DaaS / Products / End-to-End RAG with Tuned Hybrid Retrieval

End-to-End RAG with Tuned Hybrid Retrieval

A developer builds a complete RAG pipeline by first ingesting raw documents from OSS through OpenSearch embedding models into Elasticsearch vector indexes (Skill 3), then layers on Elasticsearch relevance tuning—synonym expansion, custom reranking, and hybrid vector-plus-keyword scoring (Skill 1)—to productionize retrieval quality for a semantic search application.

Products involved

Scenario

A developer builds a complete RAG pipeline by first ingesting raw documents from OSS through OpenSearch embedding models into Elasticsearch vector indexes (Skill 3), then layers on Elasticsearch relevance tuning—synonym expansion, custom reranking, and hybrid vector-plus-keyword scoring (Skill 1)—to productionize retrieval quality for a semantic search application.

How the products combine

  1. es+oss · hybrid-vector-keyword-search-system-3cb028 — Hybrid Vector + Keyword Search System

    2. See _combos/hybrid-vector-keyword-search-system-3cb028.

  2. bailian+es+es+opensearch+oss+opensearch · enterprise-rag-with-bailian-and-vector-search-d244d7 — Enterprise RAG with Bailian and Vector Search

    3. See _combos/enterprise-rag-with-bailian-and-vector-search-d244d7.

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

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

  4. airec+opensearch+es+opensearch+oss+es+oss+opensearch+airec+opensearch+es+opensearch+oss+es+oss+opensearch+airec+opensearch+es+opensearch+oss+es+oss+opensearch+bailian+bailian+es+bailian+es+airec+opensearch+es+opensearch+oss+es+oss+opensearch+bailian+bailian+es+bailian+es+es+es+opensearch+oss+es+oss+bailian+es+bailian+es+es+es+opensearch+oss+es+oss+es+opensearch+oss · ocr-enhanced-hybrid-rag-pipeline-f952fd — OCR-Enhanced Hybrid RAG Pipeline

    5. See _combos/ocr-enhanced-hybrid-rag-pipeline-f952fd.

Typical questions

FAQ

Q: How do I build an end-to-end RAG pipeline with tuned hybrid retrieval? A: You build this pipeline by ingesting raw documents from OSS through OpenSearch embedding models into Elasticsearch vector indexes, then layering on Elasticsearch relevance tuning features like synonym expansion, custom reranking, and hybrid vector-plus-keyword scoring. This architecture enables you to productionize retrieval quality for semantic search applications.