
RAGCheck
Prepare PDFs for AI with RAG Readiness Score
RAGCheck launched on May 27, 2026 with 3 votes, #3,449 of 20,538 launches that month and more than 50% of that year's launches.
What it does
Most PDF-to-text tools just extract text. But when you feed that into an LLM or vector DB, tables break, scans become unreadable, and headers repeat in every chunk. RAGCheck doesn't just convert — it diagnoses. Get a RAG Readiness Score (0-100) with actionable recommendations. Download clean Markdown, structured JSON, and pre-chunked text optimized for embeddings. Free tier: 3 conversions/day. No signup required.
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Open-source Rule-based PDF parser for RAG2024 · github.com · ▲293The PDF parser is a rule based parser which uses text co-ordinates (boundary box), graphics and font data. The PDF parser works off text layer and also offers a OCR option to automatically use OCR if there are scanned pages in your PDFs. The OCR feature is based off a modified version of tika which uses tesseract underneath. The PDF Parser offers the following features: * Sections and subsections along with their levels. * Paragraphs - combines lines. * Links between sections and paragraphs. * Tables along with the section the tables are found in. * Lists and nested lists. * Join content…
Ragas – Open-source library for evaluating RAG pipelines2024 · github.com · ▲121Ragas is an open-source library for evaluating and testing RAG and other LLM applications. Github: https://docs.ragas.io/en/stable/, docs: https://docs.ragas.io/. Ragas provides you with different sets of metrics and methods like synthetic test data generation to help you evaluate your RAG applications. Ragas started off by scratching our own itch for evaluating our RAG chatbots last year. Problems Ragas can solve - How do you choose the best components for your RAG, such as the retriever, reranker, and LLM? - How do you formulate a test dataset…

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