Learn Something New With Like-Minded People at MongoDB World

Mark Puddick
May 28, 2015 | Updated: June 4, 2015
#Company

At MongoDB World things don’t stop for lunch. Both days during lunch we’ll be hosting Birds of a Feather discussions with leaders from the MongoDB community on a variety of topics. These free-form discussions will be hosted by a subject expert and they will help guide the conversations.This is great opportunity not only to talk with a knowledgeable engineers but to meet with other like-minded MongoDB community members.

For Developers

We’ll cover topics such as Geo queries and Document Relation Diagrams. In addition the MongoDB driver teams will be hosting tables, including one of MongoDBs Java driver developers who will talk about data processing with Spark.

Looking to talk about Java,.NET, C, Python, PHP, Perl, NodeJS or Ruby? MongoDB’s drivers team will be hosting discussion tables for all of these. Meet and discuss your favorite driver with the people who create and maintain them.

Are you looking to roll out or interested in Adobe Experience Manager? Join other AEM users to discuss using the MongoMK microkernel for AEM’s storage.

For Ops

We’ll discuss how you can make the most of MongoDB’s support channels and resources. There will also be a table to talk about the best practice for using MongoDB with Docker. Security and regulatory requirement will discussed the the Big Data Big Problems table.

Summary

There is no need to pre-register for Birds of a Feather. Find a table of interest via the MongoDB conference app or look for the designated flags on the tables. Easy.

Download Guidebook today from Google Play and the App Store and search for "MongoDB World 2015". Use the app to learn more about the Birds of a Feather sessions and other useful information at MongoDB World. And if you haven’t registered yet, don’t delay - sign up now!

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The Top 5 Ways To Get Ready For MongoDB World

Are you coming to MongoDB World? Here's what you need to do: 1. Download the conference app We're mobile-only this year. Download Guidebook from the Google Play or App Store . Scan this image in the app with your mobile phone or search for 'MongoDB World 2015'. Use the app to build your MongoDB World agenda. Make sure to check out our [community events](http://mongodbworld.com/community). 2. Sign up for a pre-conference workshop Kick off your experience by attending one of our workshops run by MongoDB University, we have a few seats left - see full list of sessions . 3. Beat the line and pick up your badge early Registration opens Sunday, May 31 from 8:00am to 6:00pm on the hotel lobby level. You can also register on Monday, June 1 from 7:00am to 6:00pm. Please note - photo ID is required for badge pickup. Please bring this with you to registration. 4. Follow @MongoDB ! We'll be tweeting important and fun MongoDB World updates. And be sure to tweet with #MongoDBWorld leading up to and during the big event. Click to tweet : Can't wait for #MongoDBWorld! See you in NYC June 1 - 2 5. Bring your running shoes We're hosting Run & Jump with MongoDB - a run through Central Park and a high intensity jump rope workout on Tuesday. Meet us at 7:00am in the hotel lobby. If you haven't signed up yet, you're almost out of time. Register for MongoDB World below: Register for MongoDB World

May 28, 2015

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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024