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Dialogflow

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rasa NLU

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Dialogflow vs rasa NLU: What are the differences?

Introduction

Dialogflow and Rasa NLU are two popular natural language understanding (NLU) platforms used for building conversational agents or chatbots. Both platforms offer similar functionalities but have some key differences that set them apart.

  1. Training and Deployment: Dialogflow provides a user-friendly web interface for training and deploying chatbots, which makes it easy for non-technical users to build and manage conversational agents. On the other hand, Rasa NLU requires technical knowledge as it is a command-line tool and needs to be integrated with a developer's own application for training and deployment.

  2. Customization and Control: Rasa NLU offers more customization and control compared to Dialogflow. With Rasa NLU, developers have full control over the machine learning models and can customize them according to specific requirements. Dialogflow, on the other hand, offers limited customization options and relies on pre-trained models provided by Google.

  3. Open Source vs Proprietary: Rasa NLU is an open-source platform, which means developers can access the source code, customize it, and contribute to its development. This provides more flexibility and allows for community-driven improvements. On the contrary, Dialogflow is a proprietary platform owned by Google, and its development is controlled by Google.

  4. Speech Recognition: Dialogflow has built-in support for automatic speech recognition (ASR) and can directly process spoken input, making it suitable for voice-based applications. Rasa NLU, on the other hand, primarily focuses on text-based input and does not provide native support for speech recognition.

  5. Integration: Rasa NLU provides more flexibility for integration with other frameworks and technologies. Developers can integrate Rasa NLU with their own applications, backend systems, or even other NLU platforms. Dialogflow also allows integration with certain platforms, but it may have limitations depending on the specific use case.

  6. Cost: Dialogflow offers a free tier with limited usage quotas, but there are pricing plans available for higher usage and enterprise features. Rasa NLU, being open-source, has no direct cost associated with it, but developers may need to invest in their own infrastructure and development resources.

In Summary, Dialogflow provides a user-friendly interface and easy deployment for chatbot development, while Rasa NLU offers more customization, control, and flexibility for integration and speech recognition.

Advice on Dialogflow and rasa NLU
Needs advice
on
BotkitBotkitDialogflowDialogflow
and
rasa NLUrasa NLU

Hi, does anyone have recommendations for a chatbot framework? I am currently using Botpress, and I am not happy with it. The upside is: They pretty much have everything you can ask for in a bot solution, but the issue is: They did nothing right, the documentation is terrible, and you have this feeling of it falling apart at any time, which is what actually happened once.

My ideal solution would have:

  • Support for Messenger and web (should either have a website chat plugin or straightforward integration with a different one)
  • A visual builder (for none tech team members) | This is not a hard requirement though
  • A slick DX for building simple things like API calls or more advanced stuff.
  • We currently only have a "click bot," so no crazy NLP features required, but in the future a requirement

What I do not want: - I do not want a solution where "someone else" builds the bot for me

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Replies (1)
Samantha Delfin
Data Scientist at ArkusNexus · | 2 upvotes · 54K views
Recommends
on
DialogflowDialogflow

Dialogflow includes:

  • OOTB integration with Messenger and you may use the Web Demo integration provided to embed it to your website. For Messenger, you even have some responses such as image responses, card responses and for those that are not available you can use custom payload.
  • It has a very nice visual builder which can be easily used by non-technical builders.
  • Fulfillment allows you to easily integrate your APIs.

Coursera has a very nice two-week course to learn how to use it.

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Pros of Dialogflow
Pros of rasa NLU
  • 18
    Built-in conversational agents
  • 7
    Custom Webhooks
  • 5
    Great interface
  • 5
    Multi Lingual
  • 4
    OOTB integrations
  • 2
    Knowledge base
  • 1
    Quick display
  • 9
    Open Source
  • 6
    Docker Image
  • 6
    Self Hosted
  • 3
    Comes with rasa_core
  • 1
    Enterprise Ready

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Cons of Dialogflow
Cons of rasa NLU
  • 9
    Multi lingual
  • 2
    Can’t be self-hosted
  • 4
    No interface provided
  • 4
    Wdfsdf

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What is Dialogflow?

Give users new ways to interact with your product by building engaging voice and text-based conversational apps.

What is rasa NLU?

rasa NLU (Natural Language Understanding) is a tool for intent classification and entity extraction. You can think of rasa NLU as a set of high level APIs for building your own language parser using existing NLP and ML libraries.

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Jobs that mention Dialogflow and rasa NLU as a desired skillset
What companies use Dialogflow?
What companies use rasa NLU?
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What are some alternatives to Dialogflow and rasa NLU?
Amazon Lex
Lex provides the advanced deep learning functionalities of automatic speech recognition (ASR) for converting speech to text, and natural language understanding (NLU) to recognize the intent of the text, to enable you to build applications with highly engaging user experiences and lifelike conversational interactions.
Microsoft Bot Framework
The Microsoft Bot Framework provides just what you need to build and connect intelligent bots that interact naturally wherever your users are talking, from text/sms to Skype, Slack, Office 365 mail and other popular services.
IBM Watson
It combines artificial intelligence (AI) and sophisticated analytical software for optimal performance as a "question answering" machine.
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Twilio
Twilio offers developers a powerful API for phone services to make and receive phone calls, and send and receive text messages. Their product allows programmers to more easily integrate various communication methods into their software and programs.
See all alternatives