Embedding
ConceptDefinition
A dense numerical vector that represents text, images, or other data in a high-dimensional space where semantic similarity maps to geometric closeness. Foundation of semantic search, RAG systems, and recommendation engines.
Related Terms
Vector Database
A database optimized for storing and querying high-dimensional embedding vectors via approximate nearest neighbor (ANN) search. Core infrastructure for RAG systems. Examples: Pinecone, Weaviate, Qdrant, pgvector (PostgreSQL extension).
RAG (Retrieval-Augmented Generation)
An architecture that enhances LLM outputs by first retrieving relevant documents from a knowledge base (via vector search) and injecting them into the prompt. Grounds the model in external, up-to-date facts without requiring retraining.
Tokenizer
The component that converts raw text into tokens (integer IDs) that the model processes. Most modern LLMs use Byte-Pair Encoding (BPE) or similar subword algorithms. Token count determines cost and fits within the context window limit.