Vector databases that effectively support multimodal search are designed to handle diverse data types (text, images, audio) by converting them into embeddings and enabling cross-modal queries. These databases must manage high-dimensional vectors while offering flexible indexing, filtering, and querying across modalities. Key examples include Milvus, Pinecone, Qdrant, Elasticsearch, and Weaviate, each providing features tailored to multimodal use cases.
Milvus is a widely used open-source vector database that scales well for multimodal applications. It supports multiple index types (e.g., IVF, HNSW) and allows users to store metadata alongside vectors, making it easier to filter results by data type or other attributes. For instance, a developer could index image embeddings from a ResNet model and text embeddings from BERT in the same database, then perform hybrid queries like finding images similar to a text description. Milvus’s horizontal scaling and distributed architecture also make it suitable for large-scale multimodal datasets. Pinecone, a managed vector database, simplifies multimodal implementations by handling infrastructure while supporting real-time search. It integrates with models like CLIP (which maps text and images to a shared vector space), enabling cross-modal searches without custom pipelines. Developers can index embeddings from different modalities and query them using a unified API, reducing complexity in applications like e-commerce (searching products with images or text).
Qdrant and Weaviate offer additional flexibility. Qdrant provides built-in support for payload data (e.g., tags, geolocation) and allows combining vector similarity with metadata filters, which is useful for refining multimodal results. For example, a travel app could search for landmarks using an image query while filtering by location. Weaviate stands out with its native multimodal capabilities: it can auto-generate vectors for some data types using integrated ML models and supports hybrid keyword-vector searches. Elasticsearch, traditionally a text-search engine, now includes vector search in its 8.x releases. Its strength lies in combining lexical search (BM25) with vector similarity, enabling hybrid multimodal queries—like finding documents that match both keywords and visual patterns. Developers can leverage existing Elasticsearch ecosystems for logging or analytics while adding vector-based multimodal features.
When choosing a database, consider factors like latency, scalability, and integration with AI frameworks. Milvus and Pinecone excel in large-scale scenarios, while Weaviate simplifies setups requiring in-house vectorization. Elasticsearch is ideal for teams already using its stack. All these tools require careful schema design to map multimodal data to vectors and metadata, ensuring efficient cross-modal retrieval.