Compliant Enterprise Infra + Custom RAG Stack

Scenario

Use this stack when your organization requires MLPS 2.0 compliance for a production RAG system that cannot rely on public foundation models. It enables DevOps to provision hardened infrastructure via Terraform, fine-tune proprietary LLMs and embeddings on PAI, and serve domain-specific retrieval through Bailian while keeping all data and vector indexes isolated within a private VPC.

Integration steps

1. Provision infra: Run terraform apply with alicloud_vpc, alicloud_ecs_instance, alicloud_db_instance, and alicloud_oss_bucket. Enforce MLPS 2.0 via security_group rules (ingress 443/9200 only) and enable RDS audit_log + ssl_enforcement.
2. Ingest corpus: Upload documents via ossutil cp ./data/ oss://<bucket>/rag-corpus/ -r. Enable SSE-KMS encryption on the bucket.
3. Fine-tune on PAI: Launch PAI-DLC: pai dlc job create --image registry.cn-hangzhou.aliyuncs.com/pai/pytorch:1.12 --gpu 4 --script train.py --oss-input oss://<bucket>/rag-corpus/ --oss-output oss://<bucket>/models/. Repeat for the LLM using LoRA.
4. Index vectors in ES: Connect via VPC: client = OpenSearch(hosts=['https://<es-vpc>:9200']). Create knn mapping: {"vector": {"type": "knn_vector", "dimension": 1024}}. Bulk ingest using elasticsearch.helpers.bulk().
5. Deploy via Bailian: Register models: bailian model register --model-path oss://<bucket>/models/llm-adapter/. Create app: bailian app create --name "CompliantRAG" --llm-endpoint <pai-inference-url> --vector-store es --index-name "domain_vectors" --timeout_ms 60000.
6. Validate: Test retrieval: curl -X POST https://<bailian-app>.bailian.aliyuncs.com/v1/chat/completions -H "Authorization: Bearer <key>" -d '{"query": "..."}'.

Architecture

Terraform bootstraps the VPC, ECS, RDS, and OSS with MLPS-compliant ACLs and encryption. PAI consumes raw documents from OSS, trains embeddings/LLMs, and exports artifacts back to OSS. OpenSearch/Elasticsearch (in the same VPC) ingests embeddings and maintains the knn index. Bailian orchestrates queries, routing them to the VPC-bound ES index for retrieval, then passing context to the PAI-hosted LLM endpoint for generation. All traffic stays private except Bailian’s API gateway.

Prerequisites

• Alibaba Cloud RAM roles: AliyunPAIFullAccess, AliyunOpenSearchFullAccess, AliyunBailianFullAccess
• Terraform v1.5+ with alicloud provider
• Domain dataset and PAI GPU quota
• Bailian API key and OpenSearch VPC credentials
• MLPS 2.0 baseline security template

Common pitfalls

• VPC routing gaps: OpenSearch and PAI must share the VPC/subnet with ECS; cross-VPC routing without peering causes Bailian query timeouts.
• PAI training OOM: Fine-tuning without gradient_checkpointing or bf16 crashes DLC jobs. Set --max-seq-length 2048 and monitor VRAM.
• ES dimension mismatch: Uploading embeddings with wrong dimensions (e.g., 768 vs 1024) breaks knn queries. Validate dimension in index_settings before bulk load.
• Bailian latency spikes: Default 30s timeout is insufficient for custom LLM + vector search. Increase timeout_ms to 60s and enable connection pooling.

Typical questions

• deploy compliant enterprise app with custom RAG
• Terraform infra then deploy custom LLM RAG
• MLPS 2.0 compliant RAG deployment
• provision secure stack and add custom trained RAG
• full enterprise stack with fine-tuned RAG pipeline
• Terraform部署合规企业栈加自定义RAG
• 一键部署合规基础设施并搭建定制RAG系统
• 从基础设施到自定义大模型RAG全流程

Q: How do I deploy a compliant enterprise application with a custom RAG system using Terraform? A: You can deploy this stack by using Terraform to provision an MLPS 2.0-compliant enterprise infrastructure and then build a custom RAG system on top of it. The workflow involves fine-tuning domain LLMs and embedding models on PAI, storing vectors in OpenSearch or Elasticsearch within the VPC, and serving production retrieval via Bailian endpoints.