Production RAG with Edge-Served Inference

Scenario

Train custom embedding models on PAI and deploy them inside OpenSearch for query-time vector inference, build a retrieval pipeline across Elasticsearch and OSS, then serve the generative model on PAI-EAS with auto-scaling and model versioning behind a Cloudflare Workers edge gateway for global low-latency RAG delivery with caching and rate limiting.

How the products combine

1. alinux+alinux+cloudflare+opensearch+pai · pai-inference-with-edge-api-gateway-039c57 — PAI Inference with Edge API Gateway

See _combos/pai-inference-with-edge-api-gateway-039c57.

2. alinux+cloudflare · ai-model-with-edge-api-gateway-82b873 — AI Model with Edge API Gateway

See _combos/ai-model-with-edge-api-gateway-82b873.

3. opensearch · opensearch-deploy-model — OpenSearch — Deploy embedding model for inference

See opensearch/opensearch-deploy-model.

4. bailian+es+es+opensearch+oss+oss+pai · custom-rag-pipeline-train-embeddings-to-deploy-a-956ae5 — Custom RAG Pipeline: Train Embeddings to Deploy Application

See _combos/custom-rag-pipeline-train-embeddings-to-deploy-a-956ae5.

Typical questions

• deploy production RAG with edge gateway
• full stack RAG with Cloudflare proxy
• PAI inference RAG pipeline with CDN
• custom embeddings with managed model serving
• 部署完整RAG系统加边缘网关
• 生产级RAG部署
• OpenSearch RAG with auto-scaling inference
• train embeddings and serve model at edge

Q: How do I deploy a production RAG system with an edge gateway and custom embeddings? A: You deploy it by training custom embedding models on PAI, placing them in OpenSearch for query-time vector inference, and serving the generative model on PAI-EAS behind a Cloudflare Workers edge gateway. This architecture integrates a retrieval pipeline across Elasticsearch and OSS to deliver global low-latency responses with built-in caching and rate limiting.