No-Code Flowise LLM App : Interview Question Generator

🚀 Project Description

The Interview Question Generator Agents project is a multi-agent system designed to generate Java programming interview questions, create test cases, and provide solutions. It uses Flowise AI, which integrates supervisory AI models, worker agents, and LLM-based reasoning to automate the question-generation pipeline.

🔧 System Components

📂 Supervisor Agent

• Manages multi-agent coordination.
• Assigns tasks to worker agents in an optimal sequence.
• Ensures efficient execution of the pipeline.
• Implements recursion limits to prevent infinite loops.

👨‍💻 Worker Agents

Worker Name	Function
DataStructure Inquiry Agent	Captures user-selected data structure (Arrays, Trees, etc.).
Java Question Generator Agent	Generates Java programming questions based on the selected data structure.
Test Case Creator Agent	Designs a robust set of test cases for the generated questions.
Solution Provider Agent	Produces optimized Java solutions for the generated questions.

⚙️ System Workflow

1. User selects a data structure (Arrays, Linked Lists, Trees, Graphs).
2. Supervisor Agent assigns the task to the DataStructure Inquiry Agent.
3. Java Question Generator produces a medium-level Java programming question.
4. Test Case Creator Agent designs multiple test cases covering:
   • Edge cases
   • Typical input scenarios
   • Expected outputs
5. Solution Provider Agent writes a fully functional Java solution for the question.
6. Supervisor validates the output and returns it to the user.

🔥 Advantages of Flowise

• 🚀 **No-Code AI Workflow Builder** - Allows easy creation of AI-powered workflows with a drag-and-drop interface.
• 📡 **LLM Orchestration** - Connects **LLMs with memory, data loaders, cache, moderation, and more.**
• ⚡ **Agent & Assistant Integration** - Uses **LLM agents, assistants, and external APIs**.
• 🔗 **Seamless API Connectivity** - Supports **REST, Webhooks, and database integrations**.
• 📊 **Scalability & Deployment** - Deploys on **Docker, Kubernetes, and cloud services like AWS and GCP**.

🛠 How Flowise is a No-Code Builder

• 📌 **Drag-and-Drop Interface** - Users can build complex AI workflows without coding.
• 🖱 **Visual Workflow Editor** - Allows connecting nodes for data flow.
• 🔗 **Pre-built Components** - Includes **Chat Models, Memory, APIs, Databases, and Agents**.
• 🔧 **Customizable with Code** - Advanced users can extend functionality via **custom Python & JavaScript modules**.

🤖 Flowise LLM Orchestration

• 🔄 **Memory Management** - Allows persistent memory for chatbot sessions.
• ⚡ **Data Loaders** - Fetches structured/unstructured data dynamically.
• 🕵️ **Moderation & Security** - Prevents **unsafe, biased, or sensitive responses.**
• 📡 **Caching & Latency Optimization** - Reduces token usage and improves response speed.

🚀 How to Build No-Code Chatbots Using Flowise

1. 🖱 **Open Flowise UI** - Start a new project.
2. 📡 **Select LLM Provider** - Choose **OpenAI, Mistral, or Google AI**.
3. 🧠 **Add Memory & Moderation** - Configure **persistent memory and filtering**.
4. ⚡ **Connect APIs & Databases** - Integrate **external services for knowledge base retrieval**.
5. 🤖 **Deploy & Scale** - Host your chatbot on **Docker, AWS, or Kubernetes**.

📡 AI Model Integration

The system uses OpenAI’s LLM models (e.g., gpt-4o, gpt-3.5-turbo) to:

• Generate logically sound questions.
• Validate and refine worker responses.
• Optimize test case selection.
• Provide explanations and feedback.

🔗 AI Model Configuration

{
  "id": "chatOpenAI_0",
  "type": "ChatOpenAI",
  "inputs": {
    "modelName": "gpt-4o",
    "temperature": 0.4,
    "maxTokens": 1500
  }
}
    

🕵️‍♂️ Supervisor Agent Configuration

{
  "id": "supervisor_0",
  "type": "Supervisor",
  "inputs": {
    "supervisorName": "Interview Question Generator",
    "supervisorPrompt": "Manage a conversation between the following workers: {team_members}. Minimize steps.",
    "recursionLimit": 100
  }
}
    

🔄 Execution Flow & Connectivity

{
  "edges": [
    {"source": "supervisor_0", "target": "worker_0"},
    {"source": "worker_0", "target": "worker_1"},
    {"source": "worker_1", "target": "worker_2"},
    {"source": "worker_2", "target": "worker_3"}
  ]
}
    

🔎 Execution

🚀 Start the Chatflow

      sudo npx flowise start --PORT=3000 --DEBUG=true
      http://localhost:3000/
    

🎯 Project Summary

Feature	Description
📌 Core Functionality	Generates Java interview questions and solutions
🛠 Multi-Agent System	Supervisor manages worker agents for efficiency
🤖 AI-Powered	Uses GPT-4o for question validation
📡 Optimized Workflow	Minimizes steps via dynamic task assignment
🔄 Modular & Scalable	Deployable via Docker, Cloud Functions

🚀 Final Thoughts

The **Interview Question Generator Agents** project optimizes Java interview question generation using Flowise AI’s **multi-agent workflow**. With **no-code orchestration**, **LLM-powered decision-making**, and **scalable chatbot building**, Flowise enhances **AI-driven automation**.

🚀 Demo Videos

📖 Flowise Interview Generator Demo