Vector embeddings enable semantic search by translating text into numerical representations that capture meaning. When you create a vector embedding, you convert words, phrases, or entire documents into high-dimensional vectors (arrays of numbers). These vectors are structured so that similar concepts—like “car” and "vehicle"—end up closer to each other in the vector space than unrelated terms like “car” and “banana.” For example, using a model like Word2Vec or BERT, the word “king” might be represented as a 300-dimensional vector, and “queen” would be nearby in that space, reflecting their semantic relationship. This mathematical representation allows search systems to compare content based on meaning rather than exact keyword matches.
In semantic search, the process typically involves two steps: encoding and similarity calculation. First, a query and a set of documents (or database entries) are converted into embeddings using the same model. For instance, if a user searches for “healthy dinner ideas,” the system generates a vector for that query. Every document in the database (e.g., recipes, articles) is also precomputed into vectors. The system then calculates the similarity between the query vector and all document vectors using metrics like cosine similarity or dot product. Documents with vectors closest to the query vector are ranked highest. For example, a recipe titled “nutritious meal prep for busy nights” might match the query even if it doesn’t contain the exact words “healthy” or “dinner,” because their embeddings share semantic traits like nutrition and time efficiency.
Developers implementing semantic search need to consider factors like model selection, dimensionality, and indexing. Pretrained models (e.g., Sentence-BERT, Universal Sentence Encoder) are common starting points, but fine-tuning on domain-specific data (e.g., medical texts) can improve accuracy. High-dimensional vectors (e.g., 768 dimensions with BERT) capture nuance but require efficient storage and retrieval. Tools like FAISS or Annoy index vectors for fast approximate nearest neighbor searches, balancing speed and precision. For example, a job search platform might use FAISS to index thousands of job postings as vectors, enabling real-time matches to a candidate’s resume embedding. However, trade-offs exist: larger models offer better accuracy but increase latency, while dimensionality reduction techniques (like PCA) can speed up searches at the cost of semantic detail. Properly tuning these components ensures the system delivers relevant results without excessive computational overhead.