Vector Search
Vector Search maps queries and documents into high-dimensional embeddings and retrieves by cosine distance — the mainstream recall layer in RAG and AI engines, complementary to keyword inverted-index search.
Vector Search maps queries and documents into high-dimensional embeddings and retrieves by cosine distance — the mainstream recall layer in RAG and AI engines, complementary to keyword inverted-index search.
What is Vector Search?
Vector Search maps queries and documents into high-dimensional embeddings and retrieves by cosine distance — the mainstream recall layer in RAG and AI engines, complementary to keyword inverted-index search.
Details and examples
Implication: semantic paraphrases get recalled too, but paragraphs must be self-contained — orphan pronouns and choppy sentences hurt retrieval scores.
Related terms
RAG (Retrieval-Augmented Generation), Chunking, Semantic Search.
Vector Search · term at a glance
| Field | Value |
|---|---|
| Term | Vector Search |
| Category | Technical |
| Related | RAG (Retrieval-Augmented Generation), Chunking, Semantic Search |
Frequently asked questions
What is Vector Search?
Vector Search maps queries and documents into high-dimensional embeddings and retrieves by cosine distance — the mainstream recall layer in RAG and AI engines, complementary to keyword inverted-index search.
References
By the 23SEOGEO team · Last updated: