Semantic Search

PLAI utilizes a semantic search algorithm to provide relevant context for the LLM. Semantic search, also known as vector search, goes beyond traditional text-based search algorithms. Its aim is not only to find exact keyword matches but also to understand the meaning of words and the context of a query. Vector search employs vectors to measure semantic similarities between words or documents. Through machine learning techniques, words are transformed into high-dimensional vector spaces. This allows search algorithms to not only look for exact matches but also for words in a similar context or with related meanings. This enables more accurate and contextually relevant search results, especially for complex queries or interpreting user intentions.