Yes, multimodal queries that combine vector data with metadata can be supported in systems designed to handle hybrid search scenarios. This approach merges the strengths of vector-based similarity search (common in AI-driven applications) with structured metadata filtering (typical in databases). For example, a system might use a vector to represent semantic content (like an image or text embedding) and combine it with metadata fields like timestamps, categories, or user tags. This allows queries such as, “Find images similar to this one that were tagged as ‘outdoor’ and created after 2022,” where the vector captures visual similarity and the metadata narrows results by specific attributes.
A practical implementation might involve a database or search engine that indexes both vectors and metadata. For instance, in an e-commerce application, product images could be encoded as vectors to capture visual features, while metadata like price range, brand, or availability is stored separately. A query could retrieve products visually similar to a reference image (vector search) but filtered to only include items under $50 and in stock (metadata). Systems like Elasticsearch with plugins for vector search, or dedicated vector databases like Pinecone or Milvus, enable this by allowing joint queries—first applying metadata filters to reduce the dataset, then performing a vector similarity search on the filtered subset. This two-step process balances precision (via metadata) with semantic relevance (via vectors).
Developers need to consider how to structure indexes and optimize queries. For example, metadata filters should be applied early in the pipeline to limit the number of vectors compared, improving performance. Tools like FAISS for vector search can be integrated with traditional databases (e.g., PostgreSQL) using custom logic to handle hybrid queries. Challenges include ensuring low latency when combining operations and designing scoring mechanisms that weigh both vector similarity and metadata matches. For instance, a weighted score might prioritize metadata matches (e.g., “in stock”) over slight differences in vector similarity. Proper indexing strategies, such as composite indexes for metadata and efficient vector storage, are critical for scalability.