Glossary · AI & Agents
Vector Database
Overview
How a vector database works
Text, images, or other data are first converted into embeddings—long lists of numbers that capture meaning—by an embedding model. The vector database stores these vectors and indexes them so that, given a new query vector, it can quickly find the stored vectors closest to it in that mathematical space, which correspond to the most similar content.
To stay fast at scale, vector databases use approximate nearest-neighbor algorithms rather than comparing every record. This trades a small amount of accuracy for search that stays quick across millions of items.
Why it matters
Traditional databases match exact values or keywords. A vector database matches meaning, so a search for "cancel my plan" can surface a document titled "ending your subscription" even with no shared words. That capability underlies retrieval-augmented generation, semantic search, recommendations, and duplicate detection.
Options range from dedicated services like Pinecone to the pgvector extension that adds vector search to PostgreSQL. The right choice depends on scale, existing infrastructure, and how tightly it must integrate with your stack.
How we use vector databases
In our RAG development and knowledge-assistant projects, the vector database is where your documents live once embedded. We select and tune the store—Pinecone, pgvector, or another—so retrieval returns the right context, then connect it to the language model. Getting indexing and chunking right here is often the difference between an assistant that feels accurate and one that doesn't.
Where we use it
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How our team puts Vector Database to work in real projects.
FAQ
Vector Database — common questions
Vector database vs traditional database?
A traditional database retrieves rows by exact matches and filters; a vector database retrieves by semantic similarity between embeddings. They're complementary—many applications use a relational database for structured data and a vector database (or pgvector inside it) for meaning-based search.
Do I need a dedicated vector database?
Not always. For smaller datasets, the pgvector extension can add vector search to an existing PostgreSQL database. Dedicated services like Pinecone make sense at larger scale or when you need managed performance and minimal ops overhead.
What are vector databases used for?
Common uses include RAG systems, semantic search, recommendation engines, image and document similarity, and deduplication—anywhere you need to find items by meaning rather than exact keywords.
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