Traditional search looks for words.
AI applications often need something different.
Suppose a user searches:
“cheap server for a small business”
A semantic search system can look for content with a similar meaning even if the exact words are different.
This is where vector search becomes useful.
What Is a Vector?
A vector is a numerical representation of information.
Text can be converted into a vector called an embedding.
For example:
"cheap cloud hosting"
↓
Embedding Model
↓
[0.12, -0.42, 0.81, ...]
The vector represents the semantic characteristics of the text.
What Is pgvector?
pgvector is a PostgreSQL extension that allows databases to store and search vector data.
For Supabase applications, this means vector data can live alongside your normal application data.
Semantic Search Workflow
A typical system looks like:
Document
↓
Embedding Model
↓
Vector
↓
PostgreSQL + pgvector
↓
Similarity Search
When the user searches, the query can also be converted into a vector.
The system then looks for similar vectors.
Example Knowledge Base
Imagine an AI support system containing thousands of support articles.
A user asks:
“Why is my website unavailable?”
The knowledge base might contain an article titled:
“Troubleshooting server connection problems”
The exact words are different, but the meanings are related.
Semantic search can help identify that relationship.
Store Metadata Too
Vectors should not exist alone.
Store useful metadata such as:
document_id
title
content
category
created_at
embedding
Then your application can filter results before or after similarity search.
Combine Semantic and Normal Search
A strong search system can combine:
- Keyword search
- Semantic search
- Filters
- Metadata
This can produce more useful results than relying on only one search method.
AI Applications
pgvector can support:
- AI chatbots
- Knowledge bases
- Recommendation systems
- Document search
- RAG applications
- Similarity matching
Watch Database Resources
Vector search can become resource-intensive as the dataset grows.
Monitor:
- Vector count
- Query duration
- CPU
- RAM
- Index size
- Database storage
Self-Hosted Supabase
If you run the Supabase stack on infrastructure you control, PostgreSQL extensions such as pgvector can become part of your AI application architecture.
Hostwares offers a self-hosted Supabase stack built around PostgreSQL and the broader Supabase services.
Final Thoughts
pgvector brings vector search into PostgreSQL.
For AI applications already using Supabase, this can reduce the need to immediately introduce a separate vector database.
Start small, measure performance, and design your indexes and data model around the actual workload.