Question reformulation with history in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the necessary class for question reformulation.

LangChain

from langchain.chains import [1]

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

ARephraseQuestionChain

BStuffDocumentsChain

CConversationalRetrievalChain

DChatVectorDBChain

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing a chain that only handles document stuffing.

Selecting a chain unrelated to conversation.

The ConversationalRetrievalChain class is used for question reformulation with history in LangChain.

2fill in blank

medium

Complete the code to create a retriever from a vector store.

LangChain

retriever = vectorstore.[1]()

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

Aas_retriever

Bget_retriever

Cretrieve

Dfetch_retriever

Attempts:

3 left

💡 Hint

Common Mistakes

Using a method that does not exist on the vector store.

Confusing retrieval with fetching.

The as_retriever() method converts a vector store into a retriever object.

3fill in blank

hard

Fix the error in the code to create a ConversationalRetrievalChain with a language model and retriever.

LangChain

qa_chain = ConversationalRetrievalChain.from_llm(llm=[1], retriever=retriever)

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

Allm_model

Bmodel

Clanguage_model

Dllm

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect parameter names causing runtime errors.

Passing the model instance with wrong variable names.

The parameter name is llm to pass the language model instance.

4fill in blank

hard

Fill both blanks to reformulate a question using chat history and a new question.

LangChain

rephrased_question = qa_chain.[1](question=new_question, chat_history=[2])

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

Arephrase_question

Bgenerate_question

Cchat_history

Dhistory

Attempts:

3 left

💡 Hint

Common Mistakes

Using incorrect method names that do not exist.

Passing chat history with wrong parameter names.

The method rephrase_question is used to reformulate the question, and the chat history is passed as history.

5fill in blank

hard

Fill all three blanks to create a question-answering chain that uses a language model, retriever, and returns source documents.

LangChain

qa_chain = ConversationalRetrievalChain.from_llm(llm=[1], retriever=[2], return_source_documents=[3])

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

Allm

Bretriever

CTrue

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Passing wrong variable names for llm or retriever.

Forgetting to set return_source_documents to True.

The llm parameter passes the language model, retriever passes the retriever object, and return_source_documents=True enables returning source documents.

Practice

1. What is the main purpose of question reformulation with history in LangChain?

easy

A. To store user personal data securely

B. To speed up the chatbot response time

C. To translate questions into multiple languages

D. To help chatbots understand follow-up questions better

Solution

1. Step 1: Understand the role of question reformulation

   It helps the chatbot by using previous conversation history to clarify follow-up questions.

2. Step 2: Identify the main benefit

   This makes the chatbot understand user intent better and respond more naturally.

3. Final Answer:

   To help chatbots understand follow-up questions better -> Option D

4. Quick Check:

   Question reformulation = better follow-up understanding [OK]

Hint: Focus on improving chatbot understanding of follow-ups [OK]

Common Mistakes:

• Confusing reformulation with speed optimization
• Thinking it stores personal data
• Assuming it translates languages

2. Which of the following is the correct way to create a ConversationChain in LangChain that uses question reformulation with history?

easy

A. chain = ConversationChain(llm=llm, memory=ConversationBufferMemory())

B. chain = ConversationChain(memory=llm, llm=ConversationBufferMemory())

C. chain = ConversationChain(llm=llm, history=ConversationBufferMemory())

D. chain = ConversationChain(buffer=llm, memory=ConversationBufferMemory())

Solution

1. Step 1: Recall the correct parameter names

   ConversationChain expects an LLM instance as 'llm' and a memory object as 'memory'.

2. Step 2: Match the correct syntax

   chain = ConversationChain(llm=llm, memory=ConversationBufferMemory()) correctly assigns 'llm=llm' and 'memory=ConversationBufferMemory()'. Others mix parameter names or use incorrect ones.

3. Final Answer:

   chain = ConversationChain(llm=llm, memory=ConversationBufferMemory()) -> Option A

4. Quick Check:

   Correct parameters = llm and memory [OK]

Hint: Remember 'llm' and 'memory' are required parameters [OK]

Common Mistakes:

• Swapping 'llm' and 'memory' parameters
• Using 'history' instead of 'memory'
• Passing wrong argument names

3. Given the following code snippet, what will be the output after the second question?

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

llm = SomeLLM()
memory = ConversationBufferMemory()
chain = ConversationChain(llm=llm, memory=memory)

print(chain.run("What is AI?"))
print(chain.run("And how does it learn?"))

medium

A. The memory will reset after each question

B. The second answer will ignore the first question

C. The second answer will consider the first question's context

D. The code will raise a syntax error

Solution

1. Step 1: Understand ConversationBufferMemory behavior

   This memory stores conversation history and passes it to the LLM for context.

2. Step 2: Analyze the chain.run calls

   The second call to run uses the stored history, so the answer will reflect the first question's context.

3. Final Answer:

   The second answer will consider the first question's context -> Option C

4. Quick Check:

   Memory stores history = context-aware answers [OK]

Hint: ConversationChain with memory keeps context across calls [OK]

Common Mistakes:

• Assuming memory resets automatically
• Thinking each run is independent
• Expecting syntax errors from correct code

4. Identify the error in this LangChain code snippet for question reformulation with history:

from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory

llm = SomeLLM()
chain = ConversationChain(llm=llm, memory=ConversationBufferMemory)

response = chain.run("Explain machine learning.")
print(response)

medium

A. The llm variable is not defined

B. ConversationBufferMemory is not instantiated with parentheses

C. The run method does not exist on ConversationChain

D. The print statement is missing parentheses

Solution

1. Step 1: Check how ConversationBufferMemory is used

   It must be instantiated with parentheses to create an object, but here it's passed as a class.

2. Step 2: Verify other parts

   llm is defined, run method exists, and print syntax is correct in Python 3.

3. Final Answer:

   ConversationBufferMemory is not instantiated with parentheses -> Option B

4. Quick Check:

   Instantiate classes with () [OK]

Hint: Always add () when creating memory instance [OK]

Common Mistakes:

• Forgetting parentheses when instantiating classes
• Assuming run method is missing
• Confusing print syntax in Python 3

5. You want to improve a chatbot to handle multi-turn conversations with question reformulation using LangChain. Which approach best achieves this?

hard

A. Use ConversationChain with ConversationBufferMemory to keep history and reformulate questions

B. Use a simple LLM call without memory for faster responses

C. Store conversation history manually in a list and pass only the last question to the LLM

D. Use ConversationChain but reset memory after each question to avoid confusion

Solution

1. Step 1: Understand multi-turn conversation needs

   Keeping conversation history helps the chatbot understand context and reformulate questions properly.

2. Step 2: Evaluate options

   Use ConversationChain with ConversationBufferMemory to keep history and reformulate questions uses ConversationChain with memory, which automates history management and reformulation. Others either ignore history or reset it, losing context.

3. Final Answer:

   Use ConversationChain with ConversationBufferMemory to keep history and reformulate questions -> Option A

4. Quick Check:

   Memory + ConversationChain = best multi-turn handling [OK]

Hint: Use ConversationChain with memory for natural multi-turn chats [OK]

Common Mistakes:

• Ignoring conversation history
• Resetting memory too often
• Manually managing history inefficiently