Miscellaneous
Vector Store
Vector stores (also called vector databases) store embedding vectors alongside metadata and provide fast approximate nearest-neighbor (ANN) search. They are the storage and retrieval backbone of RAG systems, enabling semantic search over millions of documents in milliseconds.
Popular vector stores include Pinecone, Weaviate, Chroma, Qdrant, and pgvector (Postgres extension). Selection criteria include throughput, latency, filtering capabilities, and managed vs. self-hosted trade-offs.
Authority Links
Related Terms
Model Components
Embeddings
Dense numerical vectors that represent text, images, or other content in a high-dimensional space where semantically similar items are geometrically close — the foundational data structure for semantic search and RAG retrieval.
Techniques & Methods
Retrieval Augmented Generation (RAG)
An inference-time architecture that retrieves relevant documents from a knowledge base or web index and injects them into a language model's context before generation, grounding answers in real source material.
Techniques & Methods
Semantic Search
Search technology that retrieves results based on the meaning of a query rather than exact keyword matches — using embeddings to represent queries and documents as vectors and finding nearest neighbors in semantic space.
Model Components
Retrieval Model
Model that finds and returns the most relevant documents or passages from a large corpus given a query.