How to Implement Vector Search

Beyond keyword search - the vector search architecture that finds meaning not just keywords.

Vector search powers semantic search, recommendation engines, duplicate detection, and RAG pipelines. Implemented naively - brute-force cosine similarity over millions of vectors - it does not scale. This guide covers the approximate nearest neighbor algorithms, vector database selection, and indexing strategies that make vector search production-ready.

No fluff. Production-grade answers from engineers who ship AI into real products.

The Embedding Model Choice That Determines Everything Downstream

The embedding model sets the quality ceiling for your vector search. Every downstream component operates on the representations the embedding model produces. Getting this wrong is expensive to fix later. The practical choice: OpenAI text-embedding-3-large for general-purpose quality, text-embedding-3-small for cost-sensitive high-volume, or E5-large/GTE for self-hosted open-source. Always benchmark on a sample of your actual data before committing.

At Valletta Software, we focus on:

Embedding model: benchmark text-embedding-3-large vs open-source on your domain before committing

Dimensionality: higher dimensions better quality higher cost - text-embedding-3-large supports Matryoshka (reduce to 256)

Normalization: L2-normalize all vectors before storage - required for cosine similarity via dot product

Indexing: HNSW algorithm for ANN - balance recall and query latency with ef and m parameters

Batch processing: embed in batches of 100-500 - not one at a time rate limits and throughput matter

Incremental updates: upsert not rebuild - vector databases support online updates without full reindex

Metadata: store filterable fields alongside vectors - pre-filter before ANN search reduces latency

Vector Database Selection: pgvector vs Pinecone vs Weaviate vs Qdrant

No single right answer. The choice depends on hosting preference query patterns and scale.

We give you more than just people. We give you top performers who drive results.

pgvector: PostgreSQL extension - best if you are already on Postgres no new infrastructure up to ~1M vectors

Pinecone: fully managed serverless - best for teams that want zero infra slightly higher latency at low scale

Qdrant: open-source Docker-deployable - best performance/cost for self-hosted EU deployments

Weaviate: open-source with GraphQL API - best for hybrid search and rich metadata filtering

Chroma: development and small-scale - not production-ready at millions of vectors

Milvus: enterprise scale 100M+ vectors - complexity only worth it at true enterprise scale

Test with real data: run latency and recall benchmarks on your actual vectors before production commit

Your team, your direction. We handle the rest. Rates from EUR 45/h.

EU-incorporated in Malta - NDA on day one, full GDPR compliance. Trusted by startups and enterprise teams across 12+ industries.

View AI Solutions

Build RAG pipelines, agents, and LLM integrations from day one

Ship AI features 3x faster with AI-native tooling and methodology

Deploy to production - not just Jupyter notebooks and prototypes

Evaluate output quality - hallucination detection, cost optimization, monitoring

How to Implement Vector Search - With Engineers Who Run It in Production

Forget the hype. We make AI work in the real world.

Our engineers are trained in the latest AI tooling - Copilot, Claude Code, Cursor, LangChain, and vector databases - and use them daily to ship production AI features, not just prototypes.

Choose from a solo dev, mini team, or full squad. All powered by AI and ready to build from day one.

Lets keep it simple.

Our AI engineers benchmark embedding models on your specific domain implement HNSW-based ANN with metadata pre-filtering and set up incremental upsert pipelines.

How we work