Unified Data Platform: Search + RAG Answers

Scenario

Migrate structured business data (products, inventory, records) to ApsaraDB RDS, build a hybrid retrieval layer combining Elasticsearch keyword search with OSS-hosted vector embeddings, then layer OpenSearch's RAG pipeline on top to generate natural language answers—delivering a single platform that serves both catalog browsing and conversational document Q&A.

How the products combine

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

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

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

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

3. opensearch · opensearch-build-solution — OpenSearch — Build a Retrieval-Augmented Generation (RAG) solution

See opensearch/opensearch-build-solution.

4. es+opensearch+oss+es+opensearch+oss+es+rds+es+supabase+rds+es+oss+opensearch · hybrid-search-with-rag-answer-generation-e56f9a — Hybrid Search with RAG Answer Generation

See _combos/hybrid-search-with-rag-answer-generation-e56f9a.

Typical questions

• build full search platform with AI answers
• migrate database and add hybrid search plus RAG
• structured data search with conversational Q&A
• end-to-end search and answer system
• unified catalog search and document assistant
• 搭建完整搜索+AI回答平台
• 数据库迁移加混合检索加RAG生成
• 结构化数据搜索配合文档问答系统

Q: How can I build a unified search platform that combines structured data, hybrid search, and AI-powered conversational answers? A: You can build this platform by migrating structured business data to ApsaraDB RDS, implementing an Elasticsearch and OSS hybrid retrieval layer, and layering OpenSearch's RAG pipeline to generate natural language answers. This integrated architecture delivers a single system that supports both catalog browsing and conversational document Q&A.