Parameter-Tweaking: Get Faster Answers from Your Haystack Pipeline

Optimizing a parameter-rich system like a Haystack question answering pipeline can significantly impact its performance, particularly the length of documents and the top_k_retriever parameter. Adjusting these parameters can speed up the system without sacrificing quality, with document length being crucial to avoid losing syntactic context and the retriever's vector computations playing a key role in determining the reader's processing time. By optimizing these parameters, developers can improve their system's speed, especially when scaling the number of queries, making it possible to get faster answers by adjusting top_k_retriever and hitting the right document length.