DaaS / Products / End-to-End Document Intelligence Pipeline

End-to-End Document Intelligence Pipeline

Upload raw documents (PDFs, scanned images) to OSS for durable storage, use Bailian's document understanding to extract text and structured data via OCR, index the processed content into Elasticsearch for full-text search, and optionally deploy OpenSearch embedding models to create vector indexes for semantic RAG capabilities—building a complete document intelligence system from physical scans to multi-modal search.

Products involved

Scenario

Upload raw documents (PDFs, scanned images) to OSS for durable storage, use Bailian's document understanding to extract text and structured data via OCR, index the processed content into Elasticsearch for full-text search, and optionally deploy OpenSearch embedding models to create vector indexes for semantic RAG capabilities—building a complete document intelligence system from physical scans to multi-modal search.

How the products combine

  1. es+oss · upload-files-to-oss-index-in-elasticsearch-e9ec4b — Upload Files to OSS, Index in Elasticsearch

    2. See _combos/upload-files-to-oss-index-in-elasticsearch-e9ec4b.

  2. es+opensearch+oss · vector-search-rag-pipeline-on-alibaba-cloud-96d675 — Vector Search RAG Pipeline on Alibaba Cloud

    3. See _combos/vector-search-rag-pipeline-on-alibaba-cloud-96d675.

  3. bailian+es · document-extraction-to-searchable-index-pipeline-6e55f7 — Document Extraction to Searchable Index Pipeline

    4. See _combos/document-extraction-to-searchable-index-pipeline-6e55f7.

  4. es · es-ingest-documents — Elasticsearch — Ingest and manage document data in Elasticsearch

    5. See es/es-ingest-documents.

Typical questions

FAQ

Q: What is the end-to-end document intelligence pipeline for processing scanned documents and making them searchable? A: The pipeline uploads raw documents like PDFs and scanned images to OSS for durable storage, uses Bailian's document understanding to extract text and structured data via OCR, and indexes the processed content into Elasticsearch for full-text search. It optionally deploys OpenSearch embedding models to create vector indexes for semantic RAG, forming a complete multi-modal document intelligence system.