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Unleash the Power of Large Language Models Using LangChain

Chat Models, Messages, and Prompt Templates in LangChain

Explore how to use LangChain's chat models, message formats, and prompt templates to create dynamic language model interactions. Understand roles in messaging and how to build reusable prompts for personalized AI responses.

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LangChain provides a framework for developers to swiftly create LLM-powered applications.

It streamlines development by offering simple access to different language models from various providers. This allows developers to quickly experiment, select the best model for their needs, and focus on building application logic instead of dealing with different model APIs.

In this lesson, we’ll look into how we can utilize different models in LangChain and efficiently prompt these models to ask queries and get a response accordingly.

Chat Models

A model, or chat model in the context of LangChain, is any LLM that can be used for various language-related tasks. These tasks can range from text generation or summarization to simple question answering or language translation.

Models in LangChain
Models in LangChain

LangChain provides a standard interface with several LLMs, such as ChatGPT, Claude, Mistral, etc.

Here, we’ll focus on Meta’s Llama LLM with Groq. Let’s start with a simple example of querying the Llama model using the LangChain framework.

Python 3.10.4
from langchain_groq import ChatGroq
llm = ChatGroq(model="meta-llama/llama-4-scout-17b-16e-instruct")
response = llm.invoke("What is the tallest building in the world?")
print(response.content)

  • Line 1: Import the ChatGroq method from the langchain_groq module. This was installed with a pippip install langchain-groq command.

  • Line 3: Initiate the model via the ChatGroq method. We pass the model name of our choice, which is meta-llama/llama-4-scout-17b-16e-instruct. Information about the models can be checked from the Groq playground.

  • Line 5: Generate a response from the model using the invoke method. In ...