Vector Search
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Video
Navigating the Challenges of Building Centralized gen AI Tooling
The line between “super doable” and “super hard” isn’t always obvious when bringing AI to production. In the latest episode of our new People Who Ship series, Apoorva Joshi sits down with MongoDB’s John Ziegler to explore how his team built internal generative AI tools like Central RAG and MongoGPT and how they navigated challenges like permission-aware data, integrating diverse sources (think Google Drive and Zendesk!), and making it all work using MongoDB Atlas.Jun 30, 2025
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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
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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_product
Jan 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
Video
What does Aperol Spritz have to do with MongoDB??
Let's find the closest Aperol spritz to you with MongoDB geospatial queries and Atlas Vector Search.
Dec 12, 2024
Video
Insights on Vector Search and Quantization for Developers and AI Enthusiasts
✅ Try MongoDB 8.0 → https://mdb.link/Nu70pFr88Cc-8.0 ✅ Sign-up for a free cluster → https://mdb.link/Nu70pFr88Cc-try - In this episode of the MongoDB Podcast, we dive deep into the exciting advancements in vector search technology. Join us as we discuss the recent launch of quantization capabilities and how they can significantly reduce costs while enhancing performance. Discover the implications for developers and customers alike, and learn about the future of AI integration within MongoDB. Whether you're a seasoned developer or just starting out, this episode is packed with valuable insights to help you leverage the benefits of vector search in your applications.
Dec 02, 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
Video
Trader Joe's Fall Faves Party Planner with Playwright, LlamaIndex, and MongoDB Atlas Vector Search
✅ LlamaIndex and MongoDB Atlas Vector Search Integration - https://mdb.link/H4NkAvksbR8-int ✅ Developer Forums - https://mdb.link/H4NkAvksbR8-forums - In this tutorial, we create a Trader Joe’s AI party planner using Playwright to scrape our fall faves and the LlamaIndex/Atlas Vector Search integration to build a chatbot to answer questions about our items! - ✅ Playwright Documentation - https://playwright.dev/ ✅ Gen AI Showcase - https://github.com/mongodb-developer/GenAI-Showcase/blob/main/README.md
Nov 12, 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
Video
What is Vector Search and How Do Vector Databases Work?
A vector database is a type of data storage solution that manages and searches large amounts of high-dimensional numerical data (also known as vectorised data). Learn more about vector search on Developer Center: https://mdb.link/guide-rag
Nov 07, 2024
Video
Unlocking the Power of Vector Search: Transforming Data into Insights
✅ Try MongoDB 8.0 → https://mdb.link/MsbuUTw0ciI ✅ Sign-up for a free cluster → https://mdb.link/MsbuUTw0ciI-register ✅ Subscribe to MongoDB YouTube→ https://mdb.link/subscribe - Join us for an engaging conversation about the transformative potential of vector search in data management! In this video, we delve into the innovative features of Superlinked, a platform that simplifies the integration of machine learning into various applications. Discover: The importance of vector search in utilizing unstructured data effectively. Practical applications in fields like e-commerce and finance. How the collaboration between Superlinked and MongoDB enhances data querying and personalized recommendations. Strategies for managing complex data without needing extensive machine learning expertise. We’ll explore the future of machine learning infrastructure and how you can leverage it for your projects.
Nov 04, 2024