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here: https://mdb.link/lvQ-EC5afIA-tutorial Code
Snippets:
https://gist.github.com/LuceCarter/2efd3ae606da16aed...
How to Build an AI Agent with Semantic Kernel, MongoDB Atlas, C# and OpenAI
If you prefer to follow a written tutorial, it can be foundhere: https://mdb.link/lvQ-EC5afIA-tutorial Code
Snippets:
https://gist.github.com/LuceCarter/2efd3ae606da16aed...
Mar 19, 2025 | 23 min
Tutorial
How to Deploy Vector Search, Atlas Search, and Search Nodes With the Atlas Kubernetes Operator
Mar 14, 2025 | 10 min read
Tutorial
How to Seamlessly Use MongoDB Atlas and IBM watsonx.ai LLMs in Your GenAI Applications
Mar 12, 2025 | 9 min read
Video
How to Build an AI Agent with Semantic Kernel, MongoDB Atlas, C# and OpenAI
Mar 19, 2025 | 23 min
Video
MongoDB vs PostgreSQL for AI Workloads: Speed, Scalability & Developer Wins Exposed!
Mar 06, 2025 | 58 min
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Video
How to Build an AI Agent with Semantic Kernel, MongoDB Atlas, C# and OpenAI
If you prefer to follow a written tutorial, it can be found here: https://mdb.link/lvQ-EC5afIA-tutorial Code Snippets: https://gist.github.com/LuceCarter/2efd3ae606da16aed1916ace5ef88595 Subscribe to MongoDB YouTube→ https://mdb.link/subscribe Have you ever wanted to write an AI Agent with Semantic Kernel? Join Developer Advocate Luce Carter in this tutorial to create a food agent to help you decide if you can cook tonight or should just go to a restaurant! Watch Getting Started with Microsoft Semantic Kernel with MongoDB Atlas in C# → https://youtu.be/qXswaD4IGUU?si=FacxfJK8PBYmtt3y Microsoft Learn Course: https://learn.microsoft.com/en-gb/training/paths/develop-ai-agents-azure-open-ai-semantic-kernel-sdk/%7C Vector Search Index Documentation: https://mdb.link/lvQ-EC5afIA-doc Visit Mongodb.com → https://www.mongodb.com Read the MongoDB Blog → https://www.mongodb.com/blog Check out the MongoDB Developer Center → https://www.mongodb.com/developerMar 19, 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.Mar 14, 2025
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Tutorial
How to Seamlessly Use MongoDB Atlas and IBM watsonx.ai LLMs in Your GenAI Applications
Learn how to build a RAG framework using MongoDB Atlas Vector Search and IBM watsonx LLMs.Mar 12, 2025
Tutorial
Building Generative AI Applications Using MongoDB: Harnessing the Power of Atlas Vector Search and Open Source Models
Learn how to build generative AI (GenAI) applications by harnessing the power of MongoDB Atlas and Vector Search.Mar 12, 2025
Video
MongoDB vs PostgreSQL for AI Workloads: Speed, Scalability & Developer Wins Exposed!
Check out our Generative AI Showcase Repository: https://github.com/mongodb-developer/GenAI-Showcase Which database are you using for AI? Comment below! 👇 Curious which database dominates AI workloads? We pit MongoDB against PostgreSQL (with PG Vector) in a head-to-head performance showdown for vector search, ingestion speed, and real-time retrieval. Discover why developers are switching for AI scalability! 🔍 What You’ll Learn: ✅ Benchmark Results: Ingestion speed, query latency, and throughput under scale (local machine tests with 100k+ vectors). ✅ Why MongoDB Shines: Out-of-the-box performance for JSON data, zero serialization overhead, and seamless scalability. ✅ PostgreSQL PG Vector Deep Dive: Configuration challenges and when it might still work. ✅ Developer Productivity: Avoid “Postgres Regress” and focus on building AI features faster. 📊 Key Takeaways: MongoDB handles 4x faster ingestion and 2x lower latency at scale. Postgres requires tuning (HNSW parameters, JSONB serialization) for AI workloads. Why latency matters for RAG, conversational AI, and real-time apps in 2024. ⏱ Timestamps: 00:00 - Intro: The AI Database Battle 02:15 - Benchmark Setup (Local Instances, 100k Vectors) 05:40 - Ingestion Speed Showdown: MongoDB vs PG Vector 12:30 - Retrieval Latency: Why Milliseconds Matter for AI 18:50 - Throughput: Queries/Second Under Load 25:00 - Developer Experience: MongoDB’s JSON Advantage 30:45 - When to Choose Postgres? Honest Takeaways 🔔 Subscribe for more AI tech deep dives → https://mdb.link/subscribe Visit Mongodb.com → https://www.mongodb.com Read the MongoDB Blog → https://www.mongodb.com/blog Check out the MongoDB Developer Center → https://www.mongodb.com/developerMar 06, 2025
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
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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
Video
Building a Semantic Search Application with MongoDB and Quarkus using Vector Search
✅ Try MongoDB 8.0 → https://mdb.link/91SzYGDmFoI ✅ Sign-up for a free cluster → https://mdb.link/91SzYGDmFoI-try ✅ Article link → https://mdb.link/91SzYGDmFoI-read - Discover how to harness the power of MongoDB's vector search capability to build a semantic search application using the Quarkus framework. In this comprehensive tutorial, we'll guide you step-by-step from understanding vector search fundamentals to implementing a functional Java application. Learn how to use Gemini AI for vector embeddings, create optimized queries, and set up your MongoDB Atlas cluster for seamless integration. Whether you're new to vector search or looking to enhance your generative AI applications, this video provides all the tools you need to get started. - 📚 Git repo: https://github.com/mongodb-developer/mongodb-vector-search-with-quarkus Resources: 📚 Vector Embeddings: https://mdb.link/91SzYGDmFoI-models 📚 Gemini AI: https://ai.google.dev/api?lang=python https://ai.google.dev/gemini-api/docs/api-key Similarity values: 📚 Euclidean: https://en.wikipedia.org/wiki/Euclidean_distance 📚 Cosine: https://en.wikipedia.org/wiki/Cosine_similarity 📚 Dot Product: https://en.wikipedia.org/wiki/Dot_productJan 21, 2025
Video
Transforming the Insurance Industry with MongoDB: Insights on AI and Data Modernization
✅ Try MongoDB 8.0 → https://mdb.link/oqBsHREc9PM-8.0 ✅ Sign-up for a free cluster → https://mdb.link/oqBsHREc9PM-try - In this episode, we sit down with industry experts to discuss how MongoDB is revolutionizing the insurance sector through data modernization and AI integration. Discover how MongoDB's document model simplifies data processing, enhances developer productivity, and reduces friction in application development. Learn about the exciting advancements in vector search and unstructured data handling that are set to transform customer experiences in insurance. Whether you're in the industry or just curious about the future of data management, this episode is packed with valuable insights and practical applications.Dec 23, 2024