Merge branch 'master' into cli

.gitignore

@@ -43,4 +43,5 @@ docs/key.pub
 secrets.tar
 .pytest_cache
 src
+test_download.zip
 

.travis.yml

@@ -21,7 +21,8 @@ install:
   - python -m spacy link en_core_web_md en
   - pip install https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.0.0/de_core_news_sm-2.0.0.tar.gz --no-cache-dir > jnk
   - python -m spacy link de_core_news_sm de
-  - if [[ ! -f /tmp/cached/total_word_feature_extractor.dat ]]; then wget --quiet -P /tmp/cached/ https://s3-eu-west-1.amazonaws.com/mitie/total_word_feature_extractor.dat;
+  - if [[ ! -f /tmp/cached/total_word_feature_extractor.dat ]]; then 
+    travis_wait wget --quiet -P /tmp/cached/ https://s3-eu-west-1.amazonaws.com/mitie/total_word_feature_extractor.dat;
     fi
   - mv /tmp/cached/total_word_feature_extractor.dat data/total_word_feature_extractor.dat
   - pip list

CHANGELOG.rst

@@ -9,21 +9,26 @@ This project adheres to `Semantic Versioning`_ starting with version 0.7.0.
 
 Added
 -----
+- Added a detailed warning showing which entities are overlapping
+- Authentication token can be also set with env variable `RASA_NLU_TOKEN`.
 
 Changed
 -------
 - validate training data only if used for training
 - applied spacy guidelines on how to disable pipeline components
 - starter packs now also tested when attempting to merge a branch to master
+- new consistent naming scheme for pipelines:
+  - ``tensorflow_embedding`` pipeline template renamed to ``supervised_embeddings``
+  - ``spacy_sklearn`` pipeline template renamed to ``pretrained_embeddings_spacy``
+  - requirements files, sample configs, and dockerfiles renamed accordingly
+- `/train` endpoint now returns a zipfile of the trained model.
+- replace pep8 with pycodestyle
 - renamed ``rasa_nlu.evaluate`` to ``rasa_nlu.test``
-- renamed ``rasa_nlu.test.run_cv_evaluation`` to 
+- renamed ``rasa_nlu.test.run_cv_evaluation`` to
   ``rasa_nlu.test.cross_validate``
 - renamed ``rasa_nlu.train.do_train()`` to ``rasa_nlu.train.train()``
 - train command can now also load config from file
 
-=======
-- replace pep8 with pycodestyle
-
 Removed
 -------
 - **removed python 2.7 support**
@@ -800,7 +805,6 @@ Added
 - multithreading support of build in REST server (e.g. using gunicorn)
 - multitenancy implementation to allow loading multiple models which
   share the same backend
-
 Fixed
 -----
 - error propagation on failed vector model loading (spacy)

README.md

@@ -4,9 +4,11 @@
 [![Supported Python Versions](https://img.shields.io/pypi/pyversions/rasa_nlu.svg)](https://pypi.python.org/pypi/rasa_nlu)
 [![Build Status](https://travis-ci.com/RasaHQ/rasa_nlu.svg?branch=master)](https://travis-ci.com/RasaHQ/rasa_nlu)
 [![Coverage Status](https://coveralls.io/repos/github/RasaHQ/rasa_nlu/badge.svg?branch=master)](https://coveralls.io/github/RasaHQ/rasa_nlu?branch=master)
-[![Documentation Status](https://img.shields.io/badge/docs-stable-brightgreen.svg)](https://nlu.rasa.com/)
+[![Documentation Status](https://img.shields.io/badge/docs-stable-brightgreen.svg)](https://rasa.com/docs/nlu/)
 [![FOSSA Status](https://app.fossa.io/api/projects/git%2Bgithub.com%2FRasaHQ%2Frasa_nlu.svg?type=shield)](https://app.fossa.io/projects/git%2Bgithub.com%2FRasaHQ%2Frasa_nlu?ref=badge_shield)
 
+<img align="right" height="244" src="https://www.rasa.com/assets/img/sara/sara-open-source-lg.png">
+
 Rasa NLU (Natural Language Understanding) is a tool for understanding what is being said in short pieces of text.
 For example, taking a short message like:
 
@@ -28,13 +30,13 @@ Rasa then uses machine learning to pick up patterns and generalise to unseen sen
 
 You can think of Rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.
 
