MaxKB's Model Compatibility and Deployment Flexibility
MaxKB adopts modularized design architecture to achieve extensive compatibility with large language models. Through the standardized API interface layer, the system can seamlessly interface with mainstream big models such as Llama3, GPT, Claude, etc. It supports configuring multiple model instances at the same time and intelligent routing according to scenarios. The technical implementation includes a model abstraction layer, a load balancer and a caching mechanism to ensure stable response in highly concurrent scenarios.
It provides three choices in deployment mode: public cloud SaaS service is suitable for rapid verification scenarios; hybrid cloud deployment protects core data privacy; and full offline private deployment meets the strong regulatory needs of finance, government affairs, and so on. The system's built-in model performance monitoring dashboard displays key indicators such as response latency and token consumption in real time to assist in operation and maintenance decision-making.
Typical cases include a large bank using MaxKB to dock the internally trained financial risk control grand model to realize intelligent query and interpretation of credit policy under the premise of ensuring that the data does not go out of the domain, and improving the query accuracy of traditional knowledge base from 63% to 89%.
This answer comes from the articleMaxKB: Out-of-the-box AI Knowledge Base Q&A System for Smart Customer Service and In-house Knowledge BaseThe
