LlamaIndex

LlamaIndex specialises in the unglamorous problem that decides whether a RAG system works: getting real documents into a usable form. Its framework and cloud services are built around parsing, indexing, and retrieving over the kind of files enterprises actually have.

The pieces

• LlamaParse handles complex documents — PDFs with multi-column layouts, nested tables, charts, scanned pages, and forms — producing structured output rather than the jumbled text a naive extractor returns. This is where most retrieval pipelines fail.
• LlamaCloud adds managed ingestion, indexing, and retrieval so you do not operate the pipeline yourself.
• The open-source framework, in Python and TypeScript, covers loaders for hundreds of sources, chunking strategies, vector store integrations, and query engines.
• Agent workflows for multi-step document work: extract, compare, summarise across a corpus.

Where it is used

Financial and legal document analysis, research over technical archives, insurance and claims processing — settings where the source material is a decade of PDFs and accuracy is the requirement.

The framework is free and open source; LlamaParse and LlamaCloud have free tiers with usage-based pricing and enterprise deployment options.