Create a RAG Chain using LangChain 0.1 (New version)

In this video we explore a crash course of the new Langchain version (0.1.0) in python. This will allow you to create RAG chains in Langchain to chat with your documents. We will be showcasing LangChain, a revolutionary Python library that empowers developers to create context-aware and reasoning-driven applications using powerful language models. In the video, we will create several chains using the new version of LangChain to chat with a website. The final chain that we build here is a history-aware chain that takes the history of the conversation into account to answer your questions. ----- LINKS 📒 Google Colab: https://colab.research.google.com/drive/1yv1JHVCKD3oUp4XAiCGbTw7av5Cy50Xj 👉 Official LangChain Documentation: https://python.langchain.com/docs/get_started/quickstart 💬 Join the Discord Help Server - https://link.alejandro-ao.com/HrFKZn ❤️ Buy me a coffee... or a beer (thanks): https://link.alejandro-ao.com/l83gNq ✉️ Join the mail list: https://link.alejandro-ao.com/AIIguB ------------------- 🔗 What is LangChain? LangChain is a framework designed to elevate your applications to new heights. It enables the creation of context-aware applications by connecting language models to various sources of context, allowing them to reason and provide intelligent responses. 🚨 Quickstart Highlights: 👉 1. Get Set Up with LangChain: Learn how to seamlessly set up the LangChain ecosystem to kickstart your development journey. 👉 2. Basic Components Mastery: Explore the fundamental components of LangChain, including prompt templates, models, and output parsers. Harness the power of these components to enhance your applications. 👉 3. LangChain Expression Language: Delve into the protocol that serves as the backbone of LangChain. Discover how the LangChain Expression Language (LCEL) facilitates component chaining, enabling seamless integration and communication between different elements. 👉 4. Build Your First Chains: Follow our step-by-step guide to cons