MongoDB Developer Blog
Deep dives into technical concepts, architectures, and innovations with MongoDB.
Vision RAG: Enabling Search on Any Documents
January 12, 2026
Developer Blog
Build an Agentic Video Search System Using Voyage AI, MongoDB, and Anthropic
As natural language queries replace keyword searches and search systems embrace multimodal data, a single information retrieval strategy can no longer capture the full spectrum of user intent.
Vision RAG: Enabling Search on Any Documents
Information comes in many shapes and forms. While retrieval-augmented generation (RAG) primarily focuses on plain text, it overlooks vast amounts of data along the way. Most enterprise knowledge resides in complex documents, slides, graphics, and other multimodal sources. Yet, extracting useful information from these formats using optical character recognition (OCR) or other parsing techniques is often low-fidelity, brittle, and expensive.
Announcing Kinesis Support for MongoDB Atlas Stream Processing
AWS Kinesis Data Streams enable the capture of events from applications, IoT devices, clickstreams, or logs. For those building on MongoDB, getting streaming data into MongoDB Atlas for operational queries or enriching Kinesis streams with data from MongoDB collections presented challenges. Connecting these systems required custom integration code, ongoing maintenance, and deep expertise in both platforms.
Using Instruction-Following Rerankers As A Context Engineering Tool
As we've moved from simple large language model (LLM) interactions to building long-running AI agents, prompt engineering has naturally evolved into context engineering—optimizing not just system instructions but any information that lands in the LLM’s context window. This includes external knowledge, reasoning tokens, tool definitions and outcomes, short-term conversational history, long-term memory, and so on. Identifying and prioritizing the most important information at every LLM call is crucial, since all this content competes for the same limited real estate.
AI Fraud Detection With MongoDB Atlas and Temporal
When processing millions of financial transactions daily, most financial institutions face an impossible tradeoff: approve transactions quickly to maintain customer satisfaction, or scrutinize every transaction thoroughly to prevent fraud. Traditional approaches rely on static rules and basic pattern matching that don’t scale with modern fraud sophistication. This disconnect creates a fundamental problem where legitimate transactions get delayed while sophisticated fraud slips through undetected.
Automate Atlas Backups Transfer to S3 for Cost-Effective and Long-Term Archival
As organizations’ business footprints expand, their digital footprints also grow. In today’s world, data is the fuel that keeps organizations growing and running, driving insights, innovation, and resilience.
Building An Offline-first App Using AWS Amplify, AppSync and MongoDB Atlas
This blog post shows you how to use AWS Amplify, AWS AppSync, and MongoDB Atlas to build a fully AWS-native foundation for offline-first apps—and how to quickly adapt this architecture to your own business needs.
Contextualized Chunk Embeddings: Combining Local Detail with Global Context
Large documents present a fundamental challenge in LLM applications—even with today's extended context windows, including entire documents is inefficient and drowns the signal in irrelevant information. Chunking solves this by breaking documents into smaller segments, allowing retrieval systems to surgically extract only the most relevant chunks into the LLM's context window. However, this precision comes at a cost—context loss.
Harness the Power of Atlas Search and Vector Search with $rankFusion
For modern data-rich applications, it is fundamental to deliver users highly relevant search results. However, relying on a single search mechanism often falls short of capturing the full complexity of user intent. This post will demonstrate how to overcome this by implementing a hybrid search system in
Cost Optimization with Optimal Document Size
We all know that document sizes play a crucial role in both the cost and performance of a MongoDB database. MongoDB stores data in a developer-friendly, document-oriented format — JSON (more precisely, BSON, which is a binary-encoded superset of JSON). This flexible structure allows developers to model data naturally, similar to how it’s represented in application objects.
Enhancing Business Communication: The Evolution of A2P Messaging with RCS and MongoDB
Over time, humans have developed numerous ways to communicate, but technology has dramatically impacted how we interact. Communication technology has evolved significantly over the past few decades, starting with the invention of the telegraph and the telephone, which laid the foundation for long-distance communication. The rise of mobile technology further transformed communication. SMS, introduced in the early 1990s, became one of the most popular means of communication, offering a simple and efficient way to send short text messages between mobile phones. With the advent of smartphones, people could access the internet, make calls, send SMS, and use instant messaging apps from almost anywhere in the world.