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Buttons block
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How to open a new URL in another tab (window)
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How to ask a question based on a variable not being set (empty URL params) (v3)
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ActiveCampaign
Airtable
Airtable integration block
Get data filtered from Airtable with a Brick- Shop example
20 different ways to GET and filter data from Airtable
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How to Create, Update, Retrieve and Delete records in Airtable (POST, PATCH, GET & DELETE)
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Insert Multiple Records to Airtable with a Loop
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Update Multiple Records in Airtable Using a Loop
Automate.io
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DeepL
Dialogflow
Dialogflow & Landbot course
Dialogflow & Landbot intro: What is NLP, Dialogflow and what can you do with it?
Dialogflow & Landbot lesson 1: Create your first agent and intent in Dialogflow
Dialogflow & Landbot lesson 2: Get the JSON Key
Dialogflow & Landbot lesson 3: Setting up of Dialogflow in Landbot
Dialogflow & Landbot lesson 4: Training phrases and responses for a FAQ
Dialogflow & Landbot lesson 5: Entities and Landbot variables
Dialogflow & Landbot lesson 6: Redirect user depending on Dialogflow response parameters (intent, entities and more)
Learn more about Dialogflow - Courses and communities
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Dialogflow - How to get JSON Key
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Google Tag Manager
HubSpot
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Integromat
Connecting MySQL with Make.com (formerly Integromat)
How to upload a file to Google Drive using Make.com (formerly Integromat)
Send WhatsApp Message Template from Make (ex Integromat)
Make.com (formerly Integromat) trigger
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Get Opt-ins (Subscribers) from Facebook Leads using Make (ex Integromat)
How to extract data from an external source with Make.com and use it in Landbot
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Wordpress
Xano
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Zapier
Zapier block
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How to complete a digital signature flow by Zapier
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Trigger Automation block
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Facebook Business Verification - Best Practices 🇬🇧
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Send WhatsApp Messages with Landbot API
Audience block
Recognise the users input when sending a Message Template with buttons
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New Opt-in block for WhatsApp 🚀
Opt-in Check Block
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Build a WhatsApp Bot - Best Practices and User guide
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Types of Content and Media you can use in WhatsApp 🖼
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How to calculate the number of days between two selected dates (WhatsApp)
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Enhancing WhatsApp Bots with Delayed Responses Using External APIs
Different ways to format numbers with JS (WhatsApp)
Notify Teammates of Chat via WhatsApp
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1. WhatsApp Article Directory
2. WhatsApp Integration FAQ's
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CSS and Design Customizations
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CSS Customization Examples: "Back to School" Theme
CSS Customization Examples: Call To Action: WhatsApp
CSS Customization Example: "Lead Gen"
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CSS Customization Examples: CV Template
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How to remove Landbot branding
HTML Template for Emails
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Customized display in the bot
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Components CSS Library
Components CSS Library Index
Background Class CSS
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Header Class CSS
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Message Bubble Class CSS
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Dynamic Data CSS
Form Block CSS
Multi Questions CSS
Get started guide for CSS Design in Landbot
Landbot APIs
APIs
reply from Slack: How to create an integration to allow agents reply WhatsApp users from Slack (with Node JS)
MessageHooks - Landbot Webhooks
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How to redirect user to another url in your site with Livechat open to continue conversation
How to let user select a time of booking (with a minimum 45 minutes notice)
Dynamic Data in WhatsApp (Get Index Array selection)
Get the array's index of the user selection and extract information from array
How to redirect visitors to a URL (web only)
How to add a Click-to-Call/Email/WhatsApp button
How to disable a bot
Airtable usecase: Create an event registration bot with limited availability
Get the time between certain messages
How to calculate the number of days between two selected dates
Reservation bot with Airtable
How to encode a variable
Workarounds & Codes
How to Detect Visitors Browser
Create a Chat Transcript Variable with Pipedream (web bots)
Proactive message on mobile
Conditional Welcome (Non-embedded bots)
Conditional Welcome: Direct the Flow of Your Bot Based on External Parameters
Two-Step Email Verification
Different ways to format numbers with JS
Trigger Event if User Abandons Chat
Calculate Distances in WhatsApp
Customized Behavior in Mobile Browsers
Trigger Event if User Abandons Chat Using AWS
Launch Bot On Exit Intent
Display Bot During Business Hours Only (Livechat & Popup)
Delete Customer Data in Flow
Cookie consent banner (full page / full page embed)
Add Captcha Verification (Non-Embedded Bots)
Trigger a Global Keyword with JS (web v3)
Create a Scale out of Buttons - Workaround
How to pass WordPress logged in user data to Landbot
Create Dynamic Shopping Cart with JS and CSS
Set the flow depending on the url path (for embedded landbots)
Generate a URL that has variables from user answers
Resume flow based on external process with Landbot API (Request, Set, Go)
Popup on Exit Intent
Embed Google Maps
Detect if bot was opened
Launching a bot depending on browser language
How to set up questions with a countdown
Creating Masks for User Input (2 examples)
Set a timer to get the time spent during the flow
More Topics
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- OpenAI
- Fine-Tune GPT3 with Postman
Fine-Tune GPT3 with Postman
Updated
by Abby
In this tutorial we'll explain how you can fine-tune your GPT3 model only using Postman
Keep in mind that OpenAI charges for fine-tuning, so you'll need to be aware of the tokens you are willing to use, you can check out their pricing here
In this example we'll train the Davinci model, if you'd like you can train a different (cheaper) model, but for our chatbot use case, the Davinci is the best option
Creating learning data
In order to train the model to our needs we'll need to create data to feed it, the data should be a collection of key-value/prompt-completion pairs
The prompt is the question and the completion is the answer we want the bot to respond with
Ideally it should have at least a couple hundred prompt and completion pairs, any less than 100 and it won't work (hint: use GPT to create prompts and completions based on existing documentation)
The data needs to follow this format: prompt:"what are your hours?" completion:"9am-5pm", the prompts and completions should also have stopping points, here's more information
Here's a simplified example of what that would look like with Google Sheets:

