How does Haystack handle multi-step document retrieval processes?

Haystack handles multi-step document retrieval by breaking the process into sequential stages, each designed to refine and improve the results. Instead of relying on a single method, it uses a pipeline architecture where different components (like retrievers, rankers, or filters) work together. For example, a pipeline might first use a fast keyword-based retriever to fetch a broad set of documents, then apply a neural re-ranker to prioritize the most relevant ones. This approach balances speed and accuracy, ensuring the system scales while maintaining precision.

A typical pipeline starts with an initial retriever like BM25 or a sparse vector search to quickly scan large document collections. These methods excel at matching keywords but may miss contextual nuances. To address this, Haystack allows adding a second stage, such as a dense retriever (e.g., Dense Passage Retriever) or a cross-encoder re-ranker. Dense retrievers use neural embeddings to capture semantic meaning, while cross-encoders like BERT evaluate document-query pairs for deeper relevance. For instance, a pipeline might retrieve 1,000 documents using BM25, then re-rank the top 200 with a cross-encoder to surface the best 10. Developers can also insert filters (e.g., by metadata or date) between steps to narrow the pool early, reducing computational overhead.

The framework’s flexibility lets developers customize pipelines for specific needs. You could combine multiple retrievers (e.g., hybrid sparse-dense search) or add post-processing steps like deduplication or answer extraction. Haystack’s predefined components and modular design make it straightforward to experiment with configurations. For example, a medical search system might first filter documents by publication year, use a biomedical-specific retriever, then apply a domain-tuned re-ranker. By separating concerns into discrete steps, Haystack ensures each stage is optimized independently, while the pipeline as a whole delivers accurate, context-aware results efficiently.