Similarity search vs MMR retrieval in LangChain - Interactive Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to perform a similarity search using LangChain.

LangChain

results = vectorstore.[1](query="What is AI?", k=3)

Drag options to blanks, or click blank then click option'

Asearch_documents

Bsimilarity_search

Cmmr_search

Dget_relevant_documents

Attempts:

3 left

💡 Hint

Common Mistakes

Using MMR search method instead of similarity search.

The similarity_search method finds documents most similar to the query.

2fill in blank

medium

Complete the code to perform an MMR retrieval with LangChain.

LangChain

results = vectorstore.[1](query="Explain machine learning", k=4, lambda_mult=0.5)

Drag options to blanks, or click blank then click option'

Amax_marginal_relevance_search

Bsimilarity_search

Cretrieve_mmr

Dsearch_mmr

Attempts:

3 left

💡 Hint

Common Mistakes

Using similarity search instead of MMR search.

The max_marginal_relevance_search method performs Maximal Marginal Relevance retrieval balancing relevance and diversity.

3fill in blank

hard

Fix the error in the MMR search call by completing the missing method name.

LangChain

docs = vectorstore.[1](query="Benefits of AI", k=5, lambda_mult=0.7)

Drag options to blanks, or click blank then click option'

Asearch

Bsimilarity_search

Cmax_marginal_relevance_search

Dretrieve

Attempts:

3 left

💡 Hint

Common Mistakes

Using generic search methods without MMR support.

The correct method for MMR retrieval is max_marginal_relevance_search.

4fill in blank

hard

Fill both blanks to create a dictionary comprehension that maps documents to their similarity scores greater than 0.8.

LangChain

high_scores = {doc: score for doc, score in vectorstore.similarity_search_with_score(query) if score [1] 0.8 and doc.[2] is not None}

Drag options to blanks, or click blank then click option'

A>

B<

Cmetadata

Dcontent

Attempts:

3 left

💡 Hint

Common Mistakes

Using '<' instead of '>' for score comparison.

Checking wrong document attribute.

The comprehension filters scores greater than 0.8 and checks if document metadata exists.

5fill in blank

hard

Fill all three blanks to create a dictionary comprehension that maps document titles to their content length for documents retrieved by MMR with score above 0.5.

LangChain

doc_info = {doc.[1]: len(doc.[2]) for doc, score in vectorstore.[3](query, k=6, lambda_mult=0.6) if score > 0.5}

Drag options to blanks, or click blank then click option'

Ametadata['title']

Bpage_content

Cmax_marginal_relevance_search_with_score

Dsimilarity_search

Attempts:

3 left

💡 Hint

Common Mistakes

Using similarity_search instead of max_marginal_relevance_search_with_score.

Accessing wrong document attributes.

This comprehension extracts titles from metadata, counts content length, and uses max_marginal_relevance_search_with_score for MMR retrieval.

Practice

1. What is the main difference between Similarity search and MMR retrieval in Langchain?

easy

A. Similarity search uses machine learning; MMR retrieval uses rule-based filtering.

B. Similarity search finds closest matches; MMR retrieval balances closeness and diversity.

C. Similarity search always returns random results; MMR retrieval returns exact matches.

D. Similarity search is slower than MMR retrieval in all cases.

Solution

1. Step 1: Understand Similarity Search

   Similarity search finds the closest matches to the query based on similarity scores.

2. Step 2: Understand MMR Retrieval

   MMR retrieval balances between closeness and diversity to avoid repeated or very similar results.

3. Final Answer:

   Similarity search finds closest matches; MMR retrieval balances closeness and diversity. -> Option B

4. Quick Check:

   Difference = Closest vs Balanced [OK]

Hint: Similarity = closeness; MMR = closeness plus diversity [OK]

Common Mistakes:

• Thinking MMR returns only closest matches
• Confusing MMR with random retrieval
• Assuming similarity search ensures diverse results

2. Which of the following is the correct way to initialize an MMR retriever in Langchain?

easy

A. mmr_retriever = MMRRetriever(documents, k=5, lambda_mult=0.7)

B. mmr_retriever = MMRRetriever(documents, top_k=5, lambda=0.7)

C. mmr_retriever = MMRRetriever(documents, k=5, lambda=0.7)

D. mmr_retriever = MMRRetriever(documents, top_k=5, lambda_mult=0.7)

Solution

1. Step 1: Check parameter names for MMRRetriever

   The correct parameters include k for number of results and lambda_mult for diversity weight.

