AI & Data
Vector Database
A vector database is a database designed to store and search embeddings, which are numerical representations of text, images, or other data, enabling fast similarity search across millions of items.
What vector databases are used for
Traditional databases find exact matches; vector databases find similar meaning. Content is first converted into embeddings, high-dimensional vectors where semantically similar items sit close together. The database indexes these vectors, typically with approximate nearest neighbor algorithms, so a query like "refund policy for damaged goods" retrieves relevant passages even when the documents use different wording. Options include dedicated engines such as Pinecone, Qdrant, Weaviate, and Milvus, and vector extensions to general databases such as pgvector for PostgreSQL. They power semantic search, recommendations, deduplication, and the retrieval step in RAG systems.
Why it matters for business software
Vector search is the backbone of grounding AI in company data. When a chatbot or agent must answer from your documentation, contracts, or product catalog, a vector database is what finds the right context to feed the model. Choice of database affects latency, cost, and operations: many teams start with pgvector inside an existing PostgreSQL setup and move to a dedicated engine only when scale demands it. Metadata filtering, access control, and keeping the index in sync with source data are the practical concerns that determine whether retrieval quality holds up in production.
How Wizcoder AI Labs uses it
Vector databases sit inside nearly every retrieval system we build. We design ingestion pipelines, choose between pgvector and dedicated engines based on scale, and tune retrieval quality as part of AI development services and AI chatbot projects grounded in client data.
Related terms
Where we use Vector Database
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