kushnir-tadey/multimodal-rag-batch

This project is a classic implementation of a multimodal RAG pipeline, likely created for educational or portfolio purposes. With 0 stars and no forks after 134 days, it has zero market traction. It follows standard industry patterns: scraping a specific source, generating CLIP-based embeddings for images/text, and using a LLM for final synthesis. There is no technical moat; the architecture is easily reproducible using frameworks like LangChain or LlamaIndex. Frontier labs (Google, OpenAI, Anthropic) are rapidly commoditizing this space—Google's NotebookLM and OpenAI's GPT-4o native multimodal capabilities essentially render this specific tool obsolete for most users. The platform domination risk is high because cloud providers (AWS Bedrock, Azure AI Search) are integrating multimodal vector search as a managed service, and the market for RAG middleware is consolidating around established players like Unstructured.io for ingestion and Pinecone/Milvus for storage.