Chat history management in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a chat history object using LangChain.

LangChain

from langchain.memory import [1]

chat_history = [1]()

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AChatMemory

BConversationBufferMemory

CConversationSummaryMemory

DMemoryBuffer

Attempts:

3 left

💡 Hint

Common Mistakes

Using a class that summarizes instead of buffering the chat.

Using a class name that does not exist in LangChain memory module.

The ConversationBufferMemory class is used to create a chat history buffer that stores the conversation.

2fill in blank

medium

Complete the code to add chat history memory to a LangChain chat model.

LangChain

from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory

memory = ConversationBufferMemory()
chat = ChatOpenAI(memory=[1])

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Amemory

Bhistory

Cchat_memory

Dbuffer

Attempts:

3 left

💡 Hint

Common Mistakes

Using a parameter name that does not exist in the constructor.

Passing the wrong variable name.

The ChatOpenAI class accepts a memory parameter to manage chat history.

3fill in blank

hard

Fix the error in the code to retrieve the chat history as a string.

LangChain

history_str = memory.[1]

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Abuffer

Bload_memory()

Cget_history()

Dchat_memory

Attempts:

3 left

💡 Hint

Common Mistakes

Trying to call a method that does not exist.

Using an incorrect attribute name.

The ConversationBufferMemory stores the chat history in the buffer attribute as a string.

4fill in blank

hard

Fill both blanks to create a chat memory that summarizes conversation instead of buffering.

LangChain

from langchain.memory import [1]

memory = [2](llm=llm)

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AConversationSummaryMemory

BConversationBufferMemory

CChatMemory

DSummaryMemory

Attempts:

3 left

💡 Hint

Common Mistakes

Using the buffer memory class instead of summary memory.

Using a class name that does not exist.

The ConversationSummaryMemory class creates a memory that summarizes the chat using the language model llm.

5fill in blank

hard

Fill all three blanks to create a chat memory with a custom key and return messages enabled.

LangChain

memory = ConversationBufferMemory(
    memory_key=[1],
    return_messages=[2],
    output_key=[3]
)

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A"chat_history"

BTrue

C"output"

DFalse

Attempts:

3 left

💡 Hint

Common Mistakes

Using booleans as strings or vice versa.

Using incorrect key names.

Setting memory_key to "chat_history" names the stored memory key. return_messages=True makes the memory return message objects. output_key="output" sets the key for the output.