Vector Search Isn't the Answer to Everything. So What Is? A Technical Deep Dive

The blog post by Jacky Liang discusses the limitations and potential of vector search in AI applications, arguing that while vector search has its uses, it is not a one-size-fits-all solution. Hybrid search, which combines vector search with other methods like full-text and exact search, is proposed as a more effective approach for achieving relevance in search results. The article details how to implement a hybrid search engine using PostgreSQL and pgvector, emphasizing the importance of reranking to ensure the most relevant results are prioritized. The effectiveness of hybrid search is demonstrated through test cases that show improved precision and recall compared to vector-only searches. The post also highlights emerging trends such as agentic search and context engineering, which focus on providing AI systems with the context they need to perform effectively. Liang underscores the importance of choosing the right tool for the right job, suggesting that hybrid search can address the shortcomings of single-method searches and improve user satisfaction.