Basic RAG chain with LCEL in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LangChain RetrievalQA class.

LangChain

from langchain.chains import [1]

Drag options to blanks, or click blank then click option'

AVectorStore

BTextSplitter

CRetrievalQA

DOpenAI

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated classes like TextSplitter or OpenAI instead of RetrievalQA.

The RetrievalQA class is used to create a retrieval-augmented generation chain in LangChain.

2fill in blank

medium

Complete the code to initialize the RetrievalQA chain with the retriever.

LangChain

qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=[1])

Drag options to blanks, or click blank then click option'

Aquery

Bembedding

Cdocument

Dretriever

Attempts:

3 left

💡 Hint

Common Mistakes

Passing embedding or document instead of the retriever.

The retriever object is passed to the RetrievalQA chain to fetch relevant documents.

3fill in blank

hard

Fix the error in the code to run the RAG chain and get the answer.

LangChain

result = qa_chain.[1](query)

Drag options to blanks, or click blank then click option'

Arun

Bsearch

Cpredict

Danswer

Attempts:

3 left

💡 Hint

Common Mistakes

Using search or predict instead of run to execute the chain.

The run() method executes the chain with the input query and returns the answer.

4fill in blank

hard

Fill both blanks to create a LangChain LLMChain with a prompt template and an LLM.

LangChain

from langchain.chains import [1]
from langchain.prompts import [2]
llm_chain = LLMChain(llm=llm, prompt=prompt)

Drag options to blanks, or click blank then click option'

ALLMChain

BPromptTemplate

CRetriever

DVectorStore

Attempts:

3 left

💡 Hint

Common Mistakes

Importing unrelated classes like Retriever or VectorStore here.

LLMChain and PromptTemplate are imported to create a chain with a prompt and language model.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension filtering documents by length and score.

LangChain

filtered_docs = {doc: score for doc, score in docs.items() if len(doc) [1] 100 and score [2] 0.5 and doc not in [3]

Drag options to blanks, or click blank then click option'

A<

B>

C>=

Dexcluded_docs

Attempts:

3 left

💡 Hint

Common Mistakes

Using wrong comparison operators or forgetting to exclude documents.

We filter documents shorter than 100 characters, with score greater than 0.5, and not in excluded_docs.

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