Full Custom RAG: Custom LLM + Custom Embeddings

Scenario

Fine-tune a domain-specific LLM on PAI and deploy it to Bailian as a managed inference endpoint, while simultaneously training custom embedding models on PAI to power a vector search pipeline (OpenSearch/Elasticsearch/OSS) that feeds context to the custom LLM—delivering a production RAG application where both the retrieval and generation layers are custom-trained on proprietary data.

How the products combine

1. alinux+pai · train-and-deploy-ml-model-pipeline-eab66b — Train and Deploy ML Model Pipeline

See _combos/train-and-deploy-ml-model-pipeline-eab66b.

2. bailian+es+es+opensearch+oss+oss+pai · 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. bailian+pai · fine-tune-on-pai-deploy-via-bailian-eb4485 — Fine-Tune on PAI, Deploy via Bailian

See _combos/fine-tune-on-pai-deploy-via-bailian-eb4485.

4. bailian+bailian+es+es+opensearch+oss+oss+pai+es+opensearch+oss+oss+pai · 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.

Typical questions

• build fully custom RAG system
• train custom LLM and custom embeddings for RAG
• end-to-end custom RAG with custom model and embeddings
• full stack RAG with fine-tuned LLM and custom embeddings
• custom trained RAG pipeline
• 搭建完全自定义的RAG系统
• 训练自定义大模型和嵌入模型构建RAG
• 从头构建定制化的RAG应用

Q: How do I build an end-to-end custom RAG system with a fine-tuned LLM and custom embeddings? A: You build this system by combining PAI for model training, Bailian for managed inference deployment, and OpenSearch, Elasticsearch, or OSS for vector search. This workflow allows you to fine-tune a domain-specific LLM on PAI and deploy it via Bailian, while simultaneously training custom embedding models on PAI to power the retrieval pipeline. The result is a production RAG application where both the retrieval and generation layers are custom-trained on proprietary data.