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A LangChain + OpenAI Complete Tutorial for Beginner — Lesson 2 Advanced Chatbot with RAG and Vector Databases
Latest   Machine Learning

A LangChain + OpenAI Complete Tutorial for Beginner — Lesson 2 Advanced Chatbot with RAG and Vector Databases

Last Updated on February 6, 2024 by Editorial Team

Author(s): Lorentz Yeung

Originally published on Towards AI.

Photo by Growtika on Unsplash

Remarks: our tutorials use 100% working codes as of January 2024 with LangChain version 0.1.4 and OpenAI version 1.10.0.

Introduction to Advanced Concepts (RAG)Setting Up the Environment for Advanced FeaturesLoading and Preparing DocumentsImplementing Vector DatabasesIntegrating RAG with Vector DatabasesConclusion and Further Exploration

In lesson 1, you have learned the basics of building chatbot applications using LangChain, OpenAI, and Hugging Face. We started by setting up the environment and choosing the right language model. Then, we progressed to creating a simple chatbot, enhancing it with prompt templates for structured interactions. We also delved into the crucial aspects of managing chat model memory and introduced advanced features like Conversation Chains and Summary Memory.

In lesson 2, we will learn advanced systems with RAG, and Loader. With RAG and Loader, your chatbot can tap into external information or knowledge and supercharge the answers to your questions.

Retrieval-Augmented Generation is a cutting-edge approach in AI, combining the power of language models with external knowledge sources. RAG enhances the capability of chatbots by allowing them to pull in information from a variety of documents, making responses more informative and contextually rich. This is particularly useful in commercial companies, e.g. creating a chatbot for retrieving client… Read the full blog for free on Medium.

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