Voice Agents are the core components of the Millis AI platform.
These agents can be customized to perform a variety of tasks, from answering questions to guiding users through complex processes, all via natural spoken language.
It’s important to configure your voice agent to ensure it operates effectively within your specific context. Here are the main aspects you can customize:
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{ prompt: "You're a helpful assistant.", // Example prompt voice: { provider: "elevenlabs", // Voice provider voice_id: "voice-id" // Replace 'voice-id' with the ID of the desired voice }, language: "<language_code>", // optional - use language code such as en, es tools: [ { name: "get_user_data", description: "", webhook: "https://...", header: { "Content-Type": "application/json", "Authorization": "" }, params: [ { name: "", type: "string" | "number" | "boolean", description: "", required: true } ] } ], // Replace with actual function calls you need custom_llm_websocket: "wss://...", // optional - enable custom llm llm: "", // optional - choose llm model. Ex: gpt-4o, llama-3-70b}
The system prompt is where you can provide specific instructions or information that the agent needs to remember and follow. This sets the initial context for your voice agent, guiding its responses and interactions.
(Optional) If not set, the default Millis AI model is used.
Model: Specifies the GPT model that your agent will operate on. We support OpenAI’s latest model, GPT-4o, as well as open-source models like Meta Llama 3.
Provider: The provider who provides inference for the model.
A list of function calls the agent can execute to perform tasks or retrieve information during interactions. This includes API webhooks and other integrations.
You can select the AI model for your voice agent based on your needs:
Default Millis AI Model: Automatically used if no specific LLM model is provided. This model is best optimized for low latency.
Popular Models from Providers: Like OpenAI’s GPT-4o, known for the best language processing capabilities but with a trade-off in latency.
Custom Model via WebSocket: Integrate your uniquely developed or tailored LLM to give your agent specialized abilities. You have full control over the agent’s capabilities.