Samsung SmartHome 团队利用 MongoDB Atlas 为工程师真正实现工作与生活的平衡

MongoDB
January 23, 2024 | Updated: February 8, 2024

从初创企业到全球知名企业，不论企业规模如何，都需要因应时代需求，打造及部署各类现代化应用程序，才能够在当下瞬息万变的数字环境中争得一席之位。作为这些应用程序核心的开发人员，他们要面对的挑战是如何让自己开发的应用程序紧跟最新技术发展趋势，以及如何同时提升自身的工作效率。

2023 年 9 月在首尔举办的 MongoDB.local Seoul 活动期间，三星电子 (Samsung Electronics) 数字家电 (Digital Appliances, DA) 事业部的 Sungbin Lim 做了一场演讲，主题为“MongoDB Atlas 为加班加点的开发运维工程师带来工作与生活的平衡 (MongoDB Atlas brings work-life balance to evenings of DevOps engineer)”。在演讲中，Sungbin Lim 分享了他的团队使用 MongoDB 后工作效率得到的真实转变。如需观看完整视频，请点击此处（韩语）：

MongoDB Atlas 打造高效的家电数据管理

三星电子 DA 事业部以三星的 SmartThings 为重心，借助最新 AI 技术和物联网 (IoT)，为支持各类服务而开发一系列采用 AI 技术的家用电器和技术。

SmartHome 服务是 DA 事业部最早及规模最大的服务器平台，会收集三星家用电器的设备数据，供事业部用于开发各种数据驱动型服务。

Samsung SmartHome 服务早在 2014 年就已推出，其作为用户和设备档案管理服务器，可用于借助移动应用程序来控制和监控三星家用电器，并通过 MySQL 数据库管理应用程序数据。

삼성전자 DA 사업부 임성빈 프로 — 三星电子 DA 事业部 Sungbin Lim

2015 年，团队搭建了基础平台来收集由家用电器生成的实时事件数据，并将数据用于打造数据驱动型服务，例如将数据用于训练 AI 服务。

团队还引进了社区版的 MongoDB，利用这款文档模型来灵活处理和利用 JSON 格式的应用程序数据工作负载，在开发工作负载的重要环节发挥 NoSQL 的强大作用。

为拓展服务打下坚实基础

团队一开始使用 MongoDB 时，社区版 MongoDB 的运行过程很顺畅。但随着家用电器数量以及与服务器通信的流量的持续增加，团队逐渐遇到挑战，难以确保服务可靠运行。

尤其是在盛夏时节，空调、冰箱等家用电器在这几个月的频繁使用，导致数据在某个时段集中流向服务器，使问题更加复杂。

虽然社区版 MongoDB 能够依开发人员的意愿而进行调整，但要享受这个优势，还需要开发人员在问题出现时，投入时间管理及操作数据库。

此外，在公司扩大服务范围的过程中，开发团队意识到有必要夯实技术根基，才能够更好地利用 MongoDB、灵活的集群以及经过强化的存储和监控功能。2022 年，三星电子 DA 事业部改为完全采用 MongoDB Atlas，并借其更高效地运作 SmartHome 服务。

考虑到各个选项的限制和停机时间之后，三星的 DA 事业部制定了迁移策略，即，利用 Mongomirror 实用工具将数据迁移到专用网络。

“对于 Samsung SmartHome 来说，能够在基础设施管理和业务逻辑开发之间维持平衡十分重要。迁移到 MongoDB Atlas 后，我们得以卸下基础设施管理的负担，聚焦应用程序的开发和创新。”主导迁移项目的 Sungbin Lim 表示。

“MongoDB Atlas 的平均响应时间缩短了超过一半，从8毫秒缩短到3毫秒；磁盘读取延迟从3秒降至18毫秒，这样可用性就得以大幅提高，”Sungbin Lim 强调，“作为开发人员，我们惊喜地发现迁移到 MongoDB Atlas 后，我们没有再因为数据库处理故障而碰到任何问题或警告。”

因此，在为 Samsung SmartHome 服务的扩展提供坚实基础方面，MongoDB Atlas 发挥了重要的作用，让三星团队能够轻松处理更多数据流量。

开发人员工作效率和服务质量双丰收

从本质上来说，三星电子的 SmartHome 开发团队所管理的写入密集型工作负载，持续更新设备的每个状态变化，例如空调的传感器变化（温度、湿度等）、冰箱的开和关。

与 MongoDB 韩国团队一同分析关键绩效指标后，DA 事业部得出结论：基于现有副本集的横向扩展策略将无法快速更新或管理不断增加的流量和工作负载高峰（尤其是在盛夏几个月期间）。

迁移到 MongoDB Atlas 所体验到的大幅改进让三星电子的 DA 事业部设想了新的横向扩展策略，即，利用分片集群来摆脱现有副本集存在的写入工作负载限制。

“与 MongoDB 韩国团队合作进行研讨会和概念验证 (PoC) 之后，我们得以提前识别问题并完成了分片架构部署，”Sungbin Lim 表示，“我们设置了时间点恢复 (PITR)，因此能够在分片过程中出错时高效恢复数据。”