It can be written in any of the following file formats:

Converting the data
OpenAI requires us to upload the data in JSONL format, we'll use this platform to generate a JSONL file that we'll save and upload via Postman
You'll need to remove any line breaks, or you'll get an error response when sending the file via Postman
Here's an idea of what the simplified example will look like once converted into JSONL format:

Setting up Postman
Now that we have our JSONL file, let's go to Postman and create an account
We'll then create a new workspace

Now we'll click on: create new HTTP request

Uploading the data
Now that we have an HTTP request ready, we should select 'POST' from the drop-down

We'll make a call to the following endpoint: https://api.openai.com/v1/files
In the header we'll add our OpenAI API key
key Authorization value Bearer XXAPIXXKEYXX

Now, let's go to 'Body'

In the body, we'll select 'form-data'
There will be two keys, the first key 'purpose' value 'fine-tune'
The second key 'file' and for the value we'll upload our generated JSONL file
Let's press 'Send'
It should return a training file ID, we'll copy this as we'll need it for our next petition

Fine-Tuning
To fine tune our model, we'll upload the file ID that we received from the previous petition as well as specify the model we want to tune
In this case, I've selected Davinci, keep in mind this is less capable than text-davinci out of the box, but when trained with a few hundred examples it becomes much more adept at answering questions for our use case
You can check this resource for further clarity
Now we need a new POST request, we'll call the following endpoint: https://api.openai.com/v1/fine-tunes
The headers will be the same as the previous petition
In the body we'll add the file ID that was returned to us in the previous payload, we'll also specify which model we want to train
{ "training_file": "file-XXXXXXXXXXX", "model": "davinci"}

Here's what our response should look like:

You'll notice the status is 'pending', it will take a several hours before it's resolved
Finished product
Once it's been resolved we'll see the model here:

Now we can test it with the following endpoint: https://api.openai.com/v1/completions

In the body we want to specify the model, prompt, tokens and temperature as well as the stop sequence
{ "model": "davinci:ft-your-personal-model-here", "prompt": "When are you open?", "temperature": 0, "max_tokens": 20, "stop":"\n"}
The response:

Now that it's working we can add this model directly to our bot