Saravanan666/pypi-org huggingface dataset:
Package: rasa-core-sdk
Rasa Python-SDKPython SDK for the development of custom actions for Rasa Core.InstallationTo install the SDK runpipinstallrasa_core_sdkCompatibilitySDK versioncompatible Rasa Core version0.12.x>=0.12.x0.11.x0.11.xnot compatible<=0.10.xUsageDetailed instructions can be found in the Rasa Core Documentation aboutCustom Actions.DockerUsageIn order to start an action server using implemented custom actions,
you can use the available Docker imagerasa/rasa_core_sdk:latest.Before starting the action server ensure that the folder containing
your actions is handled as Python module and therefore has to contain
a file called__init__.pyThen start the action server using:dockerrun-p5055:5055--mounttype=bind,source=<ABSOLUTE_PATH_TO_YOUR_ACTIONS>,target=/app/actions\rasa/rasa_core_sdk:latestThe action server is then avaible athttp://localhost:5055/webhook.Custom DependenciesTo add custom dependencies you enhance the given Docker image, e.g.:FROM rasa/rasa_core_sdk:latest

# To install system dependencies
RUN apt-get update -qq && \
    apt-get install -y <NAME_OF_REQUIRED_PACKAGE> && \
    apt-get clean && \
    rm -rf /var/lib/apt/lists/* /tmp/* /var/tmp/*

