Update documentation.

Author: Ghislain Fourny

README.md

@@ -30,12 +30,14 @@ JSONiq queries are invoked with rumble.jsoniq() in a way similar to the way Spar
 
 JSONiq variables can be bound to lists of JSON values (str, int, float, True, False, None, dict, list) or to Pyspark DataFrames. A JSONiq query can use as many variables as needed (for example, it can join between different collections).
 
-It will later also be possible to read tables registered in the Hive metastore, similar to spark.sql(). Alternatively, the JSONiq query can also read many files of many different formats from many places (local drive, HTTP, S3, HDFS, ...) directly with simple builtin function calls such as json-lines(), text-file(), parquet-file(), csv-file(), etc. See [RumbleDB's documentation](https://rumble.readthedocs.io/en/latest/).
+It will later also be possible to read tables registered in the Hive metastore, similar to spark.sql(). Alternatively, the JSONiq query can also read many files of many different formats from many places (local drive, HTTP, S3, HDFS, ...) directly with simple builtin function calls such as json-lines(), text-file(), parquet-file(), csv-file(), etc. See [RumbleDB's documentation](https://docs.rumbledb.org/writing-jsoniq-queries-in-python).
 
 The resulting sequence of items can be retrieved as a list of JSON values, as a Pyspark DataFrame, or, for advanced users, as an RDD or with a streaming iteration over the items using the [RumbleDB Item API](https://github.com/RumbleDB/rumble/blob/master/src/main/java/org/rumbledb/api/Item.java).
 
 It is also possible to write the sequence of items to the local disk, to HDFS, to S3, etc in a way similar to how DataFrames are written back by Pyspark.
 
+The library also contains a jsoniq magic that allows you to directly write JSONiq queries in a Jupyter notebook and see the results automatically output on the screen.
+
 The design goal is that it is possible to chain DataFrames between JSONiq and Spark SQL queries seamlessly. For example, JSONiq can be used to clean up very messy data and turn it into a clean DataFrame, which can then be processed with Spark SQL, spark.ml, etc.
 
 Any feedback or error reports are very welcome.
@@ -345,7 +347,12 @@ seq.write().mode("overwrite").text("outputtext");
 
 Even more queries can be found [here](https://colab.research.google.com/github/RumbleDB/rumble/blob/master/RumbleSandbox.ipynb) and you can look at the [JSONiq documentation](https://www.jsoniq.org) and tutorials.
 
-# Last updates
+# Latest updates
+
+## Version 0.2.0 alpha 2
+- You can change the result size cap through to the now accessible Rumble configuration (for example rumble .getRumbleConf().setResultSizeCap(10)). This controls how many items can be retrieved at most with a json() call. You can increase it to whichever number you would like if you reach the cap.
+- Add the JSONiq magic to execute JSONiq queries directly in a notebook cell, using the RumbleDB instance shipped with the library.
+- RumbleSession.builder.getOrCreate() now correctly reuses an existing session instead of creating a new object. It preserves the configuration. 
 
 ## Version 0.2.0 alpha 1
 - Allow to bind JSONiq variables to pandas dataframes