Merge branch 'master' into external-triggers

CHANGELOG.rst

@@ -6,9 +6,30 @@ Rasa Change Log
 All notable changes to this project will be documented in this file.
 This project adheres to `Semantic Versioning`_ starting with version 1.0.
 
-[Unreleased 1.1.8] - `master`_
+
+[Unreleased 1.1.9] - `master`_
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
+Added
+-----
+- add root route to server started without ``--enable-api`` parameter
+- add ``--evaluate-model-directory`` to ``rasa test core`` to evaluate models from ``rasa train core -c <config-1> <config-2>``
+
+Changed
+-------
+
+Removed
+-------
+
+Fixed
+-----
+- ``rasa test core`` can handle compressed model files
+- Rasa can handle story files containing multi line comments
+
+
+[1.1.8] - 2019-07-25
+^^^^^^^^^^^^^^^^^^^^
+
 Added
 -----
 - ``TrainingFileImporter`` interface to support customizing the process of loading
@@ -23,10 +44,6 @@ Changed
   or a policy ensemble
 - Update pytype to ``2019.7.11``
 
-Removed
--------
-
-
 Fixed
 -----
 - interactive learning bug where reverted user utterances were dumped to training data
@@ -36,6 +53,7 @@ Fixed
 - ``rasa train core`` in comparison mode stores the model files compressed (``tar.gz`` files)
 - slot setting in interactive learning with the TwoStageFallbackPolicy
 
+
 [1.1.7] - 2019-07-18
 ^^^^^^^^^^^^^^^^^^^^
 

data/test_stories/stories_with_multiline_comments.md

@@ -0,0 +1,36 @@
+<!-- a
+fancy
+multiline
+comment -->
+## happy path
+* greet
+  - utter_greet
+* mood_great
+  - utter_happy
+
+## sad path 1
+* greet                 <!-- inline comment -->
+  - utter_greet
+* mood_unhappy
+  - utter_cheer_up
+  - utter_did_that_help
+* affirm
+  - utter_happy
+
+<!-- just one line comment-->
+## sad path 2
+* greet
+  - utter_greet
+* mood_unhappy
+  - utter_cheer_up
+  - utter_did_that_help
+* deny
+  - utter_goodbye
+
+<!--
+one more
+-->
+
+## say goodbye
+* goodbye
+  - utter_goodbye

docs/core/actions.rst

@@ -6,16 +6,17 @@
 Actions
 =======
 
-.. contents::
-
 Actions are the things your bot runs in response to user input.
 There are three kinds of actions in Rasa:
 
- 1. **Default actions**: e.g. ``action_listen``, ``action_restart``,
-    ``action_default_fallback``
- 2. **Utterance actions**: start with ``utter_``, just send a message
+ 1. **Utterance actions**: start with ``utter_``, just send a message
     to the user
- 3. **Custom actions**: any other action, these actions can run arbitrary code
+ 2. **Custom actions**: any other action, these actions can run arbitrary code
+ 3. **Default actions**: e.g. ``action_listen``, ``action_restart``,
+    ``action_default_fallback``
+
+.. contents::
+   :local:
 
 Utterance Actions
 -----------------
@@ -66,7 +67,7 @@ other language and define your actions there - but we provide
 a small python SDK to make development there even easier.
 
 Custom Actions Written in Python
---------------------------------
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
 
 For actions written in python, we have a convenient SDK which starts
 this action server for you.
@@ -147,16 +148,15 @@ events in :ref:`Events <events>`.
 Execute Actions in Other Code
 -----------------------------
 
-Rasa will send an HTTP ``POST`` request to the server defined as your ``action_endpoint`` containing
-information on which action to run. In addition, this request will contain all
+Rasa will send an HTTP ``POST`` request to your server containing
+information on which action to run. Furthermore, this request will contain all
 information about the conversation. :ref:`action-server` shows the detailed API spec.
 
