Merge branch 'master' into tf_py3

RasaHQ · Oct 4, 2018 · 5f79c93 · 5f79c93
2 parents e19cb91 + 237ea80
commit 5f79c93
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diff --git a/CHANGELOG.rst b/CHANGELOG.rst
@@ -14,6 +14,7 @@ Added
 
 Changed
 -------
+- ``boto3`` is now loaded lazily in ``AWSPersistor`` and is not included in ``requirements_bare.txt`` anymore
 
 Removed
 -------

diff --git a/alt_requirements/requirements_bare.txt b/alt_requirements/requirements_bare.txt
@@ -1,7 +1,6 @@
 gevent==1.2.2
 klein==17.10.0
 hyperlink==17.3.1
-boto3==1.5.20
 typing==3.6.2
 future==0.16.0
 six==1.11.0

diff --git a/alt_requirements/requirements_dev.txt b/alt_requirements/requirements_dev.txt
@@ -16,3 +16,4 @@ httpretty==0.9.5
 # other
 google-cloud-storage==1.7.0
 azure-storage-blob==1.0.0
+boto3==1.5.20
diff --git a/docs/faq.rst b/docs/faq.rst
@@ -4,6 +4,19 @@
 Frequently Asked Questions
 ==========================
 
+Which languages does the Rasa NLU support?
+------------------------------------------
+Rasa NLU can be used to understand any language that can be tokenized (on whitespace or using a custom tokenizer),
+but some backends are restricted to specific languages.
+
+The ``tensorflow_embedding`` pipeline can be used for any language because it trains custom word embeddings for your domain using the data you provide in the NLU training examples.
+
+Other backends use pre-trained word vectors and therefore are restricted to languages which have pre-trained
+vectors available. 
+
+You can read more about the Rasa NLU supported languages in :ref:`section_languages`.
+
+
 How many training examples do I need?
 -------------------------------------
 Unfortunately, the answer is *it depends*. 

diff --git a/docs/index.rst b/docs/index.rst
@@ -55,8 +55,8 @@ You can see an independent benchmark comparing Rasa NLU to closed source alterna
    :hidden:
 
    choosing_pipeline
-   entities
    languages
+   entities
    evaluation
    fallback
    faq

diff --git a/docs/languages.rst b/docs/languages.rst
@@ -4,11 +4,14 @@
 Language Support
 ================
 
-Rasa NLU can be used to understand any language, but some backends are 
-restricted to specific languages.
+**You can use Rasa NLU to build assistants in any language you want!** The 
+``tensorflow_embedding`` pipeline can be used for **any language** because
+it trains custom word embeddings for your domain. Read more about this
+pipeline in :ref:`choosing_pipeline`.
+
+Other backends have some restrictions and support those languages
+which have pre-trained word vectors available.
 
-The ``tensorflow_embedding`` pipeline can be used for any language, because
-it trains custom word embeddings for your domain.
 
 Pre-trained Word Vectors
 ------------------------

diff --git a/rasa_nlu/persistor.py b/rasa_nlu/persistor.py
@@ -9,8 +9,6 @@
 import shutil
 import tarfile
 
-import boto3
-import botocore
 from builtins import object
 from typing import Optional, Tuple, List, Text
 
@@ -143,6 +141,7 @@ class AWSPersistor(Persistor):
 
     def __init__(self, bucket_name, endpoint_url=None):
         # type: (Text, Optional[Text]) -> None
+        import boto3
         super(AWSPersistor, self).__init__()
         self.s3 = boto3.resource('s3', endpoint_url=endpoint_url)
         self._ensure_bucket_exists(bucket_name)
@@ -173,6 +172,8 @@ def list_projects(self):
             return []
 
     def _ensure_bucket_exists(self, bucket_name):
+        import boto3
+        import botocore
         bucket_config = {
             'LocationConstraint': boto3.DEFAULT_SESSION.region_name}
         try:

diff --git a/tests/base/test_persistor.py b/tests/base/test_persistor.py
@@ -7,6 +7,8 @@
 
 import mock
 import pytest
+import boto3
+import botocore
 from moto import mock_s3
 
 from tests import utilities