MISBAHULLL/Information-Retrieval-RAG

This is a beginner-level educational RAG project with no users (1 star, 0 forks, 0 velocity over 112 days). It demonstrates a standard RAG pipeline—embedding → retrieval → reranking → generation—using commodity models from Hugging Face. The approach is entirely conventional and trivially reproducible by anyone following RAG tutorials. There is no novel architecture, custom dataset preprocessing, domain-specific optimization, or production hardening. The code serves as a learning artifact rather than a defensible product. Platform domination risk is HIGH because every major platform (AWS SageMaker, Google Vertex AI, Azure OpenAI, LangChain, LlamaIndex) offers native or near-native RAG capabilities as managed services or integrated libraries—often with better UX, scaling, and model selection. A user looking to build a RAG system would reach for these mature platforms rather than fork or extend this repo. Market consolidation risk is LOW only because there is no active market here; this is not competing for revenue or users. The displacement horizon is 6 months because any platform investing in RAG functionality (which they already are) will obsolete this approach through feature parity and network effects. This project has zero defensibility: no moat, no differentiation, no adoption, and no technical depth beyond standard patterns.