Fixed documentation.

README.md

@@ -4,7 +4,7 @@ For example, for the query with the two animated movies, ["Lilo & Stitch" and "U
 
 This module also adds the novel ability to combine full text queries with items. For example a query can be a combination of items and full text search keywords. In this case the results match the keywords and are re-ranked by similary to the queried items.
 
-It is important to note that Bayesian Sets does not care about how the actual [feature][3] engineering. As an example SimSearch implements a simple [bag of words][4] model. However any other feature binary features are possible. In this case you will need to create the index directly. The index is a set of files in a .xco and .yco format (more in the [tutorial][6]) that represents the presence of a feature value in a given item. So as long as you can create these files, SimSearch can read them and perform the matching.
+It is important to note that Bayesian Sets does not care about the actual [feature][3] engineering. For example SimSearch implements a simple [bag of words][4] model. However other feature types are possible as long as they can be binarized. In this case you will need to create the index directly. The index is a set of files in a .xco and .yco format (more in the [tutorial][6]) that represents the presence of a feature value in a given item. So as long as you can create these files, SimSearch can read them and perform the matching.
 
 SimSearch has been [tested][5] on datasets with millions of documents and hundreds of thousands of features. Future plans include distributed search and real time indexing. For more information, feel free please to follow the [tutorial][6].
 

simsearch/simsphinx.py

@@ -132,7 +132,7 @@ def _SetupSphinxClient(self, item_ids, log_scores):
             self.sphinx_setup(self.wrap_cl)
 
     def _AddStats(self, sphinx_results, item_ids):
-        scores = self._GetDetailedScores(sphinx_results['ids'], item_ids)
+        scores = self._GetDetailedScores([match['id'] for match in sphinx_results['matches']], item_ids)
         for scores, match in zip(scores, sphinx_results['matches']):
             match['attrs']['@sim_scores'] = scores
 

tutorial/README.md

@@ -160,13 +160,13 @@ First you need to install [Sphinx][2] and [fSphinx][3].
 
 After you have installed Sphinx, let it index data (assuming Sphinx indexer is in /user/local/sphinx/):
 
-    /usr/local/sphinx/bin/indexer -c ./config/indexer.conf --all
+    /usr/local/sphinx/bin/indexer -c ./config/sphinx_indexer.conf --all
 
 And now let searchd serve the index:
 
-    /usr/local/sphinx/bin/searchd -c ./config/indexer.conf
+    /usr/local/sphinx/bin/searchd -c ./config/sphinx_indexer.conf
 
-Note that the "indexer.conf" must have an attribute called "log_scores_attr" set to 1 and declared as a float.
+Note that the "sphinx_indexer.conf" must have an attribute called "log_scores_attr" set to 1 and declared as a float.
 
     # log_score_attr must be set to 1
     sql_query            = \