AI-Powered Search: Building a Semantic Search Engine with MongoDB and Python

In this blog post, we'll explore how to build a semantic search engine for a movie database using MongoDB Atlas and Python. We'll leverage the power of vector embeddings and MongoDB's vector search capabilities to create a system that understands the meaning behind search queries and returns highly relevant results.

The Problem: Limitations of Keyword Search

Imagine you're looking for movies about "Movies from India" A traditional keyword search might struggle with this query if the exact phrase doesn't appear in movie titles or descriptions. It might miss relevant movies that use different terminology or focus on specific aspects.

The Solution: Semantic Search with Vector Embeddings

Semantic search solves this problem by understanding the meaning behind words and phrases. Here's how our solution works:

1. We convert movie plots into vector embeddings using a pre-trained language model.

2. User queries are converted into the same vector space.

3. We find movies with plot embeddings that are most similar to the query embedding.

This approach allows us to find movies that are conceptually similar to the query, even if they don't share exact keywords.

Implementation Details

Tools and Technologies

- MongoDB Atlas: For storing our movie data and performing vector searches.

- Python: As our programming language of choice.

- Sentence Transformers: To generate vector embeddings for movie plots and queries.

- PyMongo: To interact with MongoDB from Python.

Step 1: Setting Up the Database

First, we set up a MongoDB Atlas cluster and loaded it with movie data. Each document in our collection contains fields like title, plot, and a vector embedding of the plot.

Step 2: Generating Embeddings

We use the 'all-MiniLM-L6-v2' model from the Sentence Transformers library to generate embeddings for movie plots. This model produces 384-dimensional vectors that capture the semantic meaning of the text.

Step 3: Creating a Vector Index

To enable efficient similarity searches, we create a vector index in MongoDB:

With our index in place, we can perform vector searches:

Step 5: Comparing with Text Search

To demonstrate the power of semantic search, we also implemented a traditional text-based search for comparison:

Results and Analysis

Let's look at some example queries and their results:

As we can see, the vector search often returns more conceptually relevant results, especially for queries that don't have exact keyword matches in the movie data.

Conclusion

By leveraging vector embeddings and MongoDB's vector search capabilities, we've created a system that understands the meaning behind queries and returns highly relevant results.

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