What is Vector Database?

What is the common confusion about Vector Database?

A vector database stores and searches vectors. It does not create high-quality embeddings or guarantee relevant retrieval.

Learn Vector Database in the course

Lessons that name Vector Database in a title or section

• Embeddings & Vector Representations

  Text is discrete. Math is continuous. Every time you ask an LLM to find "similar" documents, compare meanings, or search beyond keywords, you're relying on a bridge between these two worlds.

  Phase 11: LLM Engineering

• RAG (Retrieval-Augmented Generation)

  Your LLM knows everything up to its training cutoff. It knows nothing about your company's docs, your codebase, or last week's meeting notes.

  Phase 11: LLM Engineering

Covered in Phase 11: LLM Engineering.

Related terms

• EmbeddingA learned mapping from discrete items (words, images, users) to dense vectors in continuous space, where similar items end up close together
• Semantic SearchRetrieval that represents a query and candidates in an embedding space and ranks candidates using a vector-similarity function.
• Hybrid RetrievalRetrieval that combines signals from different methods, commonly lexical matching and dense-vector similarity, before merging or reranking…
• Approximate Nearest Neighbor (ANN)A search method that returns vectors likely to be among the nearest to a query without exhaustively comparing the query with every stored…
• HNSWAn approximate-nearest-neighbor index that organizes vectors in layered proximity graphs and searches from coarse upper layers toward…

More terms in Retrieval & generation

• Approximate Nearest Neighbor (ANN)
• BM25
• Chunking
• Dense Retrieval
• Grounding
• HNSW
• Hybrid Retrieval
• Maximum Marginal Relevance (MMR)
• RAG (Retrieval-Augmented Generation)
• Recall@K
• Reciprocal Rank Fusion (RRF)
• Reranker
• Semantic Search

Open the Retrieval & generation list in the glossary