LlamaParse

Open-source library for preprocessing unstructured documents (PDFs, Word, HTML, images) for RAG and LLM applications. Handles extraction, chunking, and cleaning of diverse document types.

RAG-powered text-to-SQL framework that enables natural language querying of SQL databases using vector search for retrieval of relevant schema, documentation, and example queries.

Techniques for managing limited LLM context windows in RAG systems, including chunk selection, summarization, and iterative retrieval. As context windows fill with retrieved documents, strategies ensure the most relevant information reaches the model while respecting token limits.

Using language models to automatically evaluate RAG system outputs, retrieval quality, and answer correctness. LLM-as-judge provides scalable, consistent evaluation of aspects like faithfulness, relevance, and coherence that are difficult to measure with traditional metrics, enabling rapid iteration on RAG systems.

An advanced RAG chunking strategy that uses LLMs to dynamically determine optimal document splitting based on semantic meaning and content structure. Agentic chunking analyzes document characteristics and adapts the chunking approach per document for superior retrieval accuracy.