OscarV123/alicorp-financial-rag

The project is a standard implementation of a RAG pipeline applied to a specific set of public financial documents (Alicorp). With only 1 star and no forks after nearly four months, it represents a personal project or a technical exercise rather than a defensible software product. Its utility is limited to a very narrow dataset, and the methodology—chunking PDFs for retrieval—is now a commodity feature provided natively by frontier models and platforms (e.g., OpenAI's File Search, Google's NotebookLM, and Claude's 200k context window). There is no technical moat, as the implementation relies on standard patterns found in tutorials for LangChain or LlamaIndex. From a competitive standpoint, any user can replicate this functionality by uploading the same public PDFs to a standard GPT-4 or Gemini 1.5 interface, which often handles financial tables more robustly than basic open-source RAG implementations.