I'm an absolute knob when it comes to programming, so I ask of your help.
Essentially I'm trying to use the api.ai interface to create a character counter (in slack) where when a user says something like "hi" the bot will respond with "2".
So far I understand that I'm supposed to use an entity in order to achieve this transformation, and I have tried mapping reference words like "hi" with the synonym "2". However, the entity transformation ends up having the bot spew out exactly what the user initially said (ex. "hi"), instead of my desired outcome ("2").
Am I going about this wrong, or am I supposed to use an already existing entity to in my new entity? I apologize in advance as I literally picked this thing up yesterday, so I don't know much about it.
Any help is appreciated!
You cannot write logic in api.ai, meaning you cannot write a function that takes as input a message and returns the number of characters.
What you would need, I believe, is to integrate the api.ai with an external web service where you can write your business logic.
Have a look at Slack + Webhook Integration Example https://docs.api.ai/docs/slack-webhook-integration-guideline
However, if you just want to count the number of chars in a message, I do not see the need for api.ai.
Related
I cannot seem to get dialogflow CX to fill out optional parameters, only when they are required.
I want to have the bot ask the user for input, like "Tell me about your experience", and then repeat it back to them, but also give the user the ability to skip the question.
I have tried different solutions for solving this. One solution was to have a requires sys.any parameter and then check if it contains the word "skip" or not. However, that does not seem very robust, as a valid comment could result in a skip if they used that word.
My second attempt is seen in image 1. I tried to have a sys.any parameter which is not required and also a custom "skip" entity which was not required. No matter what I said, it would result in default fallback. I also tried to have an intent for skipping (called "no") which would overrule sys.any if it were required. However, it is just recorded in the "AnyInput" parameter, no matter what I say.
What would be a good way to give the user the ability to make a comment or skip it? Any suggestions would be helpful. Thanks!
Screenshot 1
I am using Microsoft Bot Framework to develop chatbot and my questions is that how can I handle incorrect responses from a user.
Suppose bot asks for name of user and he or she replies "don't know".
I have seen in boiler plate code of bot framework that it handles minimum length validation, but how can I handle this logical checking.
Thanks in advance.
I am assuming you are using the v4 C# SDK, let me know if this is not correct and I can update my answer for node or v3 for you.
This Sample Does exactly what you are trying to do It has a validator that checks the length of the user's input and reprompts if the length is too short. You can see this in this method
In general name validation is fairly difficult because names can be very diverse and contain special characters like "-", "'", and others. Using a prompt with a custom validator should give you the opportunity to at least add some validation like length and numerical character checking.
An expected answer normally has a known format. If bot is asking a name then the name would not have numbers and special characters.. You can do a quick check if the words returned by user are part of standard english words (there are plenty libraries having this list of words). You can even pass the returned sentence to LUIS and see if you get a known intent, and then you can disqualify the answer.
I need to modify the body of an existing GitHub issue in a Project. All I'll be passed is the title of the issue, and a word (the word exists in the body, and I'll just need to fill the checkbox next it).
It looks like to do this I'll need to use the GET API to get the body of the issue, modify it, and then use the EDIT API to swap in the new body. However the GET API can only be called with the issue number. I need to do all this as quickly as possible. Is there some way to search via an API call?
Thoughts much appreciated!
Edit: All my issues are in the same project (and issue titles will be unique there). I've also recently discovered Github's GraphQL API, which may be applicable here.
You can use the issue search endpoint with the in and repo¹ keywords:
GET /search/issues?q=text+to+search+in:title+repo:some/repo
Of course, issue titles aren't guaranteed to be unique. You'll have to request each of the issues that comes back and see if its body contains the word you're looking for. Even in that case you could get multiple positive results.
It would be much better if you could search by issue number.
¹I've assumed that you really mean "repository" when you say "project". But if you're actually talking about GitHub Project Boards you can use the project keyword as well or instead.
I am using phrase list features of LUIS. i am adding my mandatory words in my phrase list.(correct me if i am wrong)
For single mandatory word my intent works fine. But in my another intent i have 2 mandatory words in single intent which is not working fine.
Behaviour
My phrase list- product: [moisturizer,anti wrinkle cream,laugh lines,anti aging skin treatment]
target area: [face,my face,neck,forehead]
Intent name- ste1
utterance- do you have moisturizer?
user enters- "do you have bla bla"- as expected its going to none intent.
Intent name- ste2
utterance- do you have moisturizer for my face?
user input- "do you have moisturizer for my bla bla"- As here "moisturizer" is present bt "my face" is not! This should also hit none intent but its hitting to ste1 intent because "do you have moisturizer?" is completely present in ste1.
Expected Result-
I want to validate that my these two words(moisturizer, face) should be mandate to hit the ste2 intent otherwise i want it to hit none intent.
LUIS only provides a recognition service. If you want to validate something like "face" and "moisturizer" being present in a user's utterance, this should be done in your code.
You may train your bot to direct "incomplete" utterances to the "None" intent (by your description, utterances like, "I want moisturizer", or "I want lotion") but as you yourself noted;
But user can enter any random thing so I cant predict what should be in none intent...
Therefore what you should do in your model and code is add entities for "moisturizer" and "face". With these entities, inside of your code you can take the LUIS response and quickly see if you have the required basic information to start the dialog. If one entity is provided ("moisturizer") but another entity is missing (a part of the body), your bot would help the user disambiguate by prompting them what they're looking for specifically, e.g. face moisturizer or hand moisturizer.
A good way to approach the phrase lists and pattern features is that they're augmentations; they do help the machine learned model, but the weight/impact they provide when determining an intent is less than an entity's weight. The phrase lists and pattern features are not replacements for entities.
I am playing around with the Luis stock ticker example here, GitHub MicrosoftBotBuilder Example, it works well and the entity in the utterances is identified but there are stock tickers in the world that have periods in them such as bt.a
Luis by default pre-processes utterances where word breaks are inserted around punctuation characters and therefore an utterance of "what is price of bt.a" becomes "what is price of bt. a" and therefore Luis thinks the entity is "bt" instead of "bt.a"
Does anyone know how to get around this? Thx
This is how LUIS tokenizes utterances and I don't think it'll change int he near future.
I think you can investigate one of the 2 solutions:
Preprocess the utterance and normalize entities with punctuation (maybe save them in a map), and reverse the process when LUIS is called and the entities have been extracted.
Use phrase list features and add the entities that LUIS misses in their Tokenized form, label the entity tokens in the utterance, and retrain the model (I suggest you try that in a clone of your app, so you don't lose any current progress)
I need to process sentences with website addresses in them so I had to deal with a few different symbols. I found a technique that works for me, but it is not very elegant.
I am assuming here that you have an entity setup to represent the "stock symbol"
Here is what this would look like in your case.
Detect the cases when LUIS gets the "stock symbol" entity wrong. In
your case this may be whenever it ends in a period.
When LUIS gets the entity wrong, tokenize the raw query using spaces
as the separator. Grab the proper token by looking for a match with
the wrong partial token.
So for your example....
"what is price of bt.a"
You would see the "stock symbol" entity of "bt." and know that it is wrong because it ends in a period. You would then tokenize the query and look for tokens that contain "bt.". This would identify "bt.a" as the requested symbol.
Its not pretty, but in the case of website addresses has been reliable.