Deno (denoland/deno)
15. PostgresML (postgresml/postgresml)
16. ClickHouse (ClickHouse/ClickHouse)
17. SingleStore
18. Milvus (milvus-io/milvus)
19. Weaviate (weaviate/weaviate)
20. generative-openai
21. generative-cohere
AI recommended 21 alternatives but never named postgresml/korvus. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a high-performance search solution built on Postgres for Python RAG applications.
you: not recommended
AI recommended (in order):
1. pg_vector
2. Lantern
3. Supabase Vector
4. PostgresML
5. Tembo Vector DB
AI recommended 5 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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MARKDOWN (README)

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Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite