#dialogflow

Dialogflow ES or Dialogflow Essentials

Dialogflow, a service provided by Google Cloud that enables the creation of conversational agents that comprehend and react to natural language, is frequently used by organisations seeking to develop chatbots. Because Dialogflow CX comes in two variants and has sophisticated chatbot features, many organisations opt for it. If you integrate a chatbot with your website without using a middleware solution, you run the danger of using unauthenticated URLs or directly inserting sensitive credentials like service account keys into the frontend. For production-grade systems, this is not a great practice since it can leave your agent and Google project ID vulnerable.

By adding an Apigee layer between your frontend and DialogFlow CX APIs, you can lessen this. Google Cloud provides Apigee, a full lifecycle API management platform that helps companies create, protect, publish, examine, track, and make money from their APIs. Apigee offers a wide range of capabilities and advantages by serving as a strong layer between clients that access backend services and them. In this situation, Apigee offers:

Centralised authorization and authentication: Apigee manages all authorization and authentication procedures, serving as a safe gateway. As a result, there’s no need to reveal login credentials on the front end. Controlled access: Using Apigee, you can create fine-grained access controls that specify which frontend elements are able to communicate with which DialogFlow CX APIs. Apigee facilitates the management of API keys by streamlining the rotation and revocation procedures in the event of a compromise. Threat protection: To protect your APIs from abuse and possible attacks, Apigee comes with built-in tools like rate limitation, quota management, and spike arrest. Dialogueflow CX basics

This guide covers Dialogflow CX basics. It summarises key ideas.

Agents Dialogflow CX agents are virtual agents that handle concurrent end-user discussions. This natural language understanding module understands human language subtleties. Dialogflow converts conversational text or audio into structured data your apps and services can understand. You create a Dialogflow agent for your system’s talks.

Dialogflow agents resemble call centre agents. You prepare them both to handle typical conversation scenarios without being explicit.

Flows Complex conversations often cover numerous topics. Pizza delivery agents may discuss meal order, customer details, and confirmation. An agent needs many conversational turns to get end-user information on each topic.

Flows define themes and conversational pathways. Every agent has a Default Start Flow. One flow may be enough for a simple agent. Different development team members can construct and maintain flows for more complex agents.

Dialogflow CX flows resemble Dialogflow ES mega agent sub-agents. Flows improve conversation control without costing more.

Dialogflow ES A Google Cloud service called Dialogflow ES, or Dialogflow Essentials, lets you create virtual assistants and chatbots. It’s a viable choice for building chatbots that manage easier interactions and chores.

The following is an overview of Dialogflow ES’s features: Dialogflow ES uses natural language understanding, or NLU, to decipher user text or spoken words. It determines the purpose of the user’s query and retrieves pertinent data (entities) such as names, locations, and dates. Pre-built features: To facilitate conversation management, Dialogflow ES includes pre-built functionality. In addition to handling small talk and asking customers if they need any additional help, it can refer users to human agents in the event that it is unable to understand them. Integrations: Dialogflow ES may be integrated with a number of different platforms, including websites, phone systems, and messaging apps. This enables you to use your chatbot in many locations. The following are a few Dialogflow ES usage cases: Chatbots for customer service: Respond to often asked queries, solve issues, and get user input. Make it possible for people to make appointments via a chat interface. Information retrieval: Give consumers details about goods, services, or corporate guidelines. The alternative edition of Dialogflow, called Dialogflow CX, is a viable option if you’re searching for a strong solution for sophisticated chatbots with cutting-edge features. Still, Dialogflow ES is a decent choice if you want to start building chatbots for easier jobs. Pages

A Dialogflow CX session is a state machine. Pages reflect CX session states.

For each flow, you define several pages that can cover its topics. At any one time, one page is active and its flow is active. All flows have unique start pages. The start page becomes the current page when a flow starts. Every conversational turn, the page either stays the same or changes.

You configure each page to capture end-user information related to its conversational state.

