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  • GETTING STARTED
    • Introduction
    • Getting ready
    • Billing and plans
    • Quick Start AI Agents
      • Quick Start AI Agent
      • The "Start from Scratch" AI Agent
        • Chat Management Conversation
        • Welcome Conversation
        • Topic Conversation
        • Global No Match Conversation
        • Supporting LLM Actions
        • Semantic Classifier: Query Classifier
      • A Process Handling AI Agent
  • STEP BY STEP GUIDES
    • AI Agent Creation Overview
    • Add a new topic of discussion
    • Use knowledge sources via RAG
    • Adding a structured conversation
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  • CORE CONCEPTS
    • OpenDialog Approach
      • Designing Conversational AI Agents
    • OpenDialog Platform
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  • CREATE AI APPLICATIONS
    • Designing your application
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          • Introduction to conversational patterns
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      • Message Design
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        • Subsequent Messages - Virtual Intents
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      • Webchat Interface design
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    • Leveraging Generative AI
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          • Output attributes
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        • Example-based intent classification [Deprecated]
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            • Google Dialogflow Knowledge Base
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      • LLM Actions
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        • Chatwoot
    • Previewing your application
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    • FAQ
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  • Developing With OpenDialog
    • Integrating with OpenDialog
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    • WebChat
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      • Load Webchat within page Element
      • How to enable JavaScript in your browser
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  • Release Notes
    • Version 3 Upgrade Guide
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  1. GETTING STARTED
  2. Quick Start AI Agents
  3. The "Start from Scratch" AI Agent

Topic Conversation

The Topic Conversation of the Start From Scratch Scenario

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Last updated 6 months ago

The Topic conversation is the more complex conversation in this Scenario as it lays out the structure to deal with a number of different generic topics in a fluid conversational way.

Every Scene in the Topic Conversation follows the same pattern.

  1. All Scenes have the starting behavior. So once we are in the Topic Conversation we consider all Scenes.

  2. Each Scene has one or more Turns that are also starting.

  3. Each Turn starts with a user Intent that corresponds to an Intent of the Query Classifier Semantic Classifier.

  4. Each Turn then also has a response intent.

  5. Once we've responded to the user we redirect the context to the start of the Topic Conversation once more so the cycle can repeat.

  6. Most APP intents have a dynamic response generated using an LLM action.

What this patterns allow us to do is move between the different scenes on every user query with flexibility since after every response we go back to the "top" of the Topics conversation.

In addition, the Topic Conversation has a No Match Scene that means we can capture anything that our Query Classifier has not interpreted contextuall and deal with it in the Topic Conversation.

The Topic Conversation
The Topic Conversation