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Vector Databases

Vector databases store embeddings so applications can search by semantic similarity.

What Are Vector Databases​

Embeddings are numeric representations of text, images, audio, or other data.

Vector databases index those embeddings and find nearby items, which is useful for search, recommendations, retrieval-augmented generation, and AI memory.

How To Use Vector Databases​

Choose a vector database or a database with vector search support. Generate embeddings, store them with metadata, and query by similarity.

Basic Example​

const result = await vectorStore.similaritySearch("agent memory design", 5);

for (const item of result) {
  console.log(item.text, item.score);
}

Common Concepts​

  • Embeddings represent meaning as vectors.
  • Similarity search finds nearby vectors.
  • Metadata filters narrow search results.
  • Chunking affects retrieval quality.

What To Learn Next​

Learn embeddings, chunking, metadata design, hybrid search, retrieval evaluation, and RAG pipelines.