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MONGODB ATLAS ONCE AGAIN VOTED MOST LOVED VECTOR DATABASE

Rachelle Palmer
June 21, 2024 | Updated: September 17, 2024
#Vector Search

The 2024 Retool State of AI report has just been released, and for the second
year in a row, MongoDB Atlas was named the most loved vector database. Atlas
Vector Search received the highest net promoter score (NPS), a measure of how
likely a user is to recommend a solution to their peers.

This post is also available in: Deutsch, Français, Español, Português, Italiano,
한국어, 简体中文.

Interested in discovering how to leverage AI to boost productivity, streamline
development, and solve real engineering challenges? Check out our on-demand
webinar with Retool to learn more.

The Retool State of AI report is a global annual survey of developers, tech
leaders, and IT decision-makers that provides insights into the current and
future state of AI, including vector databases, retrieval-augmented generation
(RAG), AI adoption, and challenges innovating with AI.

MongoDB Atlas commanded the highest NPS in Retool’s inaugural 2023 report, and
it was the second most widely used vector database within just five months of
its release. This year, MongoDB came in a virtual tie for the most popular
vector database, with 21.1% of the vote, just a hair behind pgvector
(PostgreSQL), which received 21.3%.

The survey also points to the increasing adoption of RAG as the preferred
approach for generating more accurate answers with up-to-date and relevant
context that large language models (LLMs) aren't trained on. Although LLMs are
trained on huge corpuses of data, not all of that data is up to date, nor does
it reflect proprietary data. And in those areas where blindspots exist, LLMs are
notorious for confidently providing inaccurate "hallucinations." Fine-tuning is
one way to customize the data that LLMs are trained on, and 29.3% of Retool
survey respondents leverage this approach. But among enterprises with more than
5,000 employees, one-third now leverage RAG for accessing time-sensitive data
(such as stock market prices) and internal business intelligence, like customer
and transaction histories.

This is where MongoDB Atlas Vector Search truly shines. Customers can easily
utilize their stored data in MongoDB to augment and dramatically improve the
performance of their generative AI applications, during both the training and
evaluation phases.

In the course of one year, vector database utilization among Retool survey
respondents rose dramatically, from 20% in 2023 to an eye-popping 63.6% in 2024.
Respondents reported that their primary evaluation criteria for choosing a
vector database were performance benchmarks (40%), community feedback (39.3%),
and proof-of-concept experiments (38%).

One of the pain points the report clearly highlights is difficulty with the AI
tech stack. More than 50% indicated they were either somewhat satisfied, not
very satisfied, or not at all satisfied with their AI stack. Respondents also
reported difficulty getting internal buy-in, which is often complicated by
procurement efforts when a new solution needs to be onboarded. One way to reduce
much of this friction is through an integrated suite of solutions that
streamlines the tech stack and eliminates the need to onboard multiple unknown
vendors. Vector search is a native feature of MongoDB's developer data platform,
Atlas, so there's no need to bolt on a standalone solution. If you're already
using MongoDB Atlas, creating AI-powered experiences involves little more than
adding vector data into your existing data collections in Atlas.

If you're a developer and want to start using Atlas Vector Search to start
building generative AI-powered apps, we have several helpful resources:

 * Learn how to build an AI research assistant agent that uses MongoDB as the
   memory provider, Fireworks AI for function calling, and LangChain for
   integrating and managing conversational components.

 * Get an introduction to LangChain and MongoDB Vector Search and learn to
   create your own chatbot that can read lengthy documents and provide
   insightful answers to complex queries.

 * Watch Sachin Smotra of Dataworkz as he delves into the intricacies of scaling
   RAG (retrieval-augmented generation) applications.

 * Read our tutorial that shows you how to combine Google Gemini's advanced
   natural language processing with MongoDB, facilitated by Vertex AI Extensions
   to enhance the accessibility and usability of your database.

 * Browse our Resources Hub for articles, analyst reports, case studies, white
   papers, and more.

Interested in discovering how to leverage AI to boost productivity, streamline
development, and solve real engineering challenges? Check out our on-demand
webinar with Retool to learn more.

Want to find out more about recent AI trends and adoption? Read the full 2024
Retool State of AI report.

Head over to our quick-start guide to get started with Atlas Vector Search
today.


