An enterprise builds a secure data-driven application with RDS (IP whitelists, SSL) as the transactional backend synced to Elasticsearch for structured full-text search, while simultaneously deploying a RAG pipeline that stores raw documents in OSS, generates vector embeddings via OpenSearch, and indexes them into the same Elasticsearch cluster—creating a unified search experience that handles both structured record queries and semantic similarity search.
An enterprise builds a secure data-driven application with RDS (IP whitelists, SSL) as the transactional backend synced to Elasticsearch for structured full-text search, while simultaneously deploying a RAG pipeline that stores raw documents in OSS, generates vector embeddings via OpenSearch, and indexes them into the same Elasticsearch cluster—creating a unified search experience that handles both structured record queries and semantic similarity search.
See _combos/supabase-to-elasticsearch-search-pipeline-ee5260.
See _combos/migrate-db-to-rds-and-add-elasticsearch-search-bbb777.
See _combos/vector-search-rag-pipeline-on-alibaba-cloud-96d675.
See _combos/secure-rds-backend-with-elasticsearch-search-0df277.
Q: How do I build a secure application that combines structured database search with semantic vector search? A: You can build this by syncing a secure RDS backend with Elasticsearch for structured full-text search while deploying a RAG pipeline that indexes vector embeddings into the same cluster. This architecture creates a unified search experience that handles both transactional record queries and semantic similarity search.