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

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

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

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

Hint

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

Practice

1. What is the main purpose of a RAG chain in Langchain?

easy

A. To combine document search with AI to answer questions

B. To train a new AI model from scratch

C. To visualize data in charts

D. To clean and preprocess raw data

Solution

1. Step 1: Understand RAG chain function

   A RAG (Retrieval-Augmented Generation) chain uses search to find relevant documents and AI to generate answers.

2. Step 2: Match purpose with options

   Only To combine document search with AI to answer questions describes combining search and AI to answer questions, which is the core of RAG chains.

3. Final Answer:

   To combine document search with AI to answer questions -> Option A

4. Quick Check:

   RAG chain = combine search + AI [OK]

Hint: RAG means search plus AI for answers [OK]

Common Mistakes:

• Thinking RAG trains new models
• Confusing RAG with data visualization
• Assuming RAG only cleans data

2. Which of the following is the correct way to create a RAG chain using LCEL in Langchain?

easy

A. rag_chain = LCEL.RAGChain.build(docs, llm)

B. rag_chain = LCEL.RAGChain.from_documents(docs, llm)

C. rag_chain = RAGChain(docs, llm).build()

D. rag_chain = LCEL.createRAG(docs, llm)

Solution

1. Step 1: Recall LCEL syntax for RAG chain

   LCEL provides a method called from_documents to create a RAG chain from documents and an LLM.

2. Step 2: Compare options with correct syntax

   Only rag_chain = LCEL.RAGChain.from_documents(docs, llm) uses LCEL.RAGChain.from_documents(docs, llm), which is the correct method.

3. Final Answer:

   rag_chain = LCEL.RAGChain.from_documents(docs, llm) -> Option B

4. Quick Check:

   LCEL.RAGChain.from_documents = correct syntax [OK]

Hint: Use from_documents method to create RAG chain [OK]

Common Mistakes:

• Using non-existent createRAG method
• Calling build() on RAGChain instance
• Mixing method names or class names

3. Given the code snippet:

docs = ["Doc1 content", "Doc2 content"]
llm = SomeLLM()
rag_chain = LCEL.RAGChain.from_documents(docs, llm)
answer = rag_chain.run("What is in Doc1?")
print(answer)

What is the expected output?

medium

A. An error because docs should be a string, not a list

B. A list of documents including Doc1 and Doc2

C. An answer generated by the LLM based on Doc1 content

D. The string 'What is in Doc1?' printed as is

Solution

1. Step 1: Understand RAG chain run method

   Running rag_chain.run(question) uses the LLM to answer based on retrieved documents.

2. Step 2: Analyze input and output

   Docs are provided as a list, which is valid. The output is an AI-generated answer about Doc1 content.

3. Final Answer:

   An answer generated by the LLM based on Doc1 content -> Option C

4. Quick Check:

   rag_chain.run(question) = AI answer [OK]

Hint: RAG chain answers questions using docs + LLM [OK]

Common Mistakes:

• Expecting raw docs list as output
• Thinking docs must be a single string
• Assuming run just echoes input question

4. You wrote this code:

rag_chain = LCEL.RAGChain.from_documents("doc.txt", llm)
answer = rag_chain.run("Explain the content.")

But it raises an error. What is the likely problem?

medium

A. The llm variable is not defined

B. LCEL.RAGChain does not have from_documents method

C. The run method requires two arguments

D. The documents argument should be a list, not a filename string

Solution

1. Step 1: Check documents argument type

   from_documents expects a list of document strings, not a filename string.

2. Step 2: Identify error cause

   Passing "doc.txt" as a string causes an error because it is not a list of documents.

3. Final Answer:

   The documents argument should be a list, not a filename string -> Option D

4. Quick Check:

   Documents must be list, not filename string [OK]

Hint: Pass list of docs, not filename string [OK]

Common Mistakes:

• Passing filename instead of document list
• Assuming run needs two arguments
• Thinking from_documents method is missing

5. You want to build a RAG chain that answers questions from multiple document types (PDF, text, and web pages) using LCEL. Which approach correctly prepares the documents before creating the RAG chain?

hard

A. Load and convert each document type to text, combine into a list, then pass to LCEL.RAGChain.from_documents

B. Pass file paths directly to LCEL.RAGChain.from_documents without loading

C. Use only one document type to avoid errors

D. Create separate RAG chains for each document type and run them independently

Solution

1. Step 1: Understand document preparation for RAG

   RAG chains require documents as text strings, so all document types must be converted to text first.

2. Step 2: Combine and pass documents correctly

   After conversion, combine all texts into a list and pass to LCEL.RAGChain.from_documents for a single chain.

3. Final Answer:

   Load and convert each document type to text, combine into a list, then pass to LCEL.RAGChain.from_documents -> Option A

4. Quick Check:

   Convert all docs to text list before RAG chain [OK]

Hint: Convert all docs to text list before RAG chain [OK]

Common Mistakes:

• Passing file paths instead of text
• Using only one document type unnecessarily
• Creating multiple chains instead of one combined