← Previous


ATLAS VECTOR SEARCH 再次被评为最受欢迎的矢量数据库

Retool 的“2024 年 AI 现状”报告刚刚发布，MongoDB Atlas Vector Search 连续第二年被评为最受欢迎的矢量数据库。
Atlas Vector Search 获得了最高净推荐值 (NPS)，该值用于衡量用户向同伴推荐解决方案的可能性。 Retool 的“AI
现状”报告是对开发者、技术领导者和 IT 决策者进行的全球年度调查，提供了对 AI 的当前和未来状态的洞察，包括矢量数据库、 检索增强生成 (RAG) 、AI
采用情况和使用 AI 创新的挑战。 MongoDB Atlas Vector Search 在 Retool 的 2023 年首份报告中获得了最高
NPS，并且在发布后仅五个月内就成为第二广泛使用的矢量数据库。今年，Atlas Vector Search 以 21.1%
的得票率并列成为最受欢迎的矢量数据库，仅次于获得 21.3% 投票率的 pgvector（PostgreSQL）。 该调查还指出，人们越来越多地采用 RAG
作为在大型语言模型 ( LLM ) 未受过训练的最新相关背景下生成更准确回答的首选方法。虽然 LLM
是在庞大的数据语料库中训练出来的，但并非所有数据都是最新的，也不能反映专有数据。在那些存在盲点的领域，LLM
因自信地提供不准确的“幻觉”而臭名昭著。微调是自定义 LLM 训练数据的一种方式，29.3% 的 Retool 调查受访者利用这种方法。但是，在拥有超过
5,000 名员工的企业中，现在有三分之一的企业利用 RAG 来访问时间敏感的数据（例如股市价格）和内部商业情报，例如客户和事务历史记录。 这是 MongoDB
Atlas Vector Search 真正大放异彩的地方。在训练和评估阶段，客户可以轻松地利用他们在 MongoDB 中存储的数据来增强和显著改善其生成式
AI 应用程序的性能。 在一年的时间里，Retool 调查受访者的矢量数据库利用率急剧上升，从 2023 年的 20% 上升到 2024 年的
63.6%，令人瞠目。受访者表示，他们选择矢量数据库的主要评估标准是性能基准 (40%)、社区反馈 (39.3%) 和概念验证实验 (38%)。
该报告明确强调的痛点之一是 AI 技术堆栈的困难 。超过 50% 的受访者表示，他们对自己的 AI
堆栈比较满意、不太满意或完全不满意。受访者还表示，在获得内部支持方面存在困难，而在需要采用新解决方案时，采购工作往往会使这一问题变得更加复杂。减少这种摩擦的一种方法是通过一套集成的解决方案，简化技术堆栈，并消除加入多个未知供应商的需要。矢量搜索是
MongoDB 的开发者数据平台 Atlas 的原生功能，因此无需依赖独立的解决方案。如果您已经在使用 MongoDB Atlas ，创建 AI
驱动的体验只需将矢量数据添加到 Atlas 现有的 collection 中即可。 如果您是开发者，并想要开始使用 Atlas Vector Search
构建生成式人工智能应用程序，我们提供以下几个有用资源： 了解如何 构建一个 AI 研究助手代理，该代理使用 MongoDB 作为内存提供商、Fireworks
AI 进行函数调用以及 LangChain 集成和管理会话组件。 了解 LangChain 和 MongoDB Vector Search
，并学习创建自己的聊天机器人，该机器人可以阅读长篇文档并为复杂的查询提供深刻的回答。 观看 Dataworkz 公司的 Sachin Smotra 深入探讨
RAG（检索增强生成）应用扩展的复杂性。 阅读我们的教程 ，了解如何在 Vertex AI 扩展的支持下将 Google Gemini 的高级自然语言处理与
MongoDB 相结合，从而增强数据库的可访问性和可用性。 浏览我们的资源中心 ，获取文章、分析报告、案例研究、白皮书等。 想要进一步了解 AI
的最新趋势和采用情况？ 阅读 Retool 的“2024 年 AI 现状”完整报告 。

