Text semantic search is a method of finding information by understanding the meaning and context of search queries rather than relying solely on exact keyword matches. Unlike traditional keyword-based search, which looks for literal word matches, semantic search interprets the intent behind the text and identifies conceptually related content. For example, a query like “How do I fix a leaking pipe?” might return results about repairing plumbing, even if the exact phrase “leaking pipe” isn’t present in the documents. This approach uses machine learning models, such as transformers or word embeddings, to map text into numerical vectors that capture semantic relationships. These vectors represent words, phrases, or entire sentences in a high-dimensional space where similar meanings are closer together.
The core of semantic search involves converting text into these numerical representations (embeddings) and measuring their similarity. For instance, sentences like “What’s the weather today?” and “Will it rain this afternoon?” might be mapped to vectors that are near each other because they share the same underlying intent. Tools like BERT or Sentence-BERT are commonly used to generate these embeddings. Once text is vectorized, similarity metrics like cosine similarity compare the query vector to a database of document vectors. Developers often use approximate nearest neighbor libraries (e.g., FAISS) to efficiently search large vector datasets. This process allows systems to retrieve results that are contextually relevant, even when the wording differs significantly from the query.
Practical applications of semantic search include chatbots, recommendation systems, and enterprise search engines. For example, an e-commerce platform could use it to return products related to “comfortable running shoes” even if product descriptions use terms like “cushioned sneakers.” However, implementing semantic search requires balancing accuracy with computational resources. Pre-trained models like Universal Sentence Encoder or OpenAI’s embeddings can reduce development time, but fine-tuning on domain-specific data (e.g., medical texts) often improves performance. Developers must also handle challenges like scaling vector databases and ensuring low-latency responses. A well-designed semantic search system can handle typos, synonyms, and ambiguous queries—like finding “Apple stock prices” when the word “company” isn’t mentioned—by focusing on the underlying meaning rather than surface-level keywords.