senghuyjr11/agentic_multimodal_qa

The project represents a standard implementation of agentic RAG (Retrieval-Augmented Generation) applied to multimodal data. With only 2 stars and no forks over nearly 6 months, it demonstrates zero market traction or community adoption. The technical approach—using agents and cosine similarity for multimodal QA—is currently the industry baseline and is being natively integrated into frontier models (e.g., OpenAI's GPT-4o and Google's Gemini 1.5 Pro). This project lacks a unique dataset, a specialized architecture, or a performance moat that would prevent it from being rendered obsolete by standard library updates from LlamaIndex or LangChain, or by native features from model providers. Its 'defensibility' is minimal as it serves more as a personal experiment or a reference pattern than a durable software product.