June 21, 2024
Next →


STAFF ENGINEERING AT MONGODB: YOUR PATH TO MAKING BROAD IMPACT

Andrew Whitaker is a Senior Staff Engineer at MongoDB. His previous experience
spans tiny startups to enormous organizations like AWS, where he held several
different roles focusing on databases. Before joining MongoDB, he worked at a
startup building optimized machine learning models in the cloud. Read on to
learn more about why Andrew decided to join MongoDB in a senior-level
engineering role and how his work is driving improvement within our engineering
organization. Why MongoDB I have long been a fan of MongoDB’s products and
services. MongoDB the database has always been a pleasure to work with – the
system “brings joy” to quote a phrase. As a Python developer, I appreciate how
the Python driver feels “Pythonic” in a completely natural way. The programmer
interacts with the database using Python constructs: dictionaries, lists, and
primitive types. By contrast, SQL databases force me to change my mental model,
and the query language feels like an add-on that does not blend with the core
language. As an engineer, I am always looking to expand my knowledge and grow my
skills. The scope of challenges engineers face at MongoDB is what triggered my
interest in the company. We obviously have people working on core databases and
distributed systems. But, we also have teams dedicated to machine learning,
streaming data, analytics, networking, developer tooling, drivers, and many more
areas. It is very hard to get bored working at MongoDB. Finally, I would be
remiss if I did not mention the people. Overall, MongoDB’s engineering culture
prioritizes intelligence, low ego, and an ability to get stuff done. CL/CI
(Continuous Learning, Continuous Improvement) Working at MongoDB has provided me
with opportunities for continued learning and growth. Though I do not program as
much as I did earlier in my career, I have recently been exploring the Rust
language. I’m excited by Rust because it avoids the tradeoffs between
predictable performance and safety. My work in the search space has given me
exposure to the fast moving world of AI: vector embeddings, RAG, etc. For
various reasons, I think MongoDB is uniquely positioned to do well in this area.
On top of this, I’m working on some initiatives that are not fully public. I can
say that one focus area is improving the sharding experience for our customers.
We believe MongoDB sharding is best-in-breed. Still, the process requires more
manual configuration than we think is ideal: customers select the shard key,
cluster type, shard count, etc. We give guidance here, but I think we can raise
the bar in terms of offering a seamless experience with less “futz”. I’m also
working with the search team. We believe there is a natural affinity between
MongoDB’s document model and AI/ML workloads. We have some features in the works
that extend this integration in new and interesting ways. I also spend a fair
bit of time driving quality improvements across our suite of products. Our CTO
Jim Scharf frequently refers to our “ big 4 ” goals: security, durability,
availability, and performance. These goals are more important than any feature
we build. I’ve been working across the company to help teams define their
availability SLO/SLAs. It turns out that measuring availability is a subtle
topic. For example, a naive approach of counting the percentage of failed
requests can underestimate downtime because customers make fewer requests when a
service is unavailable. So, the first step is to clarify the definition of
availability. Finally, as a lapsed academic (in a distant life, I was a graduate
student at the University of Washington Department of Computer Science and
Engineering), I’m always interested in finding ways to bridge theory and
practice. I’ve been collaborating with some folks in our research team to drive
improvements to our replication protocols. There are theoretical results that
suggest it is impossible to simultaneously achieve low latency and strong
consistency (“linearizability” in the technical jargon). However, we believe
there are intermediate points in the consistency/latency spectrum that have not
been fully explored. This work hasn't been made into a product yet, but stay
tuned. Flexible working MongoDB is a hybrid company. Like many of our engineers,
I work outside the company headquarters in New York City (I live in Seattle). I
appreciate MongoDB’s approach to hybrid working and that company leadership,
starting with Dev , cares about the well-being of their employees. It seems
there are companies that don’t seem to trust their employees to make decisions,
such as which days to come into the office, so I’m thankful for the autonomy I
receive at MongoDB to work in a way that’s best for me. Remote work has its
challenges, but I would say that the benefit for my work/life balance has been
transformative. Final thoughts I have found MongoDB engineers demonstrate a
strong mix of technical depth, pragmatism, and empathy. I have yet to find the
“smart jerk” prototype that seems to exist throughout the tech industry.
Overall, I have found MongoDB is open to change and growth at both the team
level and the individual level. There is a willingness to evolve and improve
that aligns with the company’s values and leadership principles and enables the
success of our technology and people. Find out more about MongoDB culture and
career opportunities by joining our talent community .

November 20, 2024



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