Source citation in RAG responses in LangChain

Introduction

Source citation helps you know where the information in your AI answers comes from. It makes the answers trustworthy and easy to check.

When you want your AI to give answers with proof from documents.

When users need to verify facts from original sources.

When building chatbots that answer questions using company manuals or articles.

When you want to avoid AI making up information.

When you want to show users exactly which document or page the answer is based on.

Syntax

LangChain

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

retriever = FAISS.load_local("my_faiss_index", OpenAIEmbeddings())
qa = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True
)

result = qa({"query": "What is the capital of France?"})
print(result['result'])
print(result['source_documents'])

Set return_source_documents=True to get source info with answers.

Source documents include metadata like page number or document name.

Examples

This example shows how to get the answer and metadata about the sources.

LangChain

qa = RetrievalQA.from_chain_type(
    llm=OpenAI(),
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True
)

result = qa({"query": "Explain photosynthesis."})
print(result['result'])
print([doc.metadata for doc in result['source_documents']])

You can also pass the question as a dictionary and print full source content and metadata.

LangChain

result = qa({'query': "Who wrote Hamlet?"})
print(result['result'])
for doc in result['source_documents']:
    print(doc.page_content)
    print(doc.metadata)

Sample Program

This program loads a FAISS index with embeddings, creates a retrieval QA chain that returns source documents, asks a question, and prints the answer along with metadata and a snippet from each source document.

LangChain

from langchain.chains import RetrievalQA
from langchain.llms import OpenAI
from langchain.vectorstores import FAISS
from langchain.embeddings.openai import OpenAIEmbeddings

# Load embeddings and FAISS index
embeddings = OpenAIEmbeddings()
retriever = FAISS.load_local("faiss_index", embeddings)

# Create QA chain with source documents returned
qa = RetrievalQA.from_chain_type(
    llm=OpenAI(temperature=0),
    chain_type="stuff",
    retriever=retriever,
    return_source_documents=True
)

# Ask a question
query = "What is the tallest mountain in the world?"
result = qa({'query': query})

# Print answer
print("Answer:", result['result'])

# Print source citations
print("Sources:")
for i, doc in enumerate(result['source_documents'], 1):
    print(f"Source {i} metadata:", doc.metadata)
    print(f"Source {i} content snippet:", doc.page_content[:100], "...")

Important Notes

Make sure your documents have metadata like source name or page number for clear citations.

Returning source documents may slow down response time slightly but improves trust.

You can customize how source info is shown by processing the metadata and content.

Summary

Source citation shows where AI answers come from, making them trustworthy.

Set return_source_documents=True in Langchain RetrievalQA to get sources.

Print metadata and content snippets to share clear citations with users.

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