 As a response to the action call from Rasa, you can modify the tracker,
-e.g. by setting slots and sending responses back to the user.
+e.g. by setting slots and send responses back to the user.
 All of the modifications are done using events.
 There is a list of all possible event types in :ref:`events`.
 
-
 Proactively Reaching Out to the User Using Actions
 --------------------------------------------------
 

docs/core/domains.rst

@@ -11,6 +11,12 @@ It specifies the ``intents``, ``entities``, ``slots``, and ``actions``
 your bot should know about. Optionally, it can also include ``templates``
 for the things your bot can say.
 
+.. contents::
+   :local:
+
+
+An example of a Domain
+----------------------
 
 As an example, the ``DefaultDomain`` has the following yaml definition:
 
@@ -51,7 +57,7 @@ For example, an action could:
 * just about anything!
 
 Custom Actions and Slots
-^^^^^^^^^^^^^^^^^^^^^^^^
+------------------------
 
 To reference slots in your domain, you need to reference them by
 their **module path**. To reference custom actions, use their **name**.
@@ -76,7 +82,7 @@ see :ref:`custom-actions`).
 .. _utter_templates:
 
 Utterance templates
-^^^^^^^^^^^^^^^^^^^
+-------------------
 
 Utterance templates are messages the bot will send back to the user. There are
 two ways to use these templates:
@@ -270,7 +276,7 @@ multiple responses and Rasa will randomly pick one of them, e.g.:
     - text: "Hey, {name}. How is your day going?"
 
 Ignoring entities for certain intents
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+-------------------------------------
 
 If you want all entities to be ignored for certain intents, you can
 add the ``use_entities: []`` parameter to the intent in your domain

docs/core/interactive-learning.rst

@@ -22,6 +22,8 @@ Some people call this `Software 2.0 <https://medium.com/@karpathy/software-2-0-a
     Rasa X provides a UI for interactive learning, and you can use any user conversation
     as a starting point. See `Annotate Conversations <https://rasa.com/docs/rasa-x/annotate-conversations/>`_ in the Rasa X docs.
 
+.. contents::
+   :local:
 
 Running Interactive Learning
 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^

docs/core/slots.rst

@@ -7,6 +7,12 @@
 Slots
 =====
 
+.. contents::
+   :local:
+
+What are slots?
+---------------
+
 **Slots are your bot's memory.** They act as a key-value store
 which can be used to store information the user provided (e.g their home city)
 as well as information gathered about the outside world (e.g. the result of a

docs/nlu/choosing-a-pipeline.rst

@@ -10,6 +10,10 @@ Choosing a Pipeline
 Choosing an NLU pipeline allows you to customize your model and finetune
 it on your dataset.
 
+.. contents::
+   :local:
+
+
 The Short Answer
 ----------------
 
@@ -154,12 +158,12 @@ final context dictionary is used to persist the model's metadata.
 
 
 
-Returned Entities Object
-------------------------
-In the object returned after parsing there are two fields that show information
-about how the pipeline impacted the entities returned. The ``extractor`` field
-of an entity tells you which entity extractor found this particular entity.
-The ``processors`` field contains the name of components that altered this
+The "entity" object explained
+-----------------------------
+After parsing, the entity is returned as a dictionary.  There are two fields that show information
+about how the pipeline impacted the entities returned: the ``extractor`` field
+of an entity tells you which entity extractor found this particular entity, and
+the ``processors`` field contains the name of components that altered this
 specific entity.
 
 The use of synonyms can also cause the ``value`` field not match the ``text``

docs/nlu/language-support.rst

@@ -14,6 +14,9 @@ pipeline in :ref:`choosing-a-pipeline`.
 Other backends have some restrictions and support those languages
 which have pre-trained word vectors available.
 
+.. contents::
+   :local:
+
 
 Training a model in any language using the ``supervised_embeddings`` pipeline
 -----------------------------------------------------------------------------

docs/nlu/training-data-format.rst

@@ -6,12 +6,12 @@
 Training Data Format
 ====================
 
-Data Format
-~~~~~~~~~~~
-
 You can provide training data as Markdown or as JSON, as a single file or as a directory containing multiple files.
 Note that Markdown is usually easier to work with.
 
