Why We’re Celebrating Developers (And Why We Always Will)

No matter the future, developers will build it.

This is the fundamental message for MongoDB’s Love Your Developers campaign, which we created to celebrate, recognize, and support all that developers have done and continue to do.

When the campaign launched earlier this year, it took a more historical view on how developers have shaped the history of computing from its earliest days — even before computers were invented.

We wanted to show that — even in the eras when hardware was the primary focus — programming language pioneers, like Grace Hopper, and innovative engineers, like Jerry Lawson, fundamentally shaped the future through software.

(To read more about the campaign, and why we launched it, check out our announcement blog article.)

Introducing the MongoDB Developer Spotlight Series

Just like in the past, today’s technologies aren’t developed in a vacuum. It takes entire teams of developers — experimenting, innovating, testing — to build and deploy applications that transform how we interact with the world.

That is why we’re introducing our MongoDB Developer Spotlight Series.

Our aim is to celebrate the diversity of experiences and approaches that exist among and throughout the global developer community. To do this, we will highlight stories from today’s community champions, developer advocates, newbies, senior engineers — and more.

It’s our hope that these stories will help inspire the next generation of developers who will further shape our future.

In this new series, we’ll introduce you to developers like Trina Yau.

Trina went from being a pharmacist to a software engineer at Cisco. While initially concerned about her nontraditional background, she was later told by recruiters that her unique journey was a strength, rather than a weakness. This revelation showed her that, in order to become a developer, one must only possess a curious mind. In other words, there is no singular path to working in software.

In a forthcoming post, Trina (who is also a MongoDB Community Creator) will share with us why developing isn’t just a way to earn a living, but a way to connect with people and share experiences through technology.

This series will also spotlight developers like Justin Jenkins, who is a Software Development Engineer at Pushpay and a MongoDB Community Creator. He got his start at an early age, building his own computers and coding in BASIC - before he even knew that developing was a thing.

In a future post, Justin will share tips based on his own experiences. For example, in his view, newbie developers know more than they think when it comes to getting started.

Through MongoDB’s social channels, we’ll share all of these developers’ insights, tips, advice, and origin stories.

So stay tuned. And let us know what you think. #LoveYourDevelopers

Because no matter what the future holds, it’s developers who will build it.

If you’d like to learn how you can get involved in MongoDB’s own Developer Community, head on over to our Community Forum. There, you’ll find updates, announcements, and ways to interact with other developers.

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Atlas Vector Search 在 Retool 2023 年 AI 现状调查中斩获开发者净推荐值最高分

