What are some advanced features for document ranking in Haystack?

Haystack offers several advanced features for document ranking that improve search accuracy and flexibility. One key feature is hybrid retrieval combined with cross-encoder re-ranking. Hybrid retrieval uses both sparse methods (like BM25) and dense embedding-based models (such as Dense Passage Retriever) to balance keyword matching and semantic understanding. For example, a pipeline might first fetch documents using BM25 for keyword relevance and DPR for contextual matches, then merge the results. After this, a cross-encoder model (e.g., a BERT-based re-ranker) evaluates the top candidates by analyzing query-document pairs in depth, improving precision. This two-step approach leverages the speed of initial retrievers and the nuance of slower but more accurate re-rankers.

Another advanced capability is support for custom ranking models and fine-tuning. Haystack allows developers to integrate pre-trained transformer models from Hugging Face or custom-trained models tailored to specific domains. For instance, you could use a SentenceTransformersRanker with a model like “multi-qa-mpnet-base-dot-v1” for similarity scoring or fine-tune it on proprietary data (e.g., medical texts) to improve domain-specific performance. Parameters like top_k can be adjusted to control how many documents are passed to the ranker, balancing speed and accuracy. This flexibility ensures the system adapts to unique use cases without requiring extensive infrastructure changes.

Finally, Haystack supports advanced ranking strategies like learning-to-rank (LTR) and ensemble methods. LTR techniques train models to combine multiple relevance signals—such as BM25 scores, embedding similarities, or metadata (e.g., document freshness)—into a unified ranking. For example, a developer could build a pipeline that extracts features from retrieved documents, applies a gradient-boosted tree model to weigh those features, and reorders results. Haystack’s modular design also enables ensembling outputs from different rankers (e.g., combining a TF-IDF scorer with a neural ranker) using weighted averages or voting. These methods allow complex ranking logic while maintaining scalability through Haystack’s pipeline-centric architecture.