How does Haystack handle document retrieval and search?

Haystack handles document retrieval and search through a modular pipeline architecture that integrates document storage, retrieval models, and optional refinement steps. At its core, Haystack uses document stores (like Elasticsearch, FAISS, or in-memory databases) to index and manage text data. Documents are preprocessed—split into chunks, embedded into vectors (if using dense retrieval), and stored with metadata. For search, developers can choose between sparse retrievers (e.g., BM25 for keyword-based matching) or dense retrievers (e.g., transformer-based models like DPR or Sentence-BERT for semantic similarity). These retrievers query the document store to fetch relevant results, which can then be reranked or processed further. This flexibility allows developers to tailor the system to their use case, balancing speed and accuracy.

For example, a developer might use Elasticsearch as a document store with BM25 for fast keyword search. They could index PDFs by splitting them into paragraphs and storing metadata like document titles. When a user searches for “machine learning,” BM25 retrieves paragraphs containing exact terms. To improve relevance, they might add a dense retriever like SentenceTransformersRetriever, which converts the query and documents into embeddings and finds semantically similar results—even if keywords don’t match. Haystack’s Pipeline class makes it easy to chain components: a retriever fetches candidates, and a TransformersSimilarityRanker reranks them using cross-encoders (e.g., MiniLM-L12) for finer-grained relevance scoring. This two-stage approach combines broad recall from the initial retriever with precise ranking.

Advanced features include hybrid search (combining sparse and dense retrievers) and metadata filtering. For instance, a news app could use BM25 to find articles mentioning “climate change” and a dense retriever to capture articles about “global warming,” then filter results by date or category. Haystack also supports custom preprocessing (e.g., using spaCy for entity extraction) and integration with LLMs for tasks like summarization. Developers can scale the system by using distributed document stores like Weaviate or Milvus, and deploy APIs via REST or FastAPI. By decoupling storage, retrieval, and post-processing, Haystack provides a composable framework for building search systems that adapt to specific data and performance needs.