LangChain architecture overview - Practice Problems & Coding Challenges

Challenge - 5 Problems

🧠 Conceptual

intermediate

Core components of LangChain architecture

Which of the following is NOT a core component of LangChain's architecture?

A Databases that store user interface layouts

B Chains that link multiple calls to language models

C Agents that decide which actions to take based on inputs

D Memory modules that keep track of conversation history

💡 Hint

Think about what LangChain focuses on: language models, decision making, and memory.

LangChain's architecture includes chains, agents, and memory to manage language model workflows. It does not include databases for UI layouts.

❓ component_behavior

intermediate

Behavior of memory in LangChain

What is the main role of the memory component in LangChain's architecture?

A To visualize the output of language models in a dashboard

B To execute external API calls during chain execution

C To store and retrieve past interactions to provide context

D To generate new language model prompts automatically

💡 Hint

Memory helps keep track of what happened before.

Memory stores past conversation or interaction data so that the language model can use context in future steps.

📝 Syntax

advanced

Correct way to define a simple LangChain chain

Which code snippet correctly creates a simple chain that calls a language model with a prompt?

A

from langchain import LLMChain, OpenAI
chain = LLMChain(llm=OpenAI(), prompt="Hello")

B

from langchain import LLMChain, OpenAI, PromptTemplate
chain = LLMChain(llm=OpenAI(), prompt=PromptTemplate(template="Hello"))

C

from langchain import Chain, OpenAI
chain = Chain(llm=OpenAI(), prompt="Hello")

D

from langchain import LLMChain
chain = LLMChain(llm=OpenAI, prompt="Hello")

💡 Hint

Prompt must be a PromptTemplate object, not a plain string.

LLMChain requires a prompt argument of type PromptTemplate, not a plain string. Option B correctly uses PromptTemplate.

❓ lifecycle

advanced

Agent decision-making process in LangChain

During execution, how does a LangChain agent decide which action to take next?

A It uses the language model output to interpret the input and select an action

B It randomly selects an action from a predefined list

C It always executes all actions in sequence regardless of input

D It waits for user input before deciding any action

💡 Hint

Agents use language models to understand and decide.

Agents use the language model's output to interpret the input and decide the next action dynamically.

🔧 Debug

expert

Identifying error in LangChain memory usage

What error will this code raise? from langchain.memory import ConversationBufferMemory memory = ConversationBufferMemory() print(memory.load_memory_variables({}))

LangChain

from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
print(memory.load_memory_variables({}))

A AttributeError: 'ConversationBufferMemory' object has no attribute 'load_memory_variables'

B TypeError: load_memory_variables() missing 1 required positional argument

C KeyError: 'history' not found in memory variables

D Returns an empty dictionary {} without error

💡 Hint

Check the method signature and default behavior of load_memory_variables.

The method load_memory_variables accepts a dictionary argument and returns stored memory variables. With an empty input, it returns an empty dictionary without error.

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