💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Related tags

Text Data & NLP nlp bot machine-learning natural-language-processing bots botkit chatbot bot-framework nlu spacy mitie chatbots machine-learning-library wit rasa conversational-agents conversational-bots chatbots-framework conversational-ai conversation-driven-development
Overview

Rasa Open Source

Join the chat on Rasa Community Forum PyPI version Supported Python Versions Build Status Coverage Status Documentation Status Documentation Build FOSSA Status PRs Welcome

Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual assistants on:

  • Facebook Messenger
  • Slack
  • Google Hangouts
  • Webex Teams
  • Microsoft Bot Framework
  • Rocket.Chat
  • Mattermost
  • Telegram
  • Twilio
  • Your own custom conversational channels

or voice assistants as:

  • Alexa Skills
  • Google Home Actions

Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forth. In order for a human to have a meaningful exchange with a contextual assistant, the assistant needs to be able to use context to build on things that were previously discussed – Rasa enables you to build assistants that can do this in a scalable way.

There's a lot more background information in this blog post.

Where to get help

There is extensive documentation in the Rasa Docs. Make sure to select the correct version so you are looking at the docs for the version you installed.

Please use Rasa Community Forum for quick answers to questions.

README Contents:

How to contribute

We are very happy to receive and merge your contributions into this repository!

To contribute via pull request, follow these steps:

  1. Create an issue describing the feature you want to work on (or have a look at the contributor board)
  2. Write your code, tests and documentation, and format them with black
  3. Create a pull request describing your changes

For more detailed instructions on how to contribute code, check out these code contributor guidelines.

You can find more information about how to contribute to Rasa (in lots of different ways!) on our website..

Your pull request will be reviewed by a maintainer, who will get back to you about any necessary changes or questions. You will also be asked to sign a Contributor License Agreement.

Development Internals

Installing Poetry

Rasa uses Poetry for packaging and dependency management. If you want to build it from source, you have to install Poetry first. This is how it can be done:

curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | python

There are several other ways to install Poetry. Please, follow the official guide to see all possible options.

Managing environments

The official Poetry guide suggests to use pyenv or any other similar tool to easily switch between Python versions. This is how it can be done:

pyenv install 3.7.6
pyenv local 3.7.6  # Activate Python 3.7.6 for the current project

By default, Poetry will try to use the currently activated Python version to create the virtual environment for the current project automatically. You can also create and activate a virtual environment manually — in this case, Poetry should pick it up and use it to install the dependencies. For example:

python -m venv .venv
source .venv/bin/activate

You can make sure that the environment is picked up by executing

poetry env info

Building from source

To install dependencies and rasa itself in editable mode execute

make install

Running and changing the documentation

First of all, install all the required dependencies:

make install install-docs

After the installation has finished, you can run and view the documentation locally using:

make livedocs

It should open a new tab with the local version of the docs in your browser; if not, visit http://localhost:3000 in your browser. You can now change the docs locally and the web page will automatically reload and apply your changes.

Running the Tests

In order to run the tests, make sure that you have the development requirements installed:

make prepare-tests-ubuntu # Only on Ubuntu and Debian based systems
make prepare-tests-macos  # Only on macOS

Then, run the tests:

make test

They can also be run at multiple jobs to save some time:

JOBS=[n] make test

Where [n] is the number of jobs desired. If omitted, [n] will be automatically chosen by pytest.

Resolving merge conflicts

Poetry doesn't include any solution that can help to resolve merge conflicts in the lock file poetry.lock by default. However, there is a great tool called poetry-merge-lock. Here is how you can install it:

pip install poetry-merge-lock

Just execute this command to resolve merge conflicts in poetry.lock automatically:

poetry-merge-lock

Build a Docker image locally

In order to build a Docker image on your local machine execute the following command:

make build-docker

The Docker image is available on your local machine as rasa:localdev.

Code Style

To ensure a standardized code style we use the formatter black. To ensure our type annotations are correct we use the type checker pytype. If your code is not formatted properly or doesn't type check, GitHub will fail to build.

Formatting

If you want to automatically format your code on every commit, you can use pre-commit. Just install it via pip install pre-commit and execute pre-commit install in the root folder. This will add a hook to the repository, which reformats files on every commit.

If you want to set it up manually, install black via poetry install. To reformat files execute

make formatter

Type Checking

If you want to check types on the codebase, install mypy using poetry install. To check the types execute

make types

Deploying documentation updates

We use Docusaurus v2 to build docs for tagged versions and for the main branch. The static site that gets built is pushed to the documentation branch of this repo.

We host the site on netlify. On main branch builds (see .github/workflows/documentation.yml), we push the built docs to the documentation branch. Netlify automatically re-deploys the docs pages whenever there is a change to that branch.

Releases

Release Timeline for Minor Releases

For Rasa Open Source, we usually commit to time-based releases, specifically on a monthly basis. This means that we commit beforehand to releasing a specific version of Rasa Open Source on a specific day, and we cannot be 100% sure what will go in a release, because certain features may not be ready.

At the beginning of each quarter, the Rasa team will review the scheduled release dates for all products and make sure they work for the projected work we have planned for the quarter, as well as work well across products.

Once the dates are settled upon, we update the respective milestones.

Cutting a Major / Minor release

A week before release day

  1. Make sure the milestone already exists and is scheduled for the correct date.
  2. Take a look at the issues & PRs that are in the milestone: does it look about right for the release highlights we are planning to ship? Does it look like anything is missing? Don't worry about being aware of every PR that should be in, but it's useful to take a moment to evaluate what's assigned to the milestone.
  3. Post a message on the engineering Slack channel, letting the team know you'll be the one cutting the upcoming release, as well as:
    1. Providing the link to the appropriate milestone
    2. Reminding everyone to go over their issues and PRs and please assign them to the milestone
    3. Reminding everyone of the scheduled date for the release

A day before release day

  1. Go over the milestone and evaluate the status of any PR merging that's happening. Follow up with people on their bugs and fixes. If the release introduces new bugs or regressions that can't be fixed in time, we should discuss on Slack about this and take a decision to go forward or postpone the release. The PR / issue owners are responsible for communicating any issues which might be release relevant.

