Apple iPhone Support RAG Chatbot
🧠 Project Overview
This project showcases a Retrieval-Augmented Generation (RAG) chatbot capable of answering Apple iPhone support questions by referencing real documentation scraped from Apple’s official site.
The chatbot runs locally or with OpenAI models and supports streaming responses using Gradio. It demonstrates modern AI implementation practices including local inference with Ollama and hybrid deployment with GPT-4 for enterprise-grade reliability.
🔧 Technologies Used
- LangChain – RAG pipeline orchestration with ConversationalRetrievalChain
- FAISS – Efficient vector similarity search with saved local index
- OpenAI Embeddings – For cloud-based text representation and semantic search
- OpenAI GPT4 – For LLM-based text generation
- Ollama – For local LLM inference using LLaMA 3 and nomic-embed-text
- Gradio – Interactive frontend for chatbot demo with model switching
- Playwright (Python) – For automated document scraping of Apple Support
🔍 Features
- 🔄 Dynamic model selection between
llama3.2(local but not shown in the current demo) andgpt-4(cloud) - 🧩 Document chunking and embedding with semantic retrieval
- 🗃️ Vector search via FAISS to enhance question answering
- 💬 Conversation memory with context persistence
- 🌐 Self-hosted or deployable via Docker
🎯 Why It Matters
This project is ideal for:
- Companies exploring AI-based customer support automation
- Teams evaluating RAG pipelines and vector databases
- Clients requiring private LLM deployment or hybrid cloud-local solutions
🚀 Try It Yourself
You can take a look at the Demo HERE.
The entire project is containerized with Docker Compose, allowing easy deployment in local or cloud environments.
Want a similar AI assistant trained on your own knowledge base or product documentation?