Difference between Vector Search and Keyword Search (BM25)

How keyword search (BM25) works

BM25 is a classic lexical model: you tokenize the query and the documents, count term frequencies, and score using IDF so that rare terms matter more. It’s fast, interpretable, and works well when the user’s wording matches the corpus (e.g. product names, codes, exact phrases). It fails when wording diverges—synonyms, paraphrases, or different languages—because there’s no match. So “affordable laptop” won’t match “cheap notebook” unless you add synonyms or expand the query. Keyword search is term-based: “contains these words.”

BM25 has tunable parameters (e.g. k1, b) that control term frequency saturation and length normalization. It requires no training and integrates easily with inverted indexes and full-text engines. For many legal, e-commerce, or document-heavy workloads, keyword search remains the backbone and is often combined with filters and facets.

How vector search works

Vector search uses vectors from an embedding model: the query and each document are turned into vectors, and you return the nearest neighbors (e.g. by cosine similarity). That captures meaning, so “car” can match “automobile” and “vehicle,” and “affordable laptop” can match “cheap notebook” even with no shared words. Vector search is meaning-based: “similar in the latent space.” It requires a vector database (or vector index) and an embedding model; latency and recall depend on the index (e.g. HNSW).

The embedding model is fixed at index time: all documents and later queries must use the same model so that distances are comparable. Vector search doesn’t explain why a document matched (no highlighted terms); it only returns a similarity score, which can be an advantage for privacy or a drawback for explainability.

When to use which

Use keyword when: exact terms matter (legal, medical codes, product IDs), you need explainability (“matched because of these words”), or you have no embedding pipeline. Use vector when: you care about semantic similarity, paraphrases, or cross-lingual or multimodal search. In practice, many systems use both: hybrid search runs vector and keyword (BM25) and merges the results (e.g. with reciprocal rank fusion or weighted combination) so you get precise term matches plus semantic recall. Sparse vectors can also represent lexical signals; some vector DBs support dense + sparse in one index.

Hybrid is especially useful when the user query mixes precise terms (e.g. “iPhone 15”) with semantic intent (e.g. “good battery life”). BM25 can nail the product name while vector search finds passages about battery performance. Fusion methods (RRF or weighted sum) combine the two rank lists into a single result set.

Comparison at a glance

Keyword = term match, fast and interpretable, no embedding needed; vector = meaning match, needs embeddings and a vector database. For RAG and chatbots, vector (or hybrid) is usually used so that user questions retrieve relevant passages by meaning. For faceted product search or exact lookup, keyword (or metadata filters) often complements vector. See also full-text search integration within VDBs.