Users expect search experiences that go beyond literal keyword matching and surface results that truly align with their intent. MongoDB brings these capabilities together with native vector search, enabling semantic similarity retrieval directly inside the database. By comparing high-dimensional embeddings, MongoDB can identify documents that are meaningfully related—even when they share no overlapping terms.
What you’ll learn
In this article, you’ll learn:
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What vector search is and how embeddings represent semantic meaning.
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How MongoDB stores Float32 embeddings as BSON Binary.
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How Lucene’s HNSW index powers fast k-nearest-neighbor similarity search.
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How to set up a local MongoDB Atlas environment using the Atlas CLI.
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How to load the sample_mflix dataset with precomputed OpenAI and Voyage AI embeddings.
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How to create vector indexes directly in MongoDB.
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How to run semantic similarity queries using the
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How to use an existing embedding (e.g., Scarface) to find movies with similar plots.
What is vector search?
Vector search uses embeddings — high-dimensional numerical representations generated by machine learning models - to find items that are semantically similar.
Instead of matching exact words, vector search measures the meaning behind text.
This allows your application to retrieve documents that are conceptually related even when they don’t share any keywords. For example, a similarity query based on the plot of Scarface may return films with comparable themes—crime, ambition, moral tension—even if the wording is entirely different.
Vector search in MongoDB is powered by Lucene’s HNSW-based k-nearest-neighbor (k-NN) index over vectors stored as BSON Binary (Float32).
Read it here: MongoDB Vector Search Similarity Query - GeeksforGeeks