A developer uploads raw scanned documents (PDFs, images) to OSS, uses Bailian's document understanding to perform OCR and extract structured text, indexes the processed content into Elasticsearch for keyword search, and simultaneously deploys OpenSearch embedding models to generate vector embeddings for semantic RAG retrieval — creating a hybrid search system that combines extracted full-text search with semantic similarity over the same document corpus.
A developer uploads raw scanned documents (PDFs, images) to OSS, uses Bailian's document understanding to perform OCR and extract structured text, indexes the processed content into Elasticsearch for keyword search, and simultaneously deploys OpenSearch embedding models to generate vector embeddings for semantic RAG retrieval — creating a hybrid search system that combines extracted full-text search with semantic similarity over the same document corpus.
See _combos/end-to-end-document-intelligence-pipeline-f087d9.
See _combos/full-stack-document-ai-ocr-to-recommendations-871ebe.
See _combos/vector-search-rag-pipeline-on-alibaba-cloud-96d675.
See _combos/document-extraction-to-searchable-index-pipeline-6e55f7.
Q: How do I build a hybrid search pipeline that combines OCR text extraction with both keyword and vector-based RAG retrieval? A: You build this pipeline by uploading scanned documents to OSS, using Bailian to perform OCR and extract structured text, indexing the results into Elasticsearch for keyword search, and deploying OpenSearch embedding models to generate vector embeddings for semantic RAG retrieval. This architecture creates a unified hybrid search system that combines extracted full-text search with semantic similarity over the same document corpus.