Term tag: vector search

Chunking

Chunking splits documents into smaller passages before embedding them for retrieval. Chunk size, overlap, and splitting strategy quietly decide RAG quality.

Embedding

What an embedding vector actually is, why cosine similarity beats Euclidean distance, what dimensions buy you, and where the geometry misleads you.

GraphRAG

GraphRAG runs RAG over a knowledge graph and community summaries built from a corpus, answering global questions that plain vector RAG cannot.

Hybrid Search (RRF)

Hybrid search runs keyword (BM25) and vector retrieval together, then fuses their ranked lists with Reciprocal Rank Fusion (RRF), a score-agnostic method.

Information Retrieval (IR)

Information retrieval ranks documents against a query. How inverted indexes, TF-IDF and BM25 score, and how precision, recall, MAP and nDCG measure it.

Reranking (Cross-Encoder)

Reranking rescores the top-N results from a fast retriever with a slow, accurate cross-encoder that reads the query and document together.

Retrieval-Augmented Generation (RAG)

A technique that retrieves relevant documents at query time and feeds them into a language model's prompt, so it answers from current, external facts.

Semantic Search

Semantic search finds results by meaning, not exact keywords: it embeds the query and documents as vectors and returns the nearest ones.

Vector Database

A vector database stores embeddings with metadata, builds an approximate-nearest-neighbour index over them, and serves filtered similarity queries at scale.

Vector Search

Vector search returns the stored vectors nearest to a query vector by a distance metric — the retrieval primitive under semantic search, RAG and recommenders.