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]

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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]()

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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)

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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])

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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])

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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.