AI Chat Assistant with Conversation Memory

A simple AI chat assistant workflow that leverages previous conversation history to generate relevant responses to user input. Includes a welcome message and uses a language model to reply contextually based on chat history.

How the AI Flow works - AI Chat Assistant with Conversation Memory

Flows

How the AI Flow works

Chat Session Initialization.
Triggers when the chat session is opened and displays a welcome message to the user.
User Message Input.
Receives input messages from the user.
Retrieve Chat History.
Fetches previous chat history to use as context for the conversation.
Generate Contextual AI Response.
Combines current user input and chat history in a prompt and uses a language model to generate a relevant response.
Display AI Response.
Outputs the AI-generated response back to the chat interface for the user to view.

Prompts used in this flow

Below is a complete list of all prompts used in this flow to achieve its functionality. Prompts are the instructions given to the AI model to generate responses or perform actions. They guide the AI in understanding user intent and generating relevant outputs.

Components used in this flow

Below is a complete list of all components used in this flow to achieve its functionality. Components are the building blocks of every AI Flow. They allow you to create complex interactions and automate tasks by connecting various functionalities. Each component serves a specific purpose, such as handling user input, processing data, or integrating with external services.

Chat Opened Trigger

The Chat Opened Trigger component detects when a chat session starts, enabling workflows to respond instantly as soon as a user opens the chat. It initiates flows with the initial chat message, making it essential for building responsive, interactive chatbots.

See Chat Opened Trigger

Message Widget

The Message Widget component displays custom messages within your workflow. Ideal for welcoming users, providing instructions, or showing any important information, it supports Markdown formatting and can be set to appear only once per session.

See Message Widget

Chat Output

Discover the Chat Output component in FlowHunt—finalize chatbot responses with flexible, multi-part outputs. Essential for seamless flow completion and creating advanced, interactive AI chatbots.

See Chat Output

ChatInput

The Chat Input component in FlowHunt initiates user interactions by capturing messages from the Playground. It serves as the starting point for flows, enabling the workflow to process both text and file-based inputs.

See ChatInput

Chat History Component

The Chat History component in FlowHunt enables chatbots to remember previous messages, ensuring coherent conversations and improved customer experience while optimizing memory and token usage.

See Chat History Component

Prompt Component in FlowHunt

Learn how FlowHunt's Prompt component lets you define your AI bot’s role and behavior, ensuring relevant, personalized responses. Customize prompts and templates for effective, context-aware chatbot flows.

See Prompt Component in FlowHunt

Generator

Explore the Generator component in FlowHunt—powerful AI-driven text generation using your chosen LLM model. Effortlessly create dynamic chatbot responses by combining prompts, optional system instructions, and even images as input, making it a core tool for building intelligent, conversational workflows.

See Generator

Flow description

Purpose and benefits

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

StepNode/ComponentPurpose
Chat StartChatOpenedTriggerDetects when the chat is opened
Welcome MessageMessageWidgetGreets and informs the user
Display WelcomeChatOutputShows the welcome message
User InputChatInputCaptures user’s task or question
Retrieve HistoryChatHistoryFetches recent conversation for context
Prompt ConstructionPromptTemplateBuilds prompt for the LLM with input and chat history
AI GenerationGeneratorProduces context-aware response using the prompt
Display AI OutputChatOutputShows the AI-generated answer to the user

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.

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