Wren AI

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.

Components in LLM frameworks that fetch and parse data from various sources (PDFs, websites, databases) into a standardized format for processing. Essential first step in RAG pipelines for converting raw data into processable documents.

RAG Assessment framework for Python providing reference-free evaluation of RAG pipelines using LLM-as-a-judge, measuring context relevancy, context recall, faithfulness, and answer relevancy with automatic test data generation.

RAG evaluation framework that trains lightweight judges for retrieval and generation scoring, refining evaluation by training specialized LLM judges on synthetic datasets to provide more reliable, confidence-aware judgments.

Open-source document parsing framework from IBM with 97.9% accuracy in complex table extraction and excellent text fidelity. Self-hostable solution for converting PDFs, spreadsheets, and scanned images into structured data for RAG pipelines.

High-performance document parsing service by LlamaIndex that consistently processes documents in about 6 seconds regardless of size. Returns rich Markdown and optional HTML tables with wide format support through hosted API.