ChatGPT Knowledge Base Assistant

AI chatbot assistant powered by OpenAI GPT-4o that automatically searches and leverages internal company documents to answer user questions. Delivers context-aware, accurate, and conversational responses using both chat history and retrieved knowledge, making it ideal for customer support, internal helpdesks, or onboarding.

How the AI Flow works - ChatGPT Knowledge Base Assistant

Flows

How the AI Flow works

User submits a question.
Receives user input via chat interface.
Retrieve relevant internal documents.
Searches internal knowledge sources for information related to the user's query.
Build context-aware prompt.
Combines user question, retrieved documents, and chat history to create a comprehensive prompt for AI.
Generate AI-powered answer.
Uses OpenAI’s GPT-4o to generate a conversational, context-aware response.
Deliver response to user.
Displays the AI-generated answer in the chat for the user.

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.

Prompt

Create a prompt template with dynamic variables ({input}, {human_input}, {context}, {chat_history}, {system_message}).

                You are an AI language model assistant.

Your task is to answer customer query in INPUT with consideration of previous conversation in CHAT HISTORY.

If CONTEXT is provided, use it to generate the answer.


--- CONTEXT START
{context} 
--- CONTEXT END

--- CHAT HISTORY START
{chat_history}
--- CHAT HISTORY END

--- INPUT START
{input}
--- INPUT END
Answer in Language: {lang}
Format answer with markdown.

ANSWER:

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.

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

Document Retriever

FlowHunt's Document Retriever enhances AI accuracy by connecting generative models to your own up-to-date documents and URLs, ensuring reliable and relevant answers using Retrieval-Augmented Generation (RAG).

See Document Retriever

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

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

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

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

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 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

Flow description

Purpose and benefits

This workflow creates a chatbot interface that combines the conversational abilities of OpenAI’s ChatGPT 4o with the power to search and respond using internal company or organizational documents. It is designed to provide accurate, context-aware answers to user queries, leveraging both chat history and relevant knowledge sources.

Overview

  • Purpose: To automate and scale customer support, internal information access, or knowledge management tasks by enabling users to chat with an AI assistant that references both previous conversations and internal documentation.
  • Key Features:
    • Responds to user queries with up-to-date, contextually relevant information.
    • Searches internal documents automatically for supporting content.
    • Maintains awareness of chat history for coherent, multi-turn conversations.
    • Presents responses in markdown format for readability.

Workflow Structure

Below is a breakdown of the main components and their roles in the workflow:

ComponentDescription
Chat InputCaptures user messages and file uploads.
Chat Opened TriggerDetects when a new chat session starts.
Message WidgetDisplays a welcome message to the user at chat start.
Chat OutputDelivers messages (including welcome and AI-generated responses) to the user interface.
Chat HistoryStores and retrieves the last 10 chat exchanges (up to 8000 tokens) for context.
Document RetrieverWhen a user asks a question, this searches internal documents for relevant content.
Prompt TemplateCrafts a prompt for the AI, including user input, document context, and chat history.
GeneratorRuns the prompt through ChatGPT 4o (or another LLM), generating a response.

How the Workflow Operates

  1. Chat Session Initialization

    • When a user opens a new chat, the Chat Opened Trigger signals the workflow.
    • A welcome message is displayed using the Message Widget and shown via Chat Output.

  2. Handling User Queries

    • When the user enters a message:
      • The Chat Input node collects it.
      • In parallel:
        • The message is sent to the Document Retriever to search for up to 2 relevant internal documents.
        • The message is also passed to the Prompt Template for AI processing.

  3. Contextual Response Preparation

    • The workflow gathers:
      • The latest chat history for context.
      • Relevant documents found by the retriever.
      • The user’s current query.
    • These elements are merged in the Prompt Template, which instructs the AI to:
      • Answer considering the user’s question, previous conversation, and any document context found.
      • Format the response in markdown, and answer in the user’s language.

  4. AI Response Generation

    • The Generator node sends the prompt to the selected language model (e.g., ChatGPT 4o).
    • The generated answer is sent to Chat Output for display to the user.

Automation & Scaling Benefits

  • Consistency & Quality: Ensures users receive consistent, high-quality answers that are grounded in internal documentation and sensitive to prior conversation context.
  • Efficiency: Automates the tedious task of searching through knowledge bases, saving time for both users and support staff.
  • Scalability: Can handle many simultaneous user sessions and queries, making it ideal for organizations with high support or information demands.
  • Customization: The prompt template and document search parameters can be tailored for specific use cases, industries, or internal policies.

Example Use Cases

  • Internal Helpdesk: Employees can quickly get answers about company policies, IT procedures, or HR matters.
  • Customer Support: Customers receive accurate product or service information without waiting for a human agent.
  • Sales Enablement: Sales teams can access the latest product specs, case studies, or pitch materials on demand.
  • Knowledge Management: Ensures valuable institutional knowledge is accessible and actionable via natural language queries.

Visual Summary

Workflow Steps:

  1. User opens chat → Welcome message displayed.
  2. User asks a question.
  3. System retrieves:
    • Chat history
    • Relevant documents
  4. AI prompt is constructed (includes user input, chat history, document context).
  5. ChatGPT generates a response.
  6. Response is shown to user.

This workflow is a powerful template for anyone looking to enhance their chatbot or virtual assistant with contextual, document-aware intelligence—significantly improving user experience and operational efficiency.

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