Can Haystack be used for multi-modal search (e.g., text, images)?

Yes, Haystack can be used for multi-modal search, including text and images, though it requires careful integration with additional tools and customization. Haystack is primarily designed as a framework for building search systems focused on natural language processing (NLP), but its modular architecture allows developers to extend its capabilities to handle other data types like images. By combining Haystack with dedicated models for image processing (e.g., CLIP, ResNet) and vector databases, developers can create pipelines that index and retrieve both text and visual content.

To enable image search, one approach is to use pre-trained models to convert images into vector embeddings, which can then be stored and queried alongside text embeddings. For example, a CLIP model could generate embeddings for both text and images in a shared vector space, allowing users to search images using text queries (or vice versa). Haystack’s EmbeddingRetriever component can work with these embeddings, and vector databases like FAISS or Milvus can handle similarity searches. Text and image data would need to be preprocessed into a unified format, such as storing image paths alongside their embeddings in Haystack’s document stores (e.g., Elasticsearch or Weaviate). Developers can then build hybrid pipelines that route queries to the appropriate retriever based on the input type.

A practical example might involve a product catalog search system. Text descriptions of products could be indexed alongside images of those products. A user query like “red sneakers” would generate a text embedding via CLIP, which is compared against both text and image embeddings. Results could include relevant text snippets and images of red sneakers. However, this requires writing custom nodes in Haystack to handle image preprocessing and ensuring the document store supports multi-modal data. While Haystack doesn’t provide native image processing tools, its flexibility allows developers to plug in external libraries (e.g., OpenCV, PyTorch) to fill these gaps. This approach balances Haystack’s NLP strengths with multi-modal extensions, though it demands familiarity with both NLP and computer vision workflows.