Create a Contextual Chatbot with LLM and Vector Database in 10 Minutes

Learn how to build a contextual chatbot using a Large Language Model (LLM) and a vector database in just 10 minutes. Explore the essential components of chatbot development, including embedding models for question translation, vector databases for efficient searching, and LLMs for answer generation. Discover how to streamline the process using Langchain for minimal development effort. Follow along as MLOps Lead Raahul Dutta demonstrates the implementation process, covering topics such as embedding, quadrant analysis, and fine-tuning. Gain practical insights from an experienced professional with a background in transforming Jupyter notebooks into production-ready endpoints and implementing various ML/AI models and pipelines.

Introduction
Implementation
Embedding
Quadrant
Fine Tuning
Demo