DaaS / Products / Scanned Document RAG with Vector Search

Scanned Document RAG with Vector Search

Upload scanned PDFs and images to OSS, use Bailian document understanding to OCR-extract text and structured data, then deploy OpenSearch embedding models to vectorize the extracted content and build searchable indexes in Elasticsearch for end-to-end RAG over previously unsearchable physical documents.

Products involved

Scenario

Upload scanned PDFs and images to OSS, use Bailian document understanding to OCR-extract text and structured data, then deploy OpenSearch embedding models to vectorize the extracted content and build searchable indexes in Elasticsearch for end-to-end RAG over previously unsearchable physical documents.

How the products combine

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

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

  2. bailian+es+es+es+opensearch+oss+es+oss · end-to-end-document-intelligence-pipeline-f087d9 — End-to-End Document Intelligence Pipeline

    3. See _combos/end-to-end-document-intelligence-pipeline-f087d9.

  3. alinux+bailian+alinux+bailian+alinux+pai+bailian+bailian+es+es+opensearch+oss+oss+pai+es+opensearch+oss+oss+pai+bailian+es+es+opensearch+oss+oss+pai+bailian+pai+bailian+pai+es+alinux+bailian+bailian+pai+es+opensearch+es+opensearch+alinux+oss+rds+alinux+oss+rds+ecs+oss+terraform+ecs+rds+terraform+alinux+rds+ecs+oss+terraform+alinux+rds+es+opensearch+oss+es+rds+es+supabase+bailian+es+es+opensearch+oss+oss+pai+es+rds+terraform+es+vercel+alinux+pai+bailian+es+es+opensearch+oss+oss+pai+bailian+pai+bailian+pai+bailian+es+es+opensearch+oss+oss+pai+es+opensearch+oss+es+oss+pai · full-stack-custom-rag-train-to-production-e68446 — Full-Stack Custom RAG: Train to Production

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

  4. es+oss+pai · ml-powered-semantic-search-pipeline-b3728a — ML-Powered Semantic Search Pipeline

    5. See _combos/ml-powered-semantic-search-pipeline-b3728a.

Typical questions

FAQ

Q: How do I build a RAG pipeline for scanned documents using OCR and vector search? A: You can build an end-to-end RAG pipeline for scanned documents by uploading them to OSS, extracting text and structured data with Bailian document understanding, vectorizing the content using OpenSearch embedding models, and indexing it in Elasticsearch. This workflow enables semantic search over previously unsearchable physical documents.