Yes, Haystack can be used for multi-modal search, which involves handling and retrieving information from different types of data, such as text and images. Haystack is designed to be flexible and can integrate with various backends and tools, allowing developers to build search systems that can process and return results across different media types. For instance, if you have a collection of documents along with images related to those documents, Haystack can help you create a search interface that retrieves both text and images based on user queries.
To implement multi-modal search with Haystack, you can incorporate various pipelines and components. For text, you can use traditional document retrieval techniques with Elasticsearch or a similar system. For images, you can utilize computer vision models to extract features from the images and enable similarity search. An example can be found in using a model like CLIP from OpenAI, which can understand and relate text and images. By extracting feature embeddings for both your text data and image data, you can perform searches that take into account the context of both types of information.
Integrating these functionalities requires defining a multi-modal retriever and perhaps a custom pipeline. You need to ensure that your input queries can be interpreted to search through both text and images accordingly. The results can then be ranked based on relevancy, combining results from text and visual data. This approach ensures that when a user searches for information, they receive a comprehensive set of results that provide richer context through both text and visuals, enhancing the search experience.