Similarity search vs MMR retrieval in LangChain - Visual Side-by-Side Comparison

Concept Flow - Similarity search vs MMR retrieval

Input Query

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Compute Embedding

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Find Top K Similar

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Return Top K Results

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Balance Relevance & Diversity

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Return Diverse Results

The flow starts with an input query. Similarity search finds the top similar items directly. MMR retrieval first finds candidates then selects results balancing relevance and diversity.

Execution Sample

LangChain

query = "What is AI?"
results_sim = similarity_search(query, k=3)
results_mmr = mmr_retrieval(query, k=3, lambda_param=0.5)
print(results_sim)
print(results_mmr)

This code runs similarity search and MMR retrieval on the same query, showing different result sets.

Execution Table

Step	Method	Action	Intermediate Result	Output
1	Both	Receive query 'What is AI?'	Query embedding computed	None
2	Similarity Search	Find top 3 most similar documents by embedding similarity	Top 3 docs by similarity scores	Docs A, B, C
3	MMR Retrieval	Find candidate documents (e.g., top 10 by similarity)	Candidate set of 10 docs	Docs A-J
4	MMR Retrieval	Iteratively select docs balancing relevance and diversity using lambda=0.5	Selected docs after each iteration	Docs A, D, F
5	Both	Return final results	Similarity Search returns top 3 similar	Docs A, B, C
6	Both	Return final results	MMR returns diverse top 3 balancing similarity and novelty	Docs A, D, F
7	Both	End	No more steps	Process complete

💡 Both methods finish after selecting k=3 documents; similarity search returns closest only, MMR balances diversity.

Variable Tracker

Variable	Start	After Step 2	After Step 4	Final
query	"What is AI?"	"What is AI?"	"What is AI?"	"What is AI?"
similarity_search_results	[]	[Doc A, Doc B, Doc C]	[Doc A, Doc B, Doc C]	[Doc A, Doc B, Doc C]
mmr_candidates	[]	N/A	[Doc A-J]	[Doc A-J]
mmr_selected	[]	N/A	[Doc A, Doc D, Doc F]	[Doc A, Doc D, Doc F]

Key Moments - 3 Insights

Why does MMR retrieval select different documents than similarity search?

MMR retrieval balances relevance and diversity by iteratively selecting documents that are both similar to the query and different from already selected ones, as shown in steps 3 and 4 of the execution_table.

Does similarity search consider diversity in results?

No, similarity search only ranks documents by similarity score and returns the top k, so results may be very similar to each other, as seen in step 2.

What role does the lambda parameter play in MMR retrieval?

Lambda controls the trade-off between relevance and diversity during selection in MMR retrieval, affecting which documents are chosen in step 4.

Visual Quiz - 3 Questions

Look at the execution_table at step 2, what documents does similarity search return?

A Docs A, D, F

B Docs A, B, C

C Docs A-J

D Docs D, E, F

💡 Hint

Check the 'Output' column for step 2 in the execution_table.

At which step does MMR retrieval balance relevance and diversity?

A Step 4

B Step 3

C Step 2

D Step 5

💡 Hint

Look at the 'Action' column for MMR retrieval in the execution_table.

If lambda in MMR retrieval is set to 1, what would happen to the results?

A No documents are selected

B Results become more diverse

C Results become identical to similarity search

D Results become random

💡 Hint

Lambda=1 means full weight on relevance; see key_moments about lambda's role.

Concept Snapshot

Similarity Search:
- Finds top k documents by embedding similarity
- Results may be similar to each other

MMR Retrieval:
- Finds candidates then selects k balancing relevance and diversity
- Uses lambda to control trade-off

Use similarity search for pure relevance
Use MMR to get diverse relevant results

Full Transcript

This visual execution compares similarity search and MMR retrieval in langchain. Both start by embedding the input query. Similarity search directly finds the top k most similar documents and returns them. MMR retrieval first finds a larger candidate set, then iteratively selects documents balancing relevance to the query and diversity from already selected documents, controlled by a lambda parameter. The execution table shows step-by-step actions and outputs for both methods. Variable tracking shows how results evolve. Key moments clarify why MMR returns different documents and the role of lambda. The quiz tests understanding of these steps and concepts. The snapshot summarizes when to use each method.