RAG Pipeline Hands-on Case Study
This series uses a real enterprise technical documentation intelligent Q&A scenario as an example, demonstrating step by step how to implement the complete workflow from document ingestion to intelligent Q&A through a RAG Pipeline.
Scenario Overview
A technology company wants to build an intelligent Q&A system for internal technical documents (in PDF format, including text, tables, and flowcharts), with the following requirements:
- Accurately retrieve multiple content types such as text, tables, and images
- Support multi-turn conversations and understand contextual references
- Ensure answers can be traced back to the original document fragments
To achieve this, we split the capabilities of the RAG Pipeline into two separate Pipelines—preprocessing and retrieval—and orchestrate them separately, completing the process step by step through two tutorials.
Tutorial Directory
| No. | Tutorial | Content |
|---|---|---|
| 1 | Configure the Preprocessing Pipeline and Knowledge Base Ingestion | Orchestrate the preprocessing Pipeline (text extraction, intelligent chunking, summary/image/table enhancement, vectorized storage), create a knowledge base, and upload documents |
| 2 | Configure the Retrieval Pipeline and Agent Q&A | Orchestrate the retrieval Pipeline (query rewriting, dual-channel retrieval, multi-level reranking, LLM-generated answers), bind an Agent, and verify the Q&A results |
Prerequisites
- SERVICEME V4.2 or later
- The operating user has administrator privileges
- PDF technical documents to be processed are already prepared
💡 It is recommended to read in order, as the second tutorial depends on the knowledge base data from the first tutorial.