# To install packages from PyPI
RUN pip install --no-cache-dir <A_REQUIRED_PACKAGE_ON_PYPI>Code StyleTo ensure a standardized code style we use the formatterblack.
If your code is not formatted properly, travis will fail to build.If you want to automatically format your code on every commit, you can usepre-commit.
Just install it viapip install pre-commitand executepre-commit install.If you want to set it up manually, install black viapip install black.
To reformat files executeblack .LicenseLicensed under the Apache License, Version 2.0. Copyright 2018 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 theLibraries Summary.
Package: rasa-dialogflow-interpreter
rasa-dialogflow-interpreterrasa_core interpreter connecting to dialogflow.com API v2Arasa_coreInterpreterthat sources intent data from dialogflow.com API v2. This means you can runrasa_coreand do Natural Language Understanding using Dialogflow.InstallationInstall using pip:pip install rasa-dialogflow-interpreterUsagefromrasa_dialogflow_interpreter.interpreterimportDialogflowInterpreterfromrasa_core.agentimportAgentagent=Agent.load('path/to/dialogue/models',interpreter=DialogflowInterpreter('dialogflow-project-name',# if you omit the `service_account_json` parameter the value# exported to GOOGLE_APPLICATION_CREDENTIALS will be used insteadservice_account_json='dialogflow-project-name.json',))msg=agent.handle_text('What does it all mean, Dialogflow?')Note that due to the way that Dialogflow currently works, the returnedentitieswill not havestartandendvalues.LicenseMIT ©Frederik Ring
Package: rasa-ext-plugin
rasa extension pluginExtends rasa core through component plugin to include:max_event_history:  limits the tracker to maximum of latest 'N ' eventsExtendedDialogueStateTracker: optimize payload to action server to exclude tracker events.InstallationInstall the plugin (requires python3)Online from pypi:pipinstallrasa-ext-pluginOffline:gitclonehttps://github.com/AppliedSoul/rasa_ext_plugin.git
pipinstall-erasa_ext_pluginIn RASAendpoints.yaml, setup the plugin tracker store############################################################ limiter tracker for existing store types :# rasa_ext_plugin.core.tracker_store.InMemoryLimiterTrackerStore# rasa_ext_plugin.core.tracker_store.RedisLimiterTrackerStore# rasa_ext_plugin.core.tracker_store.MongoLimiterTrackerStore# rasa_ext_plugin.core.tracker_store.SQLLimiterTrackerStore## All limiter tracker store optionally accepts:# max_event_history - limit maximum events tracked by tracker#############################################################tracker_store:#  type: rasa_ext_plugin.core.tracker_store.InMemoryLimiterTrackerStore#  url: localhost#  max_event_history: 40tracker_store:type:rasa_ext_plugin.core.tracker_store.RedisLimiterTrackerStoreurl:localhostport:6379db:0#    password:#    record_exp:max_event_history:40Startup the rasa server.Customizing tracker payload sent to remote action serverRefer to  [tracker_store]ExtendedDialogueStateTracker, modify the return state fromcurrent_statemethod.classExtendedDialogueStateTracker(DialogueStateTracker):# override# Actions uses this method to collect tracker's metadata# before sending payload as json.defcurrent_state(self,event_verbosity:EventVerbosity=EventVerbosity.NONE)->Dict[Text,Any]:# conversation state format#  {#    "sender_id": self.sender_id,#    "slots": self.current_slot_values(),#    "latest_message": self.latest_message.parse_data,#    "latest_event_time": latest_event_time,#    "followup_action": self.followup_action,#    "paused": self.is_paused(),#    "events": evts,#    "latest_input_channel": self.get_latest_input_channel(),#    "active_form": self.active_form,#    "latest_action_name": self.latest_action_name,# }state=super(ExtendedDialogueStateTracker,self).current_state(event_verbosity)## request payload to action server contains tracker information# from this state# removing events from state information# action server currently ignores event history# reduces size of request payload to action server## additional unwanted keys can be poped to reduce size#state.pop("events",None)returnstate
Package: rasa-gen
RasaGen: ARasachatbot training data generator(中文版) | (English)InstallationInstall the latest version ofRasaGenby running:pip install rasa_genor install with source code:pip install git+https://github.com/SchweitzerGAO/rasa-train-generatorBasic usageAn ExampleThough the template in this example is in Chinese, It supports mainstream languages like English, French and Japanese etc.fromrasa_genimportNLUTemplate,Generatorif__name__=='__main__':sentence_template=['[{}](operation_set_temp)[{}]{{"entity":"value","role":"temperature"}}度','把[{}](operation_set_temp)[{}]{{"entity":"value","role":"temperature"}}度','空调[{}](operation_set_temp)[{}]{{"entity":"value","role":"temperature"}}度','把空调[{}](operation_set_temp)[{}]{{"entity":"value","role":"temperature"}}度',]word_template=['温度降低到','温度升高到','温度升高至','温度降低至','温度调整到','温度调整至','温度调到','温度调至',]template=NLUTemplate().add_sentence(sentence_template)\.add_word(word_template)\.add_random_val(16,30)generator=Generator('test_intent').add_template(template)generator.generate_from_template(50,'./test_template.yml')A detailed example is inexample.pyCreating aNLUTemplateAs shown in the example,
