Machine Troubleshooting AI Assistant

An advanced AI-powered chatbot system for industrial machine diagnostics and troubleshooting, built with Ollama (Mistral Nemo) and dynamic semantic search capabilities.

Features

AI-Powered Diagnostics

• Mistral Nemo Model: Uses Ollama with Mistral Nemo for intelligent troubleshooting responses
• RAG (Retrieval-Augmented Generation): Combines semantic search with AI generation for accurate solutions
• Context-Aware: Leverages historical maintenance data to provide relevant solutions
• Dynamic Learning: Real-time updates to the knowledge base from user contributions

Semantic Search

• Vector Embeddings: Uses Sentence-BERT (all-MiniLM-L6-v2) for intelligent case matching
• Hybrid Search: Combines keyword matching and cosine similarity for optimal results
• Case History: Searches through past maintenance records to find similar issues

Conversational Interface

• Real-time Chat: Natural language interaction with the AI assistant
• Structured Diagnosis: Form-based troubleshooting for detailed analysis
• Multi-turn Conversations: Maintains context throughout the discussion

Analytics & Feedback

• User Feedback System: Collects ratings and comments to improve accuracy
• Statistics Dashboard: Tracks queries, success rates, and user contributions
• Export Functionality: Download chat history and correction submissions

System Requirements

Software

• Ollama: Must be installed and running locally
• OS: Windows 10/11, Linux (Ubuntu 20.04+), or macOS
• Python: 3.8 or higher

Installation

1. Install Ollama & Pull Model

First, install Ollama on your system. Once installed, pull the required model:

ollama pull mistral-nemo

2. Clone the Repository

git clone https://github.com/Romilagarwal/new_chatbot.git
cd new_chatbot

3. Create Virtual Environment

python -m venv venv

# Windows
venv\Scripts\activate

# Linux/Mac
source venv/bin/activate

4. Install Dependencies

pip install flask flask-compress sentence-transformers pandas numpy scikit-learn python-dotenv pytz requests

5. Setup Environment Variables

Create a .env file in the project root:

# Dataset path
DATABASE_PATH=mix_dataset_final.csv

# Ollama Host (default)
OLLAMA_HOST=http://localhost:11434

6. Prepare Dataset

Place your machine maintenance dataset CSV file (mix_dataset_final.csv) in the project root with the following columns:

• Machine Type
• MACHINE (Machine Name)
• Problem Description
• Root Cause
• Action Taken

Usage

Starting the Application

python app.py

The application will:

1. Connect to the local Ollama service
2. Create embeddings for the reference dataset (or load from memory)
3. Start the Flask server
4. Automatically open your web browser

Using the Chatbot

Conversational Chat

Simply type your question in the chat interface:

"APMT machine showing alarm 203"
"How to fix welding spot machine not working?"
"Printer paper jam issue"

Structured Diagnosis

1. Select Machine Type from dropdown
2. Enter Machine Name (e.g., PRE-APMT, WELDING SPOT)
3. Describe the Problem in detail
4. Click Diagnose Problem

The system will:

• Find similar cases from the database
• Analyze patterns using AI
• Provide root cause analysis
• Suggest immediate actions
• Recommend preventive measures

Contributing Solutions

Help improve the system by submitting your solved cases:

1. Click Add Solution button
2. Fill in the machine details and solution
3. Submit for review

Project Structure

new_chatbot/
├── app.py                      # Main Flask application
├── ollama_model.py             # Ollama API model wrapper
├── model_utils.py              # RAG and dynamic learning base
├── stats_cards_updater.py      # Analytics logic
├── templates/
│   └── index.html              # Frontend interface
├── data/                       # Generated data directory
│   ├── feedback.csv            # User feedback
│   ├── user_corrections.csv    # Submitted solutions
│   └── chat_logs.json          # Chat history
├── mix_dataset_final.csv       # Your maintenance dataset
├── .env                        # Environment configuration
└── README.md                   # This file

Configuration

Model Settings

Mistral Nemo configuration in ollama_model.py:

• Temperature: 0.7 (troubleshooting), 0.8 (chat)
• Top-p: 0.9

Search Parameters

Adjust in model_utils.py:

threshold = 0.60        # Similarity threshold
top_n = 3               # Number of primary results

API Endpoints

Endpoint	Method	Description
/	GET	Main application interface
/health	GET	System health check
/stats	GET	Usage statistics
/chat	POST	Conversational chat endpoint
/diagnose	POST	Structured diagnosis
/feedback	POST	Submit user feedback
/submit-correction	POST	Submit new solutions
/export/chat-history	GET	Export chat logs
/export/corrections	GET	Export user contributions

Troubleshooting

Model Not Loading / Ollama API Error

Ensure Ollama is running in the background. Open a terminal and run:

ollama serve

Dataset Issues

• Verify CSV format matches expected columns
• Check for missing values in critical columns
• Ensure proper encoding (UTF-8)

Contributing

Contributions are welcome! Here's how you can help:

1. Report Issues: Found a bug? Open an issue
2. Submit Solutions: Use the built-in correction form
3. Code Improvements: Fork, improve, and submit PRs
4. Documentation: Help improve this README

License

This project is licensed under the MIT License - see the LICENSE file for details.