Source citation in RAG responses in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to import the LangChain retriever class.

LangChain

from langchain.vectorstores import [1]

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ATensorFlow

BPandas

CNumPy

DChroma

Attempts:

3 left

💡 Hint

Common Mistakes

Choosing unrelated libraries like Pandas or TensorFlow.

The Chroma class is used as a vector store retriever in LangChain for RAG.

2fill in blank

medium

Complete the code to initialize the retriever with the vector store.

LangChain

retriever = [1](embedding_function=embeddings, persist_directory='db')

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AChroma

BWeaviate

CFAISS

DPinecone

Attempts:

3 left

💡 Hint

Common Mistakes

Using other vector stores not imported or configured.

Chroma is the vector store class used to create a retriever instance in LangChain.

3fill in blank

hard

Fix the error in the code to enable source citation in the RAG chain.

LangChain

qa_chain = RetrievalQAWithSourcesChain(llm=llm, retriever=[1], return_source_documents=True)

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Aretriever()

Bretriever

Cretriever.run()

Dretriever.get()

Attempts:

3 left

💡 Hint

Common Mistakes

Calling retriever as a function instead of passing the object.

The retriever should be passed as an object, not called as a function.

4fill in blank

hard

Fill both blanks to extract answer and sources from the RAG output.

LangChain

answer = result[[1]]
sources = result[[2]]

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A'answer'

B'sources'

C'source_documents'

D'text'

Attempts:

3 left

💡 Hint

Common Mistakes

Using 'sources' key which does not exist in output.

The RAG output dictionary contains the 'answer' key for the answer text and 'source_documents' key for the source documents.

5fill in blank

hard

Fill all three blanks to create a dictionary of source texts from documents.

LangChain

source_texts = [1](doc.page_content for doc in [2])
source_dict = {i: text for i, text in [3](source_texts)}

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Alist

Bresult['source_documents']

Cenumerate

Ddict

Attempts:

3 left

💡 Hint

Common Mistakes

Using dict() instead of enumerate() in the comprehension.

We first create a list of page contents from source documents, then use enumerate to pair indices with texts for the dictionary comprehension.

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