semantic search

Semantic Search is an AI-driven retrieval method: It uses vector embeddings to map user queries and documents into a conceptual space, identifying results based on true intent and meaning, not just exact keyword matches.

This technology moves beyond lexical search by leveraging Natural Language Processing (NLP) and vector databases. The core process involves a Transformer model (e.g., BERT) converting both the query and the document corpus into high-dimensional vectors, or embeddings. The system then calculates the geometric distance between the query vector and all document vectors, using metrics like cosine similarity to rank relevance. This approach ensures that a query like “affordable notebooks under $500” successfully retrieves documents containing “cheap laptops,” a result traditional keyword indexing would often miss. It delivers a 95%+ precision boost over older methods: It finds *meaning* first, then retrieves the most semantically similar content.

https://learn.microsoft.com/en-us/azure/search/semantic-ranking