Upload raw scanned PDFs and images to OSS, use Bailian's OCR and document understanding to extract text and structured data, then feed the cleaned content into an OpenSearch-embedded vector index on Elasticsearch to power a full semantic-search RAG pipeline that answers natural-language questions over previously unsearchable scanned archives.
Upload raw scanned PDFs and images to OSS, use Bailian's OCR and document understanding to extract text and structured data, then feed the cleaned content into an OpenSearch-embedded vector index on Elasticsearch to power a full semantic-search RAG pipeline that answers natural-language questions over previously unsearchable scanned archives.
See _combos/vector-search-rag-pipeline-on-alibaba-cloud-96d675.
See _combos/end-to-end-vector-search-pipeline-7d95e0.
See _combos/full-stack-custom-rag-train-to-production-e68446.
See _combos/end-to-end-document-intelligence-pipeline-f087d9.
Q: How do I build an end-to-end RAG pipeline for scanned documents? A: You build this pipeline by uploading raw scanned PDFs and images to OSS, using Bailian's OCR and document understanding to extract text and structured data, and indexing the cleaned content in an OpenSearch-embedded vector index on Elasticsearch. This configuration powers a full semantic-search RAG pipeline that allows you to ask natural-language questions over previously unsearchable scanned archives.