Ridhima0112/Ethical-RAG-Framework

This is a zero-star, zero-fork project created today with no velocity or adoption. It is a straightforward application of existing off-the-shelf tools (Flowise as a no-code RAG framework, Google Gemini for embeddings, Mistral for LLM inference) applied to a single research paper on AI ethics. There is no novel architecture, novel data collection, novel algorithm, or meaningful technical differentiation. The README describes a standard RAG chatbot built by plugging commercial APIs and open-source models into a visual workflow tool. This represents a learning exercise or demonstration project, not a defensible product or research contribution. The tech stack is entirely commodity: Flowise is a popular open-source RAG orchestrator, Google Gemini and Mistral are both widely available, and single-document RAG is a solved problem. Platform domination risk is high because Google, OpenAI, Anthropic, and Meta are all actively building RAG-as-a-feature into their chat products, and Flowise itself is a framework that could absorb or commoditize this exact pattern. Market consolidation risk is low because there is no incumbent market—this is too early-stage and niche to attract competitive acquisition. Displacement would be imminent if any competitive pressure existed, but the lack of stars, forks, or any real usage means this is not yet a market entry—it remains a tutorial or exercise. Implementation is prototype-level (Flowise no-code builder output), with no evidence of hardening, testing, or production deployment. Defensibility is minimal: anyone with access to Flowise, Gemini API, and Mistral can replicate this in hours.