Multimodal rag

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

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

George He, head of engineering at LlamaIndex, explores when agents need pre-indexed vector search and when plain file traversal with grep is enough. He contrasts Claude Code's embedding-free approach on code with the harder reality of large, messy, multimodal, permissioned company documents, and proposes a harness of hybrid retrieval, metadata filtering, grep, and reading tools backed by LlamaParse and LiteParse. The talk closes with a live demo of an agent building a cash-flow table from 135 Alphabet filings, grounded in source files.

What is Multimodal RAG? Unlocking LLMs with Vector Databases

What is Multimodal RAG? Unlocking LLMs with Vector Databases

A technical breakdown of three distinct approaches for implementing Multimodal Retrieval-Augmented Generation (RAG), moving from simple text conversion to fully integrated cross-modal systems. The discussion covers the architecture, trade-offs, and capabilities of each method.