you can create a NLU training data generating template by creating aNLUTemplateinstance and add sentence,
word and random value to fill
in the template in a streaming way.Using aGeneratorCreate aGeneratorYou can create aGeneratorinstance
with specifying the name of the key of the training data.
For example,
you shall specify the name ofintentwhen generating data for an intent
or the name oflookupfor a lookup table.Note: If you are generating data from a csv or tsv file
with the name oflookupin the first column,
there is no need to specify
the name when creating aGeneratorFor now, onlyintentandlookupdata types innluclass are supported.
Other types likeruleandstorywill be supported in the futureGenerate data by aGeneratorThere are 2 supported ways to generate data by aGeneratorFrom a template (Recommended forintentdata)You can add aTemplateinstance byadd_templatemethod and generate the data bygenerate_from_templatemethod.From a file (Recommended forlookupdata)There is no need to createTemplateinstances.
Just specify the input file and output file
and use thegenerate_from_filemethod will be OK.Coming Soon...Generatingstory,synonymandruledataThe detaileddocumentwill be released soon.
Package: rasa-hydra
Rasa-Hydra (Forked from Rasa)Rasa-Hydra is forked from the official Rasa repo and maintained by the Hydra team. Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa-Hydra, you can build chatbots on:Facebook MessengerSlackMicrosoft Bot FrameworkRocket.ChatMattermostTelegramTwilioYour own custom conversational channelsor voice assistants as:Alexa SkillsGoogle Home ActionsRasa-Hydra's primary purpose is to help you build contextual, layered
conversations with lots of back-and-forth. To have a real conversation,
you need to have some memory and build on things that were said earlier.
Rasa-Hydra lets you do that in a scalable way.Development InternalsRunning the TestsIn order to run the tests, make sure that you have the development requirements installed:exportPIP_USE_PEP517=falsepip3install-rrequirements-dev.txt
pip3install-e.
makeprepare-tests-ubuntu# Only on Ubuntu and Debian based systemsmakeprepare-tests-macos# Only on macOSThen, run the tests:maketestThey can also be run at multiple jobs to save some time:maketest-j[n]Where[n]is the number of jobs desired. If omitted,[n]will be automatically chosen by pytest.Steps to release a new versionReleasing a new version is quite simple, as the packages are build and distributed by travis.Terminology:patch release (third version part increases): 1.1.2 -> 1.1.3minor release (second version part increases): 1.1.3 -> 1.2.0major release (first version part increases): 1.2.0 -> 2.0.0Release steps:Make sure all dependencies are up to date (especially Rasa SDK)Switch to the branch you want to cut the release from (masterin case of a major / minor, the current feature branch for patch releases)Runmake releaseCreate a PR against master or the release branch (e.g.1.2.x)Once your PR is merged, tag a new release (this SHOULD always happen on master or release branches), e.g. usinggittag1.2.0-m"next release"gitpushorigin1.2.0--tagstravis will build this tag and push a package topypiIf 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.gitcheckout-b1.2.x
gitpushorigin1.2.xPackaging the Rasa-Hydra project. For more information, please refer to thisguide.Make sure you have the latest versions of setuptools and wheel installed:python3-mpipinstall--user--upgradesetuptoolswheelBump up the version in the dialog_manager/version.py.Build the package locally.python3setup.pysdistbdist_wheelUpload the package to PyPI/Test PyPI.Install twine from PyPI.pipinstalltwinePyPIpython3-mtwineuploaddist/*Test PyPIpython3-mtwineupload--repository-urlhttps://test.pypi.org/legacy/dist/*
Package: rasal
Resolution and Sensors and Lens.Free software: MIT licenseInstallationpip install rasalYou can also install the in-development version with:pip install https://github.com/AidanJohnston/rasal/archive/main.zipDocumentationhttps://rasal.readthedocs.io/DevelopmentTo run all the tests run:toxNote, to combine the coverage data from all the tox environments run:Windowsset PYTEST_ADDOPTS=--cov-append
toxOtherPYTEST_ADDOPTS=--cov-append toxChangelog0.0.0 (2024-01-08)First release on PyPI.
Package: rasam
LicenseVersionGithub ActionsCoverageSupported versionsWheelStatusDownloadsrasamRasa ImprovedUsageInstallationpipinstallrasamRasaconfig.ymlimporters:-name:rasam.PlaceholderImporterfake_data_count:10# default value is 1pipeline:-name:rasam.RegexEntityExtractor-name:rasam.URLEntityExtractorRasanlu.ymlPlaceholderImporterThePlaceholderImporterremoves the need to write unnecessary information (eg. name, address, numbers, etc.) and helps focus on writing test data.Using{}placeholdernlu:-intent:tell_nameexamples:|- My name is {name}- I am {name} and he is {name}Using@placeholdernlu:-intent:tell_addressexamples:|- I live in @address- I stay at @address and @addressMixing{}and@placeholdersIt is possible to mix both{}and@placeholders but it is recommended to use only one style for consistency.Available placeholdersany (if you need just any data)integerdecimalnumbernamefirst_namelast_nametextwordparagraphuriurllocal_uriemaildatetimemonthdaytimezonecompanylicense_plateaddresscitycountryuser_agentpassworduser_namefile_pathRasam decoratorsRasa relies too heavily on classes to define objects like actions, forms, etc.
