AI & Intelligence
pgvector
Vector search and RAG inside the PostgreSQL you already run.
pgvector is an open-source PostgreSQL extension that adds a vector data type plus similarity indexes (HNSW and IVFFlat). Embeddings live in the same database as your products, content and customers, so semantic search, recommendations and retrieval-augmented generation become ordinary SQL — joined to business data, wrapped in your existing transactions, backups and access control.
- 01One database for relational and vector data — far less infrastructure to run, secure and keep in sync.
- 02SQL joins between embeddings and business data make retrieval genuinely smart, not just nearest-neighbour guesses.
- 03Embeddings inherit your existing Postgres backups, access control and transactions, so AI data is governed like everything else.
- 04Open source with no per-query licence — it scales further than most products ever need before a dedicated vector store earns its cost.
- 05Runs on managed Postgres you may already use (Supabase, Neon, RDS, Cloud SQL), so adoption is a migration, not a new platform.
pgvector is the open-source extension that turns PostgreSQL into a capable vector database. For teams across Dubai and the wider UAE, that is a quietly powerful idea: the AI features everyone wants — semantic search, recommendations, retrieval-augmented generation — usually need somewhere to store and query embeddings, and the default answer is a brand-new piece of infrastructure. pgvector lets you skip that. Embeddings live in the database you already run, beside the products, content and customers they describe.
Why we keep vectors in Postgres
Three things make this our default. First, one database, not two — no separate vector store to provision, secure, back up or keep in sync, which is exactly where a lot of AI projects quietly rot. Second, SQL joins between embeddings and business data, so a similarity search can filter by stock, price, language or permissions in the same query. Third, governance for free: your AI data inherits the backups, access control and transactions Postgres already gives you.
Where pgvector actually pays off
We reach for pgvector when a feature needs to understand meaning rather than match strings:
Semantic search across products, documentation and content, ranked by intent.
Recommendations and ‘more like this’, computed as similarity joined to live inventory.
The retrieval layer of a RAG pipeline that grounds an LLM in your own knowledge base.
The retrieval half of RAG
Retrieval-augmented generation only works if retrieval is good. pgvector finds the passages that actually answer a question, and a model such as Claude turns them into a grounded reply. If you are thinking about how AI is reshaping discovery, our guide to AI search optimisation covers what changes and what to do about it.
How Karve builds with pgvector
We start small and measurable: an embedding pipeline, an HNSW index, and a hybrid query that blends Postgres full-text search with vector similarity so exact and semantic matches reinforce each other — robust across both English and Arabic. From there we tune recall against real queries and re-index as content grows. pgvector is one piece of a broader practice; to see how we scope, build and run AI features end to end, explore our AI development service.
What it does
Semantic site & catalogue search
Search that understands intent, not just keywords — ranking products, docs and content by meaning, with the speed of HNSW indexes inside your own database.
RAG retrieval layer
The retrieval half of retrieval-augmented generation: pgvector grounds an LLM in your real content so answers cite your knowledge base instead of hallucinating.
Recommendations & similar items
‘More like this’, related articles and personalised product suggestions, computed as vector similarity joined to live inventory and business rules in one query.
Hybrid keyword + vector search
We combine Postgres full-text search with vector similarity so exact matches and semantic matches reinforce each other — robust across English and Arabic queries.
Embedding pipelines & tuning
We wire up embedding generation, chunking and re-indexing, then tune index parameters and recall against real queries so quality holds as your content grows.
About pgvector
Do I really need a dedicated vector database for AI search?
Usually not, at least not at first. pgvector inside your existing PostgreSQL comfortably handles millions of embeddings, and keeping vectors next to your relational data removes a whole class of sync bugs and extra cost. A dedicated vector store earns its keep only at serious scale or with very specialised needs — and because we design the retrieval layer cleanly, you can graduate to one later without rebuilding the application.
When should we use pgvector instead of a tool like Pinecone or Weaviate?
Choose pgvector when you already run Postgres and want your AI features to filter and join against live business data — stock, price, language, permissions — in a single query. Managed stores like Pinecone or Weaviate make sense when you need billions of vectors, ultra-low latency at huge scale, or want vector infrastructure fully outsourced. For most websites and apps the simplicity of one database wins, and we will tell you honestly when it does not.
How does pgvector fit into a RAG pipeline?
pgvector is the retrieval half. We embed your content, store the vectors in Postgres, and at query time fetch the passages most relevant to a question, which a model such as Claude then turns into a grounded answer that cites your own knowledge base. Because retrieval and your business data share one database, you can scope answers to the right tenant, language or product set with ordinary SQL.
What does a pgvector search build cost, and how long does it take?
A focused first use case — semantic search or a RAG retrieval layer — is typically a few weeks from scoping to a working pilot, kept deliberately narrow so we prove value before scaling. pgvector itself is free and open source, so the recurring costs are your database hosting plus usage-based embedding and LLM API fees, which we size and budget up front. There are no per-query vector-store licence fees to surprise you later.
Will pgvector be fast enough as our content grows?
For the great majority of products, yes. pgvector’s HNSW indexes deliver low-latency approximate-nearest-neighbour search over millions of vectors, and we tune index parameters and recall against your real queries rather than benchmarks. We also combine vector and full-text search, and lean on Postgres read replicas and connection pooling when traffic demands it, so performance holds as your catalogue and knowledge base expand.
Does pgvector work for Arabic and English search?
Yes. Vector search works on meaning rather than exact characters, so a multilingual embedding model lets pgvector match Arabic and English queries — even a question in one language against content in the other. We pair it with hybrid keyword search for exact terms and brand names, which matters for bilingual UAE audiences. It is part of how we build AI search end to end in our AI development service.
Where pgvector fits
AI Development & Integration
AI development in Dubai that ships: intelligent search, content-ops automation, recommendations and production assistants built on Claude and OpenAI — measured against cost, not hype.
The service