GarciaPL/calpers-pe-rag

The project is a standard RAG implementation applied to a niche public dataset (CalPERS PE reports). With 0 stars, 0 forks, and being 0 days old, it currently represents a personal experiment or a specific use-case demo rather than a viable product or infrastructure project. The defensibility is near-zero because the primary 'value'—the data—is publicly available, and the processing logic follows standard patterns found in tutorials for LlamaIndex or LangChain. Frontier models (GPT-4o, Claude 3.5 Sonnet) with large context windows can now ingest these specific PDFs directly or via standard 'Chat with PDF' features, rendering dedicated RAG pipelines for small public datasets largely obsolete. Competitive pressure comes from fintech incumbents like AlphaSense, Bloomberg, and specialized AI startups like Hebbia, who offer far more robust parsing and cross-document synthesis capabilities for the financial sector.