aishanee-sinha/Multimodal-RAG-Chatbot

The project is a standard implementation of a multimodal RAG pattern that has become a common tutorial use case in 2024. With 0 stars and 0 forks after 73 days, it lacks any market traction or community momentum. From a technical perspective, it uses commodity components: Google Gemini for reasoning, Pinecone for vector storage, and likely standard libraries like Unstructured or PyMuPDF for document parsing. The 'moat' is non-existent as frontier labs (OpenAI, Google) are rapidly internalizing these capabilities. For example, Google's Gemini 1.5 Pro now supports massive context windows (up to 2M tokens) which allows for 'RAG-less' processing of entire PDF libraries with native multimodal understanding, effectively making simple RAG architectures for small-to-medium documents obsolete. Furthermore, platforms like Vertex AI and AWS Bedrock now offer native document processing and indexing services that perform these exact steps as a managed service. This project serves as a good personal learning exercise but has no defensive characteristics against either open-source giants (LlamaIndex, LangChain) or frontier model providers.