Vector Search
Also known as: Semantic Search, Similarity Search, Nearest Neighbor Search, ANN Search
Definition
Finding items similar to a query by comparing their vector representations (embeddings) in a high-dimensional space. Unlike keyword search which matches exact terms, vector search captures semantic similarity—queries and documents with similar meanings have similar vectors, even without shared words. This enables "meaning-based" retrieval.
What this is NOT
- Not keyword search (vector search doesn't require exact word matches)
- Not the embeddings themselves (vector search is the retrieval process)
- Not always better than keyword search (sparse retrieval wins for exact matches)
Alternative Interpretations
Different communities use this term differently:
llm-practitioners
Using embedding models to convert text to vectors, storing them in a vector database, and retrieving documents whose vectors are closest to the query vector (typically by cosine similarity or dot product).
Sources: Pinecone documentation, Weaviate, Qdrant, Chroma documentation, OpenAI embeddings guide
information-retrieval
Approximate nearest neighbor (ANN) search in high-dimensional spaces, using algorithms like HNSW, IVF, or product quantization to make search tractable at scale.
Sources: HNSW paper (Malkov & Yashunin, 2018), Faiss documentation
Examples
- Searching a documentation site by meaning rather than keywords
- Finding similar products based on description embeddings
- Retrieving relevant code snippets from a codebase
- Querying a vector database like Pinecone or Weaviate
Counterexamples
Things that might seem like Vector Search but are not:
- Full-text search matching exact keywords (that's keyword/BM25 search)
- SQL queries with WHERE clauses
- Regular expression matching
Relations
- requires embedding (Vector search operates on embeddings)
- overlapsWith retrieval-augmented-generation (RAG often uses vector search for retrieval)
- overlapsWith hybrid-search (Hybrid search combines vector and keyword search)
- inTensionWith hybrid-search (Pure vector search vs hybrid approaches)
Implementations
Tools and frameworks that implement this concept: