Production RAG Platform with Neural Reranking and Infrastructure-as-Code

Scenario

A team trains custom embedding and reranking models on PAI, deploys a hybrid retrieval pipeline (vector + BM25) with Bailian neural reranking into OpenSearch/Elasticsearch, builds a dual-channel RAG chatbot and recommendation system, then provisions and manages the entire production stack (ECS, RDS, OSS, Vercel) using Terraform for repeatable infrastructure-as-code deployment.

How the products combine

1. airec · custom-trained-rag-with-personalized-recommendat-224893 — Custom-Trained RAG with Personalized Recommendation Layer

See _combos/custom-trained-rag-with-personalized-recommendat-224893.

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 · custom-model-enhanced-rag-recommendation-platfor-ec855c — Custom Model-Enhanced RAG Recommendation Platform

See _combos/custom-model-enhanced-rag-recommendation-platfor-ec855c.

4. 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.

Typical questions

• build production RAG with Terraform deployment
• train custom models and deploy with infrastructure as code
• full stack RAG platform with reranking and IaC
• PAI training to production RAG with Terraform
• neural reranking chatbot with production infrastructure
• 训练自定义模型并用Terraform部署生产级RAG
• 从模型训练到基础设施即代码的完整RAG平台
• 自定义排序加RAG聊天机器人加自动化部署

Q: How do I build a production RAG platform with custom model training, neural reranking, and infrastructure-as-code? A: You build this platform by training custom embedding and reranking models on PAI, deploying a hybrid retrieval pipeline with Bailian neural reranking into OpenSearch or Elasticsearch, and provisioning the stack with Terraform. This architecture supports a dual-channel RAG chatbot and recommendation system while managing ECS, RDS, OSS, and Vercel resources through repeatable infrastructure-as-code. Detailed implementation steps are documented in the "Full-Stack Custom RAG: Train to Production" and "Custom-Trained RAG with Personalized Recommendation Layer" skill guides.