RAG-Powered Semantic Recommendation Platform

Scenario

Build a complete knowledge discovery platform where documents stored in OSS are embedded via OpenSearch and indexed in Elasticsearch for RAG-based search, then the same semantic embeddings power AIRec to deliver personalized content recommendations based on user behavior and semantic understanding.

How the products combine

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

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

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

See opensearch/opensearch-build-solution.

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

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

4. airec · semantic-search-powered-recommendation-system-5bbd35 — Semantic Search-Powered Recommendation System

See _combos/semantic-search-powered-recommendation-system-5bbd35.

Typical questions

• build RAG system with recommendations
• semantic search and recommendation engine
• document search with personalized recommendations
• knowledge base with AIRec integration
• 搭建RAG检索推荐一体化系统
• 文档搜索配合智能推荐
• OpenSearch向量检索加AIRec推荐
• end-to-end semantic discovery platform

Q: How do I build a RAG-powered semantic search and recommendation platform? A: You can build this integrated system by storing documents in OSS, embedding them via OpenSearch, and indexing them in Elasticsearch for RAG-based search. The same semantic embeddings then power AIRec to deliver personalized content recommendations based on user behavior and semantic understanding.