Semantic search

Semantic search, also known as vector search, is a method of finding information based on the meaning and context of queries rather than just exact keyword matches. Here’s how it works:

Traditional vs Semantic Search

Traditional keyword search looks for exact word matches. If you search for “car,” it only finds documents containing that specific word. Semantic search understands that “automobile,” “vehicle,” or “sedan” are related concepts and can return relevant results even without exact matches.

How Vector Search Works

The process involves several key steps:

Text Embedding: Documents and search queries are converted into high-dimensional numerical vectors (arrays of numbers) using machine learning models. Similar concepts end up with similar vector representations in this mathematical space.

Vector Storage: These embeddings are stored in specialized vector databases that can efficiently handle high-dimensional data.

Similarity Search: When you search, your query is converted to a vector, and the system finds documents with vectors that are mathematically “close” to your query vector, typically using measures like cosine similarity.

Key Advantages

• Context Understanding: Recognizes synonyms, related concepts, and contextual meaning
• Multilingual Capabilities: Can find relevant content across different languages
• Fuzzy Matching: Handles typos, variations, and conceptual similarities
• Intent Recognition: Better understands what users are actually looking for

Common Applications

Vector search powers many modern applications including recommendation systems, document retrieval, chatbots, and search engines. It’s particularly valuable for complex domains where exact keyword matching falls short, like legal documents, scientific literature, or customer support systems.

The technology has become increasingly important with the rise of large language models and AI-powered applications that need to understand and retrieve contextually relevant information.