ML-Powered Semantic Search Pipeline

Scenario

Use PAI to preprocess training data and train embedding models, then store generated embeddings as vector indexes in OSS for similarity search, optionally combining with Elasticsearch neural reranking for hybrid text+vector search relevance optimization.

How the products combine

1. pai · pai-manage-data — Platform for AI (PAI) — Manage and process training datasets

See pai/pai-manage-data.

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

See oss/oss-manage-data.

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

See es/es-optimize-results.

Typical questions

• build semantic search pipeline
• train embedding model and store vectors
• ML powered search system
• vector search with relevance tuning
• RAG pipeline with trained embeddings
• 训练嵌入模型并存储向量
• 构建语义搜索系统
• PAI训练数据后做向量检索

Q: How do I build a semantic search pipeline using these products? A: You build the pipeline by combining PAI, OSS, and Elasticsearch to preprocess data, store vector indexes, and optimize search relevance. PAI manages and processes training datasets, while OSS handles the vector data and indexes. Elasticsearch then optimizes search result relevance through neural reranking.

Q: How can I train an embedding model and store the resulting vectors? A: You use PAI to preprocess training data and train embedding models before storing the generated embeddings as vector indexes in OSS. This setup allows you to manage datasets in PAI and store vector data in Object Storage Service for similarity search.

Q: How is search relevance optimized in a vector-based system? A: Relevance is optimized by optionally combining the stored vector indexes with Elasticsearch neural reranking for hybrid text and vector search. This integration enhances the accuracy of search result rankings alongside standard similarity matching.