RNet AI
  • INTRODUCTION
    • About Rnet
    • Quickstart
    • Core Concepts
      • List of Model Providers
  • GETTING STARTED
    • Model
      • Add New Provider
      • Predefined Model Integration
      • Custom Model Integration
      • Interfaces
      • Schema
    • Application Orchestration
      • Overview
      • Interactive Chat Application
      • Agent
      • App Kits
    • Workflow
      • Core Concepts
      • Node Overview
        • Start
        • Question Classifier
        • Knowledge Retrieval
        • Variable Aggregator
        • LLM
        • Direct Reply
        • IF/ELSE
        • HTTP Request
        • End
    • Knowledge Base
Powered by GitBook
On this page
  • Overview
  • Common Use Cases
  1. GETTING STARTED

Workflow

PreviousApp KitsNextCore Concepts

Last updated 8 months ago

Overview

Workflow refers to a structured sequence of steps or nodes that automates complex tasks, often involving large language models (LLMs). These workflows help streamline and manage the process of building AI applications by breaking down tasks into smaller, more manageable components. Each node in the workflow can perform specific actions, such as processing data, applying logic, or interacting with other systems. Think of a workflow in Rnet like an assembly line in a factory. Each station on the assembly line performs a specific task—like adding a part, painting, or inspecting the product.

Rnet workflows are divided into two types:

  • Chatflow: Designed for conversational scenarios, including customer service, semantic search, and other conversational applications that require multi-step logic in response construction.

  • Workflow: Geared towards automation and batch processing scenarios, suitable for high-quality translation, data analysis, content generation, email automation, and more.

Common Use Cases

  • Customer Service

By integrating LLM into your customer service system, you can automate responses to common questions, reducing the workload of your support team. LLM can understand the context and intent of customer queries and generate helpful and accurate answers in real-time.

  • Content Generation

Whether you need to create blog posts, product descriptions, or marketing materials, LLM can assist by generating high-quality content. Simply provide an outline or topic, and LLM will use its extensive knowledge base to produce engaging, informative, and well-structured content.

  • Task Automation

LLM can be integrated with various task management systems like Trello, Slack, and Lark to automate project and task management. Using natural language processing, LLM can understand and interpret user input, create tasks, update statuses, and assign priorities without manual intervention.

  • Data Analysis and Reporting

LLM can analyze large datasets and generate reports or summaries. By providing relevant information to LLM, it can identify trends, patterns, and insights, transforming raw data into actionable intelligence. This is particularly valuable for businesses looking to make data-driven decisions.