Document length has a very direct impact on the speed of the Reader
which is why we recommend using the
PreProcessor class to clean and split your documents.
If you halve the length of your documents, you will halve the workload placed onto your Reader.
For sparse retrievers, very long documents pose a challenge since the signal of the relevant section of text can get washed out by the rest of the document. We would recommend making sure that documents are no longer than 10,000 words.
Dense retrievers are limited in the length of text that they can read in one pass. As such, it is important that documents are not longer than the dense retriever's maximum input length. By default, Haystack's DensePassageRetriever model has a maximum length of 256 tokens. As such, we recommend that documents contain significantly less words. We have found decent performance with documents around 100 words long.
Respecting Sentence Boundaries
When splitting documents, it is generally not a good idea to let document boundaries fall in the middle of sentences.
Doing so means that each document will contain incomplete sentence fragments
which maybe be hard for both retriever and reader to interpret.
It is therefore recommended to set
split_respect_sentence_boundary=True when initializing your
Choosing the Right top-k Values
top-k parameter in both the
Reader determine how many results they return.
top-k dictates how many retrieved documents are passed on to the next stage,
top-k determines how many answer candidates to show.
In our experiments, we have found that
gives decent overall performance and so we have set this as the default in Haystack.
The choice of
top-k is a trade-off between speed and accuracy,
especially when there is a
Reader in the pipeline.
Setting it higher means passing more documents to the
thus reducing the chance that the answer-containing passage is missed.
However, passing more documents to the
Reader will create a larger workload for the component.
These parameters can easily be tweaked as follows if using a
answers = finder.get_answers(retriever_top_k=10,reader_top_k=5)
or like this if directly calling the
retrieved_docs = retriever.retrieve(top_k=10)
Tip: The Finder class is being deprecated and has been replaced by a more powerful Pipelines class.