Streaming Chat Inference

Fee settlement for real-time AI chat inference is handled by invoking `processResponse()` after each streaming interaction to ensure accurate billing on the 0G network. This guarantees correct accounting for continuous conversational outputs.

Yes, you can maintain multi-turn conversation context during streaming chat inference. The Skill manages conversation state automatically, allowing interactive chatbots to retain context across continuous streaming interactions.

To deploy a streaming customer support chatbot on 0G, initialize the 0G serving broker, send user input, and process the streaming response while calling `processResponse()` for accurate billing. This supports both streaming and non-streaming modes.

Yes, you need the `ethers` library alongside `@0glabs/0g-serving-broker` to manage streaming AI inference. These dependencies are required to handle authentication, context management, and secure fee settlement on the 0G network.

Yes, this Skill supports both streaming and non-streaming chat inference modes. You can choose interactive streaming for instant responses as users type, or use non-streaming modes for standard conversational AI interactions.

Multi-turn AI chat requires calling `processResponse()` after each inference to ensure accurate fee settlement. Invoking this function after every streaming response guarantees correct billing and security compliance during continuous interactions.