What is a pre-built agent in Dialogflow CX?

Want to cut the design and deployment times for your chatbot? In this episode of Deconstructing Chatbots, we show how you can quickly create a virtual agent using Dialogflow CX’s pre-built agent template. We’ll cover how to change custom entities, build a custom flow, and customize this pre-built agent to fit your specific use case. Watch to learn how Dialogflow CX’s pre-built templates can help you easily and quickly build a virtual agent!

Timestamps:

  • 0:00 - Intro
  • 2:03 - Customizing the pre-built agent
  • 3:01 - Creating a route group
  • 4:13 - Testing your chatbot
  • 5:34 - Conclusion

#dialogflow #chatbot #developer

What is GEEK

Buddha Community

What is a pre-built agent in Dialogflow CX?

What is a pre-built agent in Dialogflow CX?

Want to cut the design and deployment times for your chatbot? In this episode of Deconstructing Chatbots, we show how you can quickly create a virtual agent using Dialogflow CX’s pre-built agent template. We’ll cover how to change custom entities, build a custom flow, and customize this pre-built agent to fit your specific use case. Watch to learn how Dialogflow CX’s pre-built templates can help you easily and quickly build a virtual agent!

Timestamps:

  • 0:00 - Intro
  • 2:03 - Customizing the pre-built agent
  • 3:01 - Creating a route group
  • 4:13 - Testing your chatbot
  • 5:34 - Conclusion

#dialogflow #chatbot #developer

Oral  Brekke

Oral Brekke

1624871520

Working with A Recent Technology, Dialogflow CX — FlowBuilder

We worked with AtomChat in the project FlowBuilder, it is a graphical interface for creating chat bots in an easy and simple way for businesses and people that want to automate their communications with their clients, we created a team called Ternary Devs and its members are Alejandro Franco, Esteban Castaño and Juan Pablo Montoya (myself).

We chose this project because it was something that was pretty challenging, the new technologies, working with Typescript when we came from Python, working entirely with the Dialogflow API and integrating it with an application that was easy and intuitive to use, and we weren’t disappointed, it was wild ride.

At the beginning of the development we were assigned some technologies to work with, which are, for frontend Angular and TypeScript. For the backend Node.jsExpress.jsGoogle Cloud FunctionsFirestore and Dialogflow ES, the last one is a Google technology that allows to create agents with NLP technology and allows to have conversations with real people, it has been on the market for 5 years or more, but we decided to use Dialogflow CX which is a newer version of this tool, and is more suited to the type of development we wanted to do.

I was in charge of the backend, which in short is an API and the connection with Dialogflow CX and Firestore. The development at the beginning was researching the tools, TypeScriptNode.js and Express.js were the first ones, then I continued with Dialogflow CX and found the Client Libraries, and its easy integration with Node.js, from there I started to realize that the SDK documentation (client libraries) was not very complete, and it was better to go to the REST API documentation which had much more content.

#dialogflow-cx #expressjs #nodejs #node

Pre-Engineered Buildings Manufacturer in India | Hrsinfrastructure

Hrsinfrastructure are one of the best, reliable and successful pre-Engineered Building Manufacturers, which is offering the affordable PEB steel structures and steel building solutions for all types of the customers. PEB are the best quality steel structures built over a designed structural concept of primary members, secondary members, wall and roof sheeting attached to each other and different other building elements. Hrsinfrastructure Having many years of experience into PEB industry, we are serving millions of the customers just by offering the best quality PEB sheets in the affordable prices. We mainly focus on the quality, durability, reliability, flexibility in the expansion, environment-friendly, and quicker installation and many others. Being a top-leading pre-engineering building manufacturer in India, we have earned vast experience and broad knowledge of completing many constructions projects using PEB steel sheets and structures. We have got higher admiration and reputation in the market just by offering the best quality PEB sheets. Our designing and engineering team is very experienced and fully knowledgeable to prepare the best PEB structures as per the residential and commercial building’s needs and demands. Our aim is to use the best quality row materials to prepare the sheets, so every customer can get the full satisfaction easily.

#pre-engineered building manufacturers #pre-engineered buildings #pre-engineered buildings manufacturer in india

How to Built a Slackbot with Dialogflow and FastAPI

Background

Back when we were actually in the office, our team efficiency was plagued with a recurring problem — we never knew where we wanted to eat. Team lunches became a difficult task to plan around, but as developers, we held both the great power and responsibility to automate trivial problems with new and popular technologies.

