What’s New in Atlas Charts: Streamlined Data Sources

We’re excited to announce a major improvement to managing data sources in MongoDB Atlas Charts: Atlas data is now available for visualization automatically, with zero setup required.

Every visualization relies on an underlying data source. In the past, Charts made adding Atlas data as a source fairly straightforward, but teams still needed to manually choose clusters and collections from which to power their dashboards.

Streamlined data sources, however, eliminates the manual steps required to add data sources into Charts. This feature further optimizes your data visualization workflow by automatically making clusters, serverless instances, and federated database instances in your project available as data sources within Charts.

For example, if you start up a new cluster or collection and want to create a visual quickly, you can simply go into one of your dashboards and start building a chart immediately.

Check out streamlined data sources in action:

Gif of how the new data sources experience can streamline you data visualization workflow in Charts
See how the new data sources experience streamlines your data visualization workflow in Charts.

Maintain full control of your data

Although all project data will be available automatically to project members by default, we know how important it is to be able to control what data can be used by your team. For example, you may have sensitive customer data or company financials in a cluster.

Project owners maintain full control over limiting access to data like this when needed. As shown in the following image, with a few clicks, you can select any cluster or collection, confirm whether or not any charts are using a data source, and disconnect when ready.

Screenshot of the field for selecting the Atlas cluster you are wanting to pull data to Charts for

If you have collections that you want some of your team to access but not others, this can be easily achieved under Data Access in collection settings as seen in the following image.

Screenshot of the Data Access field

With every release, our goal is to make visualizing Atlas data more frictionless and powerful. The Streamlined data sources feature helps us take a big step in this direction.

Building data visualizations just got even easier with Atlas Charts. Give it a try today!

New to Atlas Charts? Get started today by logging into or signing up for MongoDB Atlas, deploying or selecting a cluster, and activating Charts for free.

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How to Leverage Enriched Queries with MongoDB 6.0

MongoDB introduces useful new functions and features with every release, and MongoDB 6.0, released this summer, offers many notable improvements , including deeper insights from enriched queries via the MongoDB Query API . This set of query enhancements was announced at MongoDB World 2022 by senior product manager Katya Kamenieva. You can watch her presentation below. Watch Kayta Kamenieva’s MongoDB World presentation on queries. Users can now use upgraded operators and change stream features. In this post, we’ll look at several of these updates, along with examples of how you can put them to use. Top N accumulators With this new feature, users can compute top items in each group based on the sort criteria ( $topN , $bottomN ), current order of documents ($firstN, $lastN), or value of a field ( $manX , $minN ). This functionality would be useful, for example, if you have a collection of restaurants with ratings, and you want to see the top three highest-rated restaurants based on the type of cuisine. You can group by cuisine and use $topN to return the top three restaurants by rating. Ability to sort arrays The ability to sort an array allows users to sort elements in the array. For example, suppose you have posted content with hundreds of user comments, and you want to sort the comments based on how many likes they received. In this case, $sortArray can pull those comments and prioritize them to the top of the comments list. Densification and gap-filling These new additions to the aggregation framework help to build out time series data more completely. When attempting to create histograms of data over time, the new stages, $denisfy and $fill , allow you to fill gaps in that data to create smoother and more complete graphs using linear interpolation, last/next observed value carried forward, or a constant value. This capability can be helpful, for example, if you want to create a graph that shows the amount of inventory in a warehouse every day for a year, but the inventory was only recorded once a week. The $densify expression will fill the gaps in the timeline, while $fill will produce values for the inventory data based on the previous observation. Joining sharded collections With this new feature, when joining collections using $lookup or performing recursive search with $graphLookup , collections on both sides can be sharded. Before 6.0, only the originating collection could be sharded. An example use case is enriching records in the “accounts” collection with the list of the corresponding orders that are stored in the “orders” collection. In the past, only “accounts” collections could be sharded. Starting with 6.0, both “accounts” and “orders” collections can be sharded. Change streams pre- and post-images Change streams now offer point-in-time (PIT) pre- and post-image capabilities , allowing users to include the state of the document before and after changes in the output of the change stream. This functionality can be useful in many situations. For example, suppose a company is tracking flight times. If a flight is delayed, the system can compare the value of the departure and arrival times both before and after that delay and trigger an automatic rewrite of the schedule for the new flight timeline, including schedules for the entire crew. Atlas Search across multiple collections This improvement to MongoDB Atlas Search allows users to search across multiple collections with a single query using $search inside the $unionWith or $lookup stages. $search can provide these results quickly, using only one query. Enriched queries are not the only improvements in MongoDB 6.0. Read about the 7 reasons to upgrade to MongoDB 6.0 and discover the possibilities. Try MongoDB Atlas for Free Today

September 20, 2022
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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