AI Chat Assistant with Conversation Memory

This workflow is designed to facilitate an interactive chat experience where the AI assistant responds to user-defined tasks, while leveraging the chat history for context-aware answers. It is a general-purpose template, making it adaptable for a wide variety of conversational automations and scalable AI-driven chat solutions.

Step-by-Step Workflow Breakdown

1. Chat Session Initiation and Welcome Message

• Chat Opened Trigger: When the chat is opened, a trigger is activated.
• Welcome Message: A message widget displays a friendly welcome message to the user:

  👋 Welcome to the Simple Task Flow!
  This tool is designed for you to define your own task based on your input 🌟. I’ll take into account our chat history to provide relevant assistance without any additional context.
  Just let me know what you’d like to do, and let’s get started! ✨💬

• Display: The welcome message is shown in the chat output area, providing onboarding and setting expectations.

2. Capturing User Input

• Chat Input Node: Receives text (and optionally file) input from the user, representing the task or question they want to address.

3. Retrieving Chat History

• Chat History Node: Fetches up to the last 10 messages (with a token cap of 8000) from the chat. This history is later used to provide context and maintain continuity in the conversation.

4. Prompt Construction

• Prompt Template Node: Constructs a dynamic prompt for the language model. It integrates:

  • The user’s latest input.
  • The recent chat history.
  • A fixed system message that instructs the AI to generate context-aware answers.

  The prompt template used is:

  You are an AI language model assistant.
  
  Your task is to generate answer for human INPUT with consideration of previous conversation in CHAT HISTORY.
  
  --- CHAT HISTORY START
  {chat_history}
  --- CHAT HISTORY END
  
  --- INPUT START
  {input}
  --- INPUT END
  
  ANSWER:
  

5. AI Generation

• Generator Node: Receives the constructed prompt and generates a text response using a large language model (LLM). This ensures the response is contextually relevant and tailored to the user’s request.

6. Output Display

• Chat Output Node: The AI-generated answer is displayed to the user in the chat interface.

Workflow Structure Table

Why This Workflow is Useful for Scaling and Automation

• Contextual Interactions: By incorporating chat history, the system maintains context, improving response relevance and user satisfaction.
• User-Defined Tasks: The workflow is task-agnostic, allowing users to define their own objectives, making it highly flexible.
• Scalable Automation: The modular design is suitable for scaling—multiple users can interact simultaneously, with each session maintaining its own context.
• Easy Customization: The prompt template and nodes can be easily adapted for specific use-cases (e.g., support, information retrieval, onboarding).
• Consistent User Experience: Automated greeting and context-aware responses ensure that every user interaction is handled professionally and efficiently.

Example Use Cases

• Customer support chatbots that remember previous interactions.
• Onboarding assistants that guide new users based on their ongoing conversation.
• General-purpose AI helpers in apps where users can define their own queries or tasks.

This workflow provides a robust foundation for building intelligent, context-aware chat automations that can be tailored to many different applications.