Retool 首次发布 AI 现状报告 ，值得研读。AI 现状报告仿照广受赞誉的内部工具现状报告，采访了各个行业的软件工程业者、高管、产品经理、设计师等 1,500 多位科技人士，共同把握时代脉搏。本次调查的目的在于了解这些科技人士会如何使用并丰富人工智能 (AI)。 在本调查中，Retool 还深度挖掘了哪些工具广受好评，包括最常与 AI 搭配使用的向量数据库。调查发现， MongoDB Atlas Vector Search 不仅在净推荐值 (Net Promoter Score, NPS) 方面斩获最高分，也是应用范围第二广泛的向量数据库。值得一提的是，取得如此亮眼成绩的这款产品面世不到 5 个月。可以看出，与上市多年的竞争解决方案相比，这款产品拥有明显优势。 在本博文中，我们会探讨向量数据库的现象级崛起，以及开发者如何利用像 Atlas Vector Search 这样的解决方案来构建 AI 驱动的应用程序。我们还会介绍 Retool 报告中的其他主要亮点。 欢迎查看 AI 资源页 ，深入了解如何利用 MongoDB 构建 AI 驱动的应用程序。 向量数据库的采用率：超出预期（差不多可以这么说...） 不论是出于探索数学的好奇心，还是生成式 AI 和 LLM 背后所倚靠的超强技术，向量嵌入以及管理向量嵌入的数据库在很短时间内就取得了重大进展。 纵观过去 12 个月数据库模型的 DB-Engines 趋势 ，不难发现，在受欢迎度变化方面，向量数据库向上的势头远远超过其他数据库产品。下方图表中粉色线条“向上向右”的轨迹已经说明了一切。 屏幕截图由 DB-Engines 提供，2023 年 11 月 8 日 但问题是，向量数据库为何如此受欢迎？ 向量数据库是检索增强生成（也称为 RAG ）的核心组成部分。作为一种全新的架构模式，RAG 不仅强力整合了经过预先训练的通用 LLM 的推理能力，还能向 LLM 反馈特定于公司的实时数据。利用 RAG 构建出的 AI 驱动型应用程序能以独特方式满足企业需求，为企业打造新品，帮助企业重塑客户体验，将内部生产力和效率提升到前所未有的高度。 向量嵌入是解锁 RAG 强大功能所必需的基本组成部分之一。向量嵌入模型能对企业数据进行编码，可将文本、代码、视频、图片、音频流或表格编码为向量。之后，这些向量会被存储和索引，并从向量数据库或向量搜索引擎中进行查询，将相关输入数据作为上下文提供给选中的 LLM。这样一来，企业就能根据具有业务相关性，以及准确且可信的最新企业数据和知识，打造出 AI 驱动的应用程序。 从 Retool 调查可以看出，向量数据库领域仍大有可为。在受访者中，目前已经开始使用向量数据库的不到 20%，但是定制模型和 AI 基础设施的趋势愈演愈烈，采用率的提高指日可待。 开发者为何采用 Atlas Vector Search？ Retool 的 AI 现状调查中介绍了过去几年里的几款开拓性向量数据库，着重介绍了需要用到上下文感知语义搜索（例如产品目录或内容发现）的应用程序。 然而，开发者在使用这些向量数据库时遇到了难题，他们必须将这些数据库与其他数据库一起集成到应用程序技术堆栈中。 在应用程序技术堆栈中，每增加一个数据库层，都会增添额外的复杂性、延迟率和运维的开销。换句话说，使用该数据库时，开发者需要另行采购、学习、集成（以满足开发、测试和生产要求）、保护和验证、扩容、监控和备份。更不必说，这个过程中还需要跨这么多系统保持数据同步。 MongoDB 采用的独特方法能够完全避免遇到这些挑战： 开发者能够在同一个数据库系统中存储和搜索原生向量嵌入。 借助 MongoDB 的分布式架构，同时做到了隔离不同工作负载和让数据保持完全同步。 