Haystack handles non-textual data by first converting it into searchable text representations or numerical embeddings, enabling compatibility with its text-centric architecture. For data like images, audio, or PDFs, Haystack relies on preprocessing steps and machine learning models to extract meaningful features or metadata. These transformed representations are then treated as “documents” within Haystack’s framework, allowing them to be indexed, stored, and queried using the same pipeline components as textual data.
The framework supports non-textual data through specialized document types and integrations with machine learning models. For example, Haystack’s Document object can store binary files (e.g., images, audio) and their metadata, while preprocessing tools like OCR (Optical Character Recognition) extract text from scanned PDFs. For audio, speech-to-text models like Whisper can transcribe speech into searchable text. For images or video, pre-trained vision models like CLIP generate vector embeddings that capture visual semantics. These embeddings are stored in vector databases (e.g., FAISS, Milvus) integrated with Haystack, enabling similarity searches. Developers can chain these steps into pipelines using Haystack’s Pipeline class, combining preprocessing, embedding generation, and retrieval in a unified workflow.
Customization is key: Haystack allows developers to plug in their own models or third-party APIs for specific data types. For instance, a medical imaging pipeline might use a fine-tuned ResNet model to generate embeddings from X-rays, while a podcast search system could combine speech recognition with keyword extraction. Haystack’s flexibility with metadata filtering also lets users combine textual and non-textual criteria—like searching for images tagged with “sunset” and filtered by creation date. While Haystack doesn’t natively process raw non-textual data, its extensible design ensures developers can integrate domain-specific tools without rebuilding entire search infrastructures.