Add AI search to existing application

This article provides a practical guide on integrating semantic search into an existing application using embedding models and PostgreSQL with the pgvector extension. The author demonstrates that adding AI-powered search is a straightforward process: adding a vector column to the database, converting text into numerical embeddings via an API like OpenAI, and performing similarity searches using cosine distance. The guide emphasizes that this approach complements, rather than replaces, traditional keyword search, allowing for a hybrid solution that captures both exact matches and conceptual meaning. The author also covers essential implementation details, such as handling background processing for embeddings, managing data backfills, and considering the cost implications of token usage. By leveraging pgvector, developers can implement sophisticated search capabilities with minimal infrastructure changes, making it an accessible upgrade for standard CRUD applications.