Haystack supports several types of retriever models designed to efficiently search and retrieve relevant documents from a dataset. These include sparse retrievers, dense retrievers, and hybrid models that combine both approaches. Each type addresses different retrieval needs, balancing speed, accuracy, and the ability to handle semantic or keyword-based queries. Developers can choose the appropriate retriever based on their use case, dataset size, and performance requirements.
Sparse retrievers, like BM25, rely on keyword matching and term frequency to rank documents. For example, Haystack integrates with Elasticsearch to use its optimized BM25 implementation, which is fast and effective for exact keyword searches. This approach works well when queries contain specific terms that directly match document content. On the other hand, dense retrievers use neural networks to convert text into dense vector embeddings, enabling semantic similarity searches. Haystack’s EmbeddingRetriever supports models like Sentence Transformers (e.g., all-MiniLM-L6-v2) or OpenAI embeddings, paired with vector databases such as FAISS or Milvus. These retrievers excel at understanding paraphrased or contextually similar queries that lack exact keyword matches.
Hybrid retrievers merge sparse and dense methods to leverage their strengths. For instance, Haystack’s EnsembleRetriever combines results from BM25 and a dense retriever, reranking them to improve accuracy. Developers can also use the JoinDocuments node in pipelines to merge outputs from multiple retrievers. Additionally, specialized models like Facebook’s Dense Passage Retriever (DPR) are supported for tasks requiring contextual understanding, such as question answering. Haystack’s modular design allows seamless integration with document stores (e.g., Elasticsearch, FAISS) and pipelines, enabling customization for specific workflows. By offering these options, Haystack provides flexibility to optimize for speed, precision, or a balance of both, depending on the application.