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The cost of not knowing MongoDB (V5RX) - Part 2
Discover the power of MongoDB schema optimization in the second part of The Cost of Not Knowing MongoDB series. This deep dive explores the impact of different data modeling approaches, focusing on the Bucket Pattern and Computed Pattern to enhance query performance and storage efficiency. Learn how grouping data by month or quarter can significantly reduce index size and improve read operations, while pre-computing aggregations can optimize reporting. With real-world benchmarks, load test results, and practical TypeScript code, this guide demonstrates how strategic schema design can maximize MongoDB’s potential.
Jan 29, 2025
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Building a Foreign Correspondent With MongoDB, Anthropic's Claude, Python
Join guest author Marko Aleksendric to learn how to use MongoDB, Anthropic's Claude and Python to create a simple web application aimed to help a virtual friend in a foreign country translate the local news items.
Dec 09, 2024
Article
The Cost of Not Knowing MongoDB
The focus of this series is to show how much performance you can gain when using MongoDB properly, following the best practices, studying your application needs, and using it to model your data.Nov 11, 2024
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Discover Latent Semantic Structure With Vector Clustering
Leverage the mathematical properties of a population of db AI-embedded vectors to extract potential novel business intelligence.
Oct 11, 2024
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Building a Quarkus Application to Perform MongoDB Vector Search
This article explores building a Quarkus application using MongoDB's vector search for smarter, context-aware search results. We'll cover generating embeddings with Gemini AI and creating a vector index in MongoDB. Learn how to enhance your application's search capabilities with this modern approach.
Oct 07, 2024
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Using SuperDuperDB to Accelerate AI Development on MongoDB Atlas Vector Search
Discover how you can use SuperDuperDB to describe complex AI pipelines built on MongoDB Atlas Vector Search and state of the art LLMs.
Sep 18, 2024
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Multi-agent Systems With AutoGen and MongoDB
Discover how to build powerful multi-agent AI systems using AutoGen and MongoDB. This guide explores the integration of Microsoft's AutoGen framework with MongoDB's Atlas Vector Search, enabling efficient retrieval-augmented generation (RAG) and collaborative AI agents. Learn step-by-step implementation, from environment setup to agent configuration, and unlock the potential of scalable, context-aware AI solutions for complex data-driven tasks.
Sep 18, 2024
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Implementing Robust RAG Pipelines: Integrating Google's Gemma 2 (2B) Open Model, MongoDB, and LLM Evaluation Techniques
This tutorial explores building a retrieval-augmented generation (RAG) pipeline by integrating Google’s Gemma 2 (2B) model, MongoDB, and LLM evaluation techniques. Gemma 2, a lightweight model with two billion parameters, is used for efficient response generation, while MongoDB acts as the vector database, enabling semantic search for relevant documents. The tutorial demonstrates how to create an asset management assistant that analyzes market reports stored in MongoDB. It covers embedding generation, vector search, and the use of the DeepEval library to assess the relevance and faithfulness of LLM-generated responses. By combining these tools, the tutorial highlights an efficient approach to building AI-driven solutions with robust performance evaluation in a RAG pipeline.
Sep 12, 2024
