Hi.
In this lecture we will add a knowledge base or vectorstore to our Vapi AI dashboard.
Let me show you how this was the last AI assistant that we have created.
Now we can further on add knowledge base to our agent so that it will have more information, more context
about our company, about our mission or whatever we want our AI agent to perform.
So for example, we have insurance company, so let us search from any random insurance company.
Let's say we are using this Texas life insurance company, and we want our AI assistant to pick up all
the call and answer all of the FAQs about insurance or any compliances.
So let us simply go to Claims or Claims FAQ.
Let's go here.
Yeah, we have a bunch of FAQs here.
Simply we can copy all of this information and then we will copy and paste it in ChatGPT or claude.
Then we will say write me 10 FAQs for my company in markdown format while you generate.
We have to refine our system fill prompt for this company, so we'll tell ChatGPT again.
Amazing.
So as it is generating, let us copy our FAQs and then simply paste it in Google Doc.
Amazing.
I have pasted that FAQs here and I will simply just download this file.
Now let us go back to our ChatGPT again.
Amazing.
It has also rewritten the system fill prompt for our Texas insurance agent.
Let us simply just copy this.
Then here is our knowledge base.
So we will upload the file that we have downloaded from Google Docs.
We will add a new file or you can go to file section over here.
Let us upload our file.
Our file is uploaded.
Let us go back to our systems.
Click on Knowledge Base and select our file.
All right.
Let us lower our temperature because we don't want any fancy or funny answers to 0.1 or 0.2.
Play around with this.
We can use max tokens of 200 and we don't want to detect any emotion.
And this feature is more relevant for any niche or any industry of therapy or healthcare like that.
But for FAQs, we don't want this.
Now click on publish.
Amazing.
Let us talk with our insurance agent.
Hello Sarah, here from Texas Life Insurance Company.
How can I help you today?
Hi.
So what is the name of your company?
Our company is Texas Life Insurance Company.
How can I assist you with your insurance needs today?
Why do you need a death certificate for insurance?
It is recognized and legal proof that an individual is deceased.
It contains the official cause and manner of death of the decedent.
Yeah.
Can I fax my claim information to you?
Yes,
You can fax the claim form and any additional documentation to begin the claim process.
However, we do require one certified death certificate for each claim and the original forms for processing.
Alright, so where can I send the claim information?
Please send your fully completed claim form and one certified death certificate, along with any additional
required documentation to //address//
Alright!
Thank you very much.
You're welcome.
If you have any more questions or need further assistance, feel free to reach out.
Have a great day.
You too.
Yeah, as you can see, this is how you can play around with knowledge base.
And the answer was exactly like that.
And if you don't want word to word answer, you can increase the temperature and you can even add a
sentence here.
Let's say Like this.
You can add rules.
You can say don't speak everything from the knowledge base like in manner of word to word.
We have modified our system field prompt.
We have added knowledge base as well as we have fine tune or refine our first message.
Now click on publish.
And now I want to touch on functions, advance and analysis. In the function section,
Tools and custom functions are basically the same, and they are actually trying to get rid of this
custom functions.
Then we have predefined functions.
Again, don't worry about this advanced thing.
We are going to cover in a much simpler way in make.com integration and go high level integration.
Now let us come to this advanced feature.
Now in privacy.
Some of the companies have this HIPAA compliances where we cannot actually record or store any audio
transcription from our calls.
So if you are one of that company, you can enable this option as well as audio recording.
Record the conversation with the assistant.
And we have enabled this on.
It is actually very efficient.
You can actually go back and listen to all of the conversation between your AI assistant and your customers.
Also, we have the option of recording the video if you are doing a zoom call or web call.
Here we will play around with our pipeline configuration.
The first thing we have is silence timeout.
So it is set to 30 second right now, but let us decrease to 20 second.
Otherwise we will increase our costing.
So after 15 second of no response from the customer, our call will be automatically ended.
Then we have response delay,
So it is set to 0.1 second, meaning response delay is when I'm talking to my AI agent.
So it will wait 0.1 second and then after that it will answer like I am asking, hi, how are you?
So it will wait from 0.1 second.
It is known as response delay and then after 0.1 second it will provide the answer.
I hope you got my point.
Then we have LLM request delay.
It is quite a complicated thing to understand.
Meaning, when you are talking with your AI agent, you will say hi, how are you?
And after some time or after 0.1 second delay you speak.
I am Manthan Patel and I have a question.
So our AI agent won't send the first part instantly to LLM and then second part which is, "Hii, I am Manthan,
And I have a question to LLM in separate ways."
It will combine both of them and send it over them in one section.
So the chances of hallucination of our LLM or wrong answer or messing up with our AI agent is much less
so.
I like to keep it 0.1 as well as interruption threshold.
So the number of words the user must say before the assistant considers being interrupted.
Meaning if your AI agent is continuously speaking, and if you want to interrupt, you can speak more
than two words and it will automatically stop and listen for your next question or for your response.
I like to set it as two.
Lastly, we have maximum duration, meaning how long should a call last?
The maximum is 1 hour whenever and right now it is half an hour or 30 minutes.
I just like to keep it around 10 minutes, which is 600 seconds.
Otherwise, in the worst case scenario, what happens is sometimes a person will just keep talking,
talking, talking from 1 hour or 30 minutes and the costing will go much higher.
Then we have messaging, where we have server URL client message server messages.
Again, don't worry about this.
We are going to actually loop this in make integration and it is very easy.
Don't worry about that.
And we have end of call message.
So whenever we are done talking with our agent we want our AI assistant to say goodbye or talk to you later,
I like this talk to you later or see you soon.
Moving on.
We have ideal messages, meaning messages
the assistant will speak when the user hasn't responded from some time like are you there?
Are you still available?
So we have bunch of options.
I'm still here.
If you need assistant, let me know if there is anything else.
I like this classic one.
Are you still there?
But you can use,
I'm still here if you need any assistance.
And my suggestion is leave this end call message to blank.
So our LLM will be able to have more room, and it will be able to sound much more natural and human.
The last two options are maximum ideal messages.
Meaning how many times our agent should speak this word and it is currently to three.
Let us decrease to two, so our agent should only call this thing twice.
And the ideal timeout.
It is currently at 7.5, meaning after 7.5 seconds.
If the user hasn't spoken, it will say this I am still here.
If you need any assistance.
I like this after nine seconds or something like that.
Amazing.
So that's how you can configure your pipeline, your agent.
And then don't forget to click on this publish button.
Let us go to analysis. Here we have summary, success evolution and structured data.
Let us cover each one of those.
So summary, it will briefly provide the summary of the conversation.
And it is actually quite useful when you are evaluating or checking after the call has been done.
Then we have success evaluation where we can predict or we can measure the success of the call from
the conversation.
And we have a bunch of rubrics for evaluation.
We can use a simple true if the call is pass or fail if not.
We can also use percentage from 0 to 100%.
We can also use numeric from how much you would rate the conversation of the end user with our AI agent.
Lastly, we have structured data which basically is providing schema data.
For example, if you want to fetch the name or the email of the customer, we can use this structured
data so that we can easily fetch out those information.
Amazing.
And don't worry, we are going to look at all of these three things in the next lectures.
But I am just giving a brief overview or a nutshell of Vapi AI dashboard.
Okay, I will see you in the next lecture.