A multimodal search system typically combines data from different sources—like text, images, audio, or video—and enables users to search across these modalities using any type of input. The architecture generally involves three core stages: data ingestion and processing, embedding generation and storage, and query handling with cross-modal retrieval. Each stage is designed to handle diverse data types, convert them into comparable formats, and efficiently retrieve results that match the user’s intent, even when queries and stored data differ in modality.
In the first stage, data ingestion and processing, the system ingests raw data from multiple sources and preprocesses it for consistency. For example, text might be cleaned and tokenized, images resized and normalized, audio split into clips, and video frames extracted. Each modality is then passed through specialized models to extract features. Text could use transformer-based models like BERT, images might rely on CNNs like ResNet, and audio could leverage spectrogram-based models like VGGish. These models convert raw data into numerical representations (vectors) that capture semantic or perceptual features. For scalability, this stage often employs parallel processing pipelines—one per modality—to handle large datasets efficiently. Storage systems like object stores (e.g., AWS S3) or databases (e.g., PostgreSQL) are used to retain raw data and metadata for later retrieval.
The second stage focuses on embedding generation and storage. Here, the feature vectors from the first stage are transformed into embeddings—compact, dense representations that enable cross-modal comparison. For instance, CLIP (Contrastive Language-Image Pretraining) maps text and images into a shared embedding space, allowing text queries to match relevant images. These embeddings are indexed in specialized vector databases like FAISS, Milvus, or Elasticsearch with vector support, optimized for fast similarity searches. Indexing strategies, such as hierarchical navigable small worlds (HNSW), balance speed and accuracy. To handle multimodal data, embeddings from different modalities may be stored in separate indexes or combined into a unified index if they share a common embedding space. This stage ensures that regardless of the input type, all data is searchable through vector similarity.
The final stage, query handling and retrieval, processes user inputs (e.g., a text query or uploaded image) and returns cross-modal results. The query is converted into an embedding using the same models applied during ingestion. For example, a user searching with an image would have that image processed through a CNN to generate an embedding, which is then compared against stored embeddings. The system retrieves the nearest neighbors using similarity metrics like cosine distance. Post-processing steps, such as re-ranking results with a secondary model or applying filters (e.g., date ranges), refine the output. A real-world example is a shopping app where users take a photo of a chair, and the system returns similar chairs from a product database by comparing image embeddings. APIs or SDKs (e.g., TensorFlow Serving) typically wrap these components to handle requests and serve results in real time. This architecture ensures flexibility across modalities while maintaining scalability and performance.