Similarity search vs MMR retrieval in LangChain - When to Use Which

The Big Idea

What if your search results were not just relevant but also refreshingly different every time?

The Scenario

Imagine you have a huge library of documents and you want to find the most relevant ones for your question by looking for similar words or phrases.

You try to pick the top matches just by similarity scores.

The Problem

Picking only the most similar documents often gives you many that say almost the same thing.

This means you get repeated information and miss out on other useful perspectives.

The Solution

MMR retrieval smartly balances similarity with diversity.

It picks documents that are relevant but also different from each other, giving you a richer set of answers.

Before vs After

✗ Before

results = similarity_search(query, top_k=5)

✓ After

results = mmr_retrieval(query, top_k=5, lambda_=0.5)

What It Enables

You get a set of results that cover different angles of your question, not just repeats of the same idea.

Real Life Example

When searching for travel tips, similarity search might give you five tips about packing, while MMR retrieval gives you packing, safety, food, and sightseeing tips.

Key Takeaways

Similarity search finds the closest matches but can be repetitive.

MMR retrieval balances relevance and diversity for better coverage.

Using MMR helps you get richer, more useful results from your searches.