The increasing demand for AI-driven solutions in development has encouraged people to conduct various research into generating code from natural language prompts. My paper presents a Retrieval-Augmented Generation (RAG) pipeline for code generation, making use of embedding models, contextual retrieval and advanced language models such as Mistral and CodeLLama. This approach incorporates document indexing and metadata extraction to create context-aware code snippets and at the end of the process, we get a python file with the generated code present in it.