Release day! 🚀

  1. At the start of the day, post a small message on slack announcing release day!. Communicate you'll be handling the release, and the time you're aiming to start releasing (again, no later than 4pm, as issues may arise and cause delays)
  2. Make sure the milestone is empty (everything has been either merged or moved to the next milestone)
  3. Once everything in the milestone is taken care of, post a small message on Slack communicating you are about to start the release process (in case anything is missing).
  4. You may now do the release by following the instructions outlined in the Rasa Open Source README !

Steps to release a new version

Releasing a new version is quite simple, as the packages are build and distributed by GitHub Actions.

Terminology:

  • micro release (third version part increases): 1.1.2 -> 1.1.3
  • minor release (second version part increases): 1.1.3 -> 1.2.0
  • major release (first version part increases): 1.2.0 -> 2.0.0

Release steps:

  1. Make sure all dependencies are up to date (especially Rasa SDK)
    • For Rasa SDK that means first creating a new Rasa SDK release (make sure the version numbers between the new Rasa and Rasa SDK releases match)
    • Once the tag with the new Rasa SDK release is pushed and the package appears on pypi, the dependency in the rasa repository can be resolved (see below).
  2. Switch to the branch you want to cut the release from (main in case of a major / minor, the current feature branch for micro releases)
    • Update the rasa-sdk entry in pyproject.toml with the new release version and run poetry update. This creates a new poetry.lock file with all dependencies resolved.
    • Commit the changes with git commit -am "bump rasa-sdk dependency" but do not push them. They will be automatically picked up by the following step.
  3. Run make release
  4. Create a PR against main or the release branch (e.g. 1.2.x)
  5. Once your PR is merged, tag a new release (this SHOULD always happen on main or release branches), e.g. using 
    git tag 1.2.0 -m "next release"
git push origin 1.2.0
    GitHub will build this tag and publish the build artifacts.
  6. If this is a minor release, a new release branch should be created pointing to the same commit as the tag to allow for future patch releases, e.g. 
    git checkout -b 1.2.x
git push origin 1.2.x

Cutting a Micro release

Micro releases are simpler to cut, since they are meant to contain only bugfixes.

The only things you need to do to cut a micro are:

  1. Notify the engineering team on Slack that you are planning to cut a micro, in case someone has an important fix to add.
  2. Make sure the bugfix(es) are in the release branch you will use (p.e if you are cutting a 2.0.4 micro, you will need your fixes to be on the 2.0.x release branch). All micros must come from a .x branch!
  3. Once you're ready to release the Rasa Open Source micro, checkout the branch, run make release and follow the steps + get the PR merged.
  4. Once the PR is in, pull the .x branch again and push the tag!

License

Licensed under the Apache License, Version 2.0. Copyright 2020 Rasa Technologies GmbH. Copy of the license.

A list of the Licenses of the dependencies of the project can be found at the bottom of the Libraries Summary.

Comments
  • Simple Rasa Core + NLU Rest API delivering responses

    Simple Rasa Core + NLU Rest API delivering responses

    My goal is to wrap the rasa core + nlu bot into an API so that I can call it in some JavaScript-based UI, such as a simple angularjs-chat ( http://angularjs.chat/tutorials/angularjs-basic-chat-module/).

    However, I can only use CustomInput to send a message to "localhost:5005" by POST but I cannot get the response from my bot. Is there any way that I could build/modify on top of the rasa_core? I know that it is already using Flask, so I don’t want to build another Flask app on top of that.

    In summary, I guess it would be great if we can set up an API such as "localhost:5005" to receive the questions from users and send responses back to the same channel, like what we have using the consoleInput.

    opened by jenniferzhu 55
  • Getting RASA to Work on the Raspberry Pi 4 (Buster)

    Getting RASA to Work on the Raspberry Pi 4 (Buster)

    Rasa version: 1.3.8

    Rasa X version: N/A

    Python version: 3.5

    Operating system (windows, osx, ...): Raspbian Buster

    Issue:

    I am in the process of trying to install Rasa on my Raspberry Pi 4 (4GB RAM edition) and I continue running into an error with installing or-tools stating:

      ERROR: Could not find a version that satisfies the requirement ortools (from mesh-tensorflow->tensor2tensor~=1.14.0->rasa) (from versions: none)
ERROR: No matching distribution found for ortools (from mesh-tensorflow->tensor2tensor~=1.14.0->rasa)

    Any suggestions on any potential work-arounds?

    Here is what I tried so far:

    • I tried to build or-tools on an x86_64 based computer running linux. Once I had it successfully built, I transfered it to the Pi and even though I could import ortools, I could not perform any operations using it.
    • Attempted to build or-tools on the Pi via these instructions but, given that the instructions are two years old and or-tools has changed dramatically, the process fails with the RPi's CPU spiking to 300% before shutting off. I also tried this process on my linux computer and it almost overloaded my 8GB of RAM - so it is a fairly intensive task!

    Another thought was to build a wheel for or-tools that could be compatible with arm7l architecture, but I do not know how feasible this is - it could be bundled with future releases of rasa.


    Replication of Error Procedure

    Here are the steps I took to get to this point; follow it completely to replicate my error:

    1. Freshly install a new version of Raspbian Lite (Buster) on an SD Card

    2. Run the following to update the Raspberry Pi:

      sudo apt-get update && 
sudo apt-get upgrade -y &&
sudo apt-get dist-upgrade -y

    3. Install Berryconda package manager:

      wget https://github.com/jjhelmus/berryconda/releases/download/v2.0.0/Berryconda3-2.0.0-Linux-armv7l.sh &&
chmod +x Berryconda3-2.0.0-Linux-armv7l.sh &&
./Berryconda3-2.0.0-Linux-armv7l.sh && sudo reboot

    4. Add RPI channel into conda:

      conda config --add channels rpi

    5. Install Python3.5 into conda:

      conda install python=3.5 -y

    6. Set-Up conda environment (we set-up with 3.5 so as to not encounter an error with open-cv):

      A. Create conda environment: conda create --name alpha python=3.5 pip -y

      B. Update all packages in conda environment:

      conda update --all -y &&
source activate alpha &&
pip install --upgrade pip setuptools wheel

