Building a Chatbot Web Interface Using Large Language Models and RAG

Watch a 16-minute Tech Field Day Ignite Talk where Ben Young chronicles his experience developing a chatbot web interface for Veeam knowledge base articles using OpenAI models. Explore the fundamentals of retrieval augmented generation (RAG) and understand the limitations of relying exclusively on large language models, including restricted access to private data and expensive retraining costs. Learn about the essential components of chatbot functionality, from document retrieval to interpretation and response generation. Follow the step-by-step process of building a knowledge base dataset and API, with particular attention to how embeddings facilitate matching queries to relevant database information. Discover various data storage solutions, including vector databases and traditional databases with vector capabilities, while examining the implementation of tools like Superbase and Langchain for managing data interactions. Gain insights into maximizing the potential of language models like Vani GPT through effective query formulation and prompt engineering for optimal response accuracy.