I am creating a simple chatbot which contains some common college related questions and their responses. In one of the responses, I need to add a link. Lets say to navigate to the college website. I am using node.js for fulfillment and I have no idea how to implement the same.
The response is to be driven from the database.
Is there any other way to achieve the same?
Related
I am a researcher and I do conduct research on conversational agents, chatbots, anthropomorphism and human-computer interaction.
For a series of online experiments I need to implement a functioning chat. I already conduct a few online experiments with a dummy chatbot to measure the mere presence of conversational user interfaces.
Now I am looking for a functioning chatbot so that my participants can interact with the chatbot. I was already looking into Dialogflow, BotFramework and various other services. However, I do have some requirements
The chatbot should be integrated into a website. The website already exists and is developed using plain HTML,PHP,JS.
The chatbot should be able to take data from the website (i.e. user_ID, treatment condition etc.) and should be able to adapt accordingly (language, design, features).
The website should be able to access the chatbot conversation and save it into a DB (I'm using a simple MySQL)
Any recommendations?
Currently I want to use DialogFlow and the Dialogflow Messanger, which however only has limited styling options (change of color etc.). Is there any saas for integrating the chatbot on the website?
Also keep in mind, in research, we unfortunately don't have much funding :D
Thanks
Dominik
Just going to answer my own question for now, still very much interested in your opinions.
So I have chose to use Google DialogFlow and the DialogFlow Messenger, which fulfill nearly all my requirements. Using JS on the website, I can access every interaction data (conversation) between the chatbot and the user. After collecting all data with JS i can continue with the experiment, take other data and then save everything in my MySQL database.
If you want to know more, feel free to contact me.
I’m new to Dialogflow CX and after reading its node.js documentation (I’m a jr Dev) I’m still struggling to get the problem below solved.
PROBLEM: I need my chatbot to receive the question “do you have Toyota Corollas (cars!) in black with less than 20,000 miles, 2017 or newer and cheaper than $15,000 for sale?”
CONTEXT: I have a database with all car makes, models, years, versions, mileage, colors and prices available. The problem is that I can’t (and sorry for how silly this looks) even initiate the bot and I know that after initiating it I would need to create a zillion entities through code (can’t do it manually) so the bot would be able to read all car parameters of the user’s question. Then after reading those parameters (entities) the bot should query my database to check availability of those particular Corollas and then give a proper answer.
ASK: I would be very grateful if you could please help me initiate the Dialogflow CX bot, load all car makes, models, years, versions, colors and prices into it AS ENTITIES and then give the answer that the user needs.
I’ve checked the GitHub quickstarts and read the documentation multiple times but am still very confused.
It looks like you have a few different issues with some of the Dialogflow CX basics. Let's try to clear them up.
How do I initiate the Dialogflow CX bot?
It isn't clear what you mean, exactly, from this.
Dialogflow CX, itself, isn't a bot client. Instead, it provides integrations with various ways to communicate with your agent. This may include telephone integrations, web-based chat systems, and an API so you can integrate with other clients such as Slack.
Your Dialogflow CX agent, itself, is setup using Google's Cloud services, and can support one or more of these integrations.
How do I create a zillion entities through code?
It isn't clear why you need to create a zillion of them, nevermind through code itself.
You likely will want to create a custom entity type for the make/model combination. And it should probably have aliases, so that people could say "Ford Explorer" or just "Explorer" and have it resolve to the same type.
If you really wanted to use an API to do this, you could use EntityType.create to do so. That points to the REST documentation, but based on the language you want to use, there may be a library able to handle it.
Some of the other types, however, can be handled with system entity types such as #sys.color or a numeric type. There's no reason for you to create those.
But what happens if someone asks for a combination that isn't valid?
Then you'll need to tell them it isn't valid. Just like if someone was talking to you in person and asked for something that didn't make sense.
How will it check the database for a good response?
You'll need to make this database call as part of a webhook that you create to implement the business logic.
I am new to the dialogflow, I want to know if there is any method that I can make my bot feels more human when the user interact with it?
Many thanks.
