How does Haystack perform document ranking?

Haystack performs document ranking through a multi-stage process that combines traditional retrieval methods with modern neural networks to improve search accuracy. The framework typically uses a two-step approach: an initial broad retrieval followed by a more precise re-ranking phase. First, it employs fast retrieval methods like BM25 or sparse embeddings to quickly fetch a large set of potentially relevant documents. These candidates are then passed to a neural re-ranker that analyzes deeper semantic relationships to refine the results. This hybrid approach balances speed and accuracy, making it practical for real-world applications.

In the first stage, Haystack often uses keyword-based algorithms such as BM25, which matches documents based on term frequency and inverse document frequency. For example, a search for “machine learning tutorials” might retrieve documents containing those exact terms. For more nuanced queries, Haystack can also use dense vector retrievers like Dense Passage Retrieval (DPR), which encode text into embeddings to capture semantic meaning. These embeddings allow the system to find documents that are conceptually related even if they don’t share exact keywords. After retrieving a candidate pool (e.g., 100-1,000 documents), the second stage applies transformer-based models like BERT or MiniLM to re-rank results. These models compare the query against each document at a deeper contextual level, prioritizing documents that better align with the user’s intent. For instance, a query for “how to optimize Python loops” might boost a document discussing list comprehensions over one that merely mentions loops in passing.

Haystack’s flexibility allows developers to customize the ranking pipeline. You can swap out retrievers (e.g., Elasticsearch for BM25, FAISS for dense vectors) or choose different re-rankers from Hugging Face’s model hub. The framework also supports training custom models on domain-specific data—for example, fine-tuning a BERT model on medical texts to improve ranking for healthcare searches. Developers can adjust parameters like the number of initial candidates or the re-ranker’s batch size to balance latency and accuracy. By separating retrieval and ranking into modular components, Haystack enables teams to iteratively improve their search systems without overhauling the entire pipeline. This approach makes it easier to adapt to varying requirements, whether prioritizing speed for large datasets or precision for complex queries.