Full Custom RAG with Deployed Frontend

Scenario

Fine-tune a domain-specific LLM and train custom embedding models on PAI, build a vector search pipeline with OpenSearch/Elasticsearch and OSS, deploy the inference backend via Bailian, and deliver a polished chatbot UI on Vercel with infrastructure managed by Terraform.

How the products combine

1. bailian · custom-rag-train-embeddings-to-production-app-9bbc6d — Custom RAG: Train Embeddings to Production App

See _combos/custom-rag-train-embeddings-to-production-app-9bbc6d.

2. bailian · custom-rag-pipeline-train-embeddings-to-deploy-a-956ae5 — Custom RAG Pipeline: Train Embeddings to Deploy Application

See _combos/custom-rag-pipeline-train-embeddings-to-deploy-a-956ae5.

3. alinux · custom-rag-pipeline-with-deployed-frontend-ba57d2 — Custom RAG Pipeline with Deployed Frontend

See _combos/custom-rag-pipeline-with-deployed-frontend-ba57d2.

4. alinux · full-custom-rag-custom-llm-custom-embeddings-75fbf5 — Full Custom RAG: Custom LLM + Custom Embeddings

See _combos/full-custom-rag-custom-llm-custom-embeddings-75fbf5.

Typical questions

• fully custom RAG with web frontend
• train custom LLM and deploy chatbot UI
• end-to-end custom RAG from PAI to Vercel
• custom model RAG with production frontend
• fine-tune LLM and embeddings and deploy web app
• 完全自定义RAG加前端部署
• 从PAI训练定制模型到Vercel部署完整RAG
• 自定义大模型加嵌入模型加聊天机器人前端

Q: How do I build a fully custom RAG system with a deployed web frontend? A: You can implement this architecture by fine-tuning a domain-specific LLM and training custom embedding models on PAI, then delivering a polished chatbot UI on Vercel with infrastructure managed by Terraform. The setup connects these components using a vector search pipeline built with OpenSearch or Elasticsearch and OSS, while the inference backend is deployed via Bailian.