From TF-IDF to Transformers: Implementing Four Generations of Semantic Search

Semantic search has evolved significantly, progressing from early, interpretable methods to today's sophisticated deep learning models. Initial techniques relied on TF-IDF to measure word frequencies and combined this with handcrafted rules to rank documents by relevance. A major breakthrough came with the use of dense embeddings from models like Sentence Transformers, which capture the actual semantic meaning and context of text rather than just keywords. The most advanced systems now fine-tune large transformer architectures like BERT to learn the specific nuances of semantic relationships directly from data for a given task. This journey highlights a fundamental shift in AI, moving from transparent, human-designed rules to powerful models that learn abstract meaning on their own.