pywayne-llm-chat-bot

To implement streaming responses for a multi-turn LLM chat, use a synchronous interface compatible with OpenAI-like APIs that handles real-time interaction and tracks per-session conversation history.

Managing per-session history in an LLM chat application requires a ChatManager class that isolates context tracking for each user session, ensuring multi-turn conversations retain their specific history.

Yes, you can configure temperature and max_tokens for streaming chat sessions, alongside parameters like top_p and penalties, by passing these settings directly to the LLMChat initialization.

Yes, the LLM chat interface works directly with OpenAI-compatible APIs by allowing you to specify a custom base_url and api_key to route your streaming chat requests to the desired provider.

You can set dynamic system prompts for real-time chat interactions by updating the prompt configuration within your chat session before requesting streaming responses from the LLM.

Multi-turn chat loses context between sessions if per-session history tracking is not implemented, which requires isolating conversation state using a dedicated ChatManager for each active session.