documentation

pyterrier_dr/flex/flatnav_retr.py

@@ -83,6 +83,10 @@ def _flatnav_retriever(self,
         drop_query_vec (bool): whether to drop the query_vec column after retrieval
         verbose (bool): whether to show progress bars
 
+    .. note::
+        This transformer requires the ``flatnav`` package to be installed. Instructions are available
+        in the `flatnav repository <https://github.com/BlaiseMuhirwa/flatnav>`__.
+
     .. cite:: arxiv:2412.01940
         :citation: Munyampirwa et al. Down with the Hierarchy: The 'H' in HNSW Stands for "Hubs". arXiv 2024.
         :link: https://arxiv.org/abs/2412.01940

pyterrier_dr/pt_docs/indexing-retrieval.rst

@@ -37,14 +37,15 @@ API Documentation
     .. automethod:: faiss_flat_retriever
     .. automethod:: faiss_hnsw_retriever
     .. automethod:: faiss_ivf_retriever
+    .. automethod:: flatnav_retriever
     .. automethod:: scann_retriever
     .. automethod:: voyager_retriever
 
     Re-Ranking
     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
     Results can be re-ranked using indexed vectors using :meth:`scorer`. (:meth:`np_scorer` and :meth:`torch_scorer` are
-    available as specific implemenations, if needed.)
+    available as specific implementations, if needed.)
 
     :meth:`gar`, :meth:`ladr_proactive`, and :meth:`ladr_adaptive` are *adaptive* re-ranking approaches that pull in other
     documents from the corpus that may be relevant.