Demystifying Vector Databases: A Practical Guide
Vector databases are everywhere in AI applications, but the terminology can be overwhelming. Here's a practical, no-nonsense guide.
Demystifying Vector Databases: A Practical Guide
If you're building anything with LLMs, you've probably heard about vector databases. Let's cut through the jargon.
What's a Vector, Anyway?
In this context, a vector is just a list of numbers that represents the meaning of a piece of text.
When Do You Actually Need a Vector DB?
You need one when:
- Building RAG systems
- Implementing semantic search
- Finding similar items
You probably don't need one when:
- Your dataset is small (< 10k items)
- You only need keyword search
My Recommendation
Start with ChromaDB for prototyping, graduate to pgvector if you're already using PostgreSQL.