Grep or Embeddings? Agentic Search Over Company Documents — George He, LlamaIndex

This post contributes to the field of RAG and document intelligence by detailing how to build robust AI systems that can process and reason over complex, non-standard document formats like PDFs, slides, and schematics. It highlights the challenges of traditional OCR and RAG when dealing with visual encoding of meaning and large, multimodal, permissioned company data. It introduces agentic approaches for improved accuracy and reasoning by providing agents with a harness of tools, including hybrid retrieval, listing, metadata filtering, grep, and reading capabilities. The work emphasizes specialized data processing with LlamaParse and LiteParse, agent harnesses for multi-step problem-solving, and comprehensive production concerns like multi-tenancy, permissions, and data freshness. The post demonstrates an agent building a cash-flow table from financial filings, grounded in source files, showcasing the power of agentic search over company documents.