#Healthcare chatbot aws code
The source code of this solution is available in the GitHub repository. The following diagram illustrates the solution architecture. CloudWatch custom widgets to create custom visualizations like heatmaps and conversation flows using AWS Lambda functions.A CloudWatch dashboard to put together a set of charts and visualizations representing the metrics and data insights from your chatbot conversations.CloudWatch Contributor Insights to identify top contributors and outliers in highly variable data such as sessions and utterances.CloudWatch Logs Insights to query the conversation logs and create powerful aggregations from the log data.CloudWatch metric filters to create custom metrics from conversation logs.Amazon CloudWatch Logs to store your chatbot conversation logs in JSON format.Amazon Lex V2 conversation logs, which generate metrics and visualizations from interactions with your chatbot.
The solution uses the following AWS services and features: CloudWatch metrics that you can use to configure CloudWatch alarms.Serverless architecture using pay-per-use managed services that scale transparently.Rich visualizations and widgets such as metrics charts, top-N lists, heatmaps, and utterance management.Slot and session attributes most frequently used values.Utterance history and performance (missed utterances, top-N utterances).Conversation flow, trends, and history (intent path chart, intent per hour heatmaps).Conversation statistics and aggregations (average session duration, messages per session, session heatmaps).
User and session activity (sentiment analysis, top-N sessions, text/speech modality).A common view of valuable chatbot insights, such as:.The solution includes the following features: You can use it to continuously analyze and improve the experience of end users interacting with your chatbot. The Amazon Lex V2 Analytics Dashboard Solution helps you monitor and visualize the performance and operational metrics of your Amazon Lex chatbot. It creates an Amazon CloudWatch dashboard where you can track your chatbot performance, trends, and engagement insights. The solution uses your Amazon Lex bot conversation logs to automatically generate metrics and visualizations. In this post, we look at deploying an analytics dashboard solution for your Amazon Lex bot. It then becomes crucial to have a conversational analytics dashboard to gain these insights from a single place. A deeper understanding of key metrics such as trending topics, top utterances, missed utterances, conversation flow patterns, and customer sentiment help you enhance your bot to better engage with customers and improve their overall satisfaction. You want to analyze the bot’s performance over time to gather insights that can help you adapt the bot to your customers’ needs. Designing a bot and deploying it in production is only the beginning of the journey. Amazon Lex V2 console and APIs make it easier to build, deploy, and manage bots so that you can expedite building virtual agents, conversational IVR systems, self-service chatbots, or informational bots. Amazon Lex allows you to build conversational interfaces into any application using voice and text. Chatbots are increasingly becoming an important channel for companies to interact with their customers, employees, and partners.
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