Optimizing Conversation Flow with DeepInfra Multi-Model Combination
Traditional chatbots often face problems such as context loss and single responses. This is possible with DeepInfra:
- Model dynamic switching: invoke different models at different stages of the dialog (e.g., Llama 3 for knowledge quizzing, Mistral for idea generation)
- context-sensitive techniques: In the API request for the
messagesThe full dialog history is kept in the array, for example:"messages": [{"role": "system", "content": "你是一个中文客服助理"},{"role": "user", "content": "上次说的退货政策"}] - Response Optimization Parameters: Adjustments
temperature(0.3-0.7 is more stable),top_p(0.9-0.95 balanced diversity) and other parameters
Advanced solutions can be implemented in combination with the LangChain framework:
1. Dialogue status tracking
2. Automated model routing
3. Real-time optimization based on user feedback
This answer comes from the articleDeepInfra Chat: experiencing and invoking a variety of open source big model chat servicesThe
May not be reproduced without permission:AI productivity tools " How to Improve the Multi-Round Conversation Experience of AI Chatbots?
English 
简体中文 
日本語 
Deutsch 
Português do Brasil