Which vector database should I use?

Pinecone for simplicity and managed service. Qdrant or Weaviate for more features/self-hosting. pgvector if you're already using PostgreSQL. Consider: scale, budget, operational preferences, and required features.

How many vectors can a vector database handle?

Modern vector databases scale to billions of vectors. Performance depends on: dimensionality, index type, hardware, and query patterns. Most business applications are well within capacity limits.

Do I need a vector database for RAG?

Not strictly - small datasets can use in-memory search. But vector databases provide: scalability, persistence, filtering, and optimised search. For production RAG, they're strongly recommended.

Can I use a regular database with vector extensions?

Yes - pgvector for PostgreSQL, vector capabilities in MongoDB and Elasticsearch. Good for simpler use cases or when you want to minimise infrastructure. May not match specialised vector DB performance at scale.

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Vector Database

A specialised database optimised for storing and searching vector embeddings. Essential for RAG and semantic search applications.

In-Depth Explanation

A vector database is a specialised data store optimised for storing, indexing, and querying high-dimensional vector data. Unlike traditional databases that search by exact values, vector databases find similar items based on mathematical distance.

Vector database capabilities:

Vector storage: Efficiently store millions/billions of embeddings
Similarity search: Find nearest neighbors quickly
Metadata filtering: Combine semantic with attribute filters
Scalability: Handle growing data volumes
Real-time updates: Add new vectors instantly

Key technologies:

Indexing algorithms: HNSW, IVF, PQ for fast search
Distance metrics: Cosine, dot product, Euclidean
Approximate nearest neighbor: Trade accuracy for speed
Hybrid search: Combine vector and keyword search

Popular vector databases:

Managed: Pinecone, Weaviate Cloud, Qdrant Cloud
Self-hosted: Milvus, Weaviate, Qdrant, Chroma
Extensions: pgvector (PostgreSQL), Elasticsearch vector

Use cases:

RAG knowledge bases
Semantic search engines
Recommendation systems
Image/audio similarity
Anomaly detection

Business Context

Vector databases like Pinecone, Weaviate, and Qdrant enable fast similarity search over millions of embeddings for real-time AI applications.

How Clever Ops Uses This

We implement and optimise vector databases for Australian businesses, choosing the right solution based on scale, budget, and operational requirements.

Example Use Case

"Storing and searching millions of product embeddings for instant recommendations based on what users are viewing."

Frequently Asked Questions

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Understanding Vector Databases for Business

Discover how vector databases enable semantic search, power RAG systems, and revolutionize how AI accesses information. Complete guide to embeddings, similarity search, and choosing the right vector database.

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Vector Velocity