About This Architecture

Retrieval-Augmented Generation (RAG) pipeline transforms source code repositories into queryable knowledge bases through systematic data preprocessing and embedding generation. Data flows from the Source Code Repository through preprocessing stages into Embedding Generation, which populates a Vector Database for semantic search. The Retrieval System queries the Vector Database to fetch relevant context, feeding it to the Language Model to produce accurate, grounded Generated Responses. This architecture solves the hallucination problem in LLMs by anchoring outputs in verified source material, critical for code documentation, technical support, and enterprise knowledge management. Fork this RAG pipeline diagram on Diagrams.so to customize vector store choices, add caching layers, or integrate with your LLM provider—export as .drawio, .svg, or .png for technical documentation.

People also ask

How does a RAG pipeline architecture work from source code to generated response?

A RAG pipeline processes source code through data preprocessing and embedding generation into a vector database. The retrieval system queries relevant embeddings to provide context to a language model, which generates accurate, grounded responses based on the retrieved source material.