Debugging failed chains in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the correct LangChain class for creating a chain.

LangChain

from langchain.chains import [1]

chain = [1]()

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

ALLMChain

BChainBase

CBaseChain

DChain

Attempts:

3 left

💡 Hint

Common Mistakes

Using a non-existent class like 'Chain' or 'ChainBase'.

Confusing base classes with usable chain classes.

The LLMChain class is the correct class to create a chain that uses a language model in LangChain.

2fill in blank

medium

Complete the code to initialize the OpenAI LLM with the correct parameter for temperature.

LangChain

from langchain.llms import OpenAI

llm = OpenAI(temperature=[1])

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

A'0.7'

B'high'

C0.7

DTrue

Attempts:

3 left

💡 Hint

Common Mistakes

Passing temperature as a string instead of a float.

Using invalid values like 'high' or boolean True.

The temperature parameter expects a float value like 0.7 to control randomness.

3fill in blank

hard

Fix the error in the chain call by completing the method name correctly.

LangChain

response = chain.[1](input_variables={'question': 'What is AI?'})

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

Astart

Bexecute

Ccall

Drun

Attempts:

3 left

💡 Hint

Common Mistakes

Using non-existent methods like 'execute' or 'start'.

Confusing the chain's method with other APIs.

The run method is used to execute the chain with input variables.

4fill in blank

hard

Fill both blanks to create a prompt template and use it in the chain.

LangChain

from langchain.prompts import [1]

prompt = [2](template="Hello, {{name}}!")
chain = LLMChain(llm=llm, prompt=prompt)

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

APromptTemplate

BPrompt

CTemplatePrompt

DChainPrompt

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect class names like 'Prompt' or 'TemplatePrompt'.

Forgetting to import the prompt template class.

PromptTemplate is the correct class to create prompt templates in LangChain.

5fill in blank

hard

Fill both blanks to create a dictionary comprehension that filters and transforms chain outputs.

LangChain

filtered_outputs = {k:v for k, v in outputs.items() if v {BLANK_2}} 0 and k != {{BLANK_2}}

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

A:

B>

C'error'

D==

Attempts:

3 left

💡 Hint

Common Mistakes

Using '=' instead of ':' in dictionary comprehension.

Using wrong comparison operators or forgetting quotes around string keys.

The dictionary comprehension uses ':' to map keys to values, filters values greater than 0, and excludes the key 'error'.