top of page
Sin título (1440 × 766 px) (2).png

PGVector

At CodeBranch, we implement semantic search and RAG solutions using PGVector.

This technology enables vector similarity search directly inside PostgreSQL, making it ideal for AI-powered search and recommendation systems.

Do you have a project in PgVector? We can help you!

When to use PGVector?

RAG Architectures

PG_Vector is suitable for retrieval-augmented generation.
It enables semantic search within PostgreSQL.
Ideal for AI knowledge systems.

Semantic Search

It supports vector similarity queries.
This enhances traditional search capabilities.
Common in AI-driven applications.

Simplified Tech Stacks

PG_Vector reduces the need for separate vector databases.
It keeps data within PostgreSQL.
Useful for simpler architectures.

AI-Powered Recommendations

It enables similarity-based recommendations.
Common in content and product platforms.
Used in personalization systems.

Enterprise Databases

PG_Vector fits well in enterprise database setups.
It leverages existing PostgreSQL infrastructure.
Ideal for scalable systems.

Hybrid Search

It can be combined with traditional SQL queries.
This enables flexible retrieval strategies.
Useful in hybrid AI systems.

Learn more about

PGVector

bottom of page