Designing a multimodal vector database involves creating a system that can store, index, and retrieve data from multiple modalities (text, images, audio, etc.) using vector embeddings. The core challenge is unifying different data types into a shared vector space while maintaining efficient query performance. This requires careful planning of data ingestion, embedding generation, indexing strategies, and query handling to ensure scalability and accuracy across modalities.
First, define a unified data model and ingestion pipeline. Each modality (e.g., images, text) needs a dedicated encoder—like CLIP for image-text pairs or Whisper for audio—to convert raw data into vectors. For example, images might be processed using a ResNet model, while text could use BERT embeddings. These vectors should be normalized (e.g., L2-normalized) to ensure consistent similarity calculations. Metadata (like timestamps or source identifiers) should be stored alongside vectors to enable filtering during queries. Tools like Apache Parquet or JSON-based storage can organize this data, with a schema that accommodates modality-specific fields. For scalability, batch processing frameworks like Apache Spark can parallelize embedding generation for large datasets.
Next, implement hybrid indexing strategies for efficient multimodal retrieval. A single index (e.g., HNSW or IVF) might suffice if all modalities share the same embedding space (as with CLIP), but separate indexes are often needed for modality-specific optimizations. For cross-modal queries—like searching images using a text prompt—ensure encoders produce compatible embeddings. For instance, CLIP maps both text and images to the same space, enabling direct similarity comparisons. Use approximate nearest neighbor (ANN) libraries like FAISS or Milvus to handle high-dimensional vectors, and consider hierarchical indexes: HNSW for low-latency searches and IVF for scalability. For composite queries (e.g., “find videos with upbeat audio and sunny scenes”), build a pipeline that separately queries audio and visual indexes, then merges results using reciprocal rank fusion or weighted scoring.
Finally, address synchronization and scalability. Use distributed storage (e.g., Amazon S3, Cassandra) and shard data by modality or user to balance load. Implement versioning for embeddings to handle model updates—for example, when upgrading from ResNet-50 to ResNet-101, keep both embedding versions temporarily to avoid invalidating existing indexes. For real-time updates, use a log-based system (like Kafka) to stream new data to embedding workers and update indexes incrementally. Expose a REST/gRPC API with endpoints for embedding insertion, deletion, and multimodal querying (e.g., /search?modality=image&text_query="red car"). Monitor performance with metrics like recall@k and latency percentiles, and optimize hot paths—such as caching frequent queries or pre-filtering metadata before ANN searches. Tools like Prometheus and Grafana can track these metrics, while load testers like Locust simulate multimodal query loads.