Semantic Search and Vector Database
Why do we need Vector DB? Let’s start with a common scenario: You’re searching for “affordable laptop with a good battery life” on an e-commerce site. A keyword-based search engine might return results that include the words “affordable,” “laptop,” and “battery life.” But it might also miss products that are described as “budget-friendly” or “long-lasting battery,” simply because they don’t match the exact keywords. The result? A frustrating search experience where you might not find what you’re really looking for, even though the perfect product is just a few clicks away. Keyword-based search systems struggle with: Synonyms : Different words can mean the same thing (e.g., “cheap” and “affordable”). Polysemy : The same word can have multiple meanings depending on context (e.g., “bank” as in a financial institution vs. “bank” as in the side of a river). Contextual Understanding : Words and phrases can have different meanings based on the context in which they’re used. These limitations ...