Offline E-Commerce Support Chatbot with Ollama and Llama 3.2 3B

Keywords: Offline AI, Local LLM, Ollama, Llama 3.2 3B, Python, E-Commerce Chatbot, Customer Support, Prompt Engineering, Zero-Shot Prompting, One-Shot Prompting, LLM Evaluation, Privacy-First AI 🚀 Project Overview A privacy-first e-commerce support chatbot that uses Ollama to run the Llama 3.2 3B model locally. A Python client sends customer support prompts to the local inference API, exploring how local LLMs can answer common e-commerce support questions without sending prompts to an external cloud-based LLM service. The project compares zero-shot and one-shot prompting strategies across 20 e-commerce support queries, evaluating responses for relevance, coherence, and helpfulness. ✨ Key Features Local LLM inference using Ollama and Llama 3.2 3B Python-based chatbot client integrated with the local Ollama HTTP API Zero-shot and one-shot prompt engineering comparison Evaluation of 20 e-commerce support queries Response assessment based on relevance, coherence, and helpfulness Lightweight, stateless architecture for local inference 🛠️ Technology Stack Python Ollama Llama 3.2 3B Python Requests library Local HTTP API for model inference Prompt templates and Markdown-based evaluation outputs 📂 GitHub Repository https://github.com/SabbellaLaharika/P... 🎥 About This Demo This demo explores the project architecture, the integration between the Python client and the local Ollama service, and the differences between zero-shot and one-shot prompting strategies. It demonstrates the practical application of local LLM inference, prompt engineering, and response evaluation for e-commerce customer support. ⭐ If you find this project useful, consider starring the GitHub repository and subscribing for more software engineering and AI engineering project demonstrations. #Ollama #Llama32 #LocalAI #LLM #Python #PromptEngineering #Ecommerce #AIEngineering #PrivacyFirst #SoftwareEngineering