Trained RAG with Lightweight Edge Generation

Scenario

Train domain-specific embedding models and fine-tune LLMs on PAI, build a hybrid BM25+vector retrieval pipeline across OpenSearch and Elasticsearch, then deploy the generative model on Alibaba Cloud Linux behind a Cloudflare Worker edge proxy for low-latency global RAG serving.

How the products combine

1. alinux · lightweight-rag-with-edge-served-generation-290f9c — Lightweight RAG with Edge-Served Generation

See _combos/lightweight-rag-with-edge-served-generation-290f9c.

2. alinux · full-stack-custom-rag-train-to-production-e68446 — Full-Stack Custom RAG: Train to Production

See _combos/full-stack-custom-rag-train-to-production-e68446.

3. airec · full-stack-rag-with-edge-served-global-inference-125949 — Full-Stack RAG with Edge-Served Global Inference

See _combos/full-stack-rag-with-edge-served-global-inference-125949.

4. alinux · production-rag-with-edge-served-inference-a4f07c — Production RAG with Edge-Served Inference

See _combos/production-rag-with-edge-served-inference-a4f07c.

Typical questions

• train custom RAG and serve at edge with Cloudflare
• PAI training plus lightweight edge RAG deployment
• fine-tune LLM and deploy RAG behind CDN proxy
• custom embeddings with edge-served generation on Alinux
• 训练自定义RAG并通过Cloudflare边缘部署
• PAI训练加轻量级边缘网关RAG系统
• train embeddings deploy hybrid search edge gateway
• custom model training with Cloudflare edge RAG serving

Q: How do I train custom RAG components and deploy them at the edge using Cloudflare? A: You train domain-specific embedding models and fine-tune LLMs on PAI, then deploy the generative model on Alibaba Cloud Linux behind a Cloudflare Worker edge proxy for low-latency global RAG serving. The workflow also involves building a hybrid BM25+vector retrieval pipeline across OpenSearch and Elasticsearch to handle document retrieval.