How do I build a question-answering system using Haystack?

To build a question-answering system using Haystack, you’ll need to set up a pipeline that includes a document store, a retriever, and a reader. Haystack is a Python framework designed for building search and NLP systems, and it integrates well with tools like Elasticsearch and Hugging Face models. Start by installing Haystack using pip install farm-haystack, and choose a document store to index your data—Elasticsearch is a common choice for scalable setups, while the InMemoryDocumentStore works for smaller experiments. Load your documents (e.g., text files, PDFs) into the store using Haystack’s Document class, which structures data into text content and metadata. For example, you might convert a FAQ document into a list of Document objects with content and meta fields like source or category.

Next, configure a retriever to fetch relevant documents for a query. Haystack supports sparse retrievers like TF-IDFRetriever for keyword-based matching and dense retrievers like EmbeddingRetriever for semantic similarity. If using a dense retriever, pair it with a sentence embedding model (e.g., sentence-transformers/all-mpnet-base-v2). The retriever scans the document store and returns a subset of documents likely to contain the answer. Then, add a reader component to extract answers from those documents. Readers are typically transformer-based models like deepset/roberta-base-squad2, which you can load via TransformersReader. The reader processes the retrieved text and returns answers with confidence scores. For example, a query like “How do I reset my password?” would trigger the retriever to find relevant support articles, and the reader would extract steps like “Go to Settings > Account Security.”

Finally, assemble these components into a pipeline using ExtractiveQAPipeline(retriever, reader) and test it with sample queries. You can optimize performance by fine-tuning the retriever or reader on your domain-specific data. For scalability, deploy the document store and pipeline behind a REST API using Haystack’s RESTPipeline or a framework like FastAPI. If you need lower latency, consider switching to a smaller reader model or caching frequent queries. Haystack also supports advanced features like preprocessors (to clean text) and evaluators (to measure accuracy), which help iterate on the system. For example, you might evaluate your pipeline using a dataset of sample questions and adjust the retriever’s top_k parameter to balance speed and accuracy.