Full-Stack RAG with Edge-Served Global Inference

Scenario

Train custom domain-specific embedding models and fine-tune LLMs on PAI, build a hybrid retrieval pipeline combining vector search with BM25 keyword search across OpenSearch and Elasticsearch, then deploy the complete inference stack behind Cloudflare edge gateway for low-latency global production serving.

How the products combine

1. airec · airec-with-custom-models-and-semantic-search-fe8869 — AIRec with Custom Models and Semantic Search

See _combos/airec-with-custom-models-and-semantic-search-fe8869.

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 · semantic-search-powered-recommendation-system-5bbd35 — Semantic Search-Powered Recommendation System

See _combos/semantic-search-powered-recommendation-system-5bbd35.

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 deploy globally with edge inference
• full stack RAG with CDN and custom embeddings
• PAI model training to production edge deployment
• end-to-end RAG pipeline with Cloudflare gateway
• train embeddings and deploy with global edge serving
• 从模型训练到全球边缘部署的完整RAG系统
• PAI训练加OpenSearch检索加Cloudflare边缘推理
• production RAG with edge-accelerated inference

Q: How do I build and deploy an end-to-end RAG pipeline with custom models and global edge inference? A: You can achieve this by training custom domain-specific embedding models and fine-tuning LLMs on PAI, building a hybrid retrieval pipeline across OpenSearch and Elasticsearch, and deploying the stack behind a Cloudflare edge gateway. This configuration delivers low-latency global production serving by combining vector and BM25 keyword search. The workflow integrates multiple cloud services specifically designed for full-stack custom RAG development.