Do I need a vector database if my knowledge base is small?

Not necessarily.

If your knowledge base is small, a full vector database is often overkill. A simpler setup may be enough:

• Keyword search / full-text search if your documents are short and terms are well-defined
• In-memory embeddings + linear similarity search if you have a small number of items
• A regular database with embeddings stored as columns if you want something simple but a bit more scalable

Rule of thumb

• Dozens to a few thousand chunks: you probably don’t need a dedicated vector DB
• Tens of thousands and up: a vector database starts to make more sense
• Need metadata filtering, hybrid search, or fast scale: vector DB becomes more useful earlier

What matters more than size

You may still want vector search if:

• users ask questions in different wording than the source text
• semantic matching is important
• you need retrieval beyond exact keywords

Good lightweight alternatives

• FAISS or Annoy locally
• pgvector in Postgres
• SQLite + embeddings for very small prototypes
• Elasticsearch/OpenSearch if you already use them for text search

Bottom line

If your KB is small, start simple. Add a vector database only when:

• retrieval quality is poor with simpler methods
• latency or scale becomes an issue
• you need advanced filtering or hybrid retrieval

If you want, I can help you choose the simplest architecture for your specific KB size and use case.