REPOGEO REPORT · LITE

supervc-stack/VectorChord

Default branch main · commit 0768657e · scanned 5/27/2026, 8:06:53 PM

GitHub: 1,685 stars · 63 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 supervc-stack/VectorChord, 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

Strengthen README's opening paragraph to emphasize core value and category

Why:

CURRENT

VectorChord (vchord) is a PostgreSQL extension engineered for scalable, high-performance, and cost-effective vector search. To efficiently store vectors while preserving search quality, VectorChord applies RaBitQ[^1] compression together with autonomous reranking. With VectorChord, you can store 400,000 vectors for just $1, enabling significant savings: 6x more vectors compared to Pinecone's optimized storage and 26x more than pgvector/pgvecto.rs for the same price.

COPY-PASTE FIX

VectorChord (vchord) is a **PostgreSQL extension** designed for **scalable, high-performance, and cost-effective vector search** directly within your database. It's engineered for the **Billion-Scale Era**, enabling you to host 100M vectors on a single i4i.xlarge ($247/mo) and scale seamlessly to 1B+. By applying RaBitQ compression and autonomous reranking, VectorChord significantly reduces storage costs, allowing you to store 400,000 vectors for just $1 – a 6x improvement over Pinecone's optimized storage and 26x over pgvector/pgvecto.rs for the same price.

mediumtopics#2

Expand repository topics with more specific keywords

Why:

CURRENT

artificial-intelligence, llmops, postgresql, vector-database, vector-search

COPY-PASTE FIX

artificial-intelligence, llmops, postgresql, vector-database, vector-search, vector-embeddings, approximate-nearest-neighbor, ann-search, database-extension, cost-optimization, billion-scale

lowlicense#3

Add explicit license information to the README

Why:

COPY-PASTE FIX

## License

VectorChord is released under [specify license(s) here, e.g., a custom license combining X and Y, or refer to the LICENSE file for full details].

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 supervc-stack/VectorChord

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

pgvector/pgvector

Recommended in 1 of 2 queries

COMPETITOR LEADERBOARD

pgvector/pgvector · recommended 1×
lanterndb/lantern · recommended 1×
postgresml/postgresml · recommended 1×
timescale/timescaledb · recommended 1×
zombodb/zombodb · recommended 1×

CATEGORY QUERY
How to perform scalable, high-performance vector search directly within a PostgreSQL database?
you: not recommended
AI recommended (in order):
1. pgvector (pgvector/pgvector)
2. Lantern (lanterndb/lantern)
3. PostgresML (postgresml/postgresml)
4. TimescaleDB (timescale/timescaledb)
5. ZomboDB (zombodb/zombodb)
AI recommended 5 alternatives but never named supervc-stack/VectorChord. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a cost-effective solution to store and search billions of vectors in Postgres.
you: not recommended
AI recommended (in order):
1. pgvector
2. Lantern
3. Supabase
4. Neon
5. Tembo
6. Pinecone
7. Weaviate
AI recommended 7 alternatives but never named supervc-stack/VectorChord. 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 supervc-stack/VectorChord?
pass
AI named supervc-stack/VectorChord explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts supervc-stack/VectorChord in production, what risks or prerequisites should they evaluate first?
pass
AI named supervc-stack/VectorChord 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 supervc-stack/VectorChord solve, and who is the primary audience?
pass
AI named supervc-stack/VectorChord explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of supervc-stack/VectorChord. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

Live preview

MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/supervc-stack/VectorChord.svg)](https://repogeo.com/en/r/supervc-stack/VectorChord)

HTML

<a href="https://repogeo.com/en/r/supervc-stack/VectorChord"><img src="https://repogeo.com/badge/supervc-stack/VectorChord.svg" alt="RepoGEO" /></a>

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supervc-stack/VectorChord — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

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