abhinav-kimothi/A-Simple-Guide-to-RAG

This project is a pedagogical resource designed to accompany a book. With 259 stars and a two-year-old codebase, it represents a snapshot of early-to-mid RAG patterns. From a competitive standpoint, it has zero defensibility; the value lies in the explanation, not the code itself. The tech stack relies on standard commodity libraries like LangChain and ChromaDB. It faces extreme frontier risk as OpenAI, Google, and Anthropic have since released managed RAG features (Assistants API, Vertex AI Search) that abstract away the manual chunking and retrieval logic demonstrated here. Furthermore, the rapid shift toward 'Agentic RAG' and long-context windows (1M+ tokens) makes the basic RAG patterns shown in this repo increasingly obsolete for production use cases. It competes with a massive sea of free documentation from LlamaIndex, DeepLearning.ai, and LangChain, which are updated much more frequently.