With our latest internal hackathon at NCR, we were given the opportunity to hack with Slack and create Slack apps with the help and advice from the Slack team. Our hackathon team came together to create a solution to this problem once and for all in our Slack workspace. With the use of Dialogflow, a platform that offers an interface for integrating natural language into apps, and FastAPI, a simple yet performant API framework for Python, we were easily able to create an interactive chatbot, named Eatware, that would do our bidding.

GIF demonstration of Eatware, our Slack bot

If you don’t know where to eat, try Eatware!

Here’s how we quickly set up our application using these three technologies.

Application Structure

We can split up our application structure into three layers:

  • Our presentation layer (Slack), where the user interacts with the bot and receives information from it
  • Our language processing layer (Dialogflow), which directly receives info from Slack
  • Our backend layer (Eatware API), receives webhook requests from Dialogflow, pulls restaurant information from external APIs, and sends the results back to Slack.

Diagram of application structure

Beginning with the presentation layer, we have the user prompt the bot to begin the experience. We identified the need for the user to converse with the bot in natural language to be a core part of the experience, so it blends in naturally with a team channel’s conversation (and it’s also a fun excuse to use the technology).

The user tags the bot and asks it in natural language that they’re looking for some food.

@Eatware, I’m looking for a bite to eat.

Dialogflow, the language-processing layer, is configured with Slack to read all the requests that mention the registered @Eatware application. It reads the sentence, uses the information we’ve trained it with to identify the user’s intent and any parameters they may have supplied. Dialogflow will respond in a conversation flow, prompting the user for any missing parameters, and when it’s reached a satisfactory point, it will make a webhook call to a location of our choosing. In our case, it’s an API we’ve set up to receive this call.

Our backend layer, built with FastAPI, receives the now-complete payload of parameters from the user and makes a call to an external service with our restaurant search terms. After this call is performed, our API maps the external response to a Slack payload, and sends the response to Slack in order to respond as the bot.

Now that we’ve covered how the application is set up, let’s get into the finer details of each part.

Dialogflow

Dialogflow is an E2E dev tool used to create conversational interfaces for websites and apps. Once the model is trained, it is compatible with multiple platforms including Facebook, LINE, Telegram, and more. For our particular project we integrated with Slack, where the user base composed of office workers enabled us to test features with potential users directly.

Dialogflow on Eatware is our middle layer, taking in user inputs flexibly from Slack, parsing relevant information out from a statement, then sending it as a webhook request to our API. When the API returns a response, Dialogflow takes the information, pieces it together into a user-friendly format, then outputs it back to the user as a Slack message.

Image for post

The main factor here is training our Dialogflow agent to recognize common sentence patterns that the user might use and determine keywords that fall into a category.

Initially, when presented with the sentence:

“Help me find an affordable Chinese restaurant nearby”

Dialogflow will not recognize these words as data. However, by manually training it by setting up a few words in the dictionary, such as “affordable = price” , “Chinese = food preference”, and “nearby = address”, the sentence will become strings of data to our agent.

Image for post

And thanks to its detection functionality, we don’t have to individually bind every word to a category; Dialogflow will attempt to do it itself when it finds a similar sentence structure. So next time when we ask the bot to “help me find an expensive Italian restaurant in Atlanta”, the agent detects the similarities in the sentence, deduces the user’s intent, and already correctly assumes the respective categories. As with all machine learning, Dialogflow improves its NLP prowess through everyday usage and user feedback.

Image for post

Once the parameters are identified, we can allow Dialogflow to passes these to a fulfillment webhook request to our API.

#chatbot #python #dialogflow #api #developer

Kasey  Turcotte

Kasey Turcotte

1623947400

One Line of Code for a Common Text Pre-Processing Step in Pandas

A quick look at splitting text columns for use in machine learning and data analysis

ometimes you’ll want to do some processing to create new variables out of your existing data. This can be as simple as splitting up a “name” column into “first name” and “last name”.

Whatever the case may be, Pandas will allow you to effortlessly work with text data through a variety of in-built methods. In this piece, we’ll go specifically into parsing text columns for the exact information you need either for further data analysis or for use in a machine learning model.

If you’d like to follow along, go ahead and download the ‘train’ dataset here. Once you’ve done that, make sure it’s saved to the same directory as your notebook and then run the code below to read it in:

import pandas as pd
df = pd.read_csv('train.csv')

Let’s get to it!

#programming #python #one line of code for a common text pre-processing step in pandas #pandas #one line of code for a common text pre-processing #text pre-processing