OCR Document Intelligence with Personalized Search

Scenario

A team trains domain-specific embedding models on PAI, builds an OCR-powered document ingestion pipeline using Bailian to process scanned PDFs and images, deploys hybrid vector+BM25 retrieval in OpenSearch, then layers AIRec on top to deliver personalized semantic search results tailored to individual user preferences and behavior patterns.

How the products combine

1. airec · custom-trained-ocr-rag-pipeline-324afe — Custom-Trained OCR RAG Pipeline

See _combos/custom-trained-ocr-rag-pipeline-324afe.

2. alinux · full-stack-custom-rag-train-to-production-e68446 — Full-Stack Custom RAG: Train to Production

See _combos/full-stack-custom-rag-train-to-production-e68446.

3. airec · custom-rag-training-to-personalized-production-s-0a7078 — Custom RAG Training to Personalized Production Search

See _combos/custom-rag-training-to-personalized-production-s-0a7078.

4. airec · custom-trained-rag-with-personalized-recommendat-224893 — Custom-Trained RAG with Personalized Recommendation Layer

See _combos/custom-trained-rag-with-personalized-recommendat-224893.

Typical questions

• train custom embeddings OCR documents and add personalized recommendations
• PAI training plus scanned document processing with personalization layer
• OCR RAG pipeline with user-level personalization
• train domain models process scanned docs and personalize search results
• full stack OCR document intelligence with recommendation engine
• PAI训练加OCR文档处理加个性化推荐
• 从模型训练到OCR文档RAG再加AIRec推荐
• 扫描文档智能检索加个性化推荐全链路

Q: How does the OCR Document Intelligence solution integrate custom model training, document processing, and personalized search? A: The solution combines PAI for training domain-specific embedding models, Bailian for OCR-ingesting scanned documents, OpenSearch for hybrid vector and BM25 retrieval, and AIRec to deliver personalized search results. This pipeline processes scanned PDFs and images through an OCR ingestion step before indexing them alongside custom vectors, then applies AIRec to tailor semantic search outputs to individual user preferences and behavior patterns.