    7. Install rasa:

      A. Install required system packages:

      sudo apt-get install libatlas-base-dev python3-dev python3-pip libhdf5-dev -y

      B. Reboot the computer

      sudo reboot

      C. Install rasa

      source activate alpha && pip install rasa

    Error (including full traceback):

    Looking in indexes: https://pypi.org/simple, https://www.piwheels.org/simple
Collecting rasa
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Collecting python-socketio>=4.3.1 (from rasa)
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Collecting networkx~=2.3 (from rasa)
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Collecting sanic-jwt~=1.3 (from rasa)
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Collecting python-telegram-bot~=11.0 (from rasa)
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Requirement already satisfied: setuptools>=41.0.0 in ./berryconda3/envs/alpha/lib/python3.5/site-packages (from rasa) (41.4.0)
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Collecting twilio~=6.0 (from rasa)
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Collecting ortools (from mesh-tensorflow->tensor2tensor~=1.14.0->rasa)
  ERROR: Could not find a version that satisfies the requirement ortools (from mesh-tensorflow->tensor2tensor~=1.14.0->rasa) (from versions: none)
ERROR: No matching distribution found for ortools (from mesh-tensorflow->tensor2tensor~=1.14.0->rasa)

    Command which led to error:

    pip install rasa


    If you have any questions, feel free to reach out - I hope we can get this working!

    type:bug :bug: help wanted 
    opened by TheCedarPrince 48
  • got concurrent.futures._base.CancelledError

    got concurrent.futures._base.CancelledError

    Rasa version: 1.1.6-full (docker image)

    Rasa X version (if used & relevant):

    Python version:

    Operating system (windows, osx, ...): docker

    Issue: seems to timeout between rasa and action server. My action takes around 10sec. I don't know why it's timeout, because DEFAULT_REQUEST_TIMEOUT is around 5 minutes.

    My action is well executed from the actions server.

    Error (including full traceback):

    Traceback (most recent call last): File "/usr/local/lib/python3.6/site-packages/rasa-1.1.6-py3.6.egg/rasa/core/processor.py", line 439, in _run_action events = await action.run(output_channel, nlg, tracker, self.domain) File "/usr/local/lib/python3.6/asyncio/coroutines.py", line 129, in throw return self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.6/site-packages/rasa-1.1.6-py3.6.egg/rasa/core/actions/action.py", line 399, in run json=json_body, method="post", timeout=DEFAULT_REQUEST_TIMEOUT File "/usr/local/lib/python3.6/asyncio/coroutines.py", line 129, in throw return self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.6/site-packages/rasa-1.1.6-py3.6.egg/rasa/utils/endpoints.py", line 144, in request **kwargs File "/usr/local/lib/python3.6/asyncio/coroutines.py", line 129, in throw return self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.6/site-packages/aiohttp/client.py", line 1005, in aenter self._resp = await self._coro File "/usr/local/lib/python3.6/asyncio/coroutines.py", line 129, in throw return self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.6/site-packages/aiohttp/client.py", line 497, in _request await resp.start(conn) File "/usr/local/lib/python3.6/asyncio/coroutines.py", line 129, in throw return self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.6/site-packages/aiohttp/client_reqrep.py", line 844, in start message, payload = await self._protocol.read() # type: ignore # noqa File "/usr/local/lib/python3.6/asyncio/coroutines.py", line 129, in throw return self.gen.throw(type, value, traceback) File "/usr/local/lib/python3.6/site-packages/aiohttp/streams.py", line 588, in read await self._waiter concurrent.futures._base.CancelledError

    Command or request that led to error:

    Content of configuration file (config.yml) (if relevant):

    Content of domain file (domain.yml) (if relevant):

    area:rasa-oss :ferris_wheel: stale area:rasa-oss/channels 📞 area:rasa-oss/custom-actions 
    opened by tormath1 48

  • "Tried to set non existent slot" error persists after deleting all instances of slots and retraining

    Rasa version: rasa-core==0.13.7 rasa-core-sdk==0.12.2 rasa-nlu==0.14.6

    Python version: Python 3.6.7

    Operating system (windows, osx, ...): Distributor ID: Ubuntu Description: Ubuntu 18.04.2 LTS Release: 18.04 Codename: bionic

    Issue: The slots which were used for previous experiments are not resetting even after removing these slots from all files (nlu.md, stories.md, domain.yml and actions.py) I tried deleting the entire model directory and trained the models again. but still it showing the errors with previously used slot. Please find the error message below.

    2019-04-22 09:56:44 ERROR    rasa_core.trackers  - Tried to set non existent slot 'environment'. Make sure you added all your s
lots to your domain file.
2019-04-22 09:56:44 ERROR    rasa_core.trackers  - Tried to set non existent slot 'platform'. Make sure you added all your slot
s to your domain file.

    I just executed a grep command from my project directory to make sure these slots are not using anymore.

    ~/project/slackbot$ grep "environment" * -R

Notebook/Conversational_Chatbot.ipynb:        "# In your environment run:\n",
(sample_bot_env) ubuntu@ip-172-31-21-57:~/project/slackbot$ grep "platform" * -R
Notebook/Conversational_Chatbot.ipynb:            "Requirement already satisfied, skipping upgrade: greenlet>=0.4.14; platform_python_implementation == \"CPython\" in /usr/local/lib/python3.6/dist-packages (from gevent->rasa_nlu[spacy]) (0.4.15)\n",
credentials.yml:# This file contains the credentials for the voice & chat platforms
nohup.out:2019-04-03 09:58:46.340534: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA

    Kindly note that I had used these slots before (not in the current stories) for doing some experiments.

