# AI Agents Memory System + Practical Agents w Gemini Embedding 2 Simplified RAG Search w Rich Context (Find document from image) **Date de l'événement :** 02/06/2026 * Publié le 02/06/2026 ### Date 02/06/2026 ### Galerie d'image ![1.jpg](https://firebasestorage.googleapis.com/v0/b/memory-ai.appspot.com/o/prod%2FrKxsdSTpqCfzIFY8Y2hg%2FprojectsMedias%2F4rjljJFql99Wvs4UiEN1%2Fthumbs%2F1_1600x900.png?alt=media&token=b826c43e-d01f-46b3-937c-fd1b0c845c19) ### Ville `#Singapore` ## Description Welcome back to our meetup in June. The date is right before the June holidays. (If this is your first time coming to the venue, please scroll down to the bottom to find the instructions for getting to the venue.) 06:00 PM Registration, dinner, networking 06:30 PM Talk starts If you want to have practical AI Agents that can productively search and find relevant rich content for you, this event is for you. If you want to have practical AI Agents that remember with a memory system, this event is for you. Dinner will be actual food, with real protein like actual beef or chicken in it, not random baloney pizza. \--- Other community news: 1\. We are also planning to hold a social Pickleball event in Jun/Jul/Aug sponsored by Mongo and open to all previous attendees of previous Mongo community events. A separate invite and event page will be sent out later. 2\. We may also do something cool for F1 weekend in Oct for all previous attendees of previous Mongo community events. \--- Talk 1: Practical AI from Gemini Embedding 2 for Simplified RAG Search with Rich Context and Less Preprocessing - Imagine the possibilities if you could find a document using an image, or find audio from a report, or find a video with a sentence. The 1st speaker will customize the talk to be relevant for both non-technical people (from a management decision POV) and technical people (from an implementation POV). \- Simplifies RAG Systems (one multimodal retrieval pipeline instead of multiple fragmented systems) \- Less preprocessing (e.g. no transcription needed) less infrastructure complexity \- Richer context (text + visuals + audio) \- A major step from “document chatbots” to truly context-aware AI systems Gemini Embedding 2 changes what Retrieval-Augmented Generation (RAG) can fundamentally do. Instead of treating text, images, audio, video, and documents as separate systems, AI can now retrieve and reason across all of them together in one shared understanding layer. This means users can search videos with natural language, retrieve diagrams from spoken conversations, match screenshots to documentation, or ground AI agents with real-world multimodal context — capabilities that were previously complex or unreliable without multiple specialized pipelines. Talk 2: Designing Memory Systems for AI Agents AI agents need memory to maintain context across sessions, learn from experience, and handle long-running tasks. The challenge? Deciding what to remember, where to store it, and how to retrieve it when it matters. In this workshop, you'll learn a practical framework for architecting memory systems that actually work in production. We'll cover: \- Types of memory in agentic systems \- Storage patterns: Where to persist memories and how to structure them for retrieval \- Retrieval strategies: Combining vector search with metadata, recency, and other signals \- Memory lifecycle: When to create, update, or prune memories to keep your system performant You'll apply this framework by building memory into an AI agent and seeing how different design choices impact behavior. You will be provided with all the resources required, including Jupyter Notebook templates. At the end of the workshop, you will also be able to check your learning and earn a skill badge to share with your network! https://learn.mongodb.com/courses/memory-for-ai-applications \--- Future Talk: TBA Awaiting confirmation for this Future Talk: Federated Layer to Manage a Multi-Agent Synthetic Workforce Agents are starting to act as workers in real production systems, and workers need a management layer. The question everyone should take back to your laptop: in my own agent stack, what happens on the second concurrent write, and can I prove, today, under whose authority my agent acted? How do I manage my synthetic workforce in production. \--- Special Thanks: \- To SQ Collective for the venue \- This is the SQ Collective calendar that you can subscribe to: https://luma.com/Ai-labs \- This is the Technology calendar that you can subscribe to: https://luma.com/calendar/cal-ZrFfXqC7PgzbBaQ - It will include tech events from tech unicorn startups such as Amplitude, Databricks, Elastic, MongoDB, and more \--- We will be giving wrist bands to attendees. When coming, please make sure to have your Luma account or your Meetup account ready on your laptop web browser, or on the respective mobile app. \--- Tags: Technology, AI Agents, Tool-Calling, Database, NoSQL, MongoDB \--- Update: In the later part of May, we will send out a question for you to indicate your food preferences (chicken, or beef, or not meat) which will affect the food ordering for the dinner. So please keep a lookout for it and respond to it when it is sent :) **Lien de l'évènement :** [https://luma.com/event/evt-vULx7qIfgwORXqm](https://luma.com/event/evt-vULx7qIfgwORXqm) ### Pays `#Singapore` ### Continent `#Asia` **Médias associés :** [Média 1](https://80954c1d.sibforms.com/serve/MUIFABojU8UBbDiX_TdcGa7Wv5VMoVB_nBZ92mkLkGlS1pJLpP7s-pVJusyN-7cG9KPrSuv3fv7TmXwuw_AoyNUShR8jZhmNDgUbZPJO2V5xYXlNz4YXOTjSb8X7Lj7PRIPzgzEWlLbA4f4uw_F8RM51EUsjSfQQko0qaby98GHMdYJVWLIXd5JzzaXBGmqN2CcYOFuqnbnaYEnw) ## event_id evt-vULx7qIfgwORXqm@events.lu.ma ### Outils `#MongoDB` `#Gemini Embedding 2` `#Jupyter Notebook` `#Amplitude` `#Databricks` `#Elasticsearch` ### Selection `#Agents` --- ### Navigation pour IA - [Index de tous les contenus](https://ai-memory.io/llms.txt) - [Plan du site (Sitemap)](https://ai-memory.io/sitemap.xml) - [Retour à l'accueil](https://ai-memory.io/)