AI-Generated Technical Service Bulletins for EVs

Iftekhar Ahmed

Completed work

ML Engineer

AI Developer

AI Engineer

Ollama

pandas

Streamlit

Automotive

🔧 AI-Generated Technical Service Bulletins (TSBs) for EVs

This project uses local LLMs to automatically generate Technical Service Bulletins (TSBs) from electric vehicle (EV) repair logs. It includes clustering similar logs, generating markdown TSBs, and a user-friendly Streamlit interface.

🚀 Features

⚙️ TSB Generation using local LLMs (e.g. Mistral via Ollama)

🧠 Unsupervised Clustering of similar repair logs

📄 Markdown Export of generated TSBs

🎛️ Streamlit Web App for interactive use

🔌 Offline Compatible – no API calls or internet required

🗃️ Organized project structure with extensible modules

📁 Project Structure

AI-Generated-TSBs-for-Evs/
│
├── app/                          # App logic & modules
│   ├── __init__.py               # (optional for now)
│   ├── tsb_generator.py          # Core logic for TSB generation with Ollama
│   ├── clustering.py             # (optional: for clustering logic)
│   ├── utils.py                  # (optional: helper functions)
│
├── data/                         # Input data files
│   ├── repair_logs_sample.txt    # Sample repair logs
│   ├── tsb_prompt.txt            # Prompt template for TSB generation
│   └── clustered_logs/           # Logs grouped by similarity
│       ├── cluster_1.txt
│       └── ...
│
├── outputs/                      # Final generated outputs
│   ├── Tsbs/                     # Markdown TSBs
│   └── generated_tsb_clusters/   # Clustered summaries
│
├── scripts/                      # Python scripts for generation & clustering
│   ├── generate_tsb.py
│   ├── cluster_logs.py
│   ├── generate_cluster_tsbs.py
│   └── batch_generate.py
│
├── streamlit_app.py              # Streamlit interface for generating TSBs
├── requirements.txt              # Python dependencies
├── README.md                     # You're here
└── venv/                         # Virtual environment (excluded from version control)

🛠️ Requirements

Python 3.8+

Ollama installed locally

A local LLM (e.g., mistral, llama3, phi3)

pip packages:

streamlit

scikit-learn

numpy

pandas

ollama

To install and start Ollama:

Install Ollama:

brew install ollama

Download and run a model (e.g. Mistral):

ollama run mistral

Make sure the model is running before launching the app or scripts.

📦 Installation

Clone and set up the project:

git clone https://github.com/your-username/AI-Generated-TSBs-for-Evs.git
cd AI-Generated-TSBs-for-Evs

Create a virtual environment:

python3 -m venv venv
source venv/bin/activate

Install Python dependencies:

pip install -r requirements.txt

⚙️ How to Run the Scripts

1. Generate TSBs from Raw Logs

python3 scripts/generate_tsb.py

📂 Output: outputs/Tsbs/tsb_1.md, tsb_2.md, etc.

2. Cluster Logs into Groups

python3 scripts/cluster_logs.py

📂 Output: data/clustered_logs/cluster_1.txt, cluster_2.txt, etc.

3. Generate TSBs from Clustered Logs

python3 scripts/generate_cluster_tsbs.py

📂 Output: outputs/Tsbs/cluster_1.md, etc.

4. Launch the Streamlit App

streamlit run streamlit_app.py

Then open http://localhost:8501 in your browser.

🧪 Sample File Format

repair_logs_sample.txt example:

Vehicle Model X shuts down randomly while charging.
Charging port light flashes red, indicating failure.
---

Battery temperature spikes above 70°C under low load.
Frequent system reboots observed after power drain.

Each log is separated by either a double newline or ---.

🧠 Model & Prompt

The prompt used for TSB generation is stored in:

data/tsb_prompt.txt

It provides instructions to the LLM for generating concise, technical, and standardized service bulletins based on the input logs.

You can customize the prompt to match your formatting preferences or tone of voice.

✅ Current Status

✅ Offline TSB generation using Ollama

✅ Clustering of logs for grouped TSBs

✅ Markdown file generation for each TSB

✅ Streamlit UI for manual upload and generation

✅ Export to PDF

🔄 Add quality evaluation & ranking (Planned)

📊 Multi-log summarization via embedding clustering (Planned)

🙌 Acknowledgments

Ollama – local LLM orchestration

scikit-learn – clustering engine

Streamlit – web app UI

✍️ Author

Iftekhar Ahmed, Berlin, Germany AI | ML | Automation | Sustainability

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Completed work

Posted Aug 5, 2025

Automates TSB creation from repair logs using local LLMs, Python, and Streamlit for EV maintenance insights.

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Timeline

Jul 1, 2025 - Jul 30, 2025

Clients

Open Source Initiative