How does Haystack handle question-answering tasks?

Haystack is an open-source framework designed for building question-answering (QA) systems. It handles QA tasks by breaking the process into modular components: document storage, retrieval, and answer extraction. First, documents are stored in a database (like Elasticsearch or FAISS) optimized for fast search. Next, a retriever component identifies relevant text passages from the database using keyword-based methods (BM25) or neural models (DPR). Finally, a reader model (e.g., BERT-based) processes the retrieved text to extract or generate answers. This modular approach allows developers to mix and match components based on their needs, balancing speed and accuracy.

Developers can customize Haystack pipelines for specific use cases. For example, if low latency is critical, you might pair BM25 with a smaller reader model like MiniLM. For complex queries requiring semantic understanding, DPR retrievers combined with larger models like RoBERTa improve accuracy. Haystack also supports hybrid retrieval, merging results from keyword and neural searches to cover diverse query types. Pipelines can be extended with additional components, such as summarizers or translation modules. For instance, a medical QA system might first retrieve research papers, then use a domain-specific reader trained on biomedical texts to extract precise answers. The framework’s flexibility makes it adaptable to scenarios ranging from enterprise search to customer support automation.

Haystack handles both extractive QA (pulling answers directly from text) and generative QA (creating new answers). For extractive tasks, the reader identifies answer spans in retrieved documents. For generative tasks, models like GPT can be integrated to synthesize answers from multiple sources. It also supports “closed-book” QA, where a generator produces answers without retrieval, useful for factual queries. To optimize performance, Haystack allows preprocessing documents into smaller chunks, improving retrieval precision. Developers can fine-tune retrievers and readers on custom datasets, ensuring the system adapts to domain-specific language. For scalability, distributed databases and approximate nearest neighbor indexing (e.g., FAISS) enable efficient searches across millions of documents. By decoupling retrieval and reading stages, Haystack simplifies debugging and upgrades—for example, swapping a retriever without altering the rest of the pipeline.