Haystack, an open-source framework designed for building search systems, supports various retriever models that help fetch relevant documents from a larger database. The primary retriever types include keyword-based retrievers, dense retrievers, and hybrid retrievers. Each model has its strengths, making it suitable for different use cases depending on the nature of the data and the specific requirements of a project.
Keyword-based retrievers, like the TF-IDF (Term Frequency-Inverse Document Frequency) and BM25 models, operate on the principle of matching keywords in the user query to those in the document corpus. These models are straightforward and interpretable, making them easy to implement. For example, if you search for "latest technology trends," a keyword retriever will look for documents that include these exact terms, ranking them based on their frequency and relevance. These models work well in contexts where the queries and documents use similar language and are often employed in structured data environments.
On the other hand, dense retrievers, such as those based on transformer architectures like BERT or Sentence Transformers, represent words and sentences as vectors in high-dimensional space. They focus on semantic similarity rather than strict keyword matching. When using such models, the system can understand that "new advancements in AI" is closely related to "recent developments in artificial intelligence," even if the exact words differ. This semantic approach provides a more nuanced understanding of the documents, making dense retrievers ideal for unstructured data or when the queries may vary significantly from the document content. Lastly, hybrid retrievers combine both keyword and dense retrieval techniques to harness the advantages of both models, offering a more robust solution for diverse applications in search and information retrieval.