Why conversation history improves RAG in LangChain - Test Your Understanding

Practice - 5 Tasks

1fill in blank

easy

Complete the code to add conversation history to the retriever query.

LangChain

query = [1] + ' ' + user_input

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

Aconversation_history

Bempty_string

Cirrelevant_data

Drandom_text

Attempts:

3 left

💡 Hint

Common Mistakes

Using unrelated or empty strings instead of conversation history.

Not including any context in the query.

Adding the conversation history to the query helps the retriever understand the context better.

2fill in blank

medium

Complete the code to update the conversation history after each user input.

LangChain

conversation_history = conversation_history + '\nUser: ' + [1]

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

Aempty_string

Bsystem_response

Cuser_input

Dretriever_output

Attempts:

3 left

💡 Hint

Common Mistakes

Appending system responses instead of user inputs.

Not updating the conversation history at all.

We add the latest user input to the conversation history to keep context updated.

3fill in blank

hard

Fix the error in the code that combines conversation history and user input for retrieval.

LangChain

retrieval_query = [1] + ' ' + user_input

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

Aretriever_output

Bconversation_history

Csystem_response

Dempty_string

Attempts:

3 left

💡 Hint

Common Mistakes

Using system responses or empty strings instead of conversation history.

Forgetting to include conversation history in the retrieval query.

The retrieval query must include the conversation history to provide context for the retriever.

4fill in blank

hard

Fill both blanks to create a function that updates conversation history and prepares the retrieval query.

LangChain

def update_and_query(user_input, [1]):
    [2] += '\nUser: ' + user_input
    retrieval_query = [2]
    return retrieval_query, [2]

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

Aconversation_history

Bsystem_response

Duser_input

Attempts:

3 left

💡 Hint

Common Mistakes

Using system_response instead of conversation_history.

Mixing up parameter and variable names.

The function updates the conversation history and uses it to build the retrieval query.

5fill in blank

hard

Fill all three blanks to implement a retrieval-augmented generation step using conversation history.

LangChain

def rag_step(user_input, [1]):
    [2] += '\nUser: ' + user_input
    retrieval_query = [2]
    retrieved_docs = retriever.retrieve([3])
    return retrieved_docs, [2]

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

Aconversation_history

Cretrieval_query

Duser_input

Attempts:

3 left

💡 Hint

Common Mistakes

Passing user_input directly to retriever instead of the combined query.

Not updating conversation history before retrieval.

This function updates conversation history, builds a retrieval query, and retrieves documents based on that query.