Vector Database

A practical example

Example: a support agent embeds your product documentation into a vector database.

When a customer asks about invoice errors, the system retrieves the three most semantically similar help articles and feeds them to the model as context.

What to evaluate before investing

• Ask how the vendor handles embedding model changes: can you re-embed data without downtime or full re-ingestion?
• Test hybrid search support, combining vector similarity with keyword filters, since pure semantic retrieval often misses product codes or names.
• Verify metadata filtering performance at your expected scale, because filtered vector queries can degrade significantly with millions of records.

Limitations and tradeoffs

A vector database only returns similar content; it cannot judge accuracy. Poor source documents produce confident but wrong agent answers, so retrieval quality depends heavily on your content hygiene.