Tag
LlamaIndex promotes LlamaParse's ability to cleanly extract nested tables (e.g., from Micron's earnings deck) into markdown, highlighting features like layout awareness, multimodal parsing, multilingual support, and enterprise readiness.
This paper introduces AutoGrable, a method that scores candidate graph constructions for tabular data without training a GNN, using a label-alignment risk based on 1-WL color refinement, enabling cheap search for effective table-to-graph mappings.
Releasing Chandra 2.1, an improved OCR model that is smaller, faster, and significantly better at handling complex tables and multilingual content, now live on the Datalab API.
Proposes Structure-Aware RAG (SA-RAG), which uses tables as an intermediate structured representation to reduce noise in retrieval-augmented generation for conversational agents, with quality-aware metadata generation and two table generation methods, outperforming existing baselines on noisy real-world datasets.