Entity types Entity types control end-user data extraction. CX entity types like ES entity types. Dialogflow predefined system entities match many common data types. There are system entities for matching dates, timings, colours, email addresses, etc. You can also create custom entities to match specific data. You may define a vegetable object to match grocery shop agent veggie selections.

Parameters Parameters store and reference end-user session values. Parameters have names and object types. Instead of raw end-user input, parameters are structured data that can be used for logic or answers.

The syntax of CX parameters has changed, but they are the same as ES parameters in terms of usability and scope.

Forms You can construct a form for each page to collect end-user parameters. The agent talks to the end-user many times until it collects all form parameters, or page parameters. The agent collects these parameters in page order. You give the agent prompts to ask the end-user for each form parameter. A process called form filling.

A Collect Customer Info page might employ a form to collect the end-user’s name and phone number.

CX form filling resembles ES slot filling.

Intents An end-user’s conversation turn intent is categorised. Compared to ES intentions, CX intents are simpler and more reusable.

Webhook

Webhooks are programmes that can call other programmes or host your business logic. Webhooks let you use the information that Dialogflow’s natural language processing extracts throughout a session to validate data that has been collected, generate dynamic answers, or start backend activities.

There are two types of webhooks: standard and adaptable. Dialogflow defines the request and response fields for a typical webhook. You can specify the request and response fields when using a flexible webhook.

Fulfilment An agent’s response to an end-user inquiry, information request, or session termination marks the beginning of the conversational turn. In order to provide dynamic responses or take action for a turn, your agent might also need to get in touch with your service. All of this is achieved through the use of fulfilment.

Any of the following could be present in a fulfilment:

Responses that are static. Webhook requests action and/or dynamic responses. Set or override parameter values with parameter presets. It is feasible (and occasionally desirable) to call more than one fulfilment during an agent’s turn, and each fulfilment may provide a response message. These answers are kept in a response queue by Dialogflow. Dialogflow sends the end-user the ordered responses after the agent’s turn is over.

The only way to use ES fulfilment is to link a webhook service. For CX, the scope of fulfilment has been expanded to include all possible prompts and responses.

State handlers State handlers, commonly referred to as handlers, are responsible for managing the conversation by generating responses for end users and/or switching between pages. Handlers are assessed for every conversational turn and have the potential to impact the session.

Integrations Dialogflow CX has various built-in discussion platform connections. These integrations call the Dialogflow API for you and provide an end-user experience. Simply design your agent and possibly install a webhook service. The integration documentation outlines how each integration handles interactions on each platform.

Interactions Each verbal turn involves interaction. Dialogflow responds to end-user input. You can handle interactions with your system using the API or an integration.

When using API, your system must handle:

Make an agent. Create an end-user interface. For each conversational turn, transmit end-user input to the Dialogflow API. A webhook service is needed for webhook-enabled fulfilment unless agent responses are static (uncommon). Your system just needs to handle the following with an integration:

Make an agent. Webhooks are optional. By acting as a middleman, Apigee gives you a strong tool for managing and monitoring your APIs in addition to improving the security of your chatbot integration. We’ll show you how to set up Apigee to enhance conversational agent security in this post.

Setting up and customising the conversational agents Apigee Proxy

To begin, launch a new Apigee instance on Google Cloud or utilise an already-existing one. To create an instance of an assessment trail, click this link. Use this instructions to create a DialogFlow CX Agent. Retrieve the Agent ID and location from the console and save them for later use. Establish a service account and assign the function of “DialogFlow API Client.” Click “Create a Proxy” of type “Reverse Proxy” on apigee.google. cloud. Give it a name, such as “myproxy.” Set up the DialogFlow CX Detect Intent API as the target endpoint, and change the project, agent, and location as needed. Add and to the HTTP target connection in the Proxy Develop tab as indicated below. Set up the proxy in the environment that is accessible. It is crucial that you use the Service Account that you previously generated in this stage. Requests to the DialogFlow CX API will be authenticated using this Service Account. Try your proxy using any API client. An answer from the DialogFlow CX Intent API ought to be visible to you. Whoa! The DialogFlow CX should be communicating with your proxy and operating as intended.

