Haystack is designed to handle multi-step document retrieval processes effectively by breaking down the retrieval task into manageable components. At its core, Haystack utilizes a modular architecture that allows developers to integrate different retrieval techniques and components easily. This means that you can combine various methods for retrieving documents, processing natural language, and generating relevant responses to achieve more refined results.
To handle multi-step retrieval, Haystack often uses a two-component approach: a retriever and a reader. The retriever's job is to sift through a large dataset to pull out candidate documents that are most relevant to the user's query. For instance, if you're looking for information about "machine learning models," the retriever would search the indexed documents using methods like keyword matching or dense vector search. Haystack supports various retrievers, such as traditional search engines or dense retrievers, allowing developers to choose the best approach based on their needs.
Once the retriever identifies relevant documents, the reader step comes into play. The reader is a more focused component that analyzes the retrieved documents to extract specific answers or relevant content. For example, using a BERT-based or other transformer model, the reader can examine passages within the retrieved documents and highlight or extract the sentences that directly answer the user’s query. This multi-step approach ensures that users receive precise and contextually relevant information by first narrowing down the document pool and then extracting the most useful content from those documents. Overall, this layered method enhances accuracy and improves the quality of information retrieval in Haystack applications.