Ragify
Make your PDFs RAG-ready
What it does
I got tired of feeding messy PDFs to my AI, so I built Ragify. Ragify turns PDFs into clean, structured text in seconds. You choose the format: Markdown, JSON, HTML, and more. Headings, tables, and paragraphs are properly detected and organised. It helps if you run selfhosted LLMs, RAG pipelines, or any automation that needs clean data from PDFs. No more manual fixing. No more garbage in, garbage out. Use it from the browser or via API. Works with n8n, Python scripts, or any HTTP tool.
Does the same job
all alternatives →- OSOpen-source Rule-based PDF parser for RAG2024 · github.com · ▲293
The 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…
- BABuilt a tool solve the nightmare of chunking tables in PDF vs. MarkdownNov 2025 · github.com · ▲15
Hey HN, solo dev here. After years of frustration with how LLMs handle complex documents, especially PDFs with tables, I decided to build a solution myself. My approach uses a Markdown conversion step to preserve the table structure, which seems to work surprisingly well for chunking. This little parser is the first public piece of a much larger, privacy-focused AI platform I'm building. I'm pretty much running on fumes financially, so any feedback, critique, or support is massively appreciated. Happy to answer any questions about the approach!
- RIRAG in 3 Lines of PythonDec 2025 · pypi.org · ▲41
Got tired of wiring up vector stores, embedding models, and chunking logic every time I needed RAG. So I built piragi. from piragi import Ragi kb = Ragi(\["./docs", "./code/\*\*/\*.py", "https://api.example.com/docs"\]) answer = kb.ask("How do I deploy this?") That's the entire setup. No API keys required - runs on Ollama + sentence-transformers locally. What it does: - All formats - PDF, Word, Excel, Markdown, code, URLs, images, audio - Auto-updates - watches sources, refreshes in background, zero query latency - Citations - every answer includes sources…
- RCRAG-chunk – A CLI to test RAG chunking strategiesNov 2025 · github.com · ▲10
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