Source citation in RAG responses in LangChain - Performance & Optimization

Performance: Source citation in RAG responses

MEDIUM IMPACT

This concept affects the response generation speed and user experience by adding source references in retrieval-augmented generation (RAG) outputs.

Including source citations in RAG-generated answers

LangChain

const { answer, sources } = await ragModel.generateAnswerWithSources(query); // includes source citations

Provides source citations alongside answers, improving user trust and clarity.

📈 Performance Gainadds ~200-500ms delay depending on source retrieval complexity

Including source citations in RAG-generated answers

LangChain

const response = await ragModel.generateAnswer(query); // no source citation included

No source citation means faster response but less transparency and trust for users.

📉 Performance Costfast response, minimal delay

Performance Comparison

Pattern	DOM Operations	Reflows	Paint Cost	Verdict
No source citation	Minimal DOM nodes	0 reflows	Low paint cost	[OK] Good
With source citation	More DOM nodes for citations	1-2 reflows	Moderate paint cost	[!] OK

Rendering Pipeline

The RAG system first retrieves relevant documents, then generates an answer with citations. Rendering the citations adds extra text and UI elements.

→Data Fetching

→Text Generation

→DOM Update

⚠️ BottleneckData Fetching and Text Generation due to additional retrieval and processing

Core Web Vital Affected

INP

This concept affects the response generation speed and user experience by adding source references in retrieval-augmented generation (RAG) outputs.

Optimization Tips

1Adding source citations increases response time due to extra data retrieval.

2Source citations impact INP by delaying interaction readiness.

3Optimize by caching sources and lazy loading citation UI elements.

Performance Quiz - 3 Questions

What is the main performance impact of adding source citations in RAG responses?

A Increased response generation time due to extra data retrieval and processing

B Reduced DOM nodes leading to faster rendering

C No impact on user interaction speed

D Decreased network requests

DevTools: Performance

How to check: Record a performance profile while generating a RAG response with and without citations. Compare the scripting and rendering times.

What to look for: Look for increased scripting time and layout shifts when citations are added.

Practice

1. What is the main purpose of source citation in Retrieval-Augmented Generation (RAG) responses?

easy

A. To show where the AI's answers come from, making them trustworthy

B. To speed up the AI's response time

C. To reduce the size of the AI model

D. To improve the AI's grammar and spelling

Solution

1. Step 1: Understand the role of source citation

   Source citation provides the origin of information used by the AI, helping users trust the answers.

2. Step 2: Compare options with the purpose

   Only To show where the AI's answers come from, making them trustworthy correctly states that citation shows where answers come from to build trust.

3. Final Answer:

   To show where the AI's answers come from, making them trustworthy -> Option A

4. Quick Check:

   Source citation = trust [OK]

Hint: Source citation means showing where info comes from [OK]

Common Mistakes:

• Thinking citation speeds up response
• Confusing citation with model size reduction
• Assuming citation fixes grammar

2. Which Langchain parameter must be set to True to receive source documents in RetrievalQA?

easy

A. return_source_documents

B. include_metadata

C. show_sources

D. enable_citation

Solution

1. Step 1: Recall Langchain RetrievalQA parameters

   The parameter to get source documents is return_source_documents.

2. Step 2: Match options with correct parameter

   Only return_source_documents matches the exact parameter name used in Langchain.

3. Final Answer:

   return_source_documents -> Option A

4. Quick Check:

   Parameter for sources = return_source_documents [OK]

Hint: Look for 'return_source_documents=True' in docs [OK]

Common Mistakes:

• Using incorrect parameter names like include_metadata
• Assuming show_sources or enable_citation exist
• Confusing with unrelated parameters

3. Given this Langchain code snippet, what will print(sources[0].metadata['source']) output if return_source_documents=True?

qa = RetrievalQA.from_chain_type(llm=llm, retriever=retriever, return_source_documents=True)
result = qa({"query": "What is AI?"})
sources = result['source_documents']
print(sources[0].metadata['source'])

medium

A. The entire content of the first source document

B. The AI's answer to the question

C. An error because 'source' key does not exist

D. The file or URL where the first source document came from

Solution

1. Step 1: Understand what source_documents contain

   They hold documents retrieved with metadata including 'source' indicating origin like file or URL.

2. Step 2: Analyze the print statement

   Accessing metadata['source'] prints the origin of the first document, not the answer or full content.

3. Final Answer:

   The file or URL where the first source document came from -> Option D

4. Quick Check:

   metadata['source'] = document origin [OK]

Hint: metadata['source'] holds origin info, not answer text [OK]

Common Mistakes:

• Thinking it prints AI answer text
• Expecting full document content
• Assuming 'source' key is missing causing error

4. You set return_source_documents=True but get a KeyError when accessing result['source_documents']. What is the likely cause?

medium

A. The retriever has no documents indexed

B. You forgot to set return_source_documents=True

C. The RetrievalQA run method returns a string, not a dict

D. The LLM model is incompatible with source documents

Solution

1. Step 1: Check what qa.run returns

   The run method returns only the answer string, not a dict with sources.

2. Step 2: Identify correct method to get sources

   To get sources, use qa({'query': '...'}) which returns a dict including source_documents.

3. Final Answer:

   The RetrievalQA run method returns a string, not a dict -> Option C

4. Quick Check:

   run() returns string, not dict [OK]

Hint: Use qa({'query': ...}) to get dict with sources, not run() [OK]

Common Mistakes:

• Assuming run() returns dict with sources
• Forgetting to set return_source_documents=True
• Blaming retriever or LLM for KeyError

5. You want to display clear source citations alongside AI answers in a chat app using Langchain RetrievalQA. Which approach correctly combines answer and source snippets for user display?

hard

A. Manually search documents outside Langchain and append sources to answer

B. Run result = qa({'query': question}), then print answer and loop over result['source_documents'] to show metadata['source'] and content snippet

C. Set return_source_documents=False and print the answer with no sources

D. Call qa.run(question) and print the answer only, ignoring sources

Solution

1. Step 1: Use the correct method to get answer and sources

   Calling qa({'query': question}) returns a dict with answer and source documents.

2. Step 2: Format output for user

   Print the answer, then loop over source_documents to show each source's metadata and a snippet of content for clear citation.

3. Final Answer:

   Run result = qa({'query': question}), then print answer and loop over sources for metadata and snippet -> Option B

4. Quick Check:

   Use qa({'query': ...}) + show sources [OK]

Hint: Use qa({'query': ...}) and print sources with snippets [OK]

Common Mistakes:

• Using qa.run() which returns only answer string
• Ignoring source documents in output
• Trying to get sources outside Langchain