Build a complete RAG application that stores source documents in an OSS bucket secured behind a Cloudflare Worker edge proxy, indexes them as vector embeddings in OpenSearch for retrieval, and serves generative LLM responses via an Alinux-hosted model also fronted by Cloudflare — giving users secure document upload, semantic search, and grounded answer generation in one pipeline.
Build a complete RAG application that stores source documents in an OSS bucket secured behind a Cloudflare Worker edge proxy, indexes them as vector embeddings in OpenSearch for retrieval, and serves generative LLM responses via an Alinux-hosted model also fronted by Cloudflare — giving users secure document upload, semantic search, and grounded answer generation in one pipeline.
See _combos/secure-oss-delivery-via-cloudflare-worker-41294a.
See _combos/ai-model-with-edge-api-gateway-82b873.
See _combos/pai-inference-with-edge-api-gateway-039c57.
See _combos/lightweight-rag-with-edge-served-generation-290f9c.
Q: How do I build a complete RAG application with secure document storage? A: You can build this pipeline by storing source documents in an OSS bucket secured behind a Cloudflare Worker edge proxy, indexing them as vector embeddings in OpenSearch, and serving generative LLM responses via an Alinux-hosted model fronted by Cloudflare. This configuration provides secure document upload, semantic search, and grounded answer generation in a single workflow. The architecture relies on four integrated cross-product skills covering secure OSS delivery, edge API gateways, PAI inference, and lightweight RAG generation.