Lumina-AI
Lumina-AI is an intelligent platform that 'reads' entire codebases. It analyzes GitHub repositories to provide high-level summaries, answer architectural questions, and suggest code improvements, acting as an automated senior developer on demand.
Built on a Retrieval-Augmented Generation (RAG) architecture. Codebases are converted into vector embeddings and stored in Pinecone. User queries trigger a similarity search to find relevant code snippets, which are then injected into an LLM prompt for context-aware answers.
The Challenge
The core technical challenge was overcoming the context window limitations of Large Language Models (LLMs). A standard LLM cannot process an entire codebase at once. The system needed a way to provide the AI with only the most relevant pieces of code to answer a specific user query.
The Solution
The platform was built on a Retrieval-Augmented Generation (RAG) architecture. Entire repositories are ingested, broken down into manageable chunks, and converted into vector embeddings stored in a Pinecone database. When a user asks a question, the system first performs a similarity search on the vector database to find the most relevant code snippets. Only these snippets are then 'injected' into the prompt for the LLM, enabling it to provide accurate, context-aware answers about massive codebases.
Solution Architecture
A visual diagram of the system's components and data flow is available.
Gerasimos Makris is an AI Web Developer with a background in FinTech operations. He specializes in building secure, scalable web applications that solve real-world financial problems. When he's not coding, he enjoys exploring the intersection of technology, finance, and business strategy.
