LangChain Course | Beginners

Full 1 hour course explaining all of the key features of LangChain.

Why use LangChain? This LangChain course looks at how LangChain can simplify and enhance working with a Large Language Model. This also includes a project to use Qdrant vector database search and a Strreamlit app with chat history. I used OpenAI ChatGPT as it's currently the most widely used and easy to use when you only have a regular laptop computer. See below for link to all of the LangChain code used on GitHub.

🟢 This video is split into the same chapters as the official LangChain documentation. It demonstrates the Python code to use LangChain Models, Prompts, Chains, Memory, Indexes, Agents and Tools.

🟢 The video also demonstrates using Qdrant as a vector database to enable retrieval of embedded vectors along with tips on how to debug LangChain and how to set up a project from scratch.

🟢 As well as the regular examples you my find on the LangChain documentation pages I also show how to create a video suggestion chatbot and in the 'indexes' chapter I show you how to create a full project to query your documents, 'upserting' data from a text document and then querying it.

-Chapters -
00:58 why we need LangChain
02:37 register with openai
04:13 models
10:37 prompts
24:58 chains
28:35 memory
32:53 indexes
52:01 tools and agents

⛓️🦜 LangChain 🦜⛓️

• OpenAI & LangChain & chatGPT

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Get the code used in the video:
GitHub : 🌏 https://github.com/RGGH/LangChain-Course
LangChain with Python article : 🌏 https://redandgreen.co.uk/langchain-w...

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🟢 LangChain Beginner's Guide - Step-by-Step Tutorial | How to use LangChain