Haystack is a framework designed for building search systems, particularly those that utilize natural language processing to retrieve relevant documents. Some advanced features for document ranking in Haystack focus on improving the relevance and accuracy of search results by employing various techniques. One major feature is the integration of embeddings, which represent documents and queries as dense vectors in high-dimensional space. This allows for semantic search, where similar meanings can be identified even if the exact words do not match. Using embeddings from models like BERT or Sentence Transformers, Haystack can provide more context-aware results that align better with user queries.
Another important feature is the ability to implement custom ranking pipelines. Developers can fine-tune how documents are ranked using different components like ElasticSearch for initial retrieval and then applying reranking models. For instance, you might first perform a basic search to get a limited number of top documents, followed by applying a transformer-based model to refine the rankings based on not only keyword matching but also contextual relevance. This two-step approach enhances the quality of results, particularly in applications where precision is crucial, such as legal or medical document retrieval.
Finally, Haystack supports multi-stage ranking and can incorporate feedback loops to continually improve search results. This means that as users interact with the system, their choices can be considered for future queries. For example, if users consistently select certain documents over others for specific queries, the ranking model can learn from this behavior. By implementing reinforcement learning or feedback mechanisms, developers can build systems that adapt and improve over time, leading to a more personalized search experience. Overall, these advanced features empower developers to create sophisticated document ranking systems that cater to the nuanced needs of users.