Haystack is an open-source framework designed to facilitate the development of question-answering systems. At its core, Haystack allows developers to create pipelines that pull information from various data sources, such as documents, databases, or web pages. When handling question-answering tasks, Haystack typically uses a combination of natural language processing (NLP) techniques, retrieval methods, and neural models to deliver accurate answers. The framework supports various backends for document storage and supports integrations with popular NLP models like those from Hugging Face.
The question-answering process in Haystack generally splits into two main components: retrieval and reading. In the retrieval stage, the system initial application searches through a document index to identify relevant documents that might contain the answer. This is done using techniques like keyword matching or more sophisticated methods such as dense vector similarity. For instance, the use of Elasticsearch as a backend allows for efficient token-based retrieval by creating a searchable index from the documents, enabling quicker response times when answering questions.
Once relevant documents are retrieved, the reading stage takes over. This involves applying a machine learning model to analyze the text in the context of the user's question. Haystack supports various reading models, including extractive and generative approaches. For example, with an extractive model, the system identifies and extracts the exact portion of text that answers the query. If a generative model is in use, the system might rephrase or summarize the information based on the context provided in the retrieved documents. By combining these steps, Haystack efficiently handles question-answering tasks, offering flexibility and scalability for developers to build tailored solutions.