A Reader in Haystack is a component designed to extract answers from text documents in response to specific questions. It’s a key part of Haystack’s question-answering (QA) pipeline, working alongside other components like Retrievers and DocumentStores. The Reader uses natural language processing (NLP) models, often based on transformer architectures like BERT or RoBERTa, to analyze text passages and identify precise answers. For example, if you ask, “What causes climate change?” the Reader scans retrieved documents to find sentences or phrases that directly address the question, such as “greenhouse gas emissions.”
The Reader operates in two main steps. First, it receives a set of candidate documents or passages from a Retriever, which narrows down the search space from a large document collection. The Reader then processes each text snippet, using its underlying model to predict answer spans (start and end positions in the text) and assign confidence scores. For instance, if the Retriever passes a paragraph about environmental science, the Reader might highlight “carbon dioxide from burning fossil fuels” as the answer. It ranks these answers based on confidence, returning the most relevant ones. This approach balances efficiency (by relying on the Retriever to filter documents) and accuracy (using the Reader’s deep learning model for detailed analysis).
Developers can customize the Reader by choosing different pre-trained models or fine-tuning them on domain-specific data. Haystack supports several Reader implementations, such as TransformersReader (for Hugging Face models) and FARMReader (optimized for training and inference). For example, you could use a biomedical QA model like BioBERT to answer questions from medical journals. Parameters like max_seq_length (how much text the model processes at once) and top_k (number of answers returned) can be adjusted to optimize performance. By integrating the Reader into a pipeline, developers can build scalable QA systems that handle complex queries across large document sets efficiently.