Vector search is generally not the best fit for most structured data tasks, but it can be useful in specific scenarios. Structured data, like databases with clearly defined columns (e.g., customer IDs, dates, or product prices), is typically queried using exact matches, ranges, or joins—operations that relational databases or NoSQL systems handle efficiently. Vector search excels at similarity-based queries in unstructured data, such as finding images or text with similar semantic meaning. However, there are cases where structured data benefits from vector techniques, especially when relationships between data points are complex or require similarity-based analysis beyond exact matches.
One example where vector search adds value to structured data is when combining numerical or categorical features to create semantic similarity. For instance, in e-commerce, a product database might include structured attributes like price, size, and category. By converting these into a vector (e.g., [price=50, size=12, category=5]), vector search could find products with similar combinations of features, even if they don’t share exact values. Another use case is time-series data: sensor readings stored in a structured format can be vectorized to detect patterns or anomalies. For example, a sequence of temperature and pressure values could be embedded as a vector, allowing a search for similar historical patterns to predict equipment failures.
However, vector search has limitations for purely structured data. Traditional databases outperform vector systems for precise queries like filtering orders by date range or calculating revenue totals. Joining tables or enforcing transactional consistency (e.g., banking systems) also requires relational databases. Additionally, maintaining vector embeddings for structured data adds complexity. For example, converting a customer’s age, location, and purchase history into a vector might not capture meaningful relationships if those features aren’t normalized or weighted appropriately. In such cases, a hybrid approach—using a relational database for exact queries and vector search for similarity-based tasks—often works better. For instance, a recommendation system could first filter products by category (structured query) and then use vector search to rank them by similarity to a user’s preferences.