-If you are new to Rasa NLU and want to create a bot, you should start with the [**tutorial**](https://nlu.rasa.com/tutorial.html).
+If you are new to Rasa NLU and want to create a bot, you should start with the [**tutorial**](https://rasa.com/docs/nlu/quickstart/).
 
 - **What does Rasa NLU do? 🤔** [Read About the Rasa Stack](http://rasa.com/products/rasa-stack/)
 
-- **I'd like to read the detailed docs 🤓** [Read The Docs](https://nlu.rasa.com)
+- **I'd like to read the detailed docs 🤓** [Read The Docs](https://rasa.com/docs/nlu/)
 
-- **I'm ready to install Rasa NLU! 🚀** [Installation](https://nlu.rasa.com/installation.html)
+- **I'm ready to install Rasa NLU! 🚀** [Installation](https://rasa.com/docs/nlu/installation/)
 
 - **I have a question ❓** [Rasa Community Forum](https://forum.rasa.com)
 
@@ -49,7 +51,7 @@ will the next major release). If you want to use Rasa NLU with python
 
 # Quick Install
 
-For the full installation instructions, please head over to the documenation: [Installation](https://nlu.rasa.com/installation.html)
+For the full installation instructions, please head over to the documentation: [Installation](https://rasa.com/docs/nlu/installation/)
 
 **Via Docker Image**
 From docker hub:
@@ -86,7 +88,7 @@ curl 'http://localhost:5000/version'
 
 ### Training New Models
 [Examples](https://github.com/RasaHQ/rasa_nlu/tree/master/data/examples/rasa)
-and [Documentation](https://nlu.rasa.com/dataformat.html) of the training data
+and [Documentation](https://rasa.com/docs/nlu/dataformat/) of the training data
 format are provided. But as a quick start execute the below command to train
 a new model
 
@@ -118,7 +120,7 @@ curl 'http://localhost:5000/parse?q=hello&project=test_model'
 # FAQ
 
 ### Who is it for?
-The intended audience is mainly __people developing bots__, starting from scratch or looking to find a a drop-in replacement for [wit](https://wit.ai), [LUIS](https://www.luis.ai), or [Dialogflow](https://dialogflow.com). The setup process is designed to be as simple as possible. Rasa NLU is written in Python, but you can use it from any language through a [HTTP API](https://nlu.rasa.com/http.html). If your project is written in Python you can [simply import the relevant classes](https://nlu.rasa.com/python.html). If you're currently using wit/LUIS/Dialogflow, you just:
+The intended audience is mainly __people developing bots__, starting from scratch or looking to find a a drop-in replacement for [wit](https://wit.ai), [LUIS](https://www.luis.ai), or [Dialogflow](https://dialogflow.com). The setup process is designed to be as simple as possible. Rasa NLU is written in Python, but you can use it from any language through a [HTTP API](https://rasa.com/docs/nlu/http/). If your project is written in Python you can [simply import the relevant classes](https://rasa.com/docs/nlu/python/). If you're currently using wit/LUIS/Dialogflow, you just:
 
 1. Download your app data from wit, LUIS, or Dialogflow and feed it into Rasa NLU
 2. Run Rasa NLU on your machine and switch the URL of your wit/LUIS api calls to `localhost:5000/parse`.
@@ -128,14 +130,19 @@ The intended audience is mainly __people developing bots__, starting from scratc
 * You don't have to make a `https` call to parse every message.
 * You can tune models to work well on your particular use case.
 