    Thank you,

    status:more-details-needed 
    opened by anoop2503 47
  • MemoryError with tensorflow_embedding on ~73k dataset with 38 intents

    MemoryError with tensorflow_embedding on ~73k dataset with 38 intents

    As mentioned in the title, I am feeding in ~73k lines of training data classified into 38 intents. And I would end up using ~200k lines of messages to create my final model. But even for 73k, I get a MemoryError. This doesn't seem to be a RAM issue as I don't see my RAM getting fully used up while running the training code. Any inputs would be valuable. Below are the details:

    Rasa NLU version: 0.13.8 Operating system: Windows Server 2016

    Training the model as:

    python -m rasa_nlu.train -c nlu_config.yml --data rasa_classification_train_set.md -o models --fixed_model_name nlu_classify_75k_38ctgy --project current --verbose

    Content of model configuration file:

    language: "en"

pipeline: "tensorflow_embedding"

    Output / Issue:

    2019-01-14 08:40:41 INFO     rasa_nlu.training_data.loading  - Training data format of rasa_classification_train_set.md is md
2019-01-14 08:40:43 INFO     rasa_nlu.training_data.training_data  - Training data stats:
        - intent examples: 73962 (38 distinct intents)
** removing entity names **
        - entity examples: 0 (0 distinct entities)
        - found entities:

2019-01-14 08:40:46 INFO     rasa_nlu.model  - Starting to train component tokenizer_whitespace
2019-01-14 08:40:55 INFO     rasa_nlu.model  - Finished training component.
2019-01-14 08:40:55 INFO     rasa_nlu.model  - Starting to train component ner_crf
2019-01-14 08:40:55 INFO     rasa_nlu.model  - Finished training component.
2019-01-14 08:40:55 INFO     rasa_nlu.model  - Starting to train component ner_synonyms
2019-01-14 08:40:55 INFO     rasa_nlu.model  - Finished training component.
2019-01-14 08:40:55 INFO     rasa_nlu.model  - Starting to train component intent_featurizer_count_vectors
Traceback (most recent call last):
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\runpy.py", line 184, in _run_module_as_main
    "__main__", mod_spec)
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\site-packages\rasa_nlu\train.py", line 184, in <module>
    num_threads=cmdline_args.num_threads)
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\site-packages\rasa_nlu\train.py", line 154, in do_train
    interpreter = trainer.train(training_data, **kwargs)
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\site-packages\rasa_nlu\model.py", line 196, in train
    **context)
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\site-packages\rasa_nlu\featurizers\count_vectors_featurizer.py", line 214, in train
    X = self.vect.fit_transform(lem_exs).toarray()
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\site-packages\scipy\sparse\compressed.py", line 947, in toarray
    out = self._process_toarray_args(order, out)
  File "C:\Users\harsh.khadloya\AppData\Local\Continuum\Anaconda3\lib\site-packages\scipy\sparse\base.py", line 1184, in _process_toarray_args
    return np.zeros(self.shape, dtype=self.dtype, order=order)
MemoryError

    During this runtime, I dont see my RAM getting used up more than 6GB, even though I have a 16GB RAM. Thanks for your help!

    type:bug :bug: type:enhancement :sparkles: 
    opened by HarshKhadloya 46
  • Upgrade to TF 2.6

    Upgrade to TF 2.6

    TF 2.4 is out and hence we should update the dependency inside Rasa OS to use it. Introduces some breaking changes as mentioned in the changelog

    Update: we are now targeting 2.6 see here

    Note that there's a draft PR here that already contains some of the necessary changes. Known remaining tasks in order to actually update code for TF 2.5 are (this list contains only the known remaining tasks, there may be others hence it is not an exhaustive of all remaining tasks):

    • [x] Run model regression tests on all configs and datasets to see if training times / accuracy are the same
    • [x] see TODOs marked with 2.5 (ex: here)
    • [x] #9433
    • [x] #9512

    Temporarily de-prioritised followups:

    • [x] #9514
    • [x] #9515
    • [ ] #9516
    • [ ] #9764
    • [ ] https://github.com/RasaHQ/rasa/issues/9666
    • [ ] https://github.com/RasaHQ/rasa/issues/9798
    • [ ] https://github.com/RasaHQ/rasa/issues/9734

    Definition of done ~No memory tests failing~ ~No increased timeouts for tests~ ~Model regression tests passing with approx. same time and accuracy~

    • [X] Followup issue created: determine w DevRel how to communicate hits to community https://github.com/RasaHQ/research/issues/220
    • [X] Followup issue created: determine w CSE how to communicate hits to customers https://github.com/RasaHQ/research/issues/219
    • [X] Followup issue created: update CUDA drivers on GPU runners to be compatible with TF 2.5 : https://github.com/RasaHQ/rasa/issues/9611
    • [X] Model regression tests on GPU have approx. same accuracy
    • [X] Lockfile reflects correct TF dependency
    • [X] Create a PR for the bumped up dependency
    • [X] Create followup issue to investigate and address memory problems on Windows CI tests, so that this fix can be reverted https://github.com/RasaHQ/rasa/issues/9734
    • [X] Create followup issue to investigate and address memory problems for Hermit CI model regression tests so that they can be run on schedule again https://github.com/RasaHQ/rasa/issues/9798
    area:rasa-oss :ferris_wheel: type:dependencies area:rasa-oss/ml 👁 
    opened by dakshvar22 43
  • Wrong classification for words the model has never seen?

    Wrong classification for words the model has never seen?

    I'm having difficulty to understand why my model behaves in a certain way:

    My models are trained with a big but quite homogeneous dataset such as "give me some advice" (intent: advice) or "tell me a joke" (intent: joke). The trained model works very well for similar queries. However, when seeing new phrases and/or words such as "apple" or "banana" that are obviously neither advice or joke, they still get classified as either advice or joke intent with a very high confidence (>85%).

    Q: Any idea why this is happening? And do you have any advice how I can do this better?

