Handling follow-up questions in LangChain - Mini Project: Build & Apply

Handling Follow-Up Questions with Langchain

📖 Scenario: You are building a simple chatbot using Langchain that can remember the context of previous questions and handle follow-up questions smoothly.

🎯 Goal: Create a Langchain chatbot that stores conversation history and uses it to answer follow-up questions correctly.

📋 What You'll Learn

Create a list to store conversation history

Set a maximum number of previous messages to keep

Use Langchain's ConversationalRetrievalChain to process questions with context

Add the final call to run the chatbot with a sample question

💡 Why This Matters

🌍 Real World

Chatbots and virtual assistants need to remember previous messages to answer follow-up questions naturally.

💼 Career

Understanding how to manage conversation history is key for building intelligent chatbots in customer support, education, and personal assistant applications.

1

Create conversation history list

Create a list called chat_history and initialize it as an empty list to store previous messages.

LangChain

# Create a list called chat_history to store previous messages
# Your code here

Hint

Think of chat_history as a notebook where you write down all previous messages.

2

Set maximum history length

Create a variable called max_history and set it to 5 to limit how many previous messages to keep.

LangChain

chat_history = []
# Set max_history to 5 to limit stored messages
# Your code here

Hint

This helps the chatbot remember only the last few messages, like a short-term memory.

3

Create ConversationalRetrievalChain with history

Create a variable called qa_chain and assign it to ConversationalRetrievalChain.from_llm(llm, retriever). Use chat_history as the conversation history input when calling qa_chain.run() later.

LangChain

chat_history = []
max_history = 5
# Create qa_chain using ConversationalRetrievalChain.from_llm with llm and retriever
# Your code here

Hint

This chain helps the chatbot answer questions using previous conversation context.

4

Run the chatbot with follow-up question handling

Call qa_chain.run() with the question "What is Langchain?" and pass chat_history as the chat_history argument. Append the question and answer to chat_history. Keep only the last max_history messages in chat_history.

LangChain

from langchain.chains import ConversationalRetrievalChain

chat_history = []
max_history = 5
qa_chain = ConversationalRetrievalChain.from_llm(llm, retriever)
# Run qa_chain with question and chat_history, update chat_history to keep last max_history messages
# Your code here

Hint

Think of chat_history as a queue that keeps only the last few messages.

Practice

1. What is the main purpose of handling follow-up questions in LangChain conversations?

easy

A. To speed up the response time by skipping context

B. To keep the conversation natural and connected by remembering context

C. To reset the conversation after every question

D. To avoid using any previous conversation history

Solution

1. Step 1: Understand conversation flow

   Handling follow-up questions means the system remembers previous messages to keep the chat natural.

2. Step 2: Identify the purpose of context

   Context helps the system answer related questions without losing track of the topic.

3. Final Answer:

   To keep the conversation natural and connected by remembering context -> Option B

4. Quick Check:

   Follow-up questions need context = A [OK]

Hint: Follow-up means remembering past chat context [OK]

Common Mistakes:

• Thinking follow-ups reset conversation
• Ignoring conversation history
• Assuming follow-ups speed up responses
• Believing context is not needed

2. Which LangChain class is commonly used to manage conversation history for follow-up questions?

easy

A. ConversationChain

B. TextSplitter

C. DocumentLoader

D. VectorStoreRetriever

Solution

1. Step 1: Recall LangChain components

   ConversationChain is designed to handle conversations and keep track of history.

2. Step 2: Match class to purpose

   TextSplitter and DocumentLoader are for documents, VectorStoreRetriever for searching, not conversation management.

3. Final Answer:

   ConversationChain -> Option A

4. Quick Check:

   ConversationChain manages chat history = B [OK]

Hint: ConversationChain handles chat history in LangChain [OK]

Common Mistakes:

• Confusing ConversationChain with document tools
• Choosing unrelated classes like TextSplitter
• Assuming VectorStoreRetriever manages conversations
• Mixing up class purposes

3. Given this code snippet using LangChain's ConversationChain:

from langchain.chains import ConversationChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
conv = ConversationChain(llm=llm)

response1 = conv.predict(input="What is AI?")
response2 = conv.predict(input="Can you explain more?")
print(response2)

What does response2 represent?

medium

A. An error because ConversationChain does not support multiple predicts

B. An answer to 'Can you explain more?' ignoring previous questions

C. An answer to 'Can you explain more?' using the context of 'What is AI?'

D. The same answer as response1 repeated

Solution

1. Step 1: Understand ConversationChain behavior

   ConversationChain keeps track of previous inputs and outputs to provide context-aware answers.

2. Step 2: Analyze the code flow

   response2 uses the previous question 'What is AI?' to answer 'Can you explain more?' with context.

3. Final Answer:

   An answer to 'Can you explain more?' using the context of 'What is AI?' -> Option C

4. Quick Check:

   ConversationChain uses history for follow-ups = C [OK]

Hint: ConversationChain.predict uses past chat context automatically [OK]

Common Mistakes:

• Assuming each predict is independent
• Expecting an error on multiple calls
• Thinking response2 repeats response1
• Ignoring conversation history

4. Identify the error in this LangChain code snippet for handling follow-up questions:

from langchain.chains import ConversationChain
from langchain.llms import OpenAI

llm = OpenAI(temperature=0)
conv = ConversationChain(llm=llm)

response1 = conv.predict(input="Hello, who won the world cup?")
response2 = conv.predict("Who scored the most goals?")
print(response2)

medium

A. The print statement is invalid syntax

B. ConversationChain cannot be called twice

C. OpenAI LLM requires temperature > 0

D. Missing 'input=' keyword in second predict call causes error

Solution

1. Step 1: Check method call syntax

   ConversationChain.predict expects a named argument 'input=...'. The second call misses this keyword.

2. Step 2: Understand consequences

   Without 'input=', Python raises a TypeError because the argument is positional but not expected.

3. Final Answer:

   Missing 'input=' keyword in second predict call causes error -> Option D

4. Quick Check:

   predict requires input= keyword = D [OK]

Hint: Always use input= keyword in predict calls [OK]

Common Mistakes:

• Calling predict without input= keyword
• Thinking ConversationChain disallows multiple calls
• Believing temperature=0 is invalid
• Assuming print syntax is wrong

5. You want to build a LangChain app that answers follow-up questions about a user's previous queries but resets context after 3 questions. Which approach best achieves this?

hard

A. Use ConversationChain and clear its memory after every 3 predictions

B. Create a new ConversationChain instance for each question

C. Use ConversationChain without clearing memory to keep all history

D. Use OpenAI directly without ConversationChain

Solution

1. Step 1: Understand requirement to reset context

   The app needs to keep context but reset after 3 questions to avoid long history.

2. Step 2: Choose method to reset memory

   ConversationChain supports memory clearing; clearing after 3 predictions resets context properly.

3. Step 3: Evaluate other options

   Creating new instances loses all context immediately; keeping all history ignores reset; using OpenAI directly lacks conversation memory.

4. Final Answer:

   Use ConversationChain and clear its memory after every 3 predictions -> Option A

5. Quick Check:

   Reset context by clearing ConversationChain memory = A [OK]

Hint: Clear ConversationChain memory to reset context after set questions [OK]

Common Mistakes:

• Recreating ConversationChain loses partial context
• Not resetting memory causes long context buildup
• Using OpenAI directly loses conversation flow
• Ignoring how to clear memory properly