What is a chain in LangChain - Hands-On Activity

Understanding Chains in LangChain

📖 Scenario: You are building a simple assistant that uses LangChain to process user input and generate responses.

🎯 Goal: Learn how to create and use a chain in LangChain to connect a language model with a prompt template.

📋 What You'll Learn

Create a language model instance

Create a prompt template

Create a chain that connects the model and the prompt

Run the chain with an input to get a response

💡 Why This Matters

🌍 Real World

Chains help build conversational assistants, chatbots, or any app that needs to connect prompts with language models.

💼 Career

Understanding chains is key for developers working with AI tools to create interactive applications using LangChain.

1

Create a language model instance

Write code to create a variable called llm that holds an instance of OpenAI with temperature=0.

LangChain

# Your code here to create llm

Need a hint?

Use OpenAI(temperature=0) to create the language model instance.

2

Create a prompt template

Create a variable called prompt that holds a PromptTemplate with input_variables=['product'] and template='What is a {product}?' .

LangChain

from langchain.prompts import PromptTemplate

# Your code here to create prompt

Need a hint?

Use PromptTemplate with input_variables=['product'] and the given template string.

3

Create a chain connecting the model and prompt

Create a variable called chain that holds an instance of LLMChain using the llm and prompt variables.

LangChain

from langchain.chains import LLMChain

# Your code here to create chain

Need a hint?

Use LLMChain(llm=llm, prompt=prompt) to create the chain.

4

Run the chain with an input

Call chain.run with the argument product='LangChain' to get the output.

LangChain

# Your code here to run the chain

Need a hint?

Use chain.run(product='LangChain') to get the response.