Package: django-bot
django-botA django library that makes it easier to develop bots with a common interface for messaging platforms (eg. Slack, FB messenger) and natural langauge parsers (eg. api.ai).OverviewBETA version: Currently django-bot only supports Slack and api.ai. Future plans include supporting more messaging platforms (Facebook Messenger, Telegram, Kik, Google assistant, Cortana, Skype, Alexa), and more natural langauge parsers (AWS Lex, wit.ai).This library helps to maintain authenticated users and groups in the database and allows you to respond to any messages as well as initiate conversations with any of those.Requirements and Installationdjango-bot for Python works with Python 2.7, 3.4, 3.5, 3.6 and django >= 1.8, and requiresPyPIto install dependencies. The message parsing and delivery is done in the background with the help of celery. It also requires slackclient and apiai python libraries for communication with the external services.pipinstalldjango-botOf course, if you prefer doing things the hard way, by pulling down the source code directly into your project:gitclonehttps://github.com/shaileshahuja/django-bot.gitAdd toINSTALLED_APPSsettings.pyINSTALLED_APPS=[...'converse',# your apps]# must remove CSRFMiddlewareMIDDLEWARE=['django.middleware.security.SecurityMiddleware','django.contrib.sessions.middleware.SessionMiddleware','django.middleware.common.CommonMiddleware','django.contrib.auth.middleware.AuthenticationMiddleware','django.contrib.messages.middleware.MessageMiddleware','django.middleware.clickjacking.XFrameOptionsMiddleware',]# making sure reversing URLs produces https instead of http, required for Slack integrationSECURE_PROXY_SSL_HEADER=('HTTP_X_FORWARDED_PROTO','https')Getting startedUser modelFirst, we need to define the model and attributes of every user communicating with the bot.models.pyfromconverse.modelsimportAbstractUserclassMyUser(AbstractUser):credits=models.FloatField(default=0.0)This user model is automatically created for each authenticated user. For example, if a slack team is authenticated,MyUserobject will be created for each user in the team. Make sure to define defaults for all fields.Default properties availableorg: The organization this user belongs to.messenger: Returns an implementation ofconverse.messengers.MessengerBaseobject. This messenger object can be used to send messages to the user. It exposes a consistent interface for different platforms.email: The email address of the user, if availablename: The name of the user, if availableOrganization modelYou must also define a model that will be instantiated for each organization that authenticates your bot. Again, remember to define defaults for any custom fields.models.pyfromconverse.modelsimportAbstractOrganizationclassOrganization(AbstractOrganization):passDefault properties availableusers: A queryset of user objects that belong to this organizationmessenger: Returns an implementation ofconverse.messengers.MessengerBaseobject. This messenger object can be used to send messages to a group common to all members of the organization. In Slack, if your bot is added, this can send a message to #general,name: The name of the organization, if availableSending messages as the botBy usinguser.messengerororg.messenger, you can get access to an implementation ofconverse.messengers.MessengerBase, such asconverse.messengers.SlackMessenger.Methods:send: To send a plaintext message.send_text: To send a message with quick replies.send_image: To send an image with quick replies.Quick replies are instant prompts for the user to click and respond. In Slack, they are sent as actions.Example:user.messenger.send_text("Are you sure?",quick_replies=[QuickReply("yes"),QuickReply(text="Cancel",value="No")])Clicking on ‘yes’ will send a request back to your server with queryQuickReply.value.ParsersParsers are responsible for understanding the intent of the user from the text query, which receives the text to be parsed and the session id. The session id can be used to respond to queries with context.converse.parsers.APIAIParseris one such parser that connects to api.ai.Integrating with api.aisettings.py# right now this is the only supported NLP framework for chatbotsTEXT_PARSER='converse.parsers.APIAIParser'API_AI_CLIENT_TOKEN='<your api.ai client token>'To match the actions in api.ai to the actions you write, make sure the name in@Executor(action="<name>")is the same as the one the ‘actions’ field in your intent. You can access the slot filling params usingself.paramsand the conversation context usingself.contexts.Implementing your own parserIf you don’t wish to use api.ai, you can implement your own parser.parsers.pyfromconverse.parsersimportParserBase,ParserResponseclassMyParser(ParserBase):defparse(self,query,session_id):# your code...response=ParserResponse()response.text=...# this will be sent instantly to the userresponse.action=...# this action will be called, if slot filling is completeresponse.slot_filling_complete=...# determines whether the query is completeresponse.params=...# parameters extracted from 'query'response.contexts=...# context of this conversationreturnresponseHave a look at theParserResponseclass for more information.ActionsActions define a unit of execution that is called in the background using celery. These can be triggered when the user sends a message. The natural language parser will detect the intent of the user, extract parameters and the pass action be to taken back to the calling program. An action should be decorated withExecutor, which defines the name of the corresponding action. The decorated object can either be a subclass ofActionBaseand implement theexecutemethod, or a method can receives user, params and contexts as kwargs.actions.pyfromconverse.executorsimportExecutor,ActionBasefromconverse.messengersimportQuickReply@Executor(action="account.balance")classCreditsAction(ActionBase):defexecute(self):self.user.messenger.send("Please wait while we retrieve your details...")# this method is called in the background, so it is safe to make time consuming API requestsaccount_type=self.contexts["accounts"]["type"]date_from=self.params["date_from"]self.user.messenger.send_text("You have ${:.2f}left in your{}account".format(self.user.credits,account_type),quick_replies=[QuickReply("buy credits"),QuickReply("redeem gift")])We also need to tell django where the action classes / methods are written.settings.pyACTION_MODULES=['<list of modules where actions can be found>']# ['x.actions']Integrating with SlackCopy the credentials from the developer portal to your django application. If this is your first time with a Slack application, please read the documentation from Slack on getting started. You have to give bot permission, create a bot user and subscribe to bot events.settings.pySLACK_CLIENT_ID='<your slack client id>'SLACK_CLIENT_SECRET='<your slack client secret>'SLACK_VERIFICATION_TOKEN='<your slack verification token>'Next, add this to your django URLs.urls.pyurlpatterns=[...,url(r'^converse/',include('converse.urls',namespace='converse'))]Next, start your server (behind https, try ngrok if in development environment), and add these URLs to your Slack app.OAuth & Permissions -> Redirect URLs: <https base url>/converse/slack/oauthEvent Subscriptions -> Request URL: <https base url>/converse/slack/webhookInteractive Messages -> Request URL: <https base url>/converse/slack/actionAfter these steps, when someone authenticates a Slack team, the Organization and User objects will be created in an async task.