Custom LLM RAG with Intelligent Recommendations

Scenario

Developers building enterprise-grade AI applications that require both precise domain-specific Q&A and personalized content delivery use this stack. It combines PAI-trained custom models, OpenSearch vector retrieval, Bailian inference, and AIRec personalization into a single, Terraform-managed pipeline deployed via Vercel.

Integration steps

1. Provision Infrastructure: Deploy ECS (ALinux), RDS, OSS, and OpenSearch using Terraform.

``hcl resource "alicloud_opensearch_instance" "vector_db" { instance_type = "opensearch.vector" node_spec = "8C32G" } ``

2. Fine-tune Models on PAI: Train your domain LLM and embedding model via PAI-DLC.

``bash pai-dlc submit-job --image registry.cn-shanghai.aliyuncs.com/pai/llm-finetune:latest \ --config '{"base_model": "qwen-7b", "train_data": "oss://my-bucket/domain_corpus.jsonl"}' ``

3. Index Vectors in OpenSearch: Push PAI-generated embeddings to OpenSearch with dense_vector mapping.

``json PUT /rag-index/_mapping { "properties": { "embedding": { "type": "dense_vector", "dims": 1024, "index": true } } } ``

4. Deploy Bailian Inference: Register the fine-tuned model and generate a serverless endpoint.

``bash curl -X POST https://dashscope.aliyuncs.com/api/v1/services/aigc/text-generation/generation \ -H "Authorization: Bearer $BAILIAN_API_KEY" \ -d '{"model": "ft-qwen-domain-v1", "input": {"prompt": "..."}}' ``

5. Configure AIRec Personalization: Sync user interaction logs and enable hybrid neural ranking.

``python airec_client.recommend(user_id="u123", scene="doc_feed", top_k=10, strategy="neural_rerank") ``

6. Deploy Vercel Frontend: Route Next.js API calls to Bailian and AIRec.

``ts export async function POST(req: Request) { const res = await fetch(process.env.BAILIAN_API_URL, { body: JSON.stringify(req.body) }); return NextResponse.json(await res.json()); } ``

Architecture

Terraform provisions the underlying compute (ECS/ALinux), storage (OSS, RDS), and search (OpenSearch) layers. Domain documents are chunked, embedded via PAI-trained models, and stored as dense_vector fields in OpenSearch. User queries hit the Vercel frontend, which routes to Bailian for LLM generation while simultaneously calling AIRec for personalized document ranking. OpenSearch handles hybrid retrieval (BM25 + ANN), and Bailian orchestrates the final RAG synthesis.

Prerequisites

• Alibaba Cloud account with PAI, OpenSearch, Bailian, AIRec, and OSS enabled
• Terraform CLI v1.5+ and alicloud provider configured
• Pre-processed domain corpus in JSONL format stored in OSS
• Node.js 18+ environment and Vercel CLI for frontend deployment
• Valid API keys for Bailian (DASHSCOPE_API_KEY) and AIRec

Common pitfalls

• Dimension mismatch: PAI-trained embeddings often output 1024 dims, but OpenSearch defaults to 768. Explicitly set dims in the index mapping.
• AIRec cold start: Without historical click/interaction logs, AIRec defaults to popularity-based ranking. Seed with synthetic engagement data during testing.
• Bailian rate limits: Concurrent RAG requests easily hit 429 errors. Implement exponential backoff and use max_tokens: 2048 to control payload size.
• OpenSearch memory pressure: Dense vectors consume significant heap. Set index.knn.memory_limit to 50% of node RAM and use hnsw instead of flat for large datasets.

Typical questions

• train custom LLM and build RAG with recommendations
• fine-tune models and deploy chatbot plus recommendation engine
• PAI custom LLM embeddings reranker with AIRec and Vercel frontend
• full stack custom RAG recommendation platform
• domain-specific LLM with personalized recommendations and chatbot UI
• 微调大模型加排序模型构建RAG推荐双通道平台
• PAI训练自定义模型加智能推荐加聊天机器人完整系统
• 自定义大模型RAG加个性化推荐加前端部署

Q: How can I build and deploy a custom LLM RAG system with intelligent recommendations? A: You build this platform by fine-tuning domain-specific LLMs and custom embedding or reranking models on PAI, then connecting them to OpenSearch or Elasticsearch and OSS for optimized vector retrieval. The solution layers an AIRec engine for personalized recommendations alongside a RAG chatbot, manages infrastructure with Terraform, and serves both features through a Vercel-deployed frontend powered by Bailian inference.