+.. contents::
+   :local:
+
 
 Markdown Format
 ---------------

docs/user-guide/evaluating-models.rst

@@ -204,11 +204,11 @@ mode to evaluate the models you just trained:
 
 .. code-block:: bash
 
-  $ rasa test core -m comparison_models/<model-1>.tar.gz comparison_models/<model-2>.tar.gz \
-    --stories stories_folder --out comparison_results
+  $ rasa test core -m comparison_models --stories stories_folder
+  --out comparison_results --evaluate-model-directory
 
 This will evaluate each of the models on the training set and plot some graphs
-to show you which policy performs best.  By evaluating on the full set of stories, you
+to show you which policy performs best. By evaluating on the full set of stories, you
 can measure how well Rasa Core is predicting the held-out stories.
 
 If you're not sure which policies to compare, we'd recommend trying out the

rasa/cli/arguments/data.py

@@ -4,7 +4,6 @@
     add_nlu_data_param,
     add_out_param,
     add_data_param,
-    add_stories_param,
     add_domain_param,
 )
 

rasa/cli/arguments/test.py

@@ -1,7 +1,7 @@
 import argparse
 from typing import Union
 
-from rasa.constants import DEFAULT_MODELS_PATH, DEFAULT_CONFIG_PATH
+from rasa.constants import DEFAULT_MODELS_PATH, DEFAULT_RESULTS_PATH
 
 from rasa.cli.arguments.default_arguments import (
     add_stories_param,
@@ -42,7 +42,7 @@ def add_test_core_argument_group(
     )
     add_out_param(
         parser,
-        default="results",
+        default=DEFAULT_RESULTS_PATH,
         help_text="Output path for any files created during the evaluation.",
     )
     parser.add_argument(
@@ -70,6 +70,15 @@ def add_test_core_argument_group(
         "trains on it. Fetches the data by sending a GET request "
         "to the supplied URL.",
     )
+    parser.add_argument(
+        "--evaluate-model-directory",
+        default=False,
+        action="store_true",
+        help="Should be set to evaluate models trained via "
+        "'rasa train core --config <config-1> <config-2>'. "
+        "All models in the provided directory are evaluated "
+        "and compared against each other.",
+    )
 
 
 def add_test_nlu_argument_group(
@@ -150,7 +159,7 @@ def add_test_nlu_argument_group(
         required=False,
         nargs="+",
         type=int,
-        default=[0, 25, 50, 75, 90],
+        default=[0, 25, 50, 75],
         help="Percentages of training data to exclude during comparison.",
     )
 
@@ -164,6 +173,6 @@ def add_test_core_model_param(parser: argparse.ArgumentParser):
         default=[default_path],
         help="Path to a pre-trained model. If it is a 'tar.gz' file that model file "
         "will be used. If it is a directory, the latest model in that directory "
-        "will be used. If multiple 'tar.gz' files are provided, all those models "
-        "will be compared.",
+        "will be used (exception: '--evaluate-model-directory' flag is set). If multiple "
+        "'tar.gz' files are provided, all those models will be compared.",
     )

rasa/cli/arguments/train.py

@@ -88,7 +88,7 @@ def add_compare_params(
         "--percentages",
         nargs="*",
         type=int,
-        default=[0, 5, 25, 50, 70, 90, 95],
+        default=[0, 25, 50, 75],
         help="Range of exclusion percentages.",
     )
     parser.add_argument(

rasa/cli/scaffold.py

@@ -81,6 +81,7 @@ def print_run_or_instructions(args: argparse.Namespace, path: Text) -> None:
             "jwt_secret",
             "jwt_method",
             "enable_api",
+            "remote_storage",
         ]
         for a in attributes:
             setattr(args, a, None)