Handling multi-modal data (text + images) in a vector database requires a strategy that integrates both data types while maintaining efficient storage, retrieval, and query performance. The core challenge lies in representing text and images as vectors in a way that preserves their semantic relationships and enables cross-modal search. Here’s a structured approach to tackle this:
First, process each modality separately using specialized models. For text, transformer-based models like BERT or sentence-transformers convert sentences into dense vectors that capture semantic meaning. For images, convolutional neural networks (CNNs) like ResNet or vision transformers (ViTs) extract feature vectors representing visual content. For example, you might use CLIP (Contrastive Language-Image Pretraining), which aligns text and images in a shared vector space, allowing direct comparison between the two. This alignment simplifies tasks like searching for images using text queries or vice versa. Ensure both modalities produce vectors of compatible dimensions (e.g., 512-dimensional vectors for CLIP) to enable unified storage and retrieval.
Next, design your database schema to store multi-modal embeddings. Many vector databases (e.g., Pinecone, Milvus, or Weaviate) support storing multiple vectors per record. Assign each data entry (e.g., a product listing with an image and description) a unique identifier and store its text and image vectors in separate fields. For example, a product database might include fields like product_id, text_embedding, and image_embedding. If your database supports it, use metadata filtering to associate additional context (e.g., product category or tags) with embeddings. This allows queries to combine semantic similarity with structured filters, such as “find red dresses similar to this image.”
Finally, implement cross-modal search logic. When querying, generate embeddings for the input (text or image) using the same models used for storage, then perform a nearest-neighbor search across the relevant vector field. For hybrid queries (e.g., “find items that match this image and are under $50”), combine vector similarity scores with metadata filters. If your use case requires joint text-image similarity, consider fusing the vectors (e.g., averaging text and image embeddings for a product) or using a database that supports multi-index lookups. For instance, a recipe app might let users search for dishes using a photo of ingredients (image vector search) and a text query like “vegetarian” (metadata filter), returning results sorted by relevance.
By focusing on modality-specific processing, unified storage, and flexible query design, you can build a system that leverages the strengths of both text and image data while maintaining scalability and performance.