What is LangChain - Hands-On Activity

What is LangChain

📖 Scenario: You are curious about how to build smart applications that can understand and generate human-like text. LangChain is a tool that helps you connect language models with other data and logic to create powerful apps.

🎯 Goal: Learn what LangChain is by creating a simple example that shows how to set up a language model and use it to generate text.

📋 What You'll Learn

Create a variable called llm that sets up a language model

Create a variable called prompt with a simple text prompt

Use the llm to generate a response from the prompt

Print or return the generated response

💡 Why This Matters

🌍 Real World

LangChain is used to build chatbots, assistants, and apps that understand and generate natural language.

💼 Career

Knowing LangChain helps you work with modern AI tools to create intelligent software solutions.

1

Set up the language model

Create a variable called llm and set it to an instance of OpenAI() from LangChain.

LangChain

# Create a variable called llm and set it to OpenAI()
# Your code here

Hint

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

2

Create a prompt

Create a variable called prompt and set it to the string 'What is LangChain?'.

LangChain

from langchain.llms import OpenAI

llm = OpenAI()

# Create a variable called prompt with the text 'What is LangChain?'
# Your code here

Hint

Set prompt to the exact string 'What is LangChain?'.

3

Generate a response

Create a variable called response and set it to the result of calling llm(prompt).

LangChain

from langchain.llms import OpenAI

llm = OpenAI()
prompt = 'What is LangChain?'

# Generate a response by calling llm with prompt
# Your code here

Hint

Call llm(prompt) and save the result in response.

4

Show the response

Add a line to print the response variable to see the generated text.

LangChain

from langchain.llms import OpenAI

llm = OpenAI()
prompt = 'What is LangChain?'
response = llm(prompt)

# Print the response
# Your code here

Hint

Use print(response) to display the generated answer.

Practice

1. What is the main purpose of LangChain?

easy

A. To create databases for storing large text files

B. To design user interfaces for mobile apps

C. To help build applications that use language models easily

D. To compile programming languages into machine code

Solution

1. Step 1: Understand LangChain's role

   LangChain is designed to help developers build apps that use language models.

2. Step 2: Compare options

   Only To help build applications that use language models easily matches this purpose; others describe unrelated tasks.

3. Final Answer:

   To help build applications that use language models easily -> Option C

4. Quick Check:

   LangChain purpose = build language model apps [OK]

Hint: Remember LangChain connects language models to apps [OK]

Common Mistakes:

• Confusing LangChain with database tools
• Thinking LangChain is for UI design
• Assuming LangChain compiles code

2. Which of the following is the correct way to describe a 'chain' in LangChain?

easy

A. A database table storing user inputs

B. A single prompt sent directly to a language model

C. A programming language used to write LangChain

D. A sequence of steps connecting models, prompts, and tools

Solution

1. Step 1: Define 'chain' in LangChain context

   A chain is a workflow linking models, prompts, and tools in order.

2. Step 2: Eliminate incorrect options

   Options A, B, and D do not describe a chain correctly.

3. Final Answer:

   A sequence of steps connecting models, prompts, and tools -> Option D

4. Quick Check:

   Chain = workflow steps [OK]

Hint: Chains link multiple steps in LangChain workflows [OK]

Common Mistakes:

• Thinking a chain is just one prompt
• Confusing chains with databases
• Believing chain is a programming language

3. Given this LangChain code snippet, what will be the output?

from langchain import PromptTemplate, LLMChain, OpenAI
prompt = PromptTemplate(template="Translate '{text}' to French.", input_variables=["text"])
llm = OpenAI(temperature=0)
chain = LLMChain(llm=llm, prompt=prompt)
result = chain.run(text="Hello")
print(result)

medium

A. Hello

B. Error: Missing API key

C. Bonjour

D. Translate 'Hello' to French.

Solution

1. Step 1: Analyze the code's function

   The code sets up a prompt to translate text to French using OpenAI model.

2. Step 2: Consider runtime environment

   Without an API key set for OpenAI, the code will raise an error.

3. Final Answer:

   Error: Missing API key -> Option B

4. Quick Check:

   OpenAI needs API key to run [OK]

Hint: OpenAI calls require API keys or error occurs [OK]

Common Mistakes:

• Assuming output is translated text without API setup
• Thinking code prints original text
• Ignoring API key requirement

4. Identify the error in this LangChain code snippet:

from langchain import PromptTemplate, LLMChain
prompt = PromptTemplate(template="Say hello to {name}.", input_variables=["name"])
chain = LLMChain(prompt=prompt)
result = chain.run(name="Alice")
print(result)

medium

A. LLMChain missing llm argument

B. No error, code runs fine

C. Incorrect method name 'run' instead of 'execute'

D. Missing input_variables list in PromptTemplate

Solution

1. Step 1: Check PromptTemplate usage

   PromptTemplate requires input_variables list; it's missing here (but not fatal).

2. Step 2: Check LLMChain initialization

   LLMChain requires an llm (language model) argument, which is missing.

3. Final Answer:

   LLMChain missing llm argument -> Option A

4. Quick Check:

   LLMChain needs llm parameter [OK]

Hint: LLMChain always needs an llm argument [OK]

Common Mistakes:

• Ignoring missing llm argument
• Confusing method names
• Overlooking input_variables requirement

5. You want to build a chatbot using LangChain that answers questions and also fetches current weather data. Which approach best uses LangChain's features?

medium

A. Create a chain that connects a language model with a weather API tool

B. Use LangChain only for the weather API calls, ignoring language models

C. Write separate scripts for chatbot and weather, no chaining needed

D. Use LangChain to store weather data in a database

Solution

1. Step 1: Understand LangChain's chaining ability

   LangChain can connect language models with external tools in a chain.

2. Step 2: Match use case to chaining

   Combining chatbot (language model) with weather API in one chain fits LangChain's design.

3. Final Answer:

   Create a chain that connects a language model with a weather API tool -> Option A

4. Quick Check:

   LangChain chains link models and tools [OK]

Hint: Chains combine models and tools for smart apps [OK]

Common Mistakes:

• Using LangChain only for API calls without models
• Separating chatbot and weather logic unnecessarily
• Misusing LangChain as a database