Compliant Keyless RAG Platform End-to-End

Scenario

Use this workflow when a DevOps team must deploy an MLPS 2.0-compliant RAG infrastructure without embedding static AccessKeys. It combines Terraform for declarative provisioning, IDaaS OIDC for machine-to-machine authentication, and Elasticsearch/OpenSearch for vector retrieval, ensuring zero-secret operations from infrastructure bootstrapping through AI-powered query runtime.

Integration steps

1. Configure Keyless Terraform Provider: Use RAM Role assumption to eliminate AK/SK in CI/CD pipelines.

``hcl provider "alicloud" { assume_role { role_arn = "acs:ram::${var.account_id}:role/TerraformExecutionRole" } } ``

2. Provision MLPS 2.0 Baseline: Apply VPC, ECS (Alibaba Cloud Linux), and OSS modules with compliance tags and encryption.

``bash terraform apply -target=module.vpc -target=module.ecs -target=module.oss -auto-approve ``

3. Register IDaaS OIDC M2M Client: Create an OIDC application with grant_type=client_credentials. Inject client_id and client_secret into ECS via Terraform-managed KMS and cloud-init.

``yaml # cloud-init /etc/profile.d/rag.sh export OIDC_TOKEN_URL="https://idaas.aliyuncs.com/oauth2/v1/token" export OIDC_CLIENT_ID="${var.idaas_client_id}" ``

4. Deploy RDS & Enable SSL: Provision PostgreSQL with strict IP whitelisting and TLS enforcement.

``hcl resource "alicloud_db_instance" "rag_meta" { engine = "PostgreSQL" security_ip_list = "10.0.0.0/8" ssl_enabled = true } ``

5. Initialize OpenSearch Vector Index: Create a dense_vector mapping matching your embedding model.

``json PUT /rag_vectors { "mappings": { "properties": { "embedding": { "type": "dense_vector", "dims": 768 } } } } ``

6. Deploy RAG Runtime on ECS: Configure the application to fetch short-lived tokens via POST $OIDC_TOKEN_URL before querying ES or RDS. Set ALIBABA_CLOUD_ECS_METADATA_TOKEN to required for instance profile keyless auth.
7. Validate End-to-End Flow: Execute curl -X POST $OIDC_TOKEN_URL -d "grant_type=client_credentials&client_id=$OIDC_CLIENT_ID&client_secret=$OIDC_SECRET" to verify token issuance, then run a test POST /rag_vectors/_search with a vector payload.

Architecture

Terraform orchestrates the VPC, ECS, RDS, OSS, and OpenSearch layers. The RAG application runs on Alibaba Cloud Linux ECS instances and authenticates to all downstream services via IDaaS OIDC short-lived JWTs. Raw documents reside in OSS, conversation metadata/logs in RDS, and vector embeddings in OpenSearch. PAI/Bailian generates embeddings and LLM responses. All inter-service traffic uses token-based auth, with zero static credentials persisted in state or runtime.

Prerequisites

• Alibaba Cloud account with MLPS 2.0 baseline enabled
• Terraform CLI v1.5+ and terraform-provider-alicloud
• IDaaS tenant with OIDC M2M application pre-configured
• Pre-approved VPC CIDR and RAM Role policies (AliyunECSInstanceProfile, AliyunESFullAccess)
• PAI/Bailian API quota for embedding models

Common pitfalls

• Static AK/SK in Terraform State: Failing to use assume_role or remote state encryption leaks credentials. Always enable encrypt = true in the OSS backend.
• OIDC Token Expiry: RAG apps caching tokens beyond the expires_in window cause 401s. Implement token refresh logic with a 30-second buffer.
• Vector Dimension Mismatch: ES index dims must exactly match the PAI/Bailian embedding output (e.g., 768 vs 1024). Mismatches trigger mapper_parsing_exception at query time.
• Security Group Over-Blocking: MLPS 2.0 defaults restrict intra-VPC traffic. Explicitly allow TCP 9200 (ES), 5432 (RDS), and 443 (IDaaS OIDC) between ECS and data layers.

Typical questions

• terraform MLPS compliant infra then deploy keyless RAG
• deploy compliance-hardened stack with OIDC RAG app
• Terraform部署合规基础设施加无密钥RAG应用
• provision secure infra then build keyless AI search platform
• 一键部署合规栈加OIDC认证的RAG智能搜索
• full pipeline terraform compliance plus keyless RAG runtime
• MLPS 2.0 infra to keyless RAG end-to-end
• terraform合规基础设施到无AK的RAG平台

Q: How do I provision compliant infrastructure with Terraform and deploy a keyless RAG application? A: You provision an MLPS 2.0 compliance-hardened infrastructure stack using Terraform and deploy a keyless RAG application configured with IDaaS OIDC for machine-to-machine authentication. This integrated workflow eliminates static access keys and secrets across the entire pipeline from infrastructure provisioning through the AI-powered retrieval runtime.