Why the RAG chain connects retrieval to generation in LangChain - See It in Action

Why the RAG chain connects retrieval to generation

📖 Scenario: You are building a simple system that first finds relevant information from a small set of documents and then uses that information to create a helpful answer. This mimics how a smart assistant looks up facts before answering a question.

🎯 Goal: Build a basic Retrieval-Augmented Generation (RAG) chain using LangChain that connects a retrieval step to a generation step. You will first set up the documents, then configure the retriever, then create the chain that uses retrieval results to generate an answer.

📋 What You'll Learn

Create a list of documents with exact text content

Set up a retriever using LangChain's SimpleRetriever

Build a RAG chain that connects the retriever to an OpenAI language model

Run the chain with a query to get a generated answer based on retrieved documents

💡 Why This Matters

🌍 Real World

This project shows how smart assistants and chatbots find facts before answering, making their responses more accurate and trustworthy.

💼 Career

Understanding RAG chains is important for building advanced AI applications that combine search and language generation, a key skill in AI development roles.

1

Create the documents list

Create a list called documents containing three strings exactly: 'LangChain helps build language model apps.', 'Retrieval-Augmented Generation improves answers.', and 'Connecting retrieval to generation is powerful.'

LangChain

# Create the documents list with the exact three strings
# Your code here

Need a hint?

Use a Python list with the exact strings inside quotes and separated by commas.

2

Set up the retriever

Import SimpleRetriever from langchain.retrievers and create a variable called retriever by passing the documents list to SimpleRetriever.

LangChain

documents = [
    'LangChain helps build language model apps.',
    'Retrieval-Augmented Generation improves answers.',
    'Connecting retrieval to generation is powerful.'
]

# Import SimpleRetriever and create retriever with documents
# Your code here

Need a hint?

Use the exact class name SimpleRetriever and pass documents=documents when creating retriever.

3

Create the RAG chain

Import OpenAI from langchain.llms and RetrievalQA from langchain.chains. Create a variable called llm as OpenAI(). Then create a variable called rag_chain by calling RetrievalQA.from_chain_type with llm=llm, retriever=retriever, and chain_type='stuff'.

LangChain

from langchain.retrievers import SimpleRetriever

documents = [
    'LangChain helps build language model apps.',
    'Retrieval-Augmented Generation improves answers.',
    'Connecting retrieval to generation is powerful.'
]

retriever = SimpleRetriever(documents=documents)

# Import OpenAI and RetrievalQA, create llm and rag_chain
# Your code here

Need a hint?

Use the exact method RetrievalQA.from_chain_type with the parameters llm=llm, retriever=retriever, and chain_type='stuff'.

4

Run the RAG chain with a query

Call rag_chain.run with the exact string 'Why connect retrieval to generation?' and assign the result to a variable called answer.

LangChain

from langchain.retrievers import SimpleRetriever
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA

documents = [
    'LangChain helps build language model apps.',
    'Retrieval-Augmented Generation improves answers.',
    'Connecting retrieval to generation is powerful.'
]

retriever = SimpleRetriever(documents=documents)

llm = OpenAI()
rag_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, chain_type='stuff')

# Run rag_chain with the query and save to answer
# Your code here

Need a hint?

Use the exact query string inside rag_chain.run() and assign it to answer.