Haystack addresses relevance ranking in document retrieval by employing a combination of retrieval models, machine learning algorithms, and vector search techniques. At its core, Haystack uses the concept of embeddings, which are numerical representations of documents and queries. These embeddings allow for a deeper understanding of semantic relationships between the text, going beyond simple keyword matching. When a user submits a query, Haystack converts the query into an embedding and retrieves relevant documents based on similarity scores computed using techniques such as cosine similarity.
One of the key components of Haystack's relevance ranking system is the use of different retrievers that can be configured to suit various needs. For example, you can use traditional keyword-based retrievers like BM25, which ranks documents based on term frequency and inverse document frequency, alongside more advanced neural retrievers that leverage models such as BERT or other transformer-based architectures. This dual approach enables developers to balance precision and recall according to their application's requirements, providing flexibility in achieving better relevance in search results.
Additionally, Haystack incorporates a ranking stage that further refines the retrieved documents. After initial retrieval, a model can re-rank the documents based on learned relevance patterns from training data, taking into account various features like user interaction, click-through rates, or domain-specific knowledge. By continually updating the relevance model using user feedback, Haystack can improve the ranking algorithm over time, ensuring that the most relevant documents are surfaced effectively during searches. This systematic approach to relevance ranking helps developers create more effective document retrieval systems that align with user needs.