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Custom Model-Enhanced RAG Recommendation Platform

A developer fine-tunes a custom embedding and reranking model on PAI, deploys it to Bailian for Elasticsearch neural reranking, then builds on that optimized search layer a full RAG chatbot for document Q&A alongside AIRec-powered semantic recommendations—creating an end-to-end pipeline where the custom-trained model directly improves retrieval quality for both the conversational and recommendation surfaces.

Products involved

Scenario

Use this pipeline when enterprise search and recommendation surfaces require domain-specific semantic understanding that off-the-shelf models cannot provide. It is ideal for teams needing a unified retrieval layer where a single fine-tuned embedding/reranker powers both conversational Q&A and personalized content discovery without duplicating infrastructure.

Integration steps

  1. Fine-tune & Export on PAI: Train your model in PAI-DSW, then push artifacts to OSS.

    2. pai-cli model export --workspace-id <ws_id> --model-path oss://<bucket>/models/reranker-v1/

  2. Deploy to PAI-EAS: Serve the model for low-latency online inference.

    3. pai-cli eas deploy --model oss://<bucket>/models/reranker-v1 --instance-type ecs.g6.xlarge --replicas 2

  3. Register in Bailian: Link the EAS endpoint to Bailian’s model registry for ES routing.

    4. POST https://dashscope.aliyuncs.com/api/v1/models/register {"model_name": "custom-rerank-v1", "endpoint": "<pai-eas-url>", "type": "reranker"}

  4. Ingest & Chunk via Bailian → OSS → ES: Parse documents and index to Elasticsearch.

    5. POST https://dashscope.aliyuncs.com/api/v1/pipeline/ingest {"source": "oss://<bucket>/docs/", "target_index": "enterprise-kb", "chunk_size": 512}

  5. Configure ES Neural Reranking: Enable Bailian plugin and set the custom model.

    6. PUT /enterprise-kb/_settings {"index.knn": true, "neural_search.model_id": "custom-rerank-v1", "rerank.top_k": 50}

  6. Deploy RAG Chatbot: Point your retriever to the ES neural endpoint.

    7. GET /enterprise-kb/_search {"_source": ["content"], "query": {"neural": {"text_embedding": {"query_text": "user_question", "k": 10}}}}

  7. Wire AIRec for Semantic Recommendations: Sync ES schema and attach PAI-EAS for custom scoring.

    8. POST /v2/openapi/instances/<airec_id>/actions/sync-schema {"feature_source": "elasticsearch", "index_name": "enterprise-kb", "custom_ranker_url": "<pai-eas-url>"}

  8. Edge Routing: Front PAI-EAS with Cloudflare Workers for caching and rate limiting.

    9. wrangler deploy --name airec-pai-proxy --config cloudflare.toml

Architecture

Raw documents reside in OSS and are parsed/chunked by Bailian before indexing into Elasticsearch. Bailian’s neural plugin routes embedding and reranking requests to the PAI-EAS endpoint hosting your fine-tuned model. Elasticsearch acts as the unified vector/hybrid search layer. The RAG application queries ES directly for context retrieval, while AIRec consumes the same ES index for candidate generation, applying the PAI-EAS model for real-time personalized ranking. Cloudflare sits at the edge to cache frequent inference payloads and enforce rate limits.

Prerequisites

Common pitfalls

Typical questions

FAQ

Q: How do I fine-tune a custom model on PAI and use it to build a RAG chatbot with semantic recommendations? A: You can build this end-to-end pipeline by fine-tuning a custom embedding and reranking model on PAI, deploying it to Bailian for Elasticsearch neural reranking, and then constructing a RAG chatbot alongside AIRec-powered semantic recommendations. This workflow establishes an optimized search layer where the custom-trained model directly improves retrieval quality for both the conversational and recommendation surfaces.