In the previous article we introduced what embeddings are and how semantic search uses similarity between vectors to find relevant content. Embeddings give us a way to measure meaning in numeric form. What comes next is how we store and search those embeddings efficiently.

A vector database is a database that stores embeddings and makes similarity search practical at scale.

A vector database stores embeddings together with metadata. It answers queries like “which stored items are closest in meaning to this input”. Instead of looking for exact text matches, it compares vectors and returns the most similar ones.

This makes vector databases a core component of retrieval systems used in AI applications such as semantic search, knowledge lookup, and retrieval-augmented generation.

Traditional databases

Traditional databases excel at exact matches and structured queries. They are not designed for high-dimensional vector data where the goal is to measure closeness in meaning rather than equality.

Semantic retrieval requires:

• comparing many vectors quickly

• using distance or similarity metrics

• finding nearest neighbors among millions of items

Vector databases use specialized indexes and algorithms to make this fast.

How vector databases work

The basic loop looks like this:

1. Ingest and embed
Turn your text or other data into embeddings using a model. Store the vectors and any metadata.

2. Index for similarity
Build an index optimized for nearest neighbor queries in many dimensions. This avoids comparing every vector on every query.

3. Query and compare
Convert the user query into an embedding with the same model. Search for the stored vectors that are most similar by a distance measure such as cosine similarity.

4. Return results
Fetch the content linked to the best matching vectors so your application can use them.

This flow lets you find the most relevant content by meaning rather than by exact text.

Usecases

Vector databases start to matter whenever you rely on embedding similarity for retrieval. Common usecases include:

• semantic document search

• RAG (retrieval-augmented generation) workflows

• long-term memory storage for agents

• similarity-based recommendations

If your system only needs exact matches or structured fields, a traditional database may still be the right choice. Vector databases become important once embeddings are the primary retrieval signal.

Looking Ahead

Even though vector similarity is a powerful concept, it's unfortunately not perfect. Pure vector search can (and in practice often does) miss exact textual matches such as:

• matching specific codes or identifiers

• finding proper nouns

• matching on exact phrases that are critical for some queries

Because of this, production retrieval systems often combine multiple methods of search and retrieval to improve relevance and recall.

This leads us directly into the topics of the next article: hybrid search and reranking, where we will discuss blending vector and keyword methods and reorder results based on deeper evaluation.