Embeddings integrate with full-text systems by adding semantic understanding to traditional keyword-based search. Full-text systems like Elasticsearch or SQL-based solutions typically match exact terms or use scoring algorithms like TF-IDF. Embeddings, which represent text as dense numerical vectors, enable these systems to find documents with similar meanings even when keywords don’t overlap. For example, a search for “automobile” could return results containing “vehicle” or “car” by comparing vector distances rather than relying on literal text matches. This integration bridges the gap between keyword search and semantic relevance, improving result quality without replacing existing full-text features.
Technically, embeddings are stored as vectors alongside the original text data in the full-text system. During indexing, each document’s text is processed through an embedding model (e.g., BERT or Sentence Transformers) to generate its vector representation. When a query is made, the system converts the query text into an embedding and searches for the nearest vectors in the database using similarity metrics like cosine similarity. To optimize performance, vector databases or extensions (e.g., PostgreSQL’s pgvector) use approximate nearest neighbor (ANN) algorithms such as HNSW or IVF. This allows fast retrieval even with large datasets. Hybrid approaches combine traditional keyword scoring (e.g., BM25) with vector similarity scores, letting developers balance precision and semantic relevance. For instance, a search for “python error handling” might prioritize exact keyword matches for debugging guides while also surfacing semantically related posts about “exception management.”
Examples illustrate practical use cases. An e-commerce platform might use embeddings to find products described as “sturdy backpack” when users search for “durable bag,” even if the keyword “durable” isn’t in the product text. Support ticket systems could cluster similar issues using embeddings, reducing duplicate efforts. Tools like Elasticsearch’s dense_vector field type or OpenAI’s API for generating embeddings enable developers to implement this without rebuilding their search infrastructure. By combining embeddings with existing full-text features—like faceted filtering or boosting specific fields—developers create more intuitive search experiences while retaining control over performance and relevance tuning.
