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.