doc

README.md

@@ -7,11 +7,11 @@
 
 # Déjà Queue
 
-A drop-in replacement for `multiprocessing.Queue`. Faster, because it takes advantage of a shared memory ring buffer (rather than slow pipes) and [pickle protocol 5 out-of-band data](https://peps.python.org/pep-0574/) to minimize copies. `DejaQueue` supports any type of [picklable](https://docs.python.org/3/library/pickle.html#what-can-be-pickled-and-unpickled) Python object, including numpy arrays or nested dictionaries with mixed content.
+A drop-in replacement for `multiprocessing.Queue`. Faster, because it takes advantage of a shared memory ring buffer (rather than slow pipes) and [pickle protocol 5 out-of-band data](https://peps.python.org/pep-0574/) to minimize copies. [`dejaq.DejaQueue`](#dejaqdejaqueue) supports any type of [picklable](https://docs.python.org/3/library/pickle.html#what-can-be-pickled-and-unpickled) Python object, including numpy arrays or nested dictionaries with mixed content.
 
 The speed of `DejaQueue` enables efficient inter-job communication in data processing pipelines, which can be implemented in a few lines of code with [`dejaq.Parallel`](#dejaqparallel).
 
-Auto-generated API documentation: https://danionella.github.io/dejaq
+Auto-generated (minimal) API documentation: https://danionella.github.io/dejaq
 
 ## Installation
 - `conda install danionella::dejaq `
@@ -51,7 +51,7 @@ producer.start()
 ### `dejaq.Parallel`
 The following examples show how to use `dejaq.Parallel` to parallelize a function or a class, and how to create job pipelines.
 
-Here we execute a function and map iterable inputs across 10 workers. To enable pipelining, the results of each stage are provided as iterable generator. Use the `.compute()` method to get the final result (note that each stage pre-fetches results from `n_workers` calls, so some of the execution already starts before `.compute`)
+Here we execute a function and map iterable inputs across 10 workers. To enable pipelining, the results of each stage are provided as iterable generator. Use the `.compute()` method to get the final result (note that each stage pre-fetches results from `n_workers` calls, so some of the execution already starts before `.compute`). Results are always ordered.
 
 ```python
 from time import sleep