Why conversation history improves RAG in LangChain - See It in Action

Why Conversation History Improves RAG

📖 Scenario: You are building a simple chatbot using LangChain that uses Retrieval-Augmented Generation (RAG) to answer questions. To make the chatbot smarter, you want to include the conversation history so the bot can understand the context better.

🎯 Goal: Create a small LangChain setup that stores conversation history and uses it to improve the retrieval step in RAG.

📋 What You'll Learn

Create a list called conversation_history with initial messages

Create a variable query with the current user question

Use conversation_history to build a combined query string

Add the combined query string to the retriever call

💡 Why This Matters

🌍 Real World

Chatbots and virtual assistants use conversation history to understand context and provide better answers.

💼 Career

Knowing how to manage conversation history and use it in retrieval is important for building advanced AI assistants and customer support bots.

1

Create the initial conversation history

Create a list called conversation_history with these exact strings: 'Hello, how can I help you?' and 'What is Retrieval-Augmented Generation?'

LangChain

# Create the conversation_history list with two messages
# Your code here

Need a hint?

Use a Python list with the exact two strings inside.

2

Add the current user query

Create a variable called query and set it to the string 'Explain why conversation history helps in RAG.'

LangChain

conversation_history = ['Hello, how can I help you?', 'What is Retrieval-Augmented Generation?']
# Create the query variable with the exact string
# Your code here

Need a hint?

Assign the exact string to the variable query.

3

Combine conversation history into a single string

Create a variable called combined_query that joins all strings in conversation_history separated by a space, then adds a space and the query string at the end.

LangChain

conversation_history = ['Hello, how can I help you?', 'What is Retrieval-Augmented Generation?']
query = 'Explain why conversation history helps in RAG.'
# Create combined_query by joining conversation_history and adding query
# Your code here

Need a hint?

Use the join method on the list and add the query string.

4

Use combined query in retriever call

Assuming you have a retriever object called retriever, write a line that calls retriever.get_relevant_documents() with combined_query as the argument and assign the result to docs.

LangChain

conversation_history = ['Hello, how can I help you?', 'What is Retrieval-Augmented Generation?']
query = 'Explain why conversation history helps in RAG.'
combined_query = ' '.join(conversation_history) + ' ' + query
# Call retriever.get_relevant_documents with combined_query and assign to docs
# Your code here

Need a hint?

Use the retriever's method with the combined query string.