This repository contains the research and experimentation conducted during my internship in R&D and as part of my Master’s degree in Data Science. The focus of my work was captured in a document titled: "The LangChain Paradigm: Building Cost-Effective, Efficient AI Chatbots."
Here, you'll find the code snippets and experiments associated with my master's thesis, which explores the implementation of a Retrieval-Augmented Generation (RAG) architecture to develop a cost-effective AI copilot utilizing a local Large Language Model (LLM).
LangChain is highlighted in this review for its pivotal role in advancing virtual assistant technology by simplifying the integration of Large Language Models (LLMs) into applications. Its modular and customizable framework allows developers to create context-aware, accurate, and efficient virtual assistants. By enabling the seamless connection of LLMs with various data sources, LangChain significantly enhances the capabilities of virtual assistants, making them more versatile and user-friendly across both commercial and academic settings.