Hybrid Vector + Keyword Search System

Scenario

Build a hybrid search application that stores embedding vectors in OSS for similarity search while using Elasticsearch with relevance tuning (synonyms, reranking) for keyword search, combining both retrieval paths to serve higher-quality results in a RAG or product-search pipeline.

How the products combine

1. oss · oss-manage-data — Object Storage Service — Manage vector data and indexes

See oss/oss-manage-data.

2. es · es-optimize-results — Elasticsearch — Optimize search result relevance

See es/es-optimize-results.

Typical questions

• build hybrid search system
• combine vector and keyword search
• hybrid retrieval pipeline
• RAG search backend
• semantic search with Elasticsearch
• 向量检索+关键词搜索混合系统
• 搭建混合搜索引擎
• OSS向量存储配合ES搜索

Q: How do I build a hybrid search system that combines vector and keyword retrieval? A: A hybrid search system is built by storing embedding vectors in Object Storage Service (OSS) for similarity search while using Elasticsearch with relevance tuning for keyword search. Combining these two retrieval paths delivers higher-quality results for RAG or product-search pipelines.