Custom-Trained Models Power ES RAG App

Scenario

Use this workflow when off-the-shelf LLMs fail to grasp proprietary terminology, compliance constraints, or niche domain logic. By fine-tuning both the generation and embedding layers on PAI and anchoring retrieval in Elasticsearch, you achieve a fully customized, low-latency RAG system tailored to enterprise knowledge bases.

Integration steps

1. Stage domain corpus in OSS: aliyun oss cp ./domain_data/ oss://rag-bucket/corpus/ --recursive
2. Launch PAI training pipeline: Submit a DLC job using pai-dlc submit --config train_config.yaml with --model_name_or_path Qwen-7B, --output_dir oss://rag-bucket/models/llm-ft, and --embedding_dim 768.
3. Deploy inference endpoints: Run alinux-deploy-model to containerize the embedding model (docker run -p 8000:8000 pai-emb:v1). Register the fine-tuned LLM in Bailian via POST https://dashscope.aliyuncs.com/api/v1/models/deploy with {"model_id": "llm-ft", "instance_type": "ml.gu7i.c2xlarge"}.
4. Configure Elasticsearch index: PUT /rag-kb with mapping: "dense_vector": {"type": "dense_vector", "dims": 768, "index": true, "similarity": "cosine"}.
5. Build ES ingest pipeline: PUT _ingest/pipeline/rag-embed with {"processors": [{"split": {"field": "content", "separator": "\n\n"}}, {"http": {"url": "http://<alinux-ip>:8000/embed", "request_method": "POST", "field_map": {"content": "input"}, "json_path": "embedding"}}]}.
6. Ingest & vectorize: POST /rag-kb/_doc?pipeline=rag-embed with {"content": "Enterprise policy v2...", "metadata": {"source": "oss://..."}}.
7. Deploy RAG app: Execute es-deploy-application to scaffold the orchestration layer. Configure retrieval to query GET /rag-kb/_search with {"knn": {"field": "dense_vector", "query_vector": [0.12, ...], "k": 5}}, then forward context to Bailian's POST https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions with {"model": "llm-ft", "messages": [...]}.

Architecture

Raw documents reside in OSS and are pulled into PAI for supervised fine-tuning of both the LLM and embedding models. The trained LLM is hosted on Bailian for managed, scalable inference, while the embedding model runs on an Alibaba Cloud Linux ECS instance. Elasticsearch acts as the vector knowledge base, using an ingest pipeline to chunk text and call the custom embedding endpoint. The RAG application orchestrates the workflow: it queries ES for top-k context via KNN search, then passes the prompt + retrieved chunks to Bailian for domain-specialized answer synthesis.

Prerequisites

• Active Alibaba Cloud account with PAI, OSS, Elasticsearch (v8.10+), and Bailian enabled
• Domain-specific dataset (instruction-response JSONL for LLM, paired text for embeddings)
• PAI workspace with GPU quota (A10/V100)
• Bailian API key and model deployment permissions
• Python 3.10+ environment with elasticsearch, requests, and oss2 SDKs

Common pitfalls

• Dimension mismatch: ES dims must exactly match the PAI-trained embedding output (e.g., 768 vs 1024).
• Ingest pipeline timeouts: Calling external embedding endpoints from ES can exceed the default 30s timeout; increase http.timeout or pre-batch embeddings.
• Bailian cold starts: First inference requests may take >10s; implement exponential backoff or keep-alive pings.
• Chunk overlap misalignment: Overlapping chunks during ingestion but not during query embedding degrades retrieval accuracy.
• PAI training data leakage: Failing to strictly split train/eval sets causes overfitting, resulting in hallucinated domain answers.

Typical questions

• train custom models then deploy RAG app
• build custom RAG with fine-tuned models
• train LLM and deploy ES chatbot
• custom trained RAG application
• fine-tune models and deploy retrieval app
• 训练自定义模型并部署RAG应用
• 微调大模型后部署ES问答系统
• 从头构建定制化RAG聊天机器人

Q: How do I fine-tune custom models and deploy a RAG application? A: You fine-tune domain-specific LLM and embedding models on PAI and then deploy a RAG chatbot application using Elasticsearch as the retrieval engine. This configuration enables enterprise-grade, domain-specialized question answering by calling the custom-trained models during retrieval.