CiteLLM
Verifiable data extraction for PDFs
CiteLLM launched on February 21, 2026 with 4 votes, #2,349 of 16,627 launches that month and more than 75% of that year's launches.
What it does
Verify LLM outputs with precise PDF citations. API and self-hosted solutions for fintech, legal, and enterprise applications.
Extract structured data from PDFs and get exact page references with highlighted snippets. Enable human verification in seconds, not minutes. No training on your documents. Files deleted after processing. Self-host available for full data control. Simple API Integration — Send a PDF + schema, get structured data with citations. Your model extracts data in seconds. But your users still ask: "Where did that come from?" LLMs return plausible values that aren't in the document. No warning, no uncertainty flag. Hunting through a 50-page PDF to find one value doesn't scale. When users can verify in one click, they trust and use the tool. LLM hallucinations aren't going away, researchers have…from citellm.com
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LLMs can generate valid JSON 100% of the time2023 · github.com · ▲854Outlines is a Python library that focuses on text generation with large language models. Brandon and I are not LLM experts and started the project a few months ago because we wanted to understand better how the generation process works. Our original background is probabilistic, relational and symbolic programming. Recently we came up with a fast way to generate text that matches a regex (https://blog.normalcomputing.ai/posts/2023-07-27-regex-guide...). The basic idea is simple: regular expressions have an equivalent Deterministic-Finite Automaton (DFA) representation. We…
PDF to MD by LLMs – Extract Text/Tables/Image Descriptives by GPT4o2024 · github.com · ▲191I've developed a Python API service that uses GPT-4o for OCR on PDFs. It features parallel processing and batch handling for improved performance. Not only does it convert PDF to markdown, but it also describes the images within the PDF using captions like `[Image: This picture shows 4 people waving]`. In testing with NASA's Apollo 17 flight documents, it successfully converted complex, multi-oriented pages into well-structured Markdown. The project is open-source and available on GitHub. Feedback is welcome.

I just open sourced my document/website extractor for Vision-LLMs2024 · github.com · ▲37Hi HackerNews, Lately, I have seen an explosion in posts offering paid APIs/services to get unstructured data into LLMs (i.e. langchain extract, ragflow, unstructured, unstract, just to name a few) and I have been largely disappointed by them, either because they fail to implement multimodal support, fail to give good context for "really tricky" PDFs / Word docs / Powerpoints, or are just plain difficult to use. In light of all these posts I figured I'd share my solution that has been working smoothly for me and my clients. I put it up on GitHub for free so you can check it…
Open-Source Document Extraction Tool2025 · github.com · ▲13Hi, I'm building an open-source self-hostable document extraction tool powered by LLM. There are popular data extraction tools and OCR tools in the market. None of them are open source. Most accounting firms, law firms, insurance, back office, and real-estate folks would like to use a tool like this. You can add PDF documents, Images, and audio files and create columns to answer questions on documents or extract information into tabular format. Access to repo: https://github.com/harishdeivanayagam/rowfill Screenshots:…
Built a tool solve the nightmare of chunking tables in PDF vs. MarkdownNov 2025 · github.com · ▲15Hey 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!
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