AI BriefingBuilding Agentic RAG with Internal Knowledge (1/3): Establishing the RAG Pipeline Foundation
Key point
Yeogi-eottae Company designed a data pipeline to build an Agentic RAG MCP that leverages internal knowledge.
Details
The Common Platform Development Team at Yeogi-eottae Company built an Agentic RAG MCP to improve accessibility to internal knowledge. The existing Confluence/Jira MCP suffered from low precision due to the limitations of keyword matching and ignored image information.
To address this, they designed a hybrid retriever architecture combining LightRAG for semantic search and Navigator for exploring document structures. The data pipeline consists of four stages: collection, transformation, alt-text generation, and synchronization, adopting incremental data processing to reduce LLM invocation costs.
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