Tutorials
Leverage vector embeddings alongside operational data to build scalable semantic search experiences integrated within MongoDB Atlas.- Latest
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Tutorial
DeepSeek and the Future of LLMs: Why MongoDB’s LLM-agnostic Approach Matters
Discover how DeepSeek-R1—a revolutionary open-source LLM trained with innovative reinforcement learning—challenges commercial giants like GPT-4, while MongoDB’s LLM-agnostic architecture powers a cost-efficient, real-time retrieval-augmented generation system. Learn about advanced reasoning, benchmark performance, and practical implementation steps that make this cutting-edge AI solution a game-changer in the evolving AI landscape.
Feb 01, 2025
Tutorial
Boosting AI: Build Your Chatbot Over Your Data With MongoDB Atlas Vector Search and LangChain Templates Using the RAG Pattern
Learn how to enhance your AI chatbot's accuracy with MongoDB Atlas Vector Search and LangChain Templates using the RAG pattern in our guide.
Jan 29, 2025
Tutorial
How to Deploy Vector Search, Atlas Search, and Search Nodes With the Atlas Kubernetes Operator
Learn how to deploy MongoDB Atlas Vector Search, Atlas Search, and Search Nodes using the Atlas Kubernetes Operator. This tutorial covers step-by-step instructions to integrate advanced search capabilities into Kubernetes clusters, enabling scalable, high-performance workloads with MongoDB Atlas.
Dec 20, 2024
Tutorial
How to Implement Working Memory in AI Agents and Agentic Systems for Real-time AI Applications
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Nov 18, 2024
Tutorial
Using Golang for AI
This article explains in full detail the implementation of the backend for the celebrity matching application from scratch written in Go. ✅ Sign-up for a free cluster at → https://mdb.link/free-biaEuu57mbs ✅ Get help on our Community Forums → https://mdb.link/community-biaEuu57mbs
Nov 07, 2024
Tutorial
Simplify Semantic Search With LangChain and MongoDB
Dive into semantic search with our tutorial on integrating LangChain and MongoDB. Simplify loading, transforming, embedding, and storing data.
Oct 28, 2024
Tutorial
How to Use Cohere's Quantized Vectors to Build Cost-effective AI Apps With MongoDB
Learn how to build cost-effective AI apps using Cohere's quantized vectors and MongoDB Atlas. This tutorial covers vector quantization techniques, efficient embedding storage, and optimized vector search operations. Discover how to leverage BSON encoding and int8 quantization to significantly reduce storage requirements while maintaining search accuracy. Ideal for developers looking to scale their AI applications and optimize performance in production environments.
Oct 03, 2024
Tutorial
How to Implement Databricks Workflows and Atlas Vector Search for Enhanced Ecommerce Search Accuracy
Learn how to implement Databricks Workflows and Atlas Vector Search for your Ecommerce accuracy.
Sep 18, 2024