Here is a good resource from Google. It talks about the Cooperation principle which helps you design helpful and straightforward prompts and responses for your users. It focuses heavily on principles that are used within day to day human interactions so you will get a bot that will respond according to the rules of human conversations. It is also closely related to dialogflow as it used actions on google as a platform.
If you want to know more about creating more human conversations I recommend you to look into human linguistic, but that is a whole new thing to learn and out of scope for StackOverflow.
I am utilising the small talk options within the chatbot that I currently use, however, I have noticed a couple of common questions which seem to be asked which fit into small talk, such as "What is your name?" and "What does you name mean?".
Is there any way in which I can add to the list of small talk questions? If not, how can I add these questions in with their responses? My issue is that I believe that you shall need a new intent for every question that gets asked? Any help would be appreciated.
Using a new intent for every question asked (or at least different versions of the same question with one answer) is the standard Dialogflow design and isn't really a problem.
The small talk functionality is just a big list of questions and answers in separate intents - you can see by looking at the pre built small talk agent through Prebuilt Agents -> Small Talk -> Import.
Therefore I would suggest to simply do it this way.
Initially, small-talk option had this issue which you specified here where users were not able to add more phrases to existing questions or add more questions.
To solve this issue, DialogFlow has introduced Small Talk Pre-built Agent.
There are approx 86 pre-built intents in the small-talk agent.
You can add/modify the phrases in those intents,
You can add/delete intents
You can modify the responses of these intents
To use small-talk agent, go to pre-built agents option in left menu, go to Small Talk agent, then import it.
Hope it helps.
I will suggest to use QnA maker service to achieve the functionality. Basically you have to create a QnA maker service and have to integrate to Bot. It will resolve your query. Please let us know if you need more information .
Regards,
Tharak
I'm building an watson conversation service and I want to know different watson Conversation and Natural Language Understanding service.
I think Watson conversation service support Natural Language Understanding, such as intent, entity but Natural Language Understanding service also provide intent and entity.
If I just use intent and entity for conversation, do I need to bind Natural Language Understanding to conversation service or not?
Thank you.
Conversation service is separate from NLU. Conversation is about building a chatbot on your own domain. The intents/entities are only what you train it on, and the dialog is a feature only available in conversation, not NLU.
NLU is a pretrained service that returns various information back about text, but does not do anything with a response, and will give you back what it has been pretrained on. Out of the box, you can't change this. You can use a product like Watson Knowledge Studio to train a custom annotator, but NLU itself knows what it knows and thats it.
There is no need to combine these, but it is possible. Depending what problem youre trying to solve will help guide you in which you want to use. If you want to understand data about unstructured text, with no real training time required, NLU is right for you. If you want to develop a chatbot to help your users with some problem, Conversation is right for you.
If you want to build a chatbot about generic things, or if you require things like people's name, extracting locations around the world, etc, and respond accordingly, you could use NLU to extract the metadata, and then pass that to Conversation and in conjunction with your custom intents/entities/dialog have a more powerful conversation.
From the way I'm understanding the question, I pre-assume that you know that Watson conversation and Natural Language classifiers (NLC) are two different services provided by IBM Watson.
Watson conversation will basically help you build a chatbot or a bot (which has speech to text or vice-versa). This chatbot helps users in different ways. Let's say if a user asks a question to the chatbot, chatbot will answer accordingly (It depends on how you designed the dialogs/ or the responses) to the question asked.
Question 1: What's your name?
Answer 1: I'm Watson.
Instead, if the question was asked incorrectly.
Incorrect question : Wat is ur name?
Answer would still be: I'm Watson.
In order to build a chatbot using Watson conversation, you need to make sure that you have proper understanding of Intents, Entities, and most importantly Dialogs (Dialogs help you design the flow of the conversation). If you know these 3 parts then you are good to go with Watson conversation. There's no link between NLC and Watson conversation if you keep them isolated. *That being said, Watson conversation itself has an Natural language understanding where it could figure out User questions even if the questions are **incomplete, grammatically incorrect, mis-spelled words etc.*
In short, you need not bind anything (Natural language) to make the conversation start working. Just focus on those 3 (Intent, entities, & dialog) portions provided and you are good to go.