The complete course of the request can be traced using the Apigee debug console for verification.

Ever heard of magic? That is exactly what Dialogflow is.

Figure 1: Dialogflow

It is a well-known fact that today’s world is thriving with data. With so many sources of data readily available like social media platforms, be it YouTube, Facebook, or Twitter, terms like ‘Data Analysis’, ‘Machine Learning’, ‘Artificial Intelligence’ are seen to be prevailing. The question is “Is it necessary to understand these terms and Natural Language Processing (commonly known as NLP) completely to build a chatbot?” and a simple answer to this question is “NO”. This has become possible with the help of Dialogflow.

In this series of posts, we will discuss all about Dialogflow and how to create a chatbot using it.

What is Natural Language Processing (NLP)?

Figure 2: Natural Language Processing (NLP)

Here we will just discuss a tip of the iceberg named Natural Language Processing to get a gist of what Dialogflow does. Natural Language (example- English) is the language that we humans communicate in. On the other hand, programming language (example- Python) is developed to give commands to computers. Natural Language Processing makes it possible for humans and computers to interact without the use of programming languages so that computers can comprehend written or verbal inputs by us. As a result, to imitate the way humans communicate, NLP models are developed. Natural languages are quite difficult to interpret correctly all the time because of grammar, idioms, context, homophones etcetera. So, a good NLP model should be able to interpret the sarcastic tone in a tweet saying “My flight is delayed. Great!” Some of the many uses of NLP in real life:

Spam detection- The mails in our inbox are classified as spam because of detection of language which suggests spamming or phishing. For instance, bad grammar and threatening language are some factors used to perform the classification.

Social media sentiment analysis- Posts on social networking sites such as Twitter can be scanned to get hold of the reaction of people with respect to any product or event. This information can prove to be quite useful for marketing campaigns.

Virtual agents and chatbots- Virtual agents like Siri, Alexa and chatbots, which are now frequently noticed in many websites, are used to provide appropriate and helpful outputs such as text or actions, whichever suitable, after examining the input from the end-user.

Unfortunately enough, it is not possible to develop a natural language processing chatbot without using coding. Having said that, there are some user-friendly tools to help simplify the process. Well, where there is a will, there is a chatbot.

What are chatbots?

Figure 3: Chatbot

Thanks to customers’ demand for assistance round-the-clock, the use of chatbots is skyrocketing. To enable customer engagement with the means of text, speech, images etcetera, and that too at any time of the day, artificial intelligence systems called chatbots are used. Insider Intelligence reveals that 40% of internet users prefer dealing with chatbots rather than agents. Moreover, in quite a literal sense, every industry ranging from medical to retail and finance is rapidly shifting to the digital market and chatbots have become a necessity for this very reason. Some well-known chatbots are eBay ShopBot and Lyft Bot.

Some amazing usages of chatbots are:

Chatbots answer questions and queries. As an example, “What is the cost of the hair-dryer?” or “What are the working hours of your company?”

Book seats for events.

Give personalized recommendations by analyzing customer’s replies.

Collect customer feedback.

For inventory management, such as, looking for particular item in the inventory.

Integrating with ERP apps to get to know about enterprise resource availability.

A bot giving illustrations to build another bot.

A personalized travel assistant to help with tasks such as booking hotels with respect to end-user's travel plans and comfort and so on.

What is Dialogflow?

Dialogflow (formerly Api.ai, Speaktoit) is now owned by Google. It is basically a framework that provides “Natural Language Processing” and “Natural Language Understanding” facilities. In other words, based on Natural Language conversations, Dialogflow builds human-computer interaction services. On top of it, this framework provides many functionalities like webbook services, integration services which makes Dialogflow quite a good place to develop a chatbot.

According to Google Cloud:

“Dialogflow is a natural language understanding platform used to design and integrate a conversational user interface into mobile apps, web applications, devices, bots, interactive voice response systems and related uses.”

Terminologies related to Dialogflow

Firstly, we should discuss an introduction to the terminologies that will be used throughout this blog. Below is a snapshot of a part of Dialogflow console which displays some of the essentials of Dialogflow.

