MongoDB Vector Search Use Cases and Design Patterns

Semantic Search

MongoDB Vector Search allows you to perform semantic search over text, images, multimodal, and other data types by indexing and searching over data that you've converted into vector embeddings.

To get started, see How to Perform Semantic Search Against Data in Your Atlas Cluster.

Retrieval-Augmented Generation (RAG)

RAG allows you to combine language models with your own data by retrieving relevant context before generating responses. This pattern allows you to build AI applications that provide more accurate and domain-specific responses grounded in your custom data.

To learn more and get started, see Retrieval-Augmented Generation (RAG) with MongoDB.

You can also implement RAG locally, without the need for API keys from LLM providers. To learn more, see Build a Local RAG Implementation with MongoDB Vector Search.

AI Agents

MongoDB provides several features for building AI agents. As both a vector and document database, MongoDB supports various search methods for agentic RAG, as well as storing agent interactions in the same database for short and long-term agent memory.

To learn more and get started, see Build AI Agents with MongoDB.

Prerequisites

To complete the tutorials on these pages, you must have the following:

• One of the following MongoDB cluster types:

  • An Atlas cluster running MongoDB version 6.0.11, 7.0.2, or later. Ensure that your IP address is included in your Atlas project's access list.

  • A local Atlas deployment created using the Atlas CLI. To learn more, see Create a Local Atlas Deployment.

  • A MongoDB Community or Enterprise cluster with Search and Vector Search installed.

• Project Data Access Admin access to the project to create MongoDB Vector Search indexes.

• The sample data loaded into your MongoDB cluster.

• mongosh or a supported MongoDB Driver to run queries on your cluster.