User Guide
eva 3.4.1
eva 3.4.1
  • Home and Support
  • What's new
  • Changelog
  • Using eva
    • Overview
      • Virtual Agents
      • Main Concepts of eva
      • Glossary
    • Developing virtual agents
      • Quick Start with eva
      • Dialog Flows
      • Dialog Cells
        • Intent Cells
        • Entity Cells
        • Answer Cells
        • Service Cells
        • Input Cells
        • Jump Cells
        • End Cells
        • Code Cells
        • Rule Cells
      • Training task
      • Export and import agents
      • Create virtual agents from templates
      • Create and manage users
    • Channels
      • Integrating Existing Channels
    • Testing Virtual Agents
      • Test a virtual agent
      • Automated Test
    • Natural Language
      • eva NLP
      • Training with eva NLP
      • Training with eva Automated Learning
      • Using other NLP engines
    • Analytics
      • External analytics platforms
    • Experience
      • Context Management
    • Advanced Options
      • Parameters
      • Intent Navigator
      • PII Data masking
    • How Tos
      • Creating variable answers with Code and Rule cells
      • Videos
  • for technicians
    • eva server Installation guide
    • API Guidelines
      • Conversation API
      • Cloner API
      • Webhooks
    • Supported/verified third-party software
    • Appendices
    • Data Extraction
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  1. Using eva

Experience

eva's platform is based on a microservices architecture and every component has its Rest APIs. A basic understanding of this architecture helps understand the APIs and the content on the next chapters.

The Broker is the orchestrator of the solution. It is responsible for receiving the information needed to execute a conversation. Its API is the one called when a channel receives a message from the user.

Also, it is responsible for calling NLPs dynamically, depending on which NLP was selected by the user in the Cockpit, and for calling external services when these are registered in the Cockpit through the webhook fields.

The main database, which is used by the Broker, is a MySQL database that contains all configuration made through the Cockpit as well as data generated through conversations by the Broker.

Another important component is the Dialog Manager, where you can build and manage the virtual agent conversation flows. This module has its own NoSQL database, for a better performance of the virtual agent. All conversation flows created in the Cockpit are stored in this MongoDB.

The extraction details explained in the following chapters will detail only the MySQL database, since it contains the conversation logs.

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Last updated 3 years ago