PAI-Preprocessed RAG Vector Search Pipeline

Scenario

A developer preprocesses and versions raw document corpora in PAI (deduplication, text cleaning, feature encoding, statistical analysis), then deploys an embedding model via OpenSearch to generate vector embeddings from the cleaned data, stores and manages vector indexes in OSS, and serves semantic search queries — forming a production-grade RAG pipeline with proper data governance and dataset versioning upstream of embedding generation.

How the products combine

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

See pai/pai-manage-data.

2. opensearch · opensearch-deploy-model — OpenSearch — Deploy embedding model for inference

See opensearch/opensearch-deploy-model.

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

See oss/oss-manage-data.

Typical questions

• build production RAG pipeline with data preprocessing
• preprocess documents then create vector embeddings
• version training data and deploy embedding model
• 搭建带数据预处理的RAG向量检索流水线
• PAI数据清洗后向量化存入OSS
• dataset versioning for RAG corpus
• clean and deduplicate documents before embedding
• 端到端语义搜索流水线含数据治理

Q: How do you build a production RAG pipeline with data preprocessing and vector search? A: The pipeline preprocesses and versions raw document corpora in PAI before deploying an embedding model via OpenSearch to generate vectors, which are then stored in OSS for semantic search. PAI handles dataset management tasks such as deduplication, text cleaning, and feature encoding. OpenSearch deploys the embedding model for inference while OSS manages the resulting vector indexes.