Search Nodes 可以提供专用计算和工作负载隔离，这对于内存密集型的向量搜索工作负载至关重要，从而实现了更高的性能和可用性。 借助 MongoDB 灵活且动态的文档模型，开发者就能够以其他数据库做不到的方式，对向量、元数据和应用程序数据之间的关系进行建模并不断演变模型。 他们能够利用可支持各种最热门编程语言的表达式 API 和驱动程序，以应用程序需要的方式处理和筛选向量和业务数据。 使用完全托管的 MongoDB Atlas 开发者数据平台 后，开发者能够达到应用程序用户所期望的规模、安全和性能目标。 这个统一方法能给开发者带来哪些优势？更短的开发周期，提供更高性能的应用程序，提供更低的延迟和更新的数据，降低了运维开销和成本。MongoDB 取得同类最高的 NPS 得分就是最佳佐证。 Atlas Vector Search 稳健、实惠，且速度出乎意料！ Kovai 的首席执行官 Saravana Kumar 谈及所在公司的 AI 助理开发工作时如此评价 欢迎阅读 利用 MongoDB 构建 AI 系列博客（前往“入门”部分查看往期内容）。您可在该部分看到 Atlas Vector Search 广泛用于各类 GenAI 驱动的应用程序，涵盖搭载聊天机器人和语音机器人的对话式 AI、智能副驾 (Copilot)、威胁情报和网络安全、合同管理、问题解答、医疗保健合规和治疗助理、内容发现和变现等领域。 MongoDB 已将关于文档的元数据存入我们的系统中。引入 Atlas Vector Search 后，我们收获了一款全面的向量元数据数据库，在过去的 10 年里经受住种种考验，满足了密集型检索需求。无需再部署一款新数据库并另行管理和学习。我们的向量和文档元数据可以直接存储在一起。 VISO TRUST 的数据和机器学习团队高级软件工程师 Pierce Lamb 可从 Retool 报告了解到 AI 现状的哪些信息？ 除了揭晓最热门的向量数据库之外，这份调查还从不同角度介绍了 AI。调查的开头部分谈到了受访者对 AI 的看法。（果然不出所料，高管比普通员工更看好 AI。）之后又谈到了投资方案、AI 对未来就业前景的影响，以及它将如何影响开发者及其未来需要具备的技能。 调查还探讨了 AI 采用率和成熟度的等级。超过 75% 的调查受访者表示，他们所在公司正努力启动 AI 项目，这其中有近一半表示项目尚处于早期阶段，且主要面向内部应用程序。调查也接着介绍了这些应用程序，以及在受访者眼中，这些应用程序能给企业带来的实际意义。调查发现，不论是否得到允许，几乎所有人都会在工作中使用 AI，然后，调查还借此识别出了使用 AI 时的主要痛点。毫无悬念的是，模型准确性、安全性和幻觉位列榜首。 调查的结尾部分探索了已投入使用的主要模型。同样在意料之中的是，Open AI 的产品/服务遥遥领先，但调查同样指明，为满足未来定制需求，将开源模型与 AI 基础设施搭配使用的意愿日趋明显。 若要深入了解本次调查的详细内容，请 阅读报告 。 开始使用 Atlas Vector Search 想了解 Vector Search 产品/服务？请访问 Atlas Vector Search 产品页面 。此页面罗列了各种教程、文档和关键 AI 生态系统集成的链接，方便您直接探索如何构建 genAI 驱动的应用程序 。 若要深入探索 Vector Search 能够触及哪些更高级的可能性，请下载 嵌入生成式 AI 白皮书，也可以通过我们的文档 《构建 2024 年 AI 路线图》 了解不同的 AI 用例，以及企业如何进行变革来支持 AI！

