The Reader component in Haystack is responsible for analyzing text passages to extract precise answers to user queries. It operates after the Retriever, which fetches relevant documents from a database or search index. The Reader uses machine learning models, typically based on transformer architectures, to process the retrieved text and identify the most likely answer spans. For example, if a user asks, “What is the capital of France?” the Retriever might fetch a Wikipedia paragraph about France, and the Reader would pinpoint “Paris” as the answer. This component is critical for question-answering systems where users expect direct answers rather than a list of documents.
The Reader works by tokenizing the input text and applying a model to predict the start and end positions of potential answers within the text. Models like BERT or RoBERTa are commonly used because they can understand context and relationships between words. For instance, if the retrieved text contains multiple mentions of cities and historical facts, the Reader can distinguish which segment directly answers the query. Developers can configure parameters such as the maximum answer length, confidence thresholds, and the number of answers returned. This allows balancing between precision (returning only high-confidence answers) and recall (ensuring relevant answers aren’t missed).
Haystack’s Reader is designed for flexibility. Developers can choose pre-trained models or fine-tune them on domain-specific data, such as medical journals or legal documents, to improve accuracy in specialized contexts. For example, a healthcare application might use a Reader fine-tuned on medical literature to answer questions about symptoms or treatments. The component also integrates with Haystack’s pipelines, enabling seamless combination with Retrievers and other tools. By decoupling the Retriever and Reader, Haystack allows teams to optimize each part independently—for instance, using a fast Retriever for scalability and a high-accuracy Reader for detailed analysis. This modularity makes the Reader adaptable to diverse use cases, from customer support chatbots to research assistance tools.