-These points are laid out in more detail in a [blog post](https://blog.rasa.com/put-on-your-robot-costume-and-be-the-minimum-viable-bot-yourself/). Rasa is a set of tools for building more advanced bots, developed by the company [Rasa](https://rasa.com). Rasa NLU is the natural language understanding module, and the first component to be open-sourced. 
+These points are laid out in more detail in a 
+[blog post](https://blog.rasa.com/put-on-your-robot-costume-and-be-the-minimum-viable-bot-yourself/). 
+Rasa is a set of tools for building more advanced bots, developed by 
+the company [Rasa](https://rasa.com). Rasa NLU is the natural language 
+understanding module, and the first component to be open-sourced. 
 
 ### What languages does it support?
-It depends. Some things, like intent classification with the `tensorflow_embedding` pipeline, work in any language. 
-Other features are more restricted. See details [here](https://nlu.rasa.com/languages.html)
+The `supervised_embeddings` pipeline works in any language.
+If you want to use pre-trained word embeddings, there are models available for
+many languages. See details [here](https://rasa.com/docs/nlu/languages/)
 
 ### How to contribute
-We are very happy to receive and merge your contributions. There is some more information about the style of the code and docs in the [documentation](https://nlu.rasa.com/contribute.html).
+We are very happy to receive and merge your contributions. There is some more information about the style of the code and docs in the [documentation](https://rasa.com/docs/contributing/).
 
