LangChain Setup & First LLM | Zero to Agent #2 | Malayalam

LangChain setup for beginners in Malayalam. In this video, we set up Jupyter Notebook, create a Python virtual environment (venv), install LangChain, and load Gemini and Groq models with init_chat_model. This is Part 2 of the LangChain Zero to Agent series. By the end, you will have a working LangChain setup and your first model responding in code. ⏱ Chapters 0:00 Intro 0:43 Getting the Gemini API key 1:17 Generating your Gemini API key 2:03 Setting up the .env file for API keys 3:30 Setting up Jupyter Notebook 4:29 Jupyter Notebook basics 5:52 Virtual environments (venv) explained 6:37 Creating and activating a venv 7:56 Listing the packages in your venv 8:18 Installing LangChain 8:52 Selecting the kernel in Jupyter 9:36 Executing your first cell 10:24 Loading API keys with dotenv 12:57 Loading a model in LangChain (init_chat_model) 16:35 Getting a response from the model 19:54 Different Gemini models 21:05 Getting your Groq API key 21:50 Loading a Groq model in LangChain What you will learn: How to get a Gemini API key and a Groq API key How to keep API keys safe in a .env file How to set up Jupyter Notebook and a venv How to install LangChain and select the right kernel How to load different models with init_chat_model How to get a response from a model in LangChain What you need: Basic Python Python and Jupyter Notebook installed ▶ Full playlist, LangChain Zero to Agent:    • What is LangChain? | LangChain Zero to Age...   ⏮ Part 1, What is LangChain?:    • What is LangChain? | LangChain Zero to Age...   Subscribe to DeepCodeX for the whole series: [subscribe link] #LangChain #Gemini #Malayalam