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Hybrid Search with RAG Answer Generation

Build a hybrid search system using Elasticsearch and OSS for vector+keyword retrieval, then layer OpenSearch's RAG pipeline on top to generate natural language answers from the retrieved documents, creating a complete search-and-answer application.

Products involved

Scenario

Build a hybrid search system using Elasticsearch and OSS for vector+keyword retrieval, then layer OpenSearch's RAG pipeline on top to generate natural language answers from the retrieved documents, creating a complete search-and-answer 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. opensearch · opensearch-build-solution — OpenSearch — Build a Retrieval-Augmented Generation (RAG) solution

    3. See opensearch/opensearch-build-solution.

  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. es+opensearch+oss+es+rds+es+supabase+rds · hybrid-search-structured-data-semantic-rag-e053d8 — Hybrid Search: Structured Data + Semantic RAG

    5. See _combos/hybrid-search-structured-data-semantic-rag-e053d8.

Typical questions

FAQ

Q: How do I build a search system that combines hybrid retrieval with AI-generated answers? A: You can build this system by using Elasticsearch and OSS for hybrid vector and keyword retrieval, then layering OpenSearch's RAG pipeline on top to generate natural language answers. This combination creates a complete search-and-answer application through several predefined cross-product skill pipelines.