Custom RAG Pipeline with Deployed Frontend

Scenario

Use this workflow when off-the-shelf embeddings fail to capture domain-specific terminology or compliance requirements. By training custom models on PAI, indexing vectors in OSS-backed OpenSearch/Elasticsearch, and serving the UI via Vercel, you build a high-precision, low-latency RAG Q&A system grounded in proprietary enterprise data.

Integration steps

1. Provision Infrastructure: Run terraform apply -var-file="prod.tfvars" using the es+rds+terraform module to spin up VPC, ECS, RDS (PostgreSQL), and an Elasticsearch cluster.
2. Stage Raw Data in OSS: Upload domain documents: ossutil cp -r ./data oss://<bucket>/raw/.
3. Train Custom Embeddings on PAI: Mount the OSS path in PAI-DSW and submit: pai submit --job-name custom-emb --oss-path oss://<bucket>/raw/ --output oss://<bucket>/models/.
4. Index Vectors in OpenSearch/ES: Generate embeddings and push via the _bulk API: curl -X POST "https://<es-endpoint>:9200/_bulk" -H "Content-Type: application/json" -d @vectors.json.
5. Configure Vector Search Mapping: Create the index with knn support: PUT /rag_index { "mappings": { "properties": { "embedding": { "type": "knn_vector", "dimension": 768 } } } }.
6. Link Backend to RDS: Configure the ES RAG pipeline to write conversation metadata and session logs to the provisioned RDS instance via JDBC.
7. Deploy Frontend to Vercel: Set NEXT_PUBLIC_ES_ENDPOINT and ES_API_KEY in .env, then run vercel --prod to publish the chatbot UI.

Architecture

Terraform provisions the foundational network, compute (ECS), and storage (OSS, RDS, ES). Domain data flows from OSS into PAI for custom embedding training. The resulting model generates vectors stored in Elasticsearch/OpenSearch with knn indexing. The RAG backend runs on ES, querying vectors and logging metadata to RDS. The Vercel-hosted frontend communicates with the ES API gateway, delivering a seamless chat interface to end users.

Prerequisites

• Alibaba Cloud CLI and ossutil configured with RAM credentials
• PAI workspace with GPU quota and DSW notebook access
• Terraform v1.5+ with alicloud provider initialized
• Vercel CLI installed and linked to a GitHub repository
• Domain dataset pre-cleaned and formatted as .txt or .pdf

Common pitfalls

• Dimension mismatch: PAI-trained model outputs 1024-dim vectors, but ES knn index is configured for 768. Always verify dimension in the mapping matches the model output.
• Vercel CORS blocking: The ES cluster rejects cross-origin requests from .vercel.app. Add http.cors.allow-origin: "https://.vercel.app" to elasticsearch.yml.
• RDS connection pooling exhaustion: High concurrent chat sessions overwhelm RDS. Use PgBouncer or configure max_connections in the Terraform RDS module.
• OSS-to-PAI latency: Mounting large OSS buckets directly in PAI causes slow I/O. Pre-copy to PAI NAS or use ossutil sync before training.

Typical questions

• train custom embeddings and deploy chatbot frontend
• PAI model training to Vercel frontend RAG
• full stack custom RAG with web deployment
• end-to-end RAG from training to production frontend
• custom embedding RAG app deploy to web
• 训练自定义嵌入模型并部署聊天机器人前端
• 从PAI训练到Vercel上线完整RAG应用
• 自定义模型RAG加前端部署

Q: How do I build and deploy an end-to-end custom RAG application with a web frontend? A: You can achieve this by training custom embedding models on PAI, storing the resulting vectors in OSS via OpenSearch or Elasticsearch, and deploying the chatbot interface to Vercel. This integrated workflow creates a complete production AI Q&A application that spans from initial model training directly to end-user web access.