November 13, 2023
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How the NFSA is Using MongoDB Atlas and AI to Make Aussie Culture Accessible

Where can you find everything from facts about Kylie Minogue, to more than 6,000 Australian home movies, to a 60s pop group playing a song with a drum-playing kangaroo ? The NFSA! Founded in 1935, the National Film and Sound Archive of Australia (NFSA) is one of the oldest archives of its kind in the world. It is tasked with collecting, preserving, and sharing Australia’s audiovisual culture. According to its website, the NFSA “represents not only [Australia’s] technical and artistic achievements, but also our stories, obsessions and myths; our triumphs and sorrows; who we were, are, and want to be.” The NFSA’s collection includes petabytes of audiovisual data—including broadcast-quality news footage, TV shows, and movies, high-resolution photographs, radio shows, and video games—plus millions of physical and contextual items like costumes, scripts, props, photographs, and promotional materials, all tucked away in a warehouse. “Today, we have eight petabytes of data, and our data is growing from one to two petabytes each year,” said Shahab Qamar, software engineering manager at NFSA. Making this wealth of data easily accessible to users across Australia (not to mention all over the world) has led to a number of challenges, which is where MongoDB Atlas—which helps developers simplify and accelerate building with data—comes in. Don’t change (but apply a few updates) Because of its broad appeal, the NFSA's collection website alone receives an average of 100,000 visitors each month. When Qamar joined the NFSA in 2020, he saw an opportunity to improve the organization’s web platform. His aim was to ensure the best possible experience for the site’s high number of daily visitors, which had begun to plateau. This included a website refresh, as well as addressing technical issues related to handling site traffic, due to the site being hosted on on-premises servers. The site also wasn’t “optimized for Google Analytics,” said Qamar. In fact, the NFSA website was invisible to Google and other search engines, so he knew it was time for a significant update, which also presented an opportunity to set up strong data foundations to build deeper capabilities down the line. But first, Qamar and team needed to find a setup that could serve the needs of the NFSA and Australia’s 26 million residents more robustly than their previous solution. Specifically, Qamar said, the NFSA was looking for a fully managed database that could also implement search at scale, as well as a system that his small team of five could easily manage. It also needed to ensure high levels of resiliency and the ability to work with more than one cloud provider. The previous NFSA site also didn’t support content delivery networks , he added. MongoDB Atlas supported all of the use cases the NFSA was looking for, Qamar said, including the ability to support multi-cloud hosting. And because Atlas is fully managed, it would readily meet the NFSA's requirements. In July 2023, after months of development, the new and greatly improved NFSA website was launched in July 2023. The redesign was immediately impactful: Since the NFSA’s redesigned site was launched, the number of users visiting the collection search website has gone up 200%, and content requests—which the NFSA access team responds to on a case-by-case basis—have gone up 16%. (Getting search) back in black While the previous version of the NFSA site included search, the prior functionality was prone to crashing, and the quality of the results was often poor, Qamar said. For example, search results were delivered alphabetically rather than based on relevance, and the previous search didn’t support fine-tuning of relevance based on matches in specific fields. So, as part of its site redesign, the NFSA was looking to add full text search, relevance-based search results, faceting, and pagination. MongoDB Atlas Search —which integrates the database, search engine, and sync mechanism into a single, unified, fully managed platform—ticked all of those boxes. A search results page on the NFSA website Indeed, the NFSA compared search results from its old site to its new MongoDB Atlas site and “found that MongoDB Atlas-based searches were more relevant and targeted,” Qamar said. Previously, configuring site search required manual coding and meant downtime for the site, he noted. “The whole setup wasn’t very developer friendly and, therefore, a barrier to working efficiently with search configuration and fine-tuning,” Qamar said. In comparison, MongoDB Atlas allowed for simple configuration and fine-tuning of the NFSA's search requirements. The NFSA has also been using MongoDB Atlas Charts . Charts help the NFSA easily visualize its collection by custom grouping (like production year or genre), as well as helping the NFSA see which items are most popular with users. “Charts have helped us understand how our collection is growing and evolving over time,” Qamar said. NFSA’s use of MongoDB Charts Can’t get you (AI) out of my head Now, the NFSA—inspired by Qamar’s own training in machine learning and the broad interest in all things AI—is exploring how it can use Atlas Vector Search and generative AI tools to allow users to explore content buried in the NFSA collection. One example cited is putting transcriptions of audiovisual files in NFSA’s collection into a vector database for retrieval-augmented generation (RAG). The NFSA has approximately 27 years worth—meaning, it would take 27 years to play it all back—of material to transcribe, and is currently developing a model to accurately capture the Australian dialect so the work is transcribed correctly. Ultimately, the NFSA is interested in building a RAG-powered AI bot to provide historically and contextually accurate information about work in the NFSA’s archive. The NFSA is also exploring how it can use RAG to deliver accurate, conversation-like search results without training large language models itself, and whether it can leverage AI to help restore some of the older videos in its collection. Qamar and team are also interested in vectorizing audio-visual material for semantic analysis and genre-based classification of collection material at scale, he said. “Historically, we’ve been very metadata-driven and keyword-driven, and I think that’s a missed opportunity. Because when we talk about what an archive does, we archive stories,” Qamar said of the possibilities offered by vectors. “An example I use is, what if the world ended tomorrow? And what if aliens came to Earth and only saw our metadata, what image of Australia would they see? Is that a true image of what Australia is really like?” Qamar said. “How content is described is important, but content’s imagery, the people in it, and the audio and words being spoken are really important. Full-text search can take you somewhere along the way, but vector search allows you to look things up in a semantic manner. So it’s more about ideas and concepts than very specific keywords,” he said. If you’re interested in learning how MongoDB helps accelerate and simplify time-to-mission for federal, state, and local governments, defense agencies, education, and across the public sector, check out MongoDB for Public Sector . Check out MongoDB Atlas Vector Search to learn more about how Vector Search helps organizations like the NFSA build applications powered by semantic search and gen AI. *Note that this story’s subheads come from Australian song titles!

May 14, 2024