Upload raw scanned documents and PDFs to OSS as durable source-of-truth storage, use Bailian's document understanding to extract text and structured data via OCR, index the processed content into Elasticsearch for full-text keyword search, and simultaneously build a RAG pipeline with OpenSearch that chunks, embeds, and indexes the same documents for semantic retrieval-augmented generation — creating a dual-mode search system over scanned content.
Upload raw scanned documents and PDFs to OSS as durable source-of-truth storage, use Bailian's document understanding to extract text and structured data via OCR, index the processed content into Elasticsearch for full-text keyword search, and simultaneously build a RAG pipeline with OpenSearch that chunks, embeds, and indexes the same documents for semantic retrieval-augmented generation — creating a dual-mode search system over scanned content.
See _combos/upload-files-to-oss-index-in-elasticsearch-e9ec4b.
See _combos/end-to-end-document-intelligence-pipeline-f087d9.
See _combos/document-extraction-to-searchable-index-pipeline-6e55f7.
See _combos/oss-document-store-for-opensearch-rag-pipeline-847274.
Q: How do I build a pipeline that uses OCR on scanned documents to support both keyword search and RAG? A: You can build this dual-mode search system by uploading scanned documents to OSS, extracting text via Bailian's OCR, and indexing the results into both Elasticsearch for keyword search and OpenSearch for semantic RAG. The pipeline simultaneously chunks and embeds the processed content in OpenSearch for retrieval-augmented generation while maintaining a full-text index in Elasticsearch.