How to Implement Semantic Search in Your Database

Traditional keyword search is dying. Users expect to find information based on intent and meaning. Adding "Semantic Search" to your existing database is easier than you think, especially with tools like `pgvector` for PostgreSQL.

Storing Vector Embeddings

The first step is to add a `vector` column to your table. You then use an embedding model (like those from OpenAI or Cohere) to generate a high-dimensional vector for each row of text in your database. These vectors are then stored in your new column, representing the "meaning" of the data in a mathematical space.

Querying with Similarity

When a user searches, you vectorize their query and then use a "cosine similarity" or "Euclidean distance" query to find the rows whose vectors are "closest" to the query vector. This allows your search to find "fruit" when a user types "apple," even if the word "fruit" never appears in the document, providing a vastly superior user experience.