    What I have tried:

    • I have trained a fallback intent to include some of the wrong ways to get advice or joke intent. I understand that I could also add "apple" or "banana" to the list of things that should be classified as a fallback intent. However, to include all possible English words doesn't sound like a smart strategy.
    • Also might be relevant is that my dataset consists of some 20,000 examples for each advice and joke intent, and the dataset for fallback intent is only around 20 examples. The imbalance in the data might contribute to the issue.
    type:question :speech_balloon: 
    opened by kuriakinzeng 42
  • I want to add google's bert as a new feature

    I want to add google's bert as a new feature

    I have tested Google's BERT as a text classifier, it performances very well in my company. So, I want to migrate BERT's power to RASA NLU. I'm workding on it recently, does anyone else interest?

    type:enhancement :sparkles: 
    opened by winstarwang 41
  • Exploration of new docs build system

    Exploration of new docs build system

    Proposed changes:

    • Exploration of new docs build system

    • related to https://github.com/RasaHQ/growth/issues/1436

    • not all pages have been added to the new system. all of them are converted, if you are missing a page you'd like to take a look at you need to add it to newdocs/sidebars.js

    To view the page, navigate into newdocs, run yarn to install dependencies and then yarn start. A browser page with the docs should pop up.

    The converter and necessary changes to fix some of the markdown issues can be found here https://github.com/RasaHQ/sphinx-markdown-builder/pull/1 - should be extended and adapted to fix more issues.

    How to build the MDX docs based on RST

    All of the new docs pages have been created automatically. If there are any changes in the RST docs (e.g. by new features or changes merged into master), the MDX docs can be regenerated:

    • checkout and install https://github.com/RasaHQ/sphinx-markdown-builder/pull/1 using git clone git://github.com/RasaHQ/sphinx-markdown-builder.git, git checkout convert-docs and pip install -e . (this should be in your rasa poetry env, e.g. try running poetry shell before the pip install)
    • checkout this branch and cd docs and run make markdown

    This will tell sphinx to build the docs and output markdown (instead of the usual HTML). The converter is pretty flexible and if we notice any issues, we can further customize the sphinx-markdown-builder based on the above PR.

    While converting, sphinx will create somewhat of an AST and then walk through these nodes. for each node in the doc there is a hook that decides how to convert that node into markdown.

    Remaining work before this can be merged is tracked in https://github.com/RasaHQ/rasa/issues/6165

    TODO

    • [x] move newdocs to docs. rename existing occurrences, e.g. https://github.com/RasaHQ/rasa/pull/6043/files#diff-793229551c14ebd7f4c0f9236d054ee0R40 077755869f71a9771855193a0c1ebb3b88908c54 and 4b180773a7eca14f344c44727b5793369bb8c0c2
    • [x] remove FIXMEs in scripts/push_docs_to_branch.sh script 4b180773a7eca14f344c44727b5793369bb8c0c2
    • [x] fix /docs/ links everywhere f758baf68b9
    • [x] support top-level CHANGELOG in the docs (similar setup than variables.json) 968e4a1b418
    • [x] fix make docs (failing because of redoc)
    • [x] setup Netlify redirection (see https://github.com/RasaHQ/rasa-website/pull/763)
    • [x] links appear to be broken after last alpha update e298750892f
    • [x] prepare prototyper move https://github.com/RasaHQ/rasa/pull/6298

    Fixes https://github.com/RasaHQ/rasa/issues/6170

    opened by tmbo 40
  • Rasa command line interface

    Rasa command line interface

    Goal

    • intuitive
    • short commands (if default configuration)
    • reuse existing knowledge
    • unify interface for Rasa Core / NLU

    General Idea

    Assume a rasa project with a default project layout. This means we can assume the location and name of files (e.g. domain.yml is always in the project root and has this name). By looking in default locations for files the user can skip their declaration. If users deviate from the normal project layout, they have to specify the locations manually.

    Assumed project layout

    .
├── __init__.py
├── actions.py
├── endpoints.yml
├── credentials.yml
├── data
├── domain.yml
├── models
│   ├── core
│   └── nlu
├── config.yml (contains both core and nlu config)

    Suggested command line commands:

    Creating the initial project (new feature)

    • rasa init + interactive guide to create project (e.g. provide project name, etc)

    Training

    • Training of Core and NLU: rasa train
    • Training of the core model: rasa train core
    • Training of the nlu model:rasa train nlu
    • Interactive Training: rasa interactive (or rasa train interactive?)
    • Special case compare polices: rasa train core --config policy1.yml policy2.yml

    Validating

    • test both: rasa test
    • test only core: rasa test core
    • test only nlu: rasa test nlu

    Run Server

    • Start Core with trained NLU model: rasa run
    • Run Core server: rasa run core
    • Run NLU server: rasa run nlu
    • Run action server: rasa run sdk (should we include the sdk or keep that separate?)

    Visualize

    • Show story paths: rasa show (alternatives: rasa inspect, rasa visualize)

    Data

    There is currently just one command in this section, but we could have other commands like rasa data validate <file> or rasa data split here in the future

    • Convert nlu data: rasa data nlu convert

    Interacting with the server

    rasa remote

    Crucial

    • provide credentials: rasa remote login <host> (where / how do we store them)
    • select environment (prod / dev / test): rasa remote stages <env>
    • set model as active: rasa remote models select <model-id>
    • pushing models: rasa remote models push <model>
    • downloading models: rasa remote models pull <model>
    • push data: rasa remote data push
    • pull data: rasa remote data pull

    Nice to have

    • list models: rasa remote models list
    • list users: rasa remote users list
    • status : rasa remote status
    • logs: rasa remote logs
    • show config: rasa remote config

    Interacting with local server

    rasa local commands as above for server except

    • no login
    • pushing / downloading are not needed (set as active should be enough)

    Other

    • rasa --version
    • rasa --help

    Parameter Names

    • tbd, general idea is to keep them like they are, but unify them between Core and NLU

    Current Status

    • [x] rasa init
    • [x] rasa run
    • [x] rasa train
    • [x] rasa test
    • [x] rasa show
    • [ ] rasa remote
    • [ ] rasa local
    • [x] rasa data

    How to test

    https://github.com/RasaHQ/rasa_stack/issues/1#issuecomment-462871671

    opened by tmbo 39
  • Slack triggers multiple messages to rasa (does not happen with Telegram/Commandline)

    Slack triggers multiple messages to rasa (does not happen with Telegram/Commandline)

    Rasa Core version: 0.9.0a5

    Python version: 3.6

    Operating system (windows, osx, ...): ubuntu 18.04

    Issue: Slack triggers multiple messages to rasa and NO, this does not happen with Telegram and Commandline Interface (!) with the very same trained RASA_NLU and RASA_CORE models (!!) Please, Rasa-Team: Try to reproduce that with any kind of story!