Figure 4: Dialogflow Essentials

Agent: A Dialogflow agent is quite similar to a human agent. First, it needs training. This training is specifically to handle human interactions which are, in a way, expected. Here, the agent’s name is ‘demochat’. Going further in this blog, we will learn all about creating agents and using them to create chatbots.

Intent: The relationship of intent to agent is many to one. In other words, one agent has multiple intents. According to the input by the user, a particular intent is matched, and this process is known as intent classification. Each intent has training phases, action, parameters and responses.

Training phases are some of the phrases which are predicted beforehand and given to the machine. One point to note here is that there is absolutely no need to add ALL the phrases. You can add some of the phrases and the built-in machine-learning of Dialogflow envisions other phrases. In simpler words, if user is expected to enter some numbers (example- customer id) then training phrases would be some numbers.

Action is used to link the matched intent with some specific actions. In the following snapshot, action is ‘get_name’.

Values from the input from user are obtained by Parameters. Continuing the above example, if expected expression from users is a number, the entity type would be identified as ‘sys.number’. ‘sys’ suggests that it is a system-built entity and not a user-defined one. To access this value by user, ‘$name’ will be required to use.

Figure 5: Training phases, action and parameters

Response, as the name suggests, are text, image or any other response which are to be returned to the user. ‘Custom Payload’ field is used to pass rich-content to the user. Examples of rich-content include image responses, suggestion chips and so on.

Figure 6: Responses

Entities: As discussed earlier, some common sort of data like numbers, date, time, and etcetera can be identified by system itself, known as system entities. We can also define our own entities known as custom entities. As an example, an entity named drink can be created with names of all the drinks like tea and coffee. In this way, when a user enters some expression along with the name of some drink, the entity type would be identified as ‘drink’. Furthermore, this same value could be used to give out a response or else perform an action, etcetera.

Figure 7: Custom entity

Figure 8: Detection of custom entity

Fulfillment: Whenever a developer wishes to give dynamic responses, fulfillment comes into play. What I mean by dynamic responses is, for instance, the user wishes to extract name of a customer from database by using customer id provided beforehand, then fulfilment needs to be enabled for that individual intent.

Integrations: Dialogflow provides integration with a number of platforms. This can be used to create an agent on those platforms. Some examples of built-in integrations of Dialogflow are Dialogflow messenger (which we will be using), Dialogflow web demo, LINE, Slack, Telegram, etcetera along with some partner built-in telephony integrations, Google-contributed open-source integrations and independent integrations.

 Do let me know if you think any other integration than Dialogflow messenger would be better to work with.

 Conclusion

Today what we discussed was just the beginning or should I say just a trailer video of this amazing movie named Dialogflow. Although I hope that you understood today’s topics, do not feel overwhelmed at all if you didn’t understand some portions. Going further, we are going to get into depth of all the terminologies and the features of Dialogflow and implement the same to develop a chatbot. The uses of chatbots truly amaze me! Do comment about any innovative or unique chatbots that you have interacted with or heard of that has amazed you.

I am open to any suggestions or feedbacks related to this post. Thank you.

 References

https://cloud.google.com/dialogflow/es/docs

https://www.businessinsider.com/chatbot-market-stats-trends?IR=T

https://en.wikipedia.org/wiki/Dialogflow

Chatbots are a great addition to your marketing toolkit. Learn how to build a chatbot in Drupal using Dialogflow as the Natural Language Processing (NLP) agent.

https://www.specbee.com/blogs/building-chatbots-drupal-using-dialogflow

ChatBot: Construire votre propre chatbot à l’aide de DialogFlow

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Ce billet d’invité est écrit par Srini Janarthanam, un expert en systèmes conversationnels qui travaille dans le domaine depuis plus de 15 ans. Il est actuellement co-fondateur et directeur de Chatomate, une marque britannique qui construit des chatbots, de l’IA et des solutions d’automatisation sur mesure pour les entreprises. Srini est…