Build a healthcare intake assistant with an Anam avatar
Blog post from LiveKit
The tutorial provides a comprehensive guide on building a voice AI healthcare intake assistant featuring a talking avatar powered by Anam. The app enables patients to interact with "Liv," an Anam avatar, to complete a medical intake form through voice interaction. The backend supports both Python and TypeScript, and the app synchronizes the avatar and form in real-time using Remote Procedure Call (RPC). Key components include LiveKit for real-time audio processing, Deepgram for speech-to-text, OpenAI for language model processing, and ElevenLabs for text-to-speech, all requiring a sample rate of 16kHz for compatibility with Anam's lip-sync feature. The app uses function tools to update form fields via RPC, while the frontend utilizes React state to reflect changes, ensuring a seamless user interaction experience. The tutorial also covers prerequisites like installing specific software and obtaining API keys, and it emphasizes the importance of session management to connect Anam to the live audio stream for effective lip-syncing.
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