What is a Retriever in Haystack, and how does it work?

A Retriever in Haystack is a component designed to efficiently fetch relevant documents or text passages from a large dataset in response to a user’s query. It acts as the first step in a pipeline for tasks like question answering or semantic search, narrowing down potentially millions of documents to a manageable set of candidates for further processing. Retrievers in Haystack work by comparing the query against indexed documents in a Document Store (like Elasticsearch or FAISS) using algorithms that prioritize speed and relevance. Their primary goal is to balance accuracy with computational efficiency, ensuring that downstream components (like a Reader for answer extraction) receive high-quality inputs without excessive latency.

Retrievers operate in two main modes: sparse and dense. Sparse retrievers, such as BM25, rely on keyword matching and term frequency statistics to rank documents. For example, a query like “What causes climate change?” would trigger BM25 to prioritize documents containing terms like “climate,” “change,” and “causes.” Dense retrievers, like the Dense Passage Retriever (DPR), use neural networks to convert both the query and documents into vector embeddings. These embeddings capture semantic meaning, allowing the retriever to find documents that are conceptually related even if they don’t share exact keywords. For instance, DPR might retrieve a passage discussing “greenhouse gas emissions” even if the query doesn’t explicitly mention those words. Hybrid approaches, which combine sparse and dense methods, are also supported to leverage the strengths of both techniques.

In practice, Haystack’s Retriever API abstracts the complexity of these methods. Developers configure a Retriever by linking it to a pre-populated Document Store and selecting an algorithm. For example, using the BM25Retriever with Elasticsearch involves indexing documents with Elasticsearch’s inverted index, while the EmbeddingRetriever requires precomputing document embeddings using models like Sentence-BERT. During a search, the Retriever processes the query, computes relevance scores (e.g., BM25’s term-frequency weights or cosine similarity between vectors), and returns the top-k results. This modular design allows developers to experiment with different retrieval strategies—such as switching from BM25 to a transformer-based model—without overhauling their entire pipeline, making it adaptable to varying accuracy and performance needs.