Basic RAG chain with LCEL in LangChain - Performance & Optimization

Performance: Basic RAG chain with LCEL

MEDIUM IMPACT

This concept affects page load speed and interaction responsiveness by how it manages data fetching and rendering in a retrieval-augmented generation chain.

Implementing a RAG chain with LCEL for document retrieval and generation

LangChain

from langchain.chains import RetrievalQA
from langchain.chains.combine_documents.stuff import StuffDocumentsChain
from langchain.chains.llm import LLMChain

rag_chain = RetrievalQA(
  retriever=retriever,
  combine_documents_chain=StuffDocumentsChain(llm_chain=LLMChain(llm=llm))
)
result = rag_chain.run(query)

This pattern uses LCEL to combine documents efficiently, reducing blocking by streaming and incremental processing.

📈 Performance Gainreduces blocking time by 40-60%, improves INP by faster response streaming

Implementing a RAG chain with LCEL for document retrieval and generation

LangChain

rag_chain = RetrievalQA.from_chain_type(llm=llm, chain_type="stuff", retriever=retriever)
result = rag_chain.run(query)

This approach fetches and processes all documents at once, causing blocking and slow response times for large datasets.

📉 Performance Costblocks rendering for 300-500ms per query depending on data size

Performance Comparison

Pattern	DOM Operations	Reflows	Paint Cost	Verdict
Basic RAG chain without LCEL	Minimal DOM changes	0 reflows	Low paint cost	[!] OK
RAG chain with LCEL incremental combining	Minimal DOM changes	0 reflows	Low paint cost	[OK] Good

Rendering Pipeline

The RAG chain with LCEL affects how data retrieval and language model generation stages interact, impacting the time before the user sees a response.

→Data Fetching

→JavaScript Execution

→Rendering

⚠️ BottleneckJavaScript Execution during document combination and generation

Core Web Vital Affected

INP

This concept affects page load speed and interaction responsiveness by how it manages data fetching and rendering in a retrieval-augmented generation chain.

Optimization Tips

1Use incremental document combining to reduce blocking in RAG chains.

2Monitor JavaScript execution time to improve interaction responsiveness.

3Avoid fetching and processing all documents at once to prevent input delays.

Performance Quiz - 3 Questions

What is the main performance benefit of using LCEL in a RAG chain?

A Reduces blocking by incremental document processing

B Increases bundle size significantly

C Triggers multiple reflows in the browser

D Blocks rendering until all documents are fetched

DevTools: Performance

How to check: Record a performance profile while running queries. Look for long scripting tasks blocking the main thread.

What to look for: Long JavaScript execution times indicate blocking; shorter tasks with streaming show better performance.

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