    Content of domain file (if used & relevant):
    opened by ewagner70 38
  • Bump httpx from 0.23.0 to 0.23.3

    Bump httpx from 0.23.0 to 0.23.3

    Bumps httpx from 0.23.0 to 0.23.3.

    Release notes

    Sourced from httpx's releases.

    Version 0.23.3

    0.23.3 (4th Jan, 2023)

    Fixed

    • Version 0.23.2 accidentally included stricter type checking on query parameters. This shouldn've have been included in a minor version bump, and is now reverted. (#2523, #2539)

    Version 0.23.2

    0.23.2 (2nd Jan, 2023)

    Added

    • Support digest auth nonce counting to avoid multiple auth requests. (#2463)

    Fixed

    • Multipart file uploads where the file length cannot be determine now use chunked transfer encoding, rather than loading the entire file into memory in order to determine the Content-Length. (#2382)
    • Raise TypeError if content is passed a dict-instance. (#2495)
    • Partially revert the API breaking change in 0.23.1, which removed RawURL. We continue to expose a url.raw property which is now a plain named-tuple. This API is still expected to be deprecated, but we will do so with a major version bump. (#2481)

    Version 0.23.1

    0.23.1

    Added

    Fixed

    • Don't drop empty query parameters. (#2354)

    Removed

    • Drop .read/.aread from SyncByteStream/AsyncByteStream (#2407)
    • Drop RawURL. (#2241)
    Changelog

    Sourced from httpx's changelog.

    0.23.3 (4th Jan, 2023)

    Fixed

    • Version 0.23.2 accidentally included stricter type checking on query parameters. This shouldn've have been included in a minor version bump, and is now reverted. (#2523, #2539)

    0.23.2 (2nd Jan, 2023)

    Added

    • Support digest auth nonce counting to avoid multiple auth requests. (#2463)

    Fixed

    • Multipart file uploads where the file length cannot be determine now use chunked transfer encoding, rather than loading the entire file into memory in order to determine the Content-Length. (#2382)
    • Raise TypeError if content is passed a dict-instance. (#2495)
    • Partially revert the API breaking change in 0.23.1, which removed RawURL. We continue to expose a url.raw property which is now a plain named-tuple. This API is still expected to be deprecated, but we will do so with a major version bump. (#2481)

    0.23.1 (18th Nov, 2022)

    Note: The 0.23.1 release should have used a proper version bump, rather than a minor point release. There are API surface area changes that may affect some users. See the "Removed" section of these release notes for details.

    Added

    Fixed

    • Don't drop empty query parameters. (#2354)

    Removed

    • Upload files must always be opened in binary mode. (#2400)
    • Drop .read/.aread from SyncByteStream/AsyncByteStream. (#2407)
    • Drop RawURL. (#2241)
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    type:dependencies release:main 
    opened by dependabot[bot] 0
  • Bump tensorflow-addons from 0.17.1 to 0.19.0

    Bump tensorflow-addons from 0.17.1 to 0.19.0

    Bumps tensorflow-addons from 0.17.1 to 0.19.0.

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    type:dependencies release:main 
    opened by dependabot[bot] 0
  • Bump gitpython from 3.1.29 to 3.1.30

    Bump gitpython from 3.1.29 to 3.1.30

    Bumps gitpython from 3.1.29 to 3.1.30.

    Release notes

    Sourced from gitpython's releases.

    v3.1.30 - with important security fixes

    See gitpython-developers/GitPython#1515 for details.

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    type:dependencies release:main 
    opened by dependabot[bot] 0
  • Bump voxmedia/github-action-slack-notify-build from 1.5.0 to 1.6.0

    Bump voxmedia/github-action-slack-notify-build from 1.5.0 to 1.6.0

    Bumps voxmedia/github-action-slack-notify-build from 1.5.0 to 1.6.0.

    Release notes

    Sourced from voxmedia/github-action-slack-notify-build's releases.

    v1.6.0

    1.6.0 (2023-01-03)

    Features

    Changelog

    Sourced from voxmedia/github-action-slack-notify-build's changelog.

    1.6.0 (2023-01-03)

    Features

    1.5.0 (2021-03-05)

    Features

    • putting repo name more prominently (373da97)

    1.4.0 (2021-03-02)

    Bug Fixes

    Features

    1.3.1 (2021-03-01)

    Bug Fixes

    1.3.0 (2021-03-01)

    Bug Fixes

    Features

    • adding job name in Slack message (8189fb3)

    ... (truncated)

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  • Bump json5 from 1.0.1 to 1.0.2 in /docs

    Bump json5 from 1.0.1 to 1.0.2 in /docs

    Bumps json5 from 1.0.1 to 1.0.2.

    Release notes

    Sourced from json5's releases.

    v1.0.2

    • Fix: Properties with the name __proto__ are added to objects and arrays. (#199) This also fixes a prototype pollution vulnerability reported by Jonathan Gregson! (#295). This has been backported to v1. (#298)
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    Unreleased [code, diff]

    v2.2.3 [code, diff]

    v2.2.2 [code, diff]

    • Fix: Properties with the name __proto__ are added to objects and arrays. (#199) This also fixes a prototype pollution vulnerability reported by Jonathan Gregson! (#295).

    v2.2.1 [code, diff]

    • Fix: Removed dependence on minimist to patch CVE-2021-44906. (#266)

    v2.2.0 [code, diff]

    • New: Accurate and documented TypeScript declarations are now included. There is no need to install @types/json5. (#236, #244)

    v2.1.3 [code, diff]

    • Fix: An out of memory bug when parsing numbers has been fixed. (#228, #229)

    v2.1.2 [code, diff]

    ... (truncated)

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  • Bump actions/setup-node from 3.5.1 to 3.6.0

    Bump actions/setup-node from 3.5.1 to 3.6.0

    Bumps actions/setup-node from 3.5.1 to 3.6.0.

