Yes, Haystack can be used for clustering and categorization of documents, although it is primarily designed for building search systems and handling queries on large datasets. By utilizing different components of Haystack, developers can structure, analyze, and categorize documents effectively. For instance, you could leverage the document store and the vector database capabilities in Haystack to group similar documents based on their content.
To start with clustering, one common approach is to convert documents into embeddings using the built-in models provided by Haystack. These embeddings represent the semantic meaning of the documents in a numerical form. Once you have the embeddings, you can employ clustering algorithms like K-means or DBSCAN from libraries such as Scikit-learn. The clustering step will help in identifying groups of similar documents based on their embeddings, making it easier to manage large datasets effectively.
For categorization, Haystack allows you to integrate classification models. After you prepare your documents, you can use fine-tuned machine learning models to predict categories for each document. For example, if you have a collection of news articles, you can train a classifier to categorize them into different topics like sports, politics, or technology. By combining clustering and categorization, developers can build comprehensive systems to manage and analyze large sets of unstructured text data efficiently.