LangChain architecture overview - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the core LangChain class for building chains.

LangChain

from langchain.chains import [1]

Drag options to blanks, or click blank then click option'

AChain

BLLMChain

CPromptTemplate

DMemory

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing 'Chain' which is a base class but not commonly imported directly.

Choosing 'PromptTemplate' which is for prompts, not chains.

The LLMChain class is the core chain class that connects prompts with language models in LangChain.

2fill in blank

medium

Complete the code to create a prompt template with a variable placeholder.

LangChain

from langchain.prompts import PromptTemplate

prompt = PromptTemplate(template="Hello, {name}!", input_variables=["[1]"])

Drag options to blanks, or click blank then click option'

Aname

Buser

Cinput

Dtext

Attempts:

3 left

💡 Hint

Common Mistakes

Using a placeholder that does not match the input variable name.

Using generic words like 'user' or 'input' that are not declared.

The placeholder in the template must match the variable name in input_variables. Here, it is name.

3fill in blank

hard

Fix the error in creating an LLMChain by filling the missing argument.

LangChain

from langchain.llms import OpenAI
from langchain.chains import LLMChain

llm = OpenAI()
chain = LLMChain(llm=llm, prompt=[1])

Drag options to blanks, or click blank then click option'

Aprompt_template

BPromptTemplate

Ctemplate

Dprompt

Attempts:

3 left

💡 Hint

Common Mistakes

Using the class name instead of the variable.

Using incorrect argument names.

The LLMChain expects a prompt argument which is an instance of PromptTemplate. The variable holding the prompt is usually named prompt.

4fill in blank

hard

Fill both blanks to create a simple memory-enabled chain that remembers previous inputs.

LangChain

from langchain.memory import [1]

memory = [2]()
chain = LLMChain(llm=llm, prompt=prompt, memory=memory)

Drag options to blanks, or click blank then click option'

AConversationBufferMemory

BConversationSummaryMemory

Attempts:

3 left

💡 Hint

Common Mistakes

Confusing summary memory with buffer memory.

Using different names for import and instantiation.

The ConversationBufferMemory class stores the conversation history in a buffer. It is imported and instantiated to enable memory in the chain.

5fill in blank

hard

Fill all three blanks to create a chain that uses an OpenAI LLM, a prompt template, and conversation memory.

LangChain

from langchain.llms import [1]
from langchain.prompts import [2]
from langchain.memory import [3]

llm = [1]()
prompt = [2](template="Say hello to {name}", input_variables=["name"])
memory = [3]()
chain = LLMChain(llm=llm, prompt=prompt, memory=memory)

Drag options to blanks, or click blank then click option'

AOpenAI

BPromptTemplate

CConversationBufferMemory

DLLMChain

Attempts:

3 left

💡 Hint

Common Mistakes

Mixing up class names or forgetting to import memory.

Using the chain class as an import in place of prompt or memory.

This code imports and creates instances of OpenAI for the language model, PromptTemplate for the prompt, and ConversationBufferMemory for memory. These are combined in an LLMChain.

Practice

1. What is the main purpose of a chain in LangChain architecture?

easy

A. To link different steps like prompts and LLMs to build language apps

B. To store data permanently in a database

C. To create user interfaces for language models

D. To train new language models from scratch

Solution

1. Step 1: Understand the role of chains

   Chains connect parts like prompt templates and language models to form a workflow.

2. Step 2: Identify the main purpose

   Chains help organize and link these steps to build smart language apps easily.

3. Final Answer:

   To link different steps like prompts and LLMs to build language apps -> Option A

4. Quick Check:

   Chains link steps = D [OK]

Hint: Chains connect prompts and models to build apps [OK]

Common Mistakes:

• Thinking chains store data permanently
• Confusing chains with UI components
• Assuming chains train models

2. Which of the following is the correct way to create a prompt template in LangChain?

easy

A. PromptTemplate.create("Hello, {name}!")

B. PromptTemplate("Hello, {name}!")

C. PromptTemplate(template="Hello, {name}!")

D. PromptTemplate.new(template="Hello, {name}!")

Solution

1. Step 1: Recall PromptTemplate syntax

   PromptTemplate requires a named argument 'template' with the string pattern.

2. Step 2: Match syntax to options

   Only PromptTemplate(template="Hello, {name}!") uses PromptTemplate(template="...") correctly.

3. Final Answer:

   PromptTemplate(template="Hello, {name}!") -> Option C

4. Quick Check:

   Named 'template' argument = A [OK]

Hint: Use named 'template' argument to create PromptTemplate [OK]

Common Mistakes:

• Passing template string without argument name
• Using non-existent create() or new() methods
• Confusing positional and keyword arguments

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

from langchain import PromptTemplate, LLMChain

prompt = PromptTemplate(template="What is the capital of {country}?")
chain = LLMChain(prompt=prompt)
result = chain.run(country="France")
print(result)

medium

A. "Paris"

B. An error because LLM is missing

C. "What is the capital of France?"

D. "France"

Solution

1. Step 1: Analyze the code components

   The chain is created with a prompt but no LLM (language model) is provided.

2. Step 2: Understand LangChain requirements

   LLMChain needs an LLM to generate answers; missing it causes an error.

3. Final Answer:

   An error because LLM is missing -> Option B

4. Quick Check:

   LLM missing causes error = B [OK]

Hint: LLMChain needs an LLM instance to run [OK]

Common Mistakes:

• Assuming chain.run returns prompt text
• Expecting output without LLM
• Confusing prompt template with output

4. Identify the error in this LangChain code snippet:

from langchain import PromptTemplate, LLMChain

prompt = PromptTemplate(template="Say hello to {name}")
chain = LLMChain(prompt=prompt, llm=None)
output = chain.run(name="Alice")
print(output)

medium

A. LLMChain requires a valid LLM, not None

B. PromptTemplate syntax is incorrect

C. run() method does not accept arguments

D. Missing import for LLM class

Solution

1. Step 1: Check PromptTemplate usage

   PromptTemplate is correctly created with a template string.

2. Step 2: Check LLMChain initialization

   LLMChain requires a valid LLM object; passing None causes failure.

3. Final Answer:

   LLMChain requires a valid LLM, not None -> Option A

4. Quick Check:

   LLM must be valid, not None = A [OK]

Hint: LLMChain needs a real LLM instance, not None [OK]

Common Mistakes:

• Thinking run() can't take arguments
• Assuming PromptTemplate syntax is wrong
• Missing imports but not causing this error

5. You want to build a LangChain app that asks a user's name, then uses an LLM to greet them. Which architecture correctly links these parts?

hard

A. Create a PromptTemplate and run it directly without an LLMChain

B. Create an LLMChain without a prompt and run it with user input

C. Create an LLM instance and call it directly without prompt or chain

D. Create a PromptTemplate for the question, then an LLMChain with that prompt and an LLM, then run the chain with user input

Solution

1. Step 1: Understand LangChain app structure

   PromptTemplate creates the question, LLMChain links prompt and LLM to generate answers.

2. Step 2: Identify correct linking

   Create a PromptTemplate for the question, then an LLMChain with that prompt and an LLM, then run the chain with user input correctly creates prompt, then LLMChain with prompt and LLM, then runs with input.

3. Final Answer:

   Create a PromptTemplate for the question, then an LLMChain with that prompt and an LLM, then run the chain with user input -> Option D

4. Quick Check:

   Prompt + LLM in chain = C [OK]

Hint: Chain = prompt + LLM + run with input [OK]

Common Mistakes:

• Trying to run prompt alone without chain
• Using LLM without prompt or chain
• Skipping linking steps in LangChain