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    Add Support for Nightly, Canary and RC builds for Node.js

    In scope of this release we added support to download nightly, rc (actions/setup-node#611) and canary (actions/setup-node#619) Node.js distributions.

    For nightly versions:

    jobs:
  build:
    runs-on: ubuntu-latest
    name: Node sample
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: '16-nightly'
      - run: npm ci
      - run: npm test

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  build:
    runs-on: ubuntu-latest
    name: Node sample
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: '16-v8-canary’
      - run: npm ci
      - run: npm test

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  build:
    runs-on: ubuntu-latest
    name: Node sample
    steps:
      - uses: actions/checkout@v3
      - uses: actions/setup-node@v3
        with:
          node-version: '16.0.0-rc.1’
      - run: npm ci
      - run: npm test

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Releases(3.4.0)

  • 3.4.0(Dec 14, 2022)

    Rasa 3.4.0 (2022-12-14)

    Features

    • #11087: Add metadata to Websocket channel. Messages can now include a metadata object which will be included as metadata to Rasa. The metadata can be supplied on a user configurable key with the metadata_key setting in the socketio section of the credentials.yml.

    Improvements

    • #11517: Added ./docker/Dockerfile_pretrained_embeddings_spacy_it to include Spacy's Italian pre-trained model it_core_news_md.
    • #11765: Replace kafka-python dependency with confluent-kafka async Producer API.
    • #11773: Add support for Python 3.10 version.
    • #2546: Added CLI option --logging-config-file to enable configuration of custom logs formatting.

    Bugfixes

    • #11869: Implements a new CLI option --jwt-private-key required to have complete support for asymmetric algorithms as specified originally in the docs.

    Improved Documentation

    • #11766: Clarify in the documentation how to write testing stories if a user presses a button with payload.
    • #11801: Clarify prioritisation of used slot asking option in forms in documentation.

    Miscellaneous internal changes

    Source code(tar.gz)
    Source code(zip)

  • 3.2.11(Dec 6, 2022)

    • #11596: Caching Message and Features fingerprints unless they are altered, saving up to 2/3 of fingerprinting time in our tests.

    Bugfixes

    • #11829: Implements a new CLI option --jwt-private-key required to have complete support for asymmetric algorithms as specified originally in the docs.
    Source code(tar.gz)
    Source code(zip)

  • 3.3.3(Dec 1, 2022)

  • 3.3.2(Nov 30, 2022)

    • #11762: Added support for camembert french bert model

    Bugfixes

    • #11818: Fixes RuntimeWarning: coroutine 'Bot.set_webhook' was never awaited issue encountered when starting the rasa server, which caused the Telegram bot to be unresponsive.

    Improved Documentation

    • #11768: The documentation was updated for Buttons using messages that start with '/'. Previously, it wrongly stated that messages with '/' bypass NLU, which is not the case.
    • #11799: Add documentation for Rasa Pro Services upgrades.
    Source code(tar.gz)
    Source code(zip)

  • 3.3.1(Nov 10, 2022)

    • #11721: Add docs on how to set up additional data lakes for Rasa Pro analytics pipeline.
    • #11724: Update migration guide and form docs with prescriptive recommendation on how to implement dynamic forms with custom slot mappings.

    Improvements

    • #11732: Updated numpy and scikit learn version to fix vulnerabilities of those dependencies
    Source code(tar.gz)
    Source code(zip)

  • 3.3.0(Oct 24, 2022)

    • #11561: Added option --offset-timestamps-by-seconds to offset the timestamp of events when using rasa export
    • #11578: Rasa supports native installations on Apple Silicon (M1 / M2). Please follow the installation instructions and take a look at the limitations.
    • #11596: Caching Message and Features fingerprints unless they are altered, saving up to 2/3 of fingerprinting time in our tests.
    • #11598: Added package versions of component dependencies as an additional part of fingerprinting calculation. Upgrading an dependency will thus lead to a retraining of the component in the future. Also, by changing fingerprint calculation, the next training after this change will be a complete retraining.
    • #11623: Export events continuously rather than loading all events in memory first when using rasa export. Events will be streamed right from the start rather than loading all events first and pushing them to the broker afterwards.
    • #11568: Adds new dependency pluggy, with which it was possible to implement new plugin functionality. This plugin manager enables the extension and/or enhancement of the Rasa command line interface with functionality made available in the rasa-plus package.

    Bugfixes

    • #11575: Fix BlockingIOError when running rasa interactive, after the upgrade of prompt-toolkit dependency.
    • #11395: Fixes a bug that lead to initial slot values being incorporated into all rules by default, thus breaking most rules when the slot value changed from its initial value
    • #11429: Made logistic regression classifier output a proper intent ranking and made ranking length configurable

    Deprecations and Removals

    • #11190: Remove code related to Rasa X local mode as it is deprecated and scheduled for removal.

    Miscellaneous internal changes

    Source code(tar.gz)
    Source code(zip)

  • 3.3.0a1(Oct 11, 2022)

  • 3.2.10(Sep 29, 2022)

    • #11588: Fixes scenarios in which a slot with from_trigger_intent mapping that specifies an active_loop condition was being filled despite that active_loop not being activated. In addition, fixes scenario in which a slot with from_trigger_intent mapping without a specified active_loop mapping condition is only filled if the form gets activated. Removes unnecessary validation warning that a slot with from_trigger_intent and a mapping condition should be included in the form's required_slots.
    • #ato-161: Fixed a bug where DIETClassier crashed during training when both masked language modelling and evaluation during training were used.

    Improved Documentation

    • #11571: Rasa SDK documentation lives now in Rasa Open Source documentation under the Rasa SDK category.

    Miscellaneous internal changes

    Source code(tar.gz)
    Source code(zip)

  • 3.2.8(Sep 8, 2022)

  • 3.2.7(Aug 31, 2022)

    • #11448: Improve rasa data validate command so that it uses custom importers when they are defined in config file.

