Retrieval-augmented generation (RAG) pipeline for code intelligence transforms source repositories into queryable knowledge bases. Source Code Repository feeds Code Preprocessing, which flows through Chunking and Structuring to Embedding Generation, populating a Vector Database. User Interface queries trigger the Retrieval Module to fetch relevant code embeddings, which the Language Model uses to generate contextually accurate responses. This architecture enables semantic code search, automated documentation, and AI-powered code explanation tools that understand repository context. Fork this diagram on Diagrams.so to customize embedding models, swap vector databases, or add caching layers for production deployments. Ideal for teams building GitHub Copilot-style assistants or internal code Q&A systems.