 In general the process is rather simple:
 1. create an issue describing the feature you want to work on (or have a look at issues with the label [help wanted](https://github.com/RasaHQ/rasa_nlu/issues?q=is%3Aissue+is%3Aopen+label%3A%22help+wanted%22))
@@ -160,7 +167,7 @@ pip install -r alt_requirements/requirements_dev.txt
 pip install -e .
 ```
 
-To test the installation use (this will run a very stupid default model. you need to [train your own model](https://nlu.rasa.com/tutorial.html) to do something useful!):
+To test the installation use (this will run a very stupid default model. you need to [train your own model](https://rasa.com/docs/nlu/quickstart/) to do something useful!):
 
 ### Advanced Docker
 Before you start, ensure you have the latest version of docker engine on your machine. You can check if you have docker installed by typing ```docker -v``` in your terminal.
@@ -172,7 +179,7 @@ docker run -p 5000:5000 rasa/rasa_nlu:latest-full
 
 There are also three volumes, which you may want to map: `/app/projects`, `/app/logs`, and `/app/data`. It is also possible to override the config file used by the server by mapping a new config file to the volume `/app/config.json`. For complete docker usage instructions go to the official [docker hub readme](https://hub.docker.com/r/rasa/rasa_nlu/).
 
-To test run the below command after the container has started. For more info on using the HTTP API see [here](https://nlu.rasa.com/http.html#endpoints)
+To test run the below command after the container has started. For more info on using the HTTP API see [here](https://rasa.com/docs/nlu/http/#endpoints)
 ```
 curl 'http://localhost:5000/parse?q=hello'
 ```

alt_requirements/conda-requirements.txt

@@ -1,2 +1,2 @@
 scipy==1.1.0
-scikit-learn==0.19.1
+scikit-learn==0.20.2

alt_requirements/requirements_full.txt

@@ -2,13 +2,13 @@
 -r requirements_bare.txt
 
 # Spacy Requirements
--r requirements_spacy_sklearn.txt
+-r requirements_pretrained_embeddings_spacy.txt
 
 # Tensorflow Requirements
--r requirements_tensorflow_sklearn.txt
+-r requirements_supervised_embeddings.txt
 
 # MITIE Requirements
--r requirements_mitie.txt
+-r requirements_pretrained_embeddings_mitie.txt
 
 duckling==1.8.0
 Jpype1==0.6.2

alt_requirements/requirements_tensorflow_sklearn.txt → alt_requirements/requirements_supervised_embeddings.txt

@@ -4,5 +4,3 @@
 tensorflow==1.12.0
 scipy==1.1.0
 sklearn-crfsuite==0.3.6
-keras-applications==1.0.6
-keras-preprocessing==1.0.5

docker/Dockerfile_mitie → docker/Dockerfile_pretrained_embeddings_mitie_en

@@ -20,7 +20,7 @@ WORKDIR ${RASA_NLU_HOME}
 
 COPY . ${RASA_NLU_HOME}
 
-RUN pip install -r alt_requirements/requirements_mitie.txt
+RUN pip install -r alt_requirements/requirements_pretrained_embeddings_mitie.txt
 
 RUN pip install -e .
 

docker/Dockerfile_pretrained_embeddings_spacy_de

@@ -0,0 +1,34 @@
+FROM python:3.6-slim
+
+ENV RASA_NLU_DOCKER="YES" \
+    RASA_NLU_HOME=/app \
+    RASA_NLU_PYTHON_PACKAGES=/usr/local/lib/python3.6/dist-packages
+
+# Run updates, install basics and cleanup
+# - build-essential: Compile specific dependencies
+# - git-core: Checkout git repos
+RUN apt-get update -qq \
+    && apt-get install -y --no-install-recommends build-essential git-core openssl libssl-dev libffi6 libffi-dev curl  \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*
+
+WORKDIR ${RASA_NLU_HOME}
+
+COPY . ${RASA_NLU_HOME}
+
+# use bash always
+RUN rm /bin/sh && ln -s /bin/bash /bin/sh
+
+RUN pip install -r alt_requirements/requirements_pretrained_embeddings_spacy.txt
+
+RUN pip install -e .
+
+RUN pip install https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.0.0/de_core_news_sm-2.0.0.tar.gz --no-cache-dir > /dev/null \
+    && python -m spacy link de_core_news_sm de
+
+VOLUME ["/app/projects", "/app/logs", "/app/data"]
+
+EXPOSE 5000
+
+ENTRYPOINT ["./entrypoint.sh"]
+CMD ["start", "-c", "config.yml", "--path", "/app/projects"]

docker/Dockerfile_spacy_sklearn → docker/Dockerfile_pretrained_embeddings_spacy_en

@@ -19,16 +19,12 @@ COPY . ${RASA_NLU_HOME}
 # use bash always
 RUN rm /bin/sh && ln -s /bin/bash /bin/sh
 
-RUN pip install -r alt_requirements/requirements_spacy_sklearn.txt
+RUN pip install -r alt_requirements/requirements_pretrained_embeddings_spacy.txt
 
 RUN pip install -e .
 
 RUN pip install https://github.com/explosion/spacy-models/releases/download/en_core_web_md-2.0.0/en_core_web_md-2.0.0.tar.gz --no-cache-dir > /dev/null \
-    && python -m spacy link en_core_web_md en \
-    && pip install https://github.com/explosion/spacy-models/releases/download/de_core_news_sm-2.0.0/de_core_news_sm-2.0.0.tar.gz --no-cache-dir > /dev/null \
-    && python -m spacy link de_core_news_sm de
-
-COPY sample_configs/config_spacy.yml ${RASA_NLU_HOME}/config.yml
+    && python -m spacy link en_core_web_md en 
 
 VOLUME ["/app/projects", "/app/logs", "/app/data"]
 

docker/Dockerfile_tensorflow → docker/Dockerfile_supervised_embeddings

@@ -16,11 +16,11 @@ WORKDIR ${RASA_NLU_HOME}
 
 COPY . ${RASA_NLU_HOME}
 
-RUN pip install -r alt_requirements/requirements_tensorflow_sklearn.txt
+RUN pip install -r alt_requirements/requirements_supervised_embeddings.txt
 
 RUN pip install -e .
 
-COPY sample_configs/config_embedding.yml ${RASA_NLU_HOME}/config.yml
+COPY sample_configs/config_supervised_embedding.yml ${RASA_NLU_HOME}/config.yml
 
 VOLUME ["/app/projects", "/app/logs", "/app/data"]