分片之后的平衡效果得到改善，因为每个分片分配了 300-400 个节点连接，而之前的副本集则是大约 800 个连接，MongoDB 的逻辑数据大小从 82 GB 缩小到大约 42 GB，能够方便高效执行工作负载。磁盘 IOPS 同样得以减少近一半，从之前的 16-20k/s 减少到 8-10k/s，提高了磁盘利用率。

借助 MongoDB Atlas 实现数据库零中断和高可用性，三星电子 DA 事业部的开发运维工程师体验到了工作与生活之间的平衡，从而专注于为客户开发新服务。

通过迁移到 MongoDB Atlas 及执行分片架构，三星电子的 DA 事业部构建了高效且可靠的基础设施，它计划在未来继续与 MongoDB 韩国团队合作以更高效地管理数据工作负载。

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Building AI with MongoDB: How Devnagri Brings the Internet to 1.3 Billion People with Machine Translations

It was while on a trip to Japan that Himanshu Sharma — later to become CEO of Devnagri — made an observation that drew parallels with his native India. Despite the majority of Japan’s population not speaking English, they were still well served by an internet that was largely based on the English language. Key to doing this was translation, and specifically the early days of automated machine translation. And so the idea to found Devnagri , India’s first AI-powered translation platform, was born. “In India, 90% of the population are not fluent in English. That is close to 1.3 billion people . We wanted to bridge this gap to make it easy for non-English speakers to access the internet in their native languages. There are more than 22 Indian languages in use, but they represent just 0.1% of data on the internet,” says Sharma. “We want to give people the same access to knowledge and education in their native languages so that they can be part of the digital ecosystem. We wanted to help businesses and the government reach real people who were not online because of the language barrier.” Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Figure 1: Devnagri’s real time translation engine helps over 100 Indian brands connect with their customers over digital channels for the first time Building India’s first machine translation platform Sharma and his team at Devnagri have developed an AI-powered translation platform that can accept multiple file formats from different industry domains. Conceptually it is similar to Google Translate. Rather than a general consumer tool, it focuses on the four key industries that together make the largest impact on the everyday lives of Indian citizens: e-learning, banking, e-commerce, and media publishing. Devnagri provides API access to its platform and a plug-and-play solution for dynamically translating applications and websites. As Sharma explains, “Our platform is built on our own custom transformer model based on the MarianNMT neural machine translation framework. We train on corpuses of content in documents, chunking them into sentences and storing them in MongoDB Atlas . We use in-context learning for training, which is further augmented with reinforcement learning from human feedback (RLHF) to further tune for precise accuracy.” Sharma goes on to say, “We run on Google Vertex AI, which handles our MLops pipeline across both model training as well as inferencing. We use Google Tensor Processing Units (TPUs) to host our models so we can translate content — such as web pages, PDFs, documentation, web and mobile apps, images, and more — for users on the fly in real-time.” While the custom transformer-based models have served the company well, recent advancements in off-the-shelf models is leading Devnagri’s engineers to switch. They are evaluating a move to OpenAI GPT-4 and the Llama-2-7b foundation models, fine-tuned with the past four years of machine translation data captured by Devnagri. Why MongoDB? Flexibility and performance MongoDB is used as the database platform for Devnagri’s machine translation models. For each sentence chunk, MongoDB stores the source English language version, the machine translation, and if applicable, the human-verified sentence translation. As Sharma explains, “We use the sentences stored in MongoDB to train our models and support real-time inference. The flexibility of its document data model made MongoDB an ideal fit to store the diversity of structured and unstructured content and features our ML models translate.” We also exploit MongoDB’s scalable distributed architecture. This allows our models to parallelize read and write requests across multiple nodes in the cloud, dramatically improving training and inference throughput. We get faster time to market with higher quality results by using MongoDB. Himanshu Sharma, Devnagri co-founder and CEO What's next? Today Devnagri serves over 100 brands and several government agencies in India. The company has also joined MongoDB’s AI Innovators Program . The program provides its data science team with access to free Atlas credits to support further machine translation experiments and development, along with access to technical guidance and best practices. Head over to our quick-start guide to get started with Atlas Vector Search today.

January 23, 2024

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AI-Powered Call Centers: A New Era of Customer Service

