AI-Powered Restaurant Review Q&A System (RAG Architecture)
Developed an interactive question-answering application using Python and LangChain, implementing a Retrieval-Augmented Generation (RAG) architecture. Engineered a pipeline that ingests and embeds real-world restaurant reviews, enabling semantic search and retrieval of the most relevant feedback for user queries. Integrated advanced LLMs (Ollama/Llama3) to generate expert, context-aware answers to questions about a pizza restaurant, grounded in actual customer reviews. Utilized ChromaDB for efficient vector storage and retrieval, and implemented custom retrievers to surface the top relevant reviews for each query. Designed the system to handle natural language questions in real time, providing users with insights and recommendations based on real customer experiences. Processed and analyzed a diverse dataset of restaurant reviews, supporting nuanced, data-driven responses to a wide range of user inquiries.