End-to-End RAG: Knowledge Base to Deployed Pipeline

Scenario

Use Bailian to ingest documents, chunk, embed, and build a managed RAG knowledge base with retrieval pipelines, then deploy OpenSearch as the vector retrieval engine and Bailian as the LLM generation endpoint to serve a complete production RAG application.

How the products combine

1. bailian+opensearch · rag-pipeline-with-retrieval-and-generation-a17b40 — RAG Pipeline with Retrieval and Generation

See _combos/rag-pipeline-with-retrieval-and-generation-a17b40.

2. es · es-deploy-application — Elasticsearch — Deploy a Retrieval-Augmented Generation (RAG) AI application

See es/es-deploy-application.

3. bailian · bailian-build-system — — Build RAG knowledge bases and retrieval pipelines

See bailian/bailian-build-system.

4. opensearch+pai · rag-pipeline-embedding-search-llm-inference-b49ee9 — RAG Pipeline: Embedding Search + LLM Inference

See _combos/rag-pipeline-embedding-search-llm-inference-b49ee9.

Typical questions

• build and deploy RAG pipeline
• 搭建并部署RAG系统
• knowledge base to production RAG
• 从知识库到RAG应用
• full RAG system setup
• deploy complete RAG application
• 端到端RAG部署
• build knowledge base then deploy RAG

Q: How do I build and deploy an end-to-end RAG pipeline from a knowledge base to production? A: An end-to-end RAG pipeline is built and deployed by using Bailian to ingest documents, chunk them, generate embeddings, and create a managed knowledge base with retrieval pipelines. OpenSearch is then deployed as the vector retrieval engine while Bailian serves as the LLM generation endpoint to deliver the complete production application.