Customer satisfaction is critical for insurance companies. Studies have shown that companies with superior customer experiences consistently outperform their peers. In fact, McKinsey found that life and property/casualty insurers with superior customer experiences saw a significant 20% and 65% increase in Total Shareholder Return , respectively, over five years. A satisfied customer is a loyal customer. They are 80% more likely to renew their policies, directly contributing to sustainable growth. However, one major challenge faced by many insurance companies is the inefficiency of their call centers. Agents often struggle to quickly locate and deliver accurate information to customers, leading to frustration and dissatisfaction. This article explores how Dataworkz and MongoDB can transform call center operations. By converting call recordings into searchable vectors (numerical representations of data points in a multi-dimensional space), businesses can quickly access relevant information and improve customer service. We'll dig into how the integration of Amazon Transcribe, Cohere, and MongoDB Atlas Vector Search—as well as Dataworkz's RAG-as-a-service platform— is achieving this transformation. From call recordings to vectors: A data-driven approach Customer service interactions are goldmines of valuable insights. By analyzing call recordings, we can identify successful resolution strategies and uncover frequently asked questions. In turn, by making this information—which is often buried in audio files— accessible to agents, they can give customers faster and more accurate assistance. However, the vast volume and unstructured nature of these audio files make it challenging to extract actionable information efficiently. To address this challenge, we propose a pipeline that leverages AI and analytics to transform raw audio recordings into vectors as shown in Figure 1: Storage of raw audio files: Past call recordings are stored in their original audio format Processing of the audio files with AI and analytics services (such as Amazon Transcribe Call Analytics ): speech-to-text conversion, summarization of content, and vectorization Storage of vectors and metadata: The generated vectors and associated metadata (e.g., call timestamps, agent information) are stored in an operational data store Figure 1: Customer service call insight extraction and vectorization flow Once the data is stored in vector format within the operational data store, it becomes accessible for real-time applications. This data can be consumed directly through vector search or integrated into a retrieval-augmented generation (RAG) architecture, a technique that combines the capabilities of large language models (LLMs) with external knowledge sources to generate more accurate and informative outputs. Introducing Dataworkz: Simplifying RAG implementation Building RAG pipelines can be cumbersome and time-consuming for developers who must learn yet another stack of technologies. Especially in this initial phase, where companies want to experiment and move fast, it is essential to leverage tools that allow us to abstract complexity and don’t require deep knowledge of each component in order to experiment with and realize the benefits of RAG quickly. Dataworkz offers a powerful and composable RAG-as-a-service platform that streamlines the process of building RAG applications for enterprises. To operationalize RAG effectively, organizations need to master five key capabilities: ETL for LLMs: Dataworkz connects with diverse data sources and formats, transforming the data to make it ready for consumption by generative AI applications. Indexing: The platform breaks down data into smaller chunks and creates embeddings that capture semantics, storing them in a vector database. Retrieval: Dataworkz ensures the retrieval of accurate information in response to user queries, a critical part of the RAG process. Synthesis: The retrieved information is then used to build the context for a foundational model, generating responses grounded in reality. Monitoring: With many moving parts in the RAG system, Dataworkz provides robust monitoring capabilities essential for production use cases. Dataworkz's intuitive point-and-click interface (as seen in Video 1) simplifies RAG implementation, allowing enterprises to quickly operationalize AI applications. The platform offers flexibility and choice in data connectors, embedding models, vector stores, and language models. Additionally, tools like A/B testing ensure the quality and reliability of generated responses. This combination of ease of use, optionality, and quality assurance is a key tenet of Dataworkz's "RAG as a Service" offering. Diving deeper: System architecture and functionalities Now that we’ve looked at the components of the pre-processing pipeline, let’s explore the proposed real-time system architecture in detail. It comprises the following modules and functions (see Figure 2): Amazon Transcribe , which receives the audio coming from the customer’s phone and converts it into text. Cohere ’s embedding model, served through Amazon Bedrock , vectorizes the text coming from Transcribe. MongoDB Atlas Vector Search receives the query vector and returns a document that contains the most semantically similar FAQ in the database. Figure 2: System architecture and modules Here are a couple of FAQs we used for the demo: Q: “Can you explain the different types of coverage available for my home insurance?” A: “Home insurance typically includes coverage for the structure of your home, your personal belongings, liability protection, and additional living expenses in case you need to temporarily relocate. I can provide more detailed information on each type if you'd like.” Q: “What is the process for adding a new driver to my auto insurance policy?" A: “To add a new driver to your auto insurance policy, I'll need some details about the driver, such as their name, date of birth, and driver's license number. We can add them to your policy over the phone, or you can do it through our online portal.” Note that the question is reported just for reference, and it’s not used for retrieval. The actual question is provided by the user through the voice interface and then matched in real-time with the answers in the database using Vector Search. This information is finally presented to the customer service operator in text form (see Fig. 3). The proposed architecture is simple but very powerful, easy to implement, and effective. Moreover, it can serve as a foundation for more advanced use cases that require complex interactions, such as agentic workflows , and iterative and multi-step processes that combine LLMs and hybrid search to complete sophisticated tasks. Figure 3: App interface, displaying what has been asked by the customer (left) and how the information is presented to the customer service operator (right) This solution not only impacts human operator workflows but can also underpin chatbots and voicebots, enabling them to provide more relevant and contextual customer responses. Building a better future for customer service By seamlessly integrating analytical and operational data streams, insurance companies can significantly enhance both operational efficiency and customer satisfaction. Our system empowers businesses to optimize staffing, accelerate inquiry resolution, and deliver superior customer service through data-driven, real-time insights. To embark on your own customer service transformation, explore our GitHub repository and take advantage of the Dataworkz free tier .

November 27, 2024