Rasam aims to remove these Rasa boilerplates to make writing chatbots easier.@action decoratorThe@actiondecorator converts function into an Action class.
Here is an example of how we can write custom classes in Rasa:classActionHelloWorld(Action):defname(self)->Text:return"action_hello_world"defrun(self,dispatcher:CollectingDispatcher,tracker:Tracker,domain:Dict[Text,Any])->List[Dict[Text,Any]]:dispatcher.utter_message(text="Hello World!")return[]The above code can be simplified using Rasam's@actiondecorator.fromrasamimportaction@actiondefaction_hello_world(self:Action,dispatcher:CollectingDispatcher,tracker:Tracker,domain:Dict[Text,Any])->List[Dict[Text,Any]]:dispatcher.utter_message(text="Hello World!")return[]AuthorRonie Martinez
Package: rasa-metaform
Rasa MetaFormRasa MetForm is a small extension ofRasa'srasa-sdkwhich introduces aMetaFormAction, an extendedFormAction. Read more in ablog post.Problem statementFormsCreating Forms in Rasa requires defining a class for each form, inheriting fromFormAction, specifying few methods, as well as indomain.yml, with matching slots and templates.IdealForm would be specified in a single, human-readable, easy to edit file. From this file we would populatedomain.ymlwith all the necessary instances offorms,slotsandtemplates, as well as define a newFormActionclass, which will implement all the necessary methods, includingname,required_slots(following the logic in the form file),slot_mappings, allvalidate_{slot}andsubmit.ProposalMetaFormActionallows to create classes from a YAML file and populates the domain with all the needed information.InstallationThe easiest way to install the package is throughPyPI.pipinstallrasa-metaformHowever you might find it more useful to clone the repository and edit theMetaFormActionclass for your form(s). We will extend the functionality with additional validation methods.Form YAML fileSee thesample.ymlfor an example form YAML file.LicenseLicensed under the Apache License, Version 2.0. Copyright 2019 Magnet S Coop. Copy of thelicense.A list of the Licenses of the dependencies of the project can be found at the bottom of theLibraries Summary.
Package: rasa-mini
Rasa MiniThis is a minified version of Rasa with only the NLU component.Please check outRasafor the main repository.LicenseLicensed under the Apache License, Version 2.0.
Copyright 2022 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 theLibraries Summary.
Package: rasa-mongo-tracker-store
rasa-mongo-tracker-storeTrackerStorefor rasa_core connecting to MongoDBInstallationInstall the package using pip:pipinstallpython_mongo_tracker_storeUsageMongoTrackerStorecan be used when loading or instantiating an Agent:fromrasa_mongo_tracker_store.storeimportMongoTrackerStoreagent=Agent.load('path/to/dialogue/models',tracker_store=MongoTrackerStore(None,# rasa's internal logic will set the domain lateronhost='localhost',port=27017,database_name='rasa',collection='trackers',))LicenseMIT ©Frederik Ring
Package: rasa-paddlenlp
Rasa NLU Components using PaddleNLPFeaturesTokenizer and Dense featurizer using pre-trained models supported by PaddleNLP.Usagepipinstallrasa-paddlenlpIn your config.yml, use the following configuration:language:zhpipeline:-name:"rasa_paddlenlp.nlu.paddlenlp_tokenizer.PaddleNLPTokenizer"model_name:bertmodel_weights:bert-wwm-ext-chinese# Flag to check whether to split intentsintent_tokenization_flag:false# Symbol on which intent should be splitintent_split_symbol:"_"-name:"rasa_paddlenlp.nlu.paddlenlp_featurizer.PaddleNLPFeaturizer"model_name:bertmodel_weights:bert-wwm-ext-chinese# rest of your configurationsCurrently there is code to support BERT pre-trained models, we just need to add the model definitions and default weights in order for other PaddleNLP-supported models.CreditsThis package took inspiration from the following projects:RasaPaddleNLPThis package was created with Cookiecutter and theaudreyr/cookiecutter-pypackageproject template.Cookiecutteraudreyr/cookiecutter-pypackageLicenseMITHistory0.3.1 (2022-02-10)Made PaddleNLPTokenizer match V3 specification.0.3.0 (2022-02-10)From now on the library supports Rasa V3Added XLNet and Roberta support (ERNIE will come soon)0.2.0 (2022-01-13)First release on PyPI.
Package: rasa-recognizers-text-extractor
rasa_recognizers_text_extractorA Rasa NLU module for extracting entities using Microsoft’s Recognizers Text libraryFree software: MIT licenseDocumentation:https://rasa-recognizers-text-extractor.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2022-11-05)Put added values field injection.0.1.0 (2022-11-04)First release on PyPI.
Package: rasa-storyteller
RASA storytellerWhen working with wonderful libraryRasa Open Sourceit is rather difficult to create complex stories, especially for people who are not familiar with YAML / Markdown.This project is a small tool, which helps to make complex stories with lots of negative paths faster.
The goal of this project is to help non-technical stuff edit bot's behaviour easily.InstallationInstall package using pip:pip install rasa-storytellerand launchrasa-storytellerHow to use itLet's start a new RASA project in a usual way:$ rasa init
Welcome to Rasa! 🤖

