![]() Application Insights will enable us to monitor our chatbot’s key metrics. Quickly analyze application telemetry, allowing the detection of anomalies, application failure, performance changes. ![]() Azure Application InsightsĪccording to Microsoft, Azure’s Application Insights provides comprehensive, actionable insights through application performance management (APM) and instant analytics. We will be using Blob Storage to store publically-accessible images, used by our chatbot. Azure Blob StorageĪccording to Microsoft, Azure’s storage-as-a-service, Blob Storage, provides massively scalable object storage for any type of unstructured data, images, videos, audio, documents, and more. We will use the MongoDB SDK to store our documents in Cosmos DB, used by our chatbot. Cosmos also supports numerous database SDKs, including MongoDB, Cassandra, and Gremlin DB. Cosmos DB supports multiple data models, including document, columnar, and graph. Cosmos DBĪccording to Microsoft, Cosmos DB is a globally distributed, multi-model database-as-a-service, designed for low latency and scalable applications anywhere in the world. For this post, we will use the current Bot Builder Node.js SDK v3 release to write our chatbot. Currently, the SDK is available for C# and Node.js. The Bot Builder SDK allows you to build, connect, deploy and manage bots, which interact with users, across multiple channels, from your app or website to Facebook, Messenger, Kik, Skype, Slack, Microsoft Teams, Telegram, SMS, Twilio, Cortana, and Skype. Bot Service leverages the Bot Builder SDK. The Azure Bot Service provides an integrated environment that is purpose-built for bot development, enabling you to build, connect, test, deploy, and manage intelligent bots, all from one place. A LUIS bot contains a domain-specific natural language model, which you design. Language Understanding integrates seamlessly with the Speech service for instant Speech-to-Intent processing, and with the Azure Bot Service, making it easy to create a sophisticated bot. According to Microsoft, LUIS allows you to quickly create enterprise-ready, custom machine learning models that continuously improve.ĭesigned to identify valuable information in conversations, Language Understanding interprets user goals ( intents) and distills valuable information from sentences ( entities), for a high quality, nuanced language model. The machine learning-based Language Understanding Intelligent Service ( LUIS) is part of Azure’s Cognitive Services, used to build Natural Language Understanding (NLU) into apps, bots, and IoT devices. Here is a brief overview of the key Microsoft technologies we will incorporate into our bot’s architecture. All three of the article’s demonstrations are written in Node.js, all three leverage their cloud platform’s machine learning-based Natural Language Understanding services, and all three take advantage of NoSQL database and storage services available on their respective cloud platforms. If you want to compare Azure’s current chatbot technologies with those of AWS and Google, in addition to this post, please read my previous two posts in this series, Building Serverless Actions for Google Assistant with Google Cloud Functions, Cloud Datastore, and Cloud Storage and Building Asynchronous, Serverless Alexa Skills with AWS Lambda, DynamoDB, S3, and Node.js. Once built, we will integrate our chatbot across multiple channels, including Web Chat and Slack. We will enhance the chatbot’s functionality with Azure’s Cloud services, including Cosmos DB and Blob Storage. In this post, we will explore the development of a machine learning-based LUIS-enabled chatbot using the Azure Bot Service and the BotBuilder SDK.
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