Source citation in RAG responses in LangChain - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does RAG stand for in the context of AI?

RAG stands for Retrieval-Augmented Generation. It means combining a search or retrieval step with a text generation step to produce answers.

beginner

Why is source citation important in RAG responses?

Source citation shows where the information came from. It helps users trust the answer and check the original documents if needed.

intermediate

How does Langchain help with source citation in RAG?

Langchain can keep track of which documents were used to create the answer and include their references in the response.

beginner

What is a common way to show sources in RAG responses?

A common way is to list the document titles or URLs at the end of the answer or inline with the text using brackets or footnotes.

intermediate

What can happen if RAG responses do not include source citations?

Users may not trust the answers, and it becomes hard to verify or correct mistakes. This reduces the usefulness of the system.

What does RAG combine to generate answers?

A Image recognition and text generation

B Only text generation

C Only retrieval

D Retrieval and text generation

RAG combines retrieval of documents with generation of text to produce answers.

Why include source citations in RAG responses?

A To show where information came from

B To confuse users

C To make answers longer

D To hide the source

Source citations help users know where the information originated and build trust.

Which tool helps track sources in Langchain?

A Memory module

B Source tracker

C Document retriever

D Chain of thought

The document retriever in Langchain keeps track of which documents are used for answers.

How are sources usually shown in RAG answers?

A Listed at the end or inline with brackets

B Hidden inside the text

C Only in the code

D Not shown at all

Sources are commonly listed at the end or inline using brackets or footnotes.

What risk exists if RAG responses lack source citations?

A Users trust answers more

B Users cannot verify or trust answers

C Answers become shorter

D Answers become more accurate

Without citations, users cannot verify or trust the answers, reducing system usefulness.

Explain why source citation is important in Retrieval-Augmented Generation (RAG) responses.

Think about how users check and trust information.

Describe how Langchain supports including source citations in RAG workflows.

Focus on how Langchain manages documents used for answers.

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