What is Vector Database?
A database optimised for storing and querying high-dimensional vectors (embeddings), enabling similarity search that powers AI applications like semantic search, recommendations, and RAG.
Understanding the Details
Traditional databases find exact matches: give me the row where ID equals 123. Vector databases find similar items: give me the ten things most similar to this query. This similarity search enables AI applications. When you ask a question, the system converts it to a vector embedding, then finds the most similar vectors in the database, which represent the most relevant content. Vector databases like Pinecone, Weaviate, and Pgvector make this similarity search fast even across millions of vectors.
How It Works in Practice
RAG retrieval
A question converts to a vector, and the database returns the five most relevant documentation chunks to include as context for the AI.
Semantic search
Searching for 'compensation strategy' finds documents about salary, pay, remuneration, and benefits, not just exact matches.
Recommendation system
Finding similar products by comparing embedding vectors rather than explicit feature matching.
Why It Matters
AI applications need to find relevant context quickly. Vector databases enable the similarity search that powers RAG, semantic search, and intelligent retrieval at the core of modern AI systems.
What People Often Get Wrong
Any database can store vectors. Actually, general databases lack the specialised indexing for efficient similarity search at scale.
Vector databases replace traditional databases. Actually, they complement traditional databases for specific similarity search use cases.
Vector search is always better than keyword search. Actually, hybrid approaches often perform better than either alone.
How We Handle Vector Database
We implement vector databases as part of RAG architectures, with proper chunking strategies, embedding selection, and retrieval tuning for accurate results.
Related Terms
Common Questions
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