Arek Borucki on foojay.io - Friends of OpenJDK

Keywords Meet Vectors: Hybrid Search on MongoDB

Thu, 09 Oct 2025 14:49:49 +0000

In the previous issues, I explained how to run a local MongoDB Atlas cluster using Atlas CLI , what vector search is, and how to use it . Now, let’s take a closer look at hybrid search.

Reading time: 4–5 min

What is hybrid search?

Hybrid search in MongoDB brings together two complementary search techniques:

Full text search (BM25 via Atlas Search )—optimized for exact keyword matches, powered by Lucene inside mongot. Perfect when users expect documents that literally contain their query terms.

Vector search (kNN via Atlas Vector Search )—optimized for semantic similarity. It uses dense embeddings from ML models to find conceptually related content, even when no keywords match.

On their own, each method has advantages and limitations. Text search misses context (“non-linear crime story” won’t return Memento). Pure semantic search may return results that are semantically aligned but sometimes not practically useful. Hybrid search combines the strengths of both, ensuring results are contextually relevant and precise.

Building an AI Semantic Movie Recommender with Vector Search

Tue, 30 Sep 2025 13:23:18 +0000

Last time, we created a vector search index in a local MongoDB Atlas cluster . Now, let’s put it to work with a real case: building an AI-powered movie recommender that suggests films similar to The Matrix–without any shared keywords.

🕒 Reading time: 3-4 min

🎯 The challenge

This demo will be entirely based on the pre-generated vector embeddings already stored in the sample_mflix dataset—no API calls and no new model runs. Everything will remain local. If you haven’t done it yet, learn how in this article 👉 Loading embeddings into MongoDB

Power your AI application with Vector Search

Tue, 23 Sep 2025 16:23:37 +0000

Most major database vendors, like MongoDB , are adding vector search capabilities to their products. It’s becoming a standard feature as demand for AI-powered applications grows.

🕒 Reading time: 3-4 min

🧠 What is vector search needed for?

MongoDB Vector Search enables semantic queries. For example, finding support tickets with similar meanings even if they use different words. It also powers hybrid search by combining exact keyword matches like “error 500” with semantically similar phrases like “server failure”. Another use case is personalization, such as recommending articles similar to those a user has read.

From Zero to Vector Hero - Locally! (Vector Search)

Thu, 18 Sep 2025 15:25:23 +0000

In the previous issue, I explained how to run a local MongoDB Atlas cluster using Atlas CLI - no cloud account required. If you missed it, read it here 👉 Run an Atlas cluster locally in minutes. Now let’s see how to use Vector Search in that local environment.

🕒 Reading time: 3-4 min

🧠 Explaining the Embedding Workflow

After launching your local MongoDB Atlas cluster and running the show dbs command in mongosh, you’ll see only the default system databases: admin, config, and local. These are used internally by MongoDB and contain no user data or vector embeddings at this point.

Run an Atlas cluster locally in minutes

Tue, 05 Aug 2025 07:33:47 +0000

You no longer need a cloud account to try MongoDB Atlas features. Spin up a fully compatible local cluster in seconds, with no login, no credit card, and no friction.

🕒 Reading time: 2-3 min

🍃 What is the MongoDB Atlas Platform?

MongoDB Atlas is a fully managed, multi-cloud data platform provided by MongoDB Inc. It allows you to deploy and run MongoDB clusters on Amazon Web Services (AWS), Google Cloud Platform (GCP), and Microsoft Azure, with full support for replica sets (for high availability), sharding (for horizontal scalability), and multi-cloud deployments. Atlas handles infrastructure, backups, security, scaling, and monitoring out of the box. It also extends the core MongoDB server capabilities with a set of integrated features.