2. Step 2: Identify correct parameter usage

   Options B, C, and D use top_k or lambda which are incorrect parameter names.

3. Final Answer:

   mmr_retriever = MMRRetriever(documents, k=5, lambda_mult=0.7) -> Option A

4. Quick Check:

   Correct params = k and lambda_mult [OK]

Hint: MMR uses k and lambda_mult parameters [OK]

Common Mistakes:

• Using 'top_k' instead of 'k'
• Using 'lambda' instead of 'lambda_mult'
• Mixing parameter names from similarity search

3. Given the following code snippet using Langchain's similarity search and MMR retrieval, what will be the main difference in their outputs?

docs = ["apple pie recipe", "apple tart recipe", "banana bread recipe", "cherry pie recipe"]
query = "apple dessert"
sim_results = similarity_search(docs, query, k=2)
mmr_results = mmr_retrieval(docs, query, k=2, lambda_mult=0.5)

medium

A. sim_results will return the two most similar apple recipes; mmr_results will return one apple and one different dessert recipe.

B. sim_results will return random recipes; mmr_results will return the two closest apple recipes.

C. Both sim_results and mmr_results will return the same two apple recipes.

D. sim_results will return only one recipe; mmr_results will return three recipes.

Solution

1. Step 1: Analyze similarity_search output

   Similarity search returns the top 2 closest matches, likely both apple recipes due to query "apple dessert".

2. Step 2: Analyze mmr_retrieval output

   MMR balances similarity and diversity, so it will likely return one apple recipe and one different dessert (like cherry pie) to increase variety.

3. Final Answer:

   sim_results will return the two most similar apple recipes; mmr_results will return one apple and one different dessert recipe. -> Option A

4. Quick Check:

   Similarity = closest; MMR = closest + diverse [OK]

Hint: Similarity = closest matches; MMR adds variety [OK]

Common Mistakes:

• Assuming both return identical results
• Thinking MMR returns random results
• Confusing number of results returned

4. You wrote this code to perform MMR retrieval but get an error:

mmr = MMRRetriever(documents, k=3, lambda_mult=0.6)
results = mmr.get_relevant_documents(query)

What is the likely cause of the error?

medium

A. Calling 'get_relevant_documents' instead of 'retrieve_documents'.

B. Using 'k' instead of 'top_k' when initializing MMRRetriever.

C. Missing import statement for MMRRetriever.

D. Using 'lambda_mult' instead of 'lambda' parameter.

Solution

1. Step 1: Check parameter names in MMRRetriever initialization

   The parameters k and lambda_mult are correct.

2. Step 2: Confirm method name correctness

   get_relevant_documents is a valid method for retrievers.

3. Final Answer:

   Missing import statement for MMRRetriever. -> Option C

4. Quick Check:

   Missing import = NameError [OK]

Hint: Import MMRRetriever before use [OK]

Common Mistakes:

• Forgetting import statement for MMRRetriever
• Confusing method names
• Wrong parameter names like top_k or lambda

5. You want to retrieve 3 documents related to "climate change" using Langchain. You want results that are relevant but also cover different aspects like causes, effects, and solutions. Which retrieval method and parameters should you choose?

hard

A. Use MMR retrieval with k=3 and lambda_mult=0 to get only closest matches.

B. Use similarity search with k=3 for closest matches only.

C. Use similarity search with k=1 repeated 3 times to get variety.

D. Use MMR retrieval with k=3 and lambda_mult=0.8 to balance relevance and diversity.

Solution

1. Step 1: Identify need for relevance and diversity

   You want documents that are relevant but cover different aspects, so diversity is important.

2. Step 2: Choose retrieval method and parameters

   MMR retrieval balances relevance and diversity using lambda_mult. A value like 0.8 prioritizes relevance while introducing some diversity.

3. Final Answer:

   Use MMR retrieval with k=3 and lambda_mult=0.8 to balance relevance and diversity. -> Option D

4. Quick Check:

   Diversity + relevance = MMR with 0 < lambda_mult < 1 [OK]

Hint: Lower lambda_mult increases diversity in MMR [OK]

Common Mistakes:

• Using similarity search only for diverse results
• Setting lambda_mult to 0 disables relevance
• Repeating similarity search calls for variety