To get started quickly, an initial project will be created.
If you need some help, check out the documentation at https://rasa.com/docs/rasa.
Now let's start! 👇🏽

? Please enter a path where the project will be created [default: current directory] .
Created project directory at '/home/user/rasa-project'.
Finished creating project structure.
? Do you want to train an initial model? 💪🏽  No
No problem 👍🏼. You can also train a model later by going to the project directory and running 'rasa train'.After that we should go to the directory with our new project and find three files:domain.yml,data/nlu.md,stories.md.
Launch our utility and locate those files in the start window:After you load those files a main window with three tabs opens, they are:intents:You can easily add new intents or new nlu examples, update or remove existing intents in this window.responses:Using this window you can do CRUD operations with responses.stories:The most interesting tab: here stories are presented as trees. You can link your intents with answers or even create new intent/response in-place using right button menu.When you have created some story with complex logic and multiple branchings like this:Just press "export" button at the bottom of the window anddomain.yml,data/nlu.md,stories.mdwill be generated in<your_working_directory>/export/.
They will be suffixed with current timestamp so you can push "export" multiple times while editing to store different versions.
The stories generated from image above will look like this:## greet-mood_great
  * greet
      - utter_greet
  * mood_great
      - utter_happy
  
  ## greet-mood_unhappy-affirm
  * greet
      - utter_greet
  * mood_unhappy
      - utter_cheer_up
      - utter_did_that_help
  * affirm
      - utter_happy
  
  ## greet-mood_unhappy-deny
  * greet
      - utter_greet
  * mood_unhappy
      - utter_cheer_up
      - utter_did_that_help
  * deny
      - utter_goodbye
      
  ## greet-mood_neutral-deny
  * greet
      - utter_greet
  * mood_neutral
      - utter_ask_cheer_up
  * deny
      - utter_goodbye
  
  ## greet-mood_neutral-affirm-affirm
  * greet
      - utter_greet
  * mood_neutral
      - utter_ask_cheer_up
  * affirm
      - utter_cheer_up
      - utter_did_that_help
  * affirm
      - utter_happy
  
  ## greet-mood_neutral-affirm-deny
  * greet
      - utter_greet
  * mood_neutral
      - utter_ask_cheer_up
  * affirm
      - utter_cheer_up
      - utter_did_that_help
  * deny
      - utter_goodbye
Package: rasa-vier-cvg
Rasa and VIER Cognitive Voice GatewayRasa is the leading open-source conversational AI platform that enables both individual developers and large enterprises to build superior AI assistants and chatbots. Rasa provides the infrastructure and tools needed to build the outstanding tools and transform the way customers communicate with businesses. Rasa can be deeply customized down to levels not possible with other platforms due to the open sourced architecture and machine learning.Rasa is used by millions of developers and small teams to program enterprise conversational AI applications.VIER Cognitive Voice Gateway (CVG)enables access to telephony, speech-to-text (STT), text-to-speech (TTS) and contact center integration for chatbots built with Rasa. I.e. CVG makes your chatbot to a voicebot handling phone calls.Installing VIER CVG Channel in RasaTo build voicebots using Rasa and CVG use our VIER CVG channel provided in this package. It needs to be installed as part of your Rasa installation.Installing RasaIf you do not have installed Rasa yet, follow theinstallation guideas provided by Rasa.Installing VIER CVG Channel for RasaThe VIER CVG channel in Rasa implements all the CVG APIs relevant for bots to provide CVGs full power to you as a Rasa developer.The easiest way to install this package is through PyPI.pip install rasa-vier-cvgDockerIf you are using Rasa on Docker and you don't want to build a derived image, you can also download thechannel sourceand bind-mount the package into arasa/rasa-base container with a volume definition like this:./rasa_vier_cvg:/opt/venv/lib/python3.10/site-packages/rasa_vier_cvgConfigure RasaAdd the following content tocredentials.yml:rasa_vier_cvg.CVGInput:
  token: "CHOOSE_YOUR_TOKEN"Configuring CVGIf you do not yet have an account for CVG please contact us atinfo@vier.ai.To configure the connection between your Rasa bot andCVGjust select Rasa as the bot template, enter your Rasa URL (e.g.https://rasa.example.org/webhooks/vier-cvg) and your token, as set in credentials.yaml, in the CVG project settings.Using the VIER CVG Channel in RasaThe following APIs are part of the outgoing channel (from a bot perspective):Call API,Dialog API,Assist API,Health API,Recording API.TheBot APIis part the incoming channel (from a bot perspective).More InformationSee and try ourlittle sample voicebotbuilt with CVG and Rasa to go into more details.Find more information on our Rasa integration and how to build voicebots with Rasa and CVG in ourdocs.
Package: rasb
RASB-Python-APIAn API wrapper for the Discord RASB bot / service using Python.FeaturesGrab if a user is banned or not.Get the full RASB ban list.Grab info about an RASB report.Code Examplesfromrasb.pyimportRASBClientClient=RASBClient()checkBanned=Client.isUserBanned("ID")print(checkBanned)-------------------------------------fromrasb.pyimportRASBClientClient=RASBClient()banList=Client.getBans()print(banList)-------------------------------------fromrasb.pyimportRASBClientClient=RASBClient()getReport=Client.getReport("report_id")print(getReport)
Package: rasb.py
RASB-Python-APIAn API wrapper for the Discord RASB bot / service using Python.FeaturesGrab if a user is banned or not.Get the full RASB ban list.Grab info about an RASB report.Code Examplesfromrasb.pyimportRASBClientClient=RASBClient()checkBanned=Client.isUserBanned("ID")print(checkBanned)-------------------------------------fromrasb.pyimportRASBClientClient=RASBClient()banList=Client.getBans()print(banList)-------------------------------------fromrasb.pyimportRASBClientClient=RASBClient()getReport=Client.getReport("report_id")print(getReport)