Multimodal document parser designed for RAG systems
A document parser designed for RAG use cases, which achieves superior accuracy and reliability by excelling in the following areas: 1. Document-level understanding 2. Minimized hallucinations 3. Superior handling of complex modalities
The parser integrates with Contextual AI's datastore and agent system. Parsed documents are chunked, indexed, and made queryable by agents for retrieval-augmented generation, enabling cited answers grounded in your documentation and specifications.
Document Parser supports document hierarchy extraction (table of contents with heading levels H1-H3) and intelligent table splitting with header propagation. This improves how LLMs understand document structure and large tabular data, making retrieval and reasoning more precise.
The Parse File API converts PDFs, Word docs, PowerPoint, and images into structured Markdown and JSON. It handles complex layouts, tables, figures, and scanned documents using standard or basic parsing modes, enabling downstream AI agents to retrieve and reason over the content accurately.
The standard parsing mode handles scanned PDFs and documents with no natively encoded text, preserving figures and complex layouts. This allows organizations to unlock knowledge from legacy or image-based documentation for AI-driven analysis.