Haystack is an open-source framework developed by deepset for building search systems and question-answering applications using natural language processing (NLP). It provides modular components and pipelines to handle tasks like document retrieval, semantic search, and extractive question answering. Haystack is designed to work with large collections of documents, enabling developers to build systems that can efficiently find and extract relevant information. Instead of requiring users to build everything from scratch, it abstracts common NLP workflows into reusable parts, such as document stores, retrievers, and readers, which can be combined into customizable pipelines.
The framework operates through a pipeline-based approach. First, documents are ingested into a document store (e.g., Elasticsearch, FAISS, or Milvus), which indexes them for fast retrieval. When a query is made, a retriever component scans the document store to find the most relevant documents. For example, a sparse retriever like BM25 uses keyword matching, while a dense retriever like Dense Passage Retrieval (DPR) uses neural embeddings to find semantically similar texts. After retrieval, a reader component—often a transformer-based model like BERT or RoBERTa—processes the retrieved documents to extract precise answers. For instance, in a question-answering system, the reader might parse a paragraph about climate change to answer “What causes global warming?” by identifying the text snippet mentioning greenhouse gases.
Haystack emphasizes flexibility and extensibility. Developers can swap components to fit their needs—for example, using a different document store for scalability or a custom-trained reader model for domain-specific tasks. Preprocessing tools are included to clean and split documents, and REST APIs simplify deployment. A typical use case involves indexing research papers into Elasticsearch, using DPR to retrieve relevant sections, and a fine-tuned BERT model to answer technical questions. By modularizing complex workflows, Haystack lets developers focus on integrating NLP capabilities without reinventing infrastructure, making it practical for applications like enterprise search, chatbots, or knowledge management systems.