Haystack, an open-source framework designed for building search systems, supports various types of embeddings that can be utilized to enhance the search experience. These embeddings are essentially numerical representations of text or other data types and help in capturing the semantic meaning of the content. In Haystack, you can work with pre-trained models and other embeddings that are suitable for different tasks, such as text retrieval, question answering, and document similarity.
One common type of embedding model in Haystack is the transformer-based embeddings, which are derived from architectures like BERT, RoBERTa, or DistilBERT. These models understand context better than traditional embeddings because they take into account the entire sentence rather than individual words. For example, using the BERT model, a search for "Apple" can distinguish between the fruit and the technology company based on the surrounding words in a query. These transformer models can be fine-tuned to adapt to specific domains or applications, making them flexible for various use cases.
In addition to transformer embeddings, Haystack also allows the use of dense vector embeddings from other sources, including sentence-transformers and universal sentence encoders. These models offer ease of use and are suitable for tasks such as clustering and document similarity. For instance, if you have a large set of product descriptions, you can use these embeddings to find similar products based on user queries. Haystack provides built-in support for these models, making it easier for developers to integrate and implement effective search capabilities in their applications.