Model Compute Paradigm
Dynamically routes AI tasks like chat, summarization, and sentiment analysis to appropriate models using intelligent, LLM-powered routing within a lightweight, production-ready FastAPI backend.
About
Modern AI/LLM deployments often face challenges managing diverse model backends, complex routing decisions, and combining outputs from multiple models, all while handling production-grade concerns such as concurrency, streaming, and retries. This project offers a production-ready proof-of-concept for an MCP (Model Compute Paradigm) architecture. It provides a FastAPI-based microserver that orchestrates multiple AI/LLM models behind a unified, scalable interface, enabling dynamic task routing, LLM-based intent parsing, multi-model pipelines, and streaming chat capabilities, all built for asynchronous and Dockerized deployment.
Key Features
- Async FastAPI server for high concurrency
- Intelligent LLM-powered Model Routing to specific or sequential tasks
- Streaming Responses for chat and other real-time interactions
- Metadata-Driven Model Registry for flexible model configuration
- Dockerized for seamless production deployment
- 0 GitHub stars
Use Cases
- Develop AI pipelines (e.g., RAG, RL) by sequencing multiple model executions
- Build your own ChatGPT-style API with streaming capabilities
- Create an intelligent task router that parses user intent for AI services
