Yes, you can combine product metadata filters with vector search to create more precise and efficient search systems. Vector search uses machine learning models to find semantically similar items based on their embeddings (numerical representations), while metadata filters apply structured conditions like price ranges, categories, or dates. Combining both allows you to narrow down results using hard constraints while still leveraging the flexibility of semantic similarity. For example, an e-commerce app could use vector search to find “shoes similar to product X” but filter results to only include items under $100 or in a specific size range.
Implementing this typically involves two steps. First, apply metadata filters to reduce the dataset to a relevant subset. For instance, filter products by category="electronics" and price < $500. Then, perform vector search on this subset to find items semantically related to a query, like “noise-canceling headphones.” Some databases, like Pinecone or Milvus, support hybrid queries where metadata filters and vector search are executed in a single request, optimizing performance. Alternatively, you could post-filter vector search results, but this risks excluding relevant matches that don’t meet metadata criteria, so pre-filtering is often preferred for efficiency.
Key considerations include balancing performance and accuracy. Pre-filtering too aggressively (e.g., using strict metadata ranges) might exclude valid vector matches, while overly broad filters could negate the benefits of combining the approaches. Testing is critical: measure recall (how many relevant items are found) and latency for different filter combinations. Tools like Elasticsearch with k-NN plugins or PostgreSQL’s pgvector extension also support this hybrid approach, letting you add metadata conditions in SQL-like syntax alongside vector distance calculations. This combination is especially useful in applications like recommendation systems, where business rules (e.g., regional availability) must coexist with personalized semantic matching.