Response.appendHeader("Content-Type", "application/xml") Since we're using the response object to handle cookies we can't just pass the TwiML straight back to the callback, we need to set the appropriate header and return the TwiML in the body of the response Redirect to the Function where the is capturing the caller's speech Generate some TwiML using the cleaned up AI response Limit the conversation history to the last 10 messages you can increase this if you want but keeping things short for this demonstration improves performance Now that you've got the ingredients, let's check out the recipe in two flavors: CLI and GUI.Įxports.handler = function(context, event, callback) `) Of course, if you've got your own server rattling around someplace you can make a couple small edits to the Javascript and it will run in your node.js environment, no sweat. Beyond merely giving you the ability to get a proof of concept up and running without needing to spin up a server of your own, Functions provide auto scaling capabilities, enhanced security, and reduced latency by running your code inside Twilio. How will you get these three robots to talk to each other, and to your callers? By using Twilio Functions. By leveraging the OpenAI API, developers can integrate this AI directly into their applications, offering users a more interactive and engaging experience. It can understand context, provide relevant responses, and even engage in creative tasks like writing stories or poems. Robot #3: OpenAI's ChatGPT Conversational CompanionĬhatGPT is an advanced language model developed by OpenAI, capable of generating human-like text based on given input. With support for multiple languages, a wide range of voices, and SSML support, Twilio text-to-speech allows you to customize your chatbot's voice to match your brand's identity. Polly's neural voices offer a more natural and lifelike sound, providing an engaging listening experience for users. With the TwiML verb, Twilio provides a text-to-speech (TTS) function that uses Amazon Polly voices which leverage deep learning to synthesize human-like speech. Robot #2: Giving your robot a voice with Amazon Polly Neural Voices Historically, Twilio developers have used speech recognition as a way to navigate interactive voice response (IVRs) and other self-service automation workflows, but with the release of new experimental speech models, the only limit is ✨ your imagination ✨. It offers excellent accuracy, low latency, and support for numerous languages and dialects. Twilio's speech recognition using the TwiML verb is a powerful tool that turns words spoken on a phone call into text. Want to give this a demo a whirl before diving in? Call 1-989-4OPENAI (467-3624) to test it out! Robot #1: Decoding the human voice using Speech Recognition You'll also use the Call Event API to parse what callers are asking about and view the responses from the bot which allows us to unlock the rich first-party data captured in these interactions and send the data to customer engagement platforms like Segment where you can use it to build customer profiles, understand customer preferences, and create the personalized experiences customers expect. In this post, we'll show you how to use Twilio's native speech recognition and Amazon Polly Neural text-to-speech capabilities with ChatGPT to create a voice-activated chatbot, all hosted entirely on Twilio's serverless Functions environment. These days people talk to, listen to, and collaborate with robots all the time, but you know what's cooler than interacting with one robot? Interacting with three! Using ChatGPT to power an interactive voice chatbot is not just a novelty, it can be a way to get useful business intelligence information while reserving dedicated, expensive, and single-threaded human agents for conversations that only humans can help with.
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