    Bugfixes

    • #11311: Re-instates the REST channel metadata feature. Metadata can be provided on the metadata key.
    Source code(tar.gz)
    Source code(zip)

  • 3.1.7(Aug 31, 2022)

  • 2.8.33(Aug 30, 2022)

    • #11395: Fixes a bug that lead to initial slot values being incorporated into all rules by default, thus breaking most rules when the slot value changed from its initial value
    Source code(tar.gz)
    Source code(zip)

  • 3.2.6(Aug 12, 2022)

    • #11433: This fix makes sure that when a Domain object is loaded from multiple files where one file specifies a custom session config and the rest do not, the default session configuration does not override the custom session config.

    Miscellaneous internal changes

    Source code(tar.gz)
    Source code(zip)

  • 3.2.5(Aug 5, 2022)

    • #11294: Fix KeyError which resulted when action_two_stage_fallback got executed in a project whose domain contained slot mappings.
    • #11390: Fixes regression in which slot mappings were prioritized according to reverse order as they were listed in the domain, instead of in order from first to last, as was implicitly expected in 2.x. Clarifies this implicit priority order in the docs.
    • #11394: Enables the dispatching of bot messages returned as events by slot validation actions.
    Source code(tar.gz)
    Source code(zip)

  • 3.2.4(Jul 21, 2022)

    • #11061: Added session_config key as valid domain key during domain loading from directory containing a separate domain file with session configuration.
    • #11362: Run default action action_extract_slots after a custom action returns a UserUttered event to fill any applicable slots.
    • #11368: Handle the case when an EndpointConfig object is given as parameter to the AwaitableTrackerStore.create() method.
    Source code(tar.gz)
    Source code(zip)

  • 3.1.6(Jul 20, 2022)

  • 3.1.5(Jul 15, 2022)

    • #11333: SlotSet events will be emitted when the value set by the current user turn is the same as the existing value.

      Previously, ActionExtractSlots would not emit any SlotSet events if the new value was the same as the existing one. This caused the augmented memoization policy to lose these slot values when truncating the tracker.

    Source code(tar.gz)
    Source code(zip)

  • 2.8.31(Jul 12, 2022)

  • 3.0.12(Jul 6, 2022)

  • 3.2.2(Jul 5, 2022)

  • 2.8.30(Jul 5, 2022)

    • #11131: Responses with two newlines will now be split correctly to separate messages if buttons are present, so that buttons will be sent with the last message.
    • #11293: Make YAML sequence indent configurable via env var "DEFAULT_YAML_SEQUENCE_INDENT_ENV_VAR

    Miscellaneous internal changes

    Source code(tar.gz)
    Source code(zip)

  • 2.8.29(Jun 23, 2022)

  • 3.0.11(Jun 22, 2022)

  • 3.1.4(Jun 22, 2022)

  • 3.2.1(Jun 17, 2022)

    • #10430: Fix failed check in rasa data validate that verifies forms in rules or stories are consistent with the domain when the rule or story contains a default action as active_loop step.
    Source code(tar.gz)
    Source code(zip)

  • 3.1.3(Jun 17, 2022)

    • #11129: The azure botframework channel now validates the incoming JSON Web Tokens (including signature).

      Previously, JWTs were not validated at all.

    • #11197: Backports fix for failed check in rasa data validate that verifies forms in rules or stories are consistent with the domain when the rule or story contains a default action as active_loop step.

    Source code(tar.gz)
    Source code(zip)

  • 3.2.0(Jun 14, 2022)

    • #10989: NLU training data in JSON format is deprecated and will be removed in Rasa Open Source 4.0. Please use rasa data convert nlu -f yaml --data <path to NLU data> to convert your NLU JSON data to YAML format before support for NLU JSON data is removed.

    Improvements

    • #10696: Make TrackerStore interface methods asynchronous and supply an AwaitableTrackerstore wrapper for custom tracker stores which do not implement the methods as asynchronous.
    • #10897: Added flag use_gpu to TEDPolicy and UnexpecTEDIntentPolicy that can be used to enable training on CPU even when a GPU is available.
    • #11102: Add --endpoints command line parameter to rasa train parser.

    Bugfixes

    • #11129: The azure botframework channel now validates the incoming JSON Web Tokens (including signature).

      Previously, JWTs were not validated at all.

    • #9386: rasa shell now outputs custom json unicode characters instead of \uxxxx codes

    Improved Documentation

    • #11130: Clarify aspects of the API spec
      • GET /status endpoint: Correct response schema for model_id - a string, not an object.
      • GET /conversations/{conversation_id}/tracker: Describe each of the enum options for include_events query parameter
      • POST & PUT /conversations/{conversation_id}/tracker/eventss: Events schema added for each event type
      • GET /conversations/{conversation_id}/story: Clarified the all_sessions query parameter and default behaviour.
      • POST /model/test/intents: Remove JSON payload option since it is not supported
      • POST /model/parse: Explain what emulation_mode is and how it affects response results

    Miscellaneous internal changes

    Source code(tar.gz)
    Source code(zip)

  • 3.1.2(Jun 8, 2022)

  • 3.1.1(Jun 3, 2022)

    • #10480: Remove warning for Rasa X localmode not being supported when the --production flag is present.
    • #10908: Pin requirement for scipy<1.8.0 since scipy>=1.8.0 is not backward compatible with scipy<1.8.0 and additionally requires Python>=3.8, while Rasa supports Python 3.7 as well.
    • #11149: Fix the extraction of values for slots with mapping conditions from trigger intents that activate a form, which was possible in 2.x.
    Source code(tar.gz)
    Source code(zip)

  • 2.8.28(May 20, 2022)

    • #10482: Adds a new optional config argument prefix_suffix_case_sensitive which can be set to False to ignore cases in prefix and suffix functions.
    • #11122: Added flag use_gpu to TEDPolicy and UnexpecTEDIntentPolicy that can be used to enable training on CPU even when a GPU is available.
    Source code(tar.gz)
    Source code(zip)
Owner
Rasa
Open source machine learning tools for developers to build, improve, and deploy text-and voice-based chatbots and assistants
Rasa
GitHub https://rasa.com/docs/
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

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