Basic RAG chain with LCEL in LangChain - Mini Project: Build & Apply

Basic RAG chain with LCEL

📖 Scenario: You want to build a simple Retrieval-Augmented Generation (RAG) chain using LangChain's LCEL (LangChain Expression Language) to answer questions based on a small set of documents.This helps you combine document search with AI-generated answers.

🎯 Goal: Create a basic RAG chain using LangChain and LCEL that retrieves relevant documents and generates an answer.

📋 What You'll Learn

Create a list of documents with exact content

Set up a retriever configuration variable

Use LCEL syntax to build a RAG chain expression

Complete the chain setup with the correct final call

💡 Why This Matters

🌍 Real World

Building RAG chains helps create AI apps that answer questions using specific documents, improving accuracy and relevance.

💼 Career

Understanding RAG chains and LCEL is useful for AI developers working on chatbots, search engines, and knowledge assistants.

1

Create the documents list

Create a list called documents with these exact strings: 'LangChain is a framework for building LLM apps.', 'LCEL allows chaining with expressions.', and 'RAG combines retrieval and generation.'

LangChain

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

Need a hint?

Use a Python list with the exact strings given.

2

Set up the retriever variable

Create a variable called retriever and assign it the string 'simple_retriever' to represent the retriever configuration.

LangChain

documents = [
    'LangChain is a framework for building LLM apps.',
    'LCEL allows chaining with expressions.',
    'RAG combines retrieval and generation.'
]
# Create the retriever variable with the string 'simple_retriever'
# Your code here

Need a hint?

Assign the exact string 'simple_retriever' to the variable retriever.

3

Write the LCEL RAG chain expression

Create a variable called rag_chain_expression and assign it the string 'rag_chain(retriever, documents, question)' representing the LCEL expression for the RAG chain.

LangChain

documents = [
    'LangChain is a framework for building LLM apps.',
    'LCEL allows chaining with expressions.',
    'RAG combines retrieval and generation.'
]
retriever = 'simple_retriever'
# Create rag_chain_expression with the string 'rag_chain(retriever, documents, question)'
# Your code here

Need a hint?

Assign the exact LCEL string to rag_chain_expression.

4

Complete the RAG chain setup

Create a variable called final_chain and assign it the string f"execute({rag_chain_expression})" to represent executing the RAG chain expression.

LangChain

documents = [
    'LangChain is a framework for building LLM apps.',
    'LCEL allows chaining with expressions.',
    'RAG combines retrieval and generation.'
]
retriever = 'simple_retriever'
rag_chain_expression = 'rag_chain(retriever, documents, question)'
# Create final_chain with f"execute({rag_chain_expression})"
# Your code here

Need a hint?

Use an f-string to wrap rag_chain_expression with execute().