Dialogflow - Powerful Conversational AI Platform
Agent NLU modules create agents with included intents to manage conversation flows. Prebuilt agents provide customizable expertise in various areas like car, date, alarm, and weather. Intents map user input to actions, entities extract values, and parameters connect responses to entities.
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Presentation Transcript
Dialogflow (aka API.AI)
Agent NLU (Natural Language Understanding) modules Create new agents Import predefined agents Included in your app, product, or service Contains one or more intents Designed to manage a conversation flow in a specific way
Prebuilt Agents Collection of agents developed by the Dialogflow team Customizable agents that specialize is different areas of knowledge Examples Car Date Alarm Weather
Intent Represents a mapping between what a user says and what action should be taken by your software Intent has the following sections: User says Example Mode Written in natural language and annotated with parameter values Weather forecast in Portland tomorrow Action Steps your application will take when a specific intent has been triggered. Response Text, image, card, quick reply, custom payload Context Current context of a user's request Resolves it or them Parameter values
Entity Tool used for extracting parameter values from natural language inputs Three entity types: System -- defined by Dialogflow @sys.date matches common date references such as "January 1, 2015" or "The first of January of 2015" and returns a reference value in ISO-8601 format: 2015-01-01T12:00:00-03:00. @sys.color matches most popular colors and returns the matched color. @sys.unit-currency matches amounts of money with indication of currency name, e.g., "50 euros" or "twenty dollars and five cents Developer --defined by developer E.g., @meet, @condiments User --built for each individual end-user in every request @playlist Can be exported and downloaded
Parameters Parameters are elements generally used to connect words in a user s response, to entities.
See it in action https://www.youtube.com/watch?v=HqfN9qj Nj9A
Actions step your application will take when a specific intent has been triggered by a user s input. Actions can have parameters for extracting information from user requests
Messages Text Image Card Consist of an image, a card title, a card subtitle, and interactive buttons (for sending user queries or opening links) Quick reply Displayed as clickable buttons with pre-defined user responses. Custom payload
Events Events is a feature that allows you to invoke intents by an event name instead of a user query
Dialogs Linear dialogs - the aim of which is to collect the information necessary to complete the required action (e.g. find the best hotel, turn on the right light bulb, or play the desired song) Non-linear dialogs - which may have several branches, depending on users answers
Machine Learning Machine Learning is the tool that allows your agent to understand a user's interactions as natural language and convert them into structured data Dialogflow terminology, your agent uses machine learning algorithms to match user requests to specific intents and uses entities to extract relevant data from them.
Training As you and your users chat with your